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IEEE Transactions on Services Computing
Journal Prestige (SJR): 0.87
Citation Impact (citeScore): 5
Number of Followers: 5  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1939-1374
Published by IEEE Homepage  [228 journals]
  • A Blockchain-Based Cross-Domain and Autonomous Access Control Scheme for
           Internet of Things

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      Authors: Xiaohan Hao;Wei Ren;Yangyang Fei;Tianqing Zhu;Kim-Kwang Raymond Choo;
      Pages: 773 - 786
      Abstract: The volume, variety and value of data generated by Internet of Things (IoT) devices are expected to increase significantly in foreseeable future, hence, reinforcing the importance of secure and efficient access control solutions for these devices and their networks. However, existing access control solutions are not generally lightweight or scalable, particularly for geographically disperse, inexpensive resource constrained IoT devices. To tackle above challenges, we propose a lightweight consortium blockchain based architecture to enable intelligent autonomous access control for IoT devices. In our architecture, intelligent blockchain facilitates the storing of access policies, provision of authentication services for data access control, and trust evaluation for access request nodes through token accumulation mechanism. Specifically, the user's access request is approved only after it is confirmed by the blockchain network. To ensure the reliability of authenticity, a compromised resistant consensus algorithm is adapted and implemented to defend against at most $1/3$1/3 compromised authenticators. In addition, a cross-domain and flexible access control model is not only used to support data sharing among various users but can also be used for access control for exceptional blockchain situations. We explain how our system meets our design goals of reliability, availability, confidentiality, integrity, lightweight, security and scalability. In addition, we also analyze the proposed system's performance from computational, storage and network overheads (e.g., running cryptographic algorithms on a Raspberry Pi 4B), and the findings suggest that the time to run typical cryptographic algorith-s is in the microsecond range.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • A Delegatable Attribute Based Encryption Scheme for a Collaborative
           E-Health Cloud

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      Authors: Harsha Sandaruwan Gardiyawasam Pussewalage;Vladimir Oleshchuk;
      Pages: 787 - 801
      Abstract: With the popularization and growing utilization of electronic health records (EHRs) coupled with the advancements in cloud computing, healthcare providers are interested in storing EHRs in third-party, semi-trusted cloud platforms. Given the collaborative nature of modern e-health environments, integrating access delegation is of paramount importance to strengthen the flexibility of the sharing of health information. However, access delegation has to be enforced in a controlled manner so that it will not jeopardize the security of the system. For such applications, attribute based encryption (ABE) mechanisms are quite useful given the fact that ABE facilitates an efficient way of enforcing secure, fine-grained access control over encrypted data. However, incorporating delegatability with ABE mechanisms is tricky, and the existing schemes lack the control over the process of delegation of encrypted data. As a solution, we propose a novel ABE based access control scheme which can enforce multi-level, controlled access delegation and demonstrated how it could be deployed in an e-health environment to securely share outsourced EHRs of patients. Furthermore, we have shown that the proposed scheme is secure against chosen plaintext attacks as well as attacks mounted via attribute collusion.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • A Double-Space and Double-Norm Ensembled Latent Factor Model for Highly
           Accurate Web Service QoS Prediction

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      Authors: Di Wu;Peng Zhang;Yi He;Xin Luo;
      Pages: 802 - 814
      Abstract: Quality-of-Service (QoS), which describes the non-functional characteristics of Web service, is of great significance in service selection. Since users cannot invoke all services to obtain the corresponding QoS data, QoS prediction becomes a hot yet thorny issue. To date, a latent factor analysis (LFA)-based QoS predictor is one of the most successful and popular approaches to address this issue. However, current LFA-based QoS predictors are mostly modeled on inner product space with an L2-norm-oriented Loss function only. They cannot comprehensively represent the characteristics of target QoS data to make accurate predictions because inner product space and L2-norm have their respective limitations. To address this issue, this study proposes a Double-space and Double-norm Ensembled Latent Factor (D2E-LF) model. Its main idea is three-fold: 1) Double-space—inner product space and distance space are employed to model two kinds of LFA-based QoS predictors, respectively, 2) Double-norm—both of these two predictors adopt an L1-and-L2-norm-oriented Loss function, and 3) Ensembled—building an ensemble of these two predictors by a weighting strategy. By doing so, D2E-LF integrates multi-merits originating from inner product space, distance space, L1-norm, and L2-norm, making it achieve highly accurate QoS prediction. Experiments on two real-world QoS datasets demonstrate that D2E-LF has significantly higher prediction accuracy than state-of-the-art models.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • A Latency-Driven Availability Assessment for Multi-Tenant Service Chains

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      Authors: Luigi De Simone;Mario Di Mauro;Roberto Natella;Fabio Postiglione;
      Pages: 815 - 829
      Abstract: Nowadays, most telecommunication services adhere to the Service Function Chain (SFC) paradigm, where network functions are implemented via software. In particular, container virtualization is becoming a popular approach to deploy network functions and to enable resource slicing among several tenants. The resulting infrastructure is a complex system composed by a huge amount of containers implementing different SFC functionalities, along with different tenants sharing the same chain. The complexity of such a scenario lead us to evaluate two critical metrics: the steady-state availability (the probability that a system is functioning in long runs) and the latency (the time between a service request and the pertinent response). Consequently, we propose a latency-driven availability assessment for multi-tenant service chains implemented via Containerized Network Functions (CNFs). We adopt a multi-state system to model single CNFs and the queueing formalism to characterize the service latency. To efficiently compute the availability, we develop a modified version of the Multidimensional Universal Generating Function (MUGF) technique. Finally, we solve an optimization problem to minimize the SFC cost under an availability constraint. As a relevant example of SFC, we consider a containerized version of IP Multimedia Subsystem, whose parameters have been estimated through fault injection techniques and load tests.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • A Momentum-Accelerated Hessian-Vector-Based Latent Factor Analysis Model

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      Authors: Weiling Li;Xin Luo;Huaqiang Yuan;MengChu Zhou;
      Pages: 830 - 844
      Abstract: Service-oriented applications commonly involve high-dimensional and sparse (HiDS) interactions among users and service-related entities, e.g., user-item interactions from a personalized recommendation services system. How to perform precise and efficient representation learning on such HiDS interactions data is a hot yet thorny issue. An efficient approach to it is latent factor analysis (LFA), which commonly depends on large-scale non-convex optimization. Hence, it is vital to implement an LFA model able to approximate second-order stationary points efficiently for enhancing its representation learning ability. However, existing second-order LFA models suffer from high computational cost, which significantly reduces its practicability. To address this issue, this paper presents a Momentum-accelerated Hessian-vector algorithm (MH) for precise and efficient LFA on HiDS data. Its main ideas are two-fold: a) adopting the principle of a Hessian-vector-product-based method to utilize the second-order information without manipulating a Hessian matrix directly, and b) incorporating a generalized momentum method into its parameter learning scheme for accelerating its convergence rate to a stationary point. Experimental results on nine industrial datasets demonstrate that compared with state-of-the-art LFA models, an MH-based LFA model achieves gains in both accuracy and convergence rate. These positive outcomes also indicate that a generalized momentum method is compatible with the algorithms, e.g., a second-order algorithm, which implicitly rely on gradients.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • A Novel Graph-Based Computation Offloading Strategy for Workflow
           Applications in Mobile Edge Computing

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      Authors: Xuejun Li;Tianxiang Chen;Dong Yuan;Jia Xu;Xiao Liu;
      Pages: 845 - 857
      Abstract: With the fast development of mobile edge computing (MEC), there is an increasing demand for running complex applications on the edge. These complex applications can be represented as workflows where task dependencies are explicitly specified. To achieve better Quality of Service (QoS), computation offloading is widely used in the MEC environment. However, many existing computation offloading strategies only focus on independent computation tasks but overlook the task dependencies. Meanwhile, most of these strategies are based on search algorithms which are often time-consuming and hence not suitable for many delay-sensitive complex applications in MEC. Therefore, a highly efficient graph-based strategy was proposed in our recent work but it can only deal with simple workflow applications with linear (namely sequential) structure. For solving these problems, a novel graph-based strategy is proposed for workflow applications in MEC. Specifically, this strategy can deal with complex workflow applications with nonlinear (viz. parallel, selective and iterative) structures. Meanwhile, the offloading decision plan with the lowest energy consumption of the end-device under deadline constraint can be found by using the graph-based partition technique. We have comprehensively evaluated our strategy on FogWorkflowSim platform for complex workflow applications. Extensive numerical results demonstrate that the end device's energy consumption can be effectively reduced by 7.81% and 9.51% compared with PSO and GA by the proposed strategy. Meanwhile, the strategy running time is 1% and 0.2% of PSO and GA, respectively.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • A Semi-Centralized Trust Management Model Based on Blockchain for Data
           Exchange in IoT System

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      Authors: Yuan Liu;Chuang Zhang;Yu Yan;Xin Zhou;Zhihong Tian;Jie Zhang;
      Pages: 858 - 871
      Abstract: IoT data exchange plays a vital role in supporting various applications and services with massive IoT devices. However, the existence of malicious devices threatens the integrity and reliability of the exchanged data. Trust management has been used to mitigate the impact of malicious devices in centralized and decentralized architectures. However, most of these traditional trust management systems bear computation, storage, and communication challenges. In this study, we propose a semi-centralized trust management system architecture based on blockchain in both single and multiple domains. The IoT devices are centralized organized by cloud servers who coordinately sustain a rating data ledger within each domain based the proposed rotation based consensus protocol in a decentralized manner to support cross-domain data exchange. A computational trust model is proposed by aggregating the direct and indirect trust information, where we elaborately design decay function, recommendation credibility and adaptable weights so as to calculate the trust value of dynamic malicious devices. Finally, we evaluate the proposed system model in various situations through simulation based experiments and compare it with two classical models in the literature. The experimental results demonstrate the effectiveness of the proposed trust model in identifying malicious devices and mitigating the influence of malicious devices.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • A Workflow Scheduling Approach With Modified Fuzzy Adaptive Genetic
           Algorithm in IaaS Clouds

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      Authors: Naela Rizvi;Dharavath Ramesh;Lipo Wang;Annappa Basava;
      Pages: 872 - 885
      Abstract: The emergence of the cloud platform with substantial resources to offer on-demand instigated the researchers to migrate the scientific workflows to the cloud environment. The scheduling of workflows with diverse QoS parameters is not a trivial task, but an NP-Complete problem. Several heuristics for QoS constrained workflows have been investigated. However, most of them focus only on time and cost and do not guarantee high resource utilization. The scheduling of the workflow tasks over the minimum cloud resources under the defined time limit is a grave concern. In this article, an algorithm named MFGA (Modified Fuzzy Adaptive Genetic Algorithm) has been formulated to minimize the makespan and improve resource utilization under both deadline and budget constraints. A fuzzy logic controller has also been devised to control the crossover and mutation rates that prevent MFGA from getting stuck in a local optimum. MFGA has a novel crossover technique that adds the fittest solutions in the population. Additionally, a new mutation technique has also been introduced, which minimizes the makespan and increases the reusability of the resources. The simulation experiments with the real workflows show that the proposed MFGA outperforms other state-of-the-art algorithms.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Accurate and Reliable Service Recommendation Based on Bilateral Perception
           in Multi-Access Edge Computing

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      Authors: Zhizhong Liu;Quan Z. Sheng;Zhenxing Zhang;Xiaofei Xu;Dianhui Chu;Jian Yu;Shuang Wang;
      Pages: 886 - 899
      Abstract: Multi-access edge computing (MEC) is an emerging computing paradigm that brings services from the centralized cloud to nearby network edge to improve users’ Quality of Experience (QoE). As massive services with dynamic Quality of Service (QoS) are available in MEC, it becomes challenging for users to find reliable services that satisfy their needs. Therefore, service recommendation technology is urgently needed in MEC. Although existing service recommendation methods work well on recommending popular services that users might be interested in, they fail to recommend services with reliable QoS in the MEC environment. To tackle this issue, an accurate and reliable service recommendation (ARSR) approach based on bilateral perception is proposed, which aims to proactively recommend reliable services by perceiving both users’ service demands and multi-QoS of candidate services. ARSR consists of three main steps. First, a user's service demand is estimated by a context-aware service demand prediction method based on an improved online deep learning model. Then, multiple QoS attributes of candidate services are forecasted by a multidimensional contexts-aware QoS prediction method based on an improved multi-task deep neural network. Finally, the optimal service is recommended to the user based on the predicted QoS. Extensive experiments have been carried out to verify the proposed approach and to prove its performance superiority.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Adaptive Intrusion Detection in Edge Computing Using Cerebellar Model
           Articulation Controller and Spline Fit

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      Authors: Gulshan Kumar;Rahul Saha;Mauro Conti;Reji Thomas;Tannishtha Devgun;Joel J. P C. Rodrigues;
      Pages: 900 - 912
      Abstract: Internet-of-Thing (IoT) faces various security attacks. Different solutions exist to mitigate the intrusion problems. However, the existing solutions lack behind in dealing with heterogeneity of attack sources and features. The future anticipated demand of devices’ connections also urge the need of new solutions addressing the concerns of time consumption and complexity. In this article, we show a novel solution for the intrusion detection in IoT framework. We configure the intrusion detection in the edge computing layer so that the effect of the attack is not propagated to the clouds. Our solution uses cerebellar model articulation controller with kernel map. This combination is very new in the direction of intrusion detection; hence, it emphasizes the novelty of our proposed intrusion detection solution. We name our solution as Cerebellar Model Articulation Controller based Intrusion Detection System (CMACIDS). Additionally, we use spline fitting to the kernel mapping for the model fit; this adds on another novel contribution to CMACIDS. The results obtained with our detection system are compared with the state-of-the-art solutions in terms of complexity, false alarms, and precision of detection. The analysis of the comparative study proves the efficiency of the solution and makes CMACIDS suitable for IoT paradigm.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • An Accurate and Privacy-Preserving Retrieval Scheme Over Outsourced
           Medical Images

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      Authors: Dan Zhu;Hui Zhu;Xiangyu Wang;Rongxing Lu;Dengguo Feng;
      Pages: 913 - 926
      Abstract: With the rapid advancement in medical imaging techniques, Content-Based (medical) Image Retrieval (CBIR), which can assist in disease diagnosis, has gained much attention in both academia and industry. However, due to patients’ sensitive information involved in medical images, privacy-preserving CBIR is a challenge worth exploiting. Though several privacy-preserving CBIR schemes have been put forth, they can only resist known-background attack (KBA), and do not suffice for protecting the image privacy in outsourced settings. In this article, aiming at the above challenge, we first design a novel Privacy-preserving Mahalanobis Distance Comparison (PMDC) method to improve the accuracy of medical images retrieval. Then, combined with the Mahalanobis distance based Fuzzy C-Means (FCM-M) algorithm, a scheme named TAMMIE is proposed to achieve accurate and privacy-preserving medical image retrieval over encrypted data. With TAMMIE, an image owner can securely outsource the images and indexes to a cloud server, and query users can request retrieval services from the cloud server while keeping their queries private. Detailed security analysis shows that our proposed schemes are secure under the attack stronger than KBA. Furthermore, thorough empirical experiments conducted on two real-world and one synthetic datasets also demonstrate the efficiency of TAMMIE.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • An Online Orchestration Mechanism for General-Purpose Edge Computing

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      Authors: Xun Shao;Go Hasegawa;Mianxiong Dong;Zhi Liu;Hiroshi Masui;Yusheng Ji;
      Pages: 927 - 940
      Abstract: In recent years, the fast development of mobile communications and cloud systems has substantially promoted edge computing. By pushing server resources to the edge, mobile service providers can deliver their content and services with enhanced performance, and mobile-network carriers can alleviate congestion in the core networks. Although edge computing has been attracting much interest, most current research is application-specific, and analysis is lacking from a business perspective of edge cloud providers (ECPs) that provide general-purpose edge cloud services to mobile service providers and users. In this article, we present a vision of general-purpose edge computing realized by multiple interconnected edge clouds, analyzing the business model from the viewpoint of ECPs and identifying the main issues to address to maximize benefits for ECPs. Specifically, we formalize the long-term revenue of ECPs as a function of server-resource allocation and public data-placement decisions subject to the amount of physical resources and inter-cloud data-transportation cost constraints. To optimize the long-term objective, we propose an online framework that integrates the drift-plus-penalty and primal-dual methods. With theoretical analysis and simulations, we show that the proposed method approximates the optimal solution in a challenging environment without having future knowledge of the system.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • An Optimization Framework for Migrating and Deploying Multiclass
           Enterprise Applications Into the Cloud

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      Authors: Shiyong Li;Huan Liu;Wenzhe Li;Wei Sun;
      Pages: 941 - 956
      Abstract: Enterprises can reduce the computational burden and costs substantially by migrating and deploying their partial or even whole applications to the cloud, so as to promote and realize their digital transformation. In this article, we study the following problems in the migration and deployment of enterprise applications: i) How the migration time factor influences application migration indirectly' ii) What is the optimal deployment strategy for multiple applications' In this regard, many existing schemes that aim to optimize the economic cost can neither model the optimal migration strategy nor the optimal deployment resource allocation appropriately for enterprise applications. To tackle these limitations, first, this article aims at minimizing migration time by allocating the bandwidth of the access links for applications migration and formulates a strictly convex optimization problem. After that, the article concentrates on modelling the deployment interactions for resource allocation between enterprise application and cloud physical machines as a non-convex optimization problem. The successive approximation method is used to approximate the problem into a series of strictly convex optimization problems and an algorithm is proposed to achieve the optimal resource allocation for applications deployment problem. Numerical results illustrate the effective performance of the proposed schemes of enterprise application migration and deployment in comparison with other methods.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Attribute-Based Expressive and Ranked Keyword Search Over Encrypted
           Documents in Cloud Computing

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      Authors: Qinlong Huang;Guanyu Yan;Qinglin Wei;
      Pages: 957 - 968
      Abstract: Attribute-based keyword search (ABKS) has been proposed to realize fine-grained access control and provide search service in cloud computing. However, most ABKS schemes focus on single or conjunctive keyword search, while the recent Boolean keyword search schemes only support monotonic query formula mainly involving AND, OR and threshold operators. How to support more expressive Boolean query formulas and return the corresponding accurate search results to users have become challenges for practical ABKS over ciphertexts. In this paper, we introduce an attribute-based expressive and ranked keyword search scheme over encrypted documents named ABERKS, which allows authorized users to submit expressive Boolean query formulas involving AND, OR, NOT and threshold operators. ABERKS utilizes a non-monotonic access tree structure to construct the query formula, and further leverages extended Boolean model to rank the search results. Specifically, the users are able to define the weights in the query formula, and get the relevance score of each matched ciphertext if the attributes and keywords are both satisfied. We prove the security of ABERKS against chosen keyword attack under selective ciphertext policy model and against keyword guessing attack, and also conduct extensive experiments to show the efficiency and practicality of ABERKS.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Automated Probe Life-Cycle Management for Monitoring-As-a-Service

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      Authors: Alessandro Tundo;Marco Mobilio;Oliviero Riganelli;Leonardo Mariani;
      Pages: 969 - 982
      Abstract: Cloud services must be continuously monitored to guarantee that misbehaviors can be timely revealed, compensated, and fixed. While simple applications can be easily monitored and controlled, monitoring non-trivial cloud systems with dynamic behavior requires the operators to be able to rapidly adapt the set of collected indicators. Although the currently available monitoring frameworks are equipped with a rich set of probes to virtually collect any indicator, they do not provide the automation capabilities required to quickly and easily change (i.e., deploy and undeploy) the probes used to monitor a target system. Indeed, changing the collected indicators beyond standard platform-level indicators can be an error-prone and expensive process, which often requires manual intervention. This article presents a Monitoring-as-a-Service framework that provides the capability to automatically deploy and undeploy arbitrary probes based on a user-provided set of indicators to be collected. The life-cycle of the probes is fully governed by the framework, including the detection and resolution of the erroneous states at deployment time. The framework can be used jointly with existing monitoring technologies, without requiring the adoption of a specific probing technology. We experimented our framework with cloud systems based on containers and virtual machines, obtaining evidence of the efficiency and effectiveness of the proposed solution.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • AutoThing: A Secure Transaction Framework for Self-Service Things

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      Authors: Nikolay Ivanov;Qiben Yan;
      Pages: 983 - 995
      Abstract: Self-Service Terminals (SSTs) are increasing their presence across multiple industries, from vending machines and self-service banking to automated national border crossing checkpoints. Due to the massive integration of SSTs into critical infrastructure, their security has raised major concerns. In this work, we develop a security model for the family of SST system protocols to formally prove that traditional SST systems are not resilient against cyber-attacks. We create a comprehensive inventory of attacking configurations against SSTs. We then use this inventory to verify that enhanced resilience against compromising any major component of an SST system can be achieved via three steps: a) replacing free-range APIs with multi-signature transaction tokens; b) switching from networking interfaces in SSTs into direct device-to-device channels; and c) adding a bootstrapping service. We introduce Offline Self-Service Things (OSST), which have high attack resilience by maintaining a distributed representation without the need to be online. To enable the real-world applicability of OSSTs, we develop AutoThing, a transaction framework for OSSTs. We show the extensibility of AutoThing by building two applications upon the framework: VolgaPay, a payment system for vending machines; and VolgaGuard, an access control system. We evaluate both systems to show the portability and scalability of AutoThing.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Capturing Request Execution Path for Understanding Service Behavior and
           Detecting Anomalies Without Code Instrumentation

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      Authors: Yong Yang;Long Wang;Jing Gu;Ying Li;
      Pages: 996 - 1010
      Abstract: With the increasing scale and complexity of cloud platforms and big-data analytics platforms, it is becoming more and more challenging to understand and diagnose the processing of a service request across multi-layer software stacks of such platforms. One way that helps to deal with this problem is to accurately capture the complete end-to-end execution path of service requests among all involved components. This paper presents REPTrace, a generic methodology for capturing such execution paths in a transparent fashion. Moreover, this paper demonstrates the effectiveness of REPTrace by presenting how REPTrace can be leveraged for knowledge extraction and anomaly detection on the platforms’ request processing. Our experimental results show that, REPTrace enables capturing a holistic view of the request processing across multiple layers of the platforms (which is missing in official documentation) and discovering important undocumented features of the platforms. Fault injection experiments show execution anomalies are detected with 93% precision and 96% recall with aid of REPTrace.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • CASE-SSE: Context-Aware Semantically Extensible Searchable Symmetric
           Encryption for Encrypted Cloud Data

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      Authors: Lanxiang Chen;Yujie Xue;Yi Mu;Lingfang Zeng;Fatemeh Rezaeibagha;Robert H. Deng;
      Pages: 1011 - 1022
      Abstract: Traditional searchable symmetric encryption (SSE) schemes rarely support context-aware semantic extension, and then lead to the searched results being incomplete or deviating from the user’s query intention. To address this problem, a new context-aware semantically extensible searchable symmetric encryption based on Word2vec model (CASE-SSE) is proposed to achieve context-aware semantic extension in this article. The proposed scheme utilizes outsourced datasets as corpora to extract all keywords for training the Word2vec model, and the trained results is the ontology knowledge base that can be used to extend the semantics of query keywords directly. Further, to facilitate multi-keyword search using the extended query vector, we use the $k$k-means clustering algorithm to classify outsourced datasets. We then construct an AVL-tree index and an inverted index based on the classified results, thereby achieving efficient context-aware semantically extensible SSE. The security analysis indicates it is secure and effective. The experimental results show that our scheme is superior in both efficiency and accuracy.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • CoLocateMe: Aggregation-Based, Energy, Performance and Cost Aware VM
           Placement and Consolidation in Heterogeneous IaaS Clouds

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      Authors: Muhammad Zakarya;Lee Gillam;Khaled Salah;Omer Rana;Santosh Tirunagari;Rajkumar Buyya;
      Pages: 1023 - 1038
      Abstract: In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users’ costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this article, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users’ costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is $sim$∼9.61% more energy and $sim$∼20.0% more performance efficient than segregation-based policies. Moreover, various aggregation m-trics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users’ costs.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Cost-Effective Data Placement in Edge Storage Systems With Erasure Code

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      Authors: Hai Jin;Ruikun Luo;Qiang He;Song Wu;Zilai Zeng;Xiaoyu Xia;
      Pages: 1039 - 1050
      Abstract: In this paper, we make the first attempt to investigate the use of erasure codes in cost-effective data storage at the edge. The focus is to find the optimal strategy for placing coded data blocks on the edge servers in an ESS, aiming to minimize the storage cost while serving all the users in the system. We first model this novel Erasure Coding based Edge Data Placement (EC-EDP) problem as a constrained optimization problem and prove that it is NP-hard. Then, we propose an optimal approach named EC-EDP-O based on integer programming. We also propose an approximation algorithm named EC-EDP-V for finding approximate solutions to large-scale EC-EDP problems efficiently. The results of experiments conducted on a widely-used real-world dataset demonstrate that EC-EDP-O and EC-EDP-V can save an average of 68.58% (and up to 81.16% in large-scale scenarios) storage cost compared with replica-based storage approaches.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • CTL-Based Adaptive Service Composition in Edge Networks

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      Authors: Deng Zhao;Zhangbing Zhou;Patrick C. K. Hung;Shuiguang Deng;Xiao Xue;Walid Gaaloul;
      Pages: 1051 - 1065
      Abstract: With the recent adoption of edge computing, Internet of Things (IoT) devices collaborate at the network edge to facilitate edge-native applications. In this setting, IoT devices are typically encapsulated as IoT services to encode their functionalities, and their collaboration is achieved through IoT service composition. Due to the continuous resource occupancy, release, and consumption of IoT devices at runtime, a composition, which is functionally compatible and non-functionally optimal at this moment, may not hold in the forthcoming time durations, when certain IoT services may significantly downgrade in their Quality-of-Services (QoS). To guarantee the compatibility of compositions with QoS variations, this article proposes an adaptive composition mechanism leveraging Computation Tree Logic (CTL) specifications. Specifically, we formalize the composition as a temporal task, and convert it to CTL formulae with the abstractions of required functionalities and composite structures. Functional compatibility is formally interpreted by CTL semantics during the execution of compositions. Besides, we construct a QoS Dependency Graph (QoSDG) to capture QoS variations, and achieve adaptive composition with dynamic QoS satisfactions. Extensive experiments are conducted upon publicly-available datasets, and comparison results demonstrate that our technique outperforms the state-of-the-art counterparts in heterogenous scenarios with higher QoS dependencies ranging from 0.3$%$% to 27.8$%$%.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • DUASVS: A Mobile Data Saving Strategy in Short-Form Video Streaming

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      Authors: Guanghui Zhang;Jie Zhang;Ke Liu;Jing Guo;Jack Y. B. Lee;Haibo Hu;Vaneet Aggarwal;
      Pages: 1066 - 1078
      Abstract: Fueled by the emerging short video applications (e.g., TikTok), streaming short-form videos nowadays is ubiquitous among mobile users. During the viewing, one common action is to scroll the screen to switch videos, which is a handy operation for the viewers to quickly search for content of interest. However, our empirical measurements reveal that frequent video switching can result in nearly half of the mobile data quota being used for transferring the video data that is never watched. This problem is called data loss in this work. Given the immense cost of the network infrastructure, such a high proportion of data loss is financially tremendous to both mobile users and streaming vendors. To tackle the problem, this study proposes a novel system called Data Usage Aware Short Video Streaming (DUASVS), where a new Integrated Learning is used to capture the characters of past network conditions and then trains intelligent adaptation models to reduce data loss and save data usage. Extensive evaluations show that DUASVS is able to save 70.7%∼83.2% of mobile data usage without incurring any QoE degradation. Moreover, the system exhibits strong robustness, performing consistently over a wide range of network environments as well as video streaming sessions.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Dynamic Multi-Resource Optimization for Storage Acceleration in Cloud
           Storage Systems

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      Authors: Kyungtae Lee;Jinhwi Kim;Jeongho Kwak;Yeongjin Kim;
      Pages: 1079 - 1092
      Abstract: Demand for using cloud object storage has been increasing in order to efficiently manage a large number of binary large objects (BLOBs), including videos, photos and documents. Although many companies and institutions are currently trying to utilize public cloud object storage services such as AWS Simple Storage Service (S3), most of existing encoding systems for safe storage of data have not been optimized for current cloud object storage architecture. In this article, we propose a novel dynamic extreme erasure encoding algorithm, namely DexEncoding aiming to maximize the utility of clients where the encoding locations in the cloud storage architecture are dynamically optimized between gateway and storage servers with respect to the time-varying cloud environment. Here, the utility captures the satisfaction of clients for the speed of data storage and fairness among clients. DexEncoding efficiently resolves resource bottlenecks by adapting to the dynamic network, processing and storage resource availability and storage request. Real measurement-driven simulations demonstrate that the proposed DexEncoding algorithm drastically outperforms that applied in the state-of-the-art object storage systems in a perspective of clients’ satisfaction.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Edge Resource Pricing and Scheduling for Blockchain: A Stackelberg Game
           Approach

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      Authors: Sijie Huang;He Huang;Guoju Gao;Yu-E Sun;Yang Du;Jie Wu;
      Pages: 1093 - 1106
      Abstract: Blockchain came to prominence as the distributed ledger underneath Bitcoin, which protects the transaction histories in a fully-connected, peer-to-peer network. The blockchain mining process requires high computing power to solve a Proof-of-Work (PoW) puzzle, which is hard to implement on users’ mobile devices. So these miners may leverage the edge/cloud service providers (ESPs/CSP) to calculate the PoW puzzle. The existing edge-assisted blockchain networks assumed that all ESPs have a uniform propagation delay, which is unrealistic. In this article, we consider a more practical scene where ESPs locate in diverse positions of the blockchain network, which causes different propagation delays when supporting the computation of the PoW puzzle. Additionally, these ESPs connect to a remote CSP for resource scheduling when the computing tasks exceed their maximum capacity. The blockchain mining process generally involves complicated competition and games among CSP, ESPs, and miners. Each service provider focuses on how to determine his resource price so that he can maximize his utility. According to the set resource price, each miner concentrates on scheduling his resource requests for each ESP to maximize individual personal utility, which depends on ESPs’ resource price and propagation delays. We first model such a resource pricing and scheduling problem as a three-stage multi-leader multi-follower Stackelberg game and aim at finding the Stackelberg equilibrium. Then, we analyze the subgame optimization problem in each stage and propose an iterative algorithm based on backward induction to achieve the Nash equilibrium of the Stackelberg game. Finally, extensive simulations are conducted to verify the significant performance of the proposed solution.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Edge-Assisted Public Key Homomorphic Encryption for Preserving Privacy in
           Mobile Crowdsensing

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      Authors: Ramin Ganjavi;Ahmad R. Sharafat;
      Pages: 1107 - 1117
      Abstract: Mobile crowdsensing (MCS) is becoming an increasingly important topic due to rapid proliferation of mobile apps where participants’ anonymity is a pivotal requirement with direct impacts on their safety and well being. There are two main challenges in crypto-based privacy-preserving aggregation in MCS, namely, participants joining or leaving the crowd randomly at will, and adversaries injecting fake data. The conventional approach for preserving privacy is to provide blanket anonymity to all, including adversaries, which enables the latter to cause harm without being identified. In addition, with the proliferation of edge servers, there is a need to develop edge-assisted MCS, which would be more efficient in terms of less back-haul traffic and less delay as compared to cloud-assisted-only MCS. In this paper, we present an efficient edge-assisted MCS scheme which preserves the participants’ privacy and anonymity, protects the service against adversaries, and can be used to verify that aggregation is free of anomalies. Our scheme is transparent to the join-and-leave problem; and its computational cost and communication overhead are small and fixed, i.e., it is insensitive to crowd count. We utilize group signature for source authentication to identify and block adversaries that cause harm while providing anonymity to ordinary participants.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Efficient Container Assignment and Layer Sequencing in Edge Computing

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      Authors: Jiong Lou;Hao Luo;Zhiqing Tang;Weijia Jia;Wei Zhao;
      Pages: 1118 - 1131
      Abstract: Containers are becoming a popular way of running applications in edge computing. Before running the application, the edge node must download the application’s container image consisting of multiple layers. However, given the limited bandwidth in edge computing, the container startup latency due to long image download time seriously affects the real-time performance. In this article, we jointly determine the container assignment and the layer download sequence to reduce the total startup latency. We formulate the Container Assignment and Layer Sequencing (CALS) problem and prove its NP-hardness. A Layer-Aware Scheduling Algorithm (LASA) is proposed, fully considering layer sharing among images. First, layers shared by the same set of images are grouped to reduce CALS’s problem scale without affecting the optimal result. Second, considering both layer sharing and existing layer size on edge nodes, a layer-aware algorithm is designed to assign containers to appropriate edge nodes. Finally, to determine the layer download sequence on each edge node, an approximation algorithm is proposed. We further analyze the approximation ratio of LASA in the case of identical edge nodes with sufficient capacity. Extensive experiments based on real-world data show the effectiveness of LASA, which reduces the total startup latency by 40% to 60%.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Efficient Point-of-Interest Recommendation Services With Heterogenous
           Hypergraph Embedding

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      Authors: Chen Wang;Mengting Yuan;Rui Zhang;Kai Peng;Ling Liu;
      Pages: 1132 - 1143
      Abstract: Point-of-interest (POI) recommendation service has drawn growing attention with the widespread popularity of location- based social networks (LBSNs). Recent research methods on POI recommendation based on graph embedding have mainly focused on explicit interactions of LBSN objects such as user's check-ins on POIs and social relationships, while neglecting implicit relationship that cannot be directly observed but may notably contribute to the POI recommendation. This paper presents VirHpoi, a heterogeneous hypergraph embedding method for POI recommendation in LBSNs with three original contributions. First, we model the LBSNs as a hypergraph to capture the complex interactions in LBSNs and learn the hypergraph by preserving homophily and interaction attribute affinity of the LBSNs. Second, we introduce the notion of “virtual hyperedges” to capture the intrinsic correlations of POIs. Virtual hyperedges incorporate implicit yet informative connections of the check-in patterns in LBSNs in terms of geographical and semantic characteristics. Third, we propose techniques to learn heterogenous hypergraph embedding on the complex LBSN graph with both homogenous edges and heterogenous hyperedges with dual objectives: we aim to preserve the homophily of objects intra domain by maximizing the co-occurrence probability of all homogenous edges, and we want to learn the interaction attribute affinity across domains by maximizing the probability of predicting the target object in the hyperedges. As a result, our approach can preserve both the intra domain homophily of objects and the interaction attribute affinity across domains by learning low-dimensional embeddings of LBSN objects and then make more effective recommendations based on the embeddings. Extensive experiments on four real-world datasets show the effectiveness and superiority of VirHpoi compared with the state-of-the-art methods.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Energy Consumption Scheduling as a Fog Computing Service in Smart Grid

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      Authors: Samira Chouikhi;Moez Esseghir;Leila Merghem-Boulahia;
      Pages: 1144 - 1157
      Abstract: The advent of smart grid technologies provides new tools and services to optimally manage the electricity grids. One of the most interesting services that emerged with the development of Information and Communication Technologies (ICTs) is energy demand management. This service permits us to face the issues caused by the ever-increasing energy demand such as grid congestion during peak hours, increasing energy generation costs, and even blackouts. In this paper, we investigate the problem of consumer-side optimization of residential energy demand. Our main aim is to better distribute the energy consumption over a day to avoid or reduce the demand during peak hours. Hence, we propose a fog computing-based model for energy demand scheduling using energy consumption cost as an incentive. In this model, the fog nodes schedule the appliances’ operations in order to reduce the individual and global energy bills whilst respecting consumers’ preferences. The proposed approach performs a multi-agent system-based cooperative scheduling game with minimal interactions between the nodes. Moreover, we present a fog nodes’ assignment scheme to decide which node will handle which appliances’ schedules. The nodes’ assignment strategy aims to optimize the use of fog nodes’ resources whilst reducing the scheduling process latency. The performance evaluation shows that the use of fog computing can achieve interesting results in terms of the reduction of energy consumption cost. For instance, the energy consumption during peak hour decreases by more than 25% from 670 kWh to 500 kWh when the scheduling game is performed. As a consequence, the energy consumption cost decreases by 7% from 806 € to 750 € .
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Energy-Aware Service Function Chaining Embedding in NFV Networks

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      Authors: Rongping Lin;Liu He;Shan Luo;Moshe Zukerman;
      Pages: 1158 - 1171
      Abstract: Network function virtualization (NFV) is a new networking paradigm based on decoupling network functions from dedicated hardware, so these network functions can be run as pieces of software on general-purpose computation servers, which are called virtual network functions. In addition to guarantee the service qualities provided by NFV networks comparable to those provided by traditional telecommunication networks, energy consumption becomes one of the challenges faced by NFV. This is due to a large number of general computation servers that consume a significant amount of energy. We address here the problem of how to provide an energy-aware service function chaining (SFC) embedding in NFV networks with a hierarchical resource allocation, where an SFC has a set of virtual network functions to be executed in a specific sequential order providing a specific network service. Assuming a dynamic traffic scenario, we introduce for this new problem an integer linear programming (ILP) and three polynomial heuristic algorithms for resource allocation. All three heuristic algorithms achieve energy savings by shutting down idle devices and balance the tradeoff between energy cost and SFC request acceptance ratio. Numerical results demonstrate the quality of the proposed heuristic algorithms in terms of acceptance ratio by comparing them with the ILP method and a method extended from an exiting algorithm despite the fact that they save energy.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Experimental Evaluation of Rule-Based Autonomic Computing Management
           Framework for Cloud-Native Applications

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      Authors: Joanna Kosińska;Krzysztof Zieliński;
      Pages: 1172 - 1183
      Abstract: The policy-based management paradigm in a flexible manner governs the system behavior. For Cloud-native applications, additionally, it simplifies the compliance with CI/CD objectives. Hence, the velocity of changes in requirements made at runtime does not influence the system implementation. Continuously the adjustments are integrated into the system on the fly. This paper evaluates the rule-based approach to representing policies in the context of Cloud-native applications. Deploying applications in orchestrated environments is one of the main principles of Cloud-native. Our approach represents the extension of the management characteristics that are available in current implementations of the orchestrators. The presented study also shows a general methodology for experimental evaluation of complex Cloud-native environments. We propose two categories of experiments. Both evaluate the rule-based approach. The first category evaluates the impacts of dynamic adjustment of resources in the context of the Cloud-native execution environment. The second category assesses the influence of the rule engine approach on the autonomic management process. Given the wide range of available experiments, we additionally assume that evaluation is performed from the point of view of the execution environment's resources. This approach tightly embraces the capabilities of the proposed solution realized by the AMoCNA system and demonstrates its usability.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • FAST: A Forecasting Model With Adaptive Sliding Window and Time Locality
           Integration for Dynamic Cloud Workloads

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      Authors: Binbin Feng;Zhijun Ding;Changjun Jiang;
      Pages: 1184 - 1197
      Abstract: The workload predictor has attracted attention as a key component of the proactive service operation management framework. However, the request and resource workloads of cloud applications are highly dynamic. Existing approaches decompose the original workload into trend, seasonal, and random components, establish models accordingly, and then combine all outputs to generate results. Indeed, the random component usually has significant heteroscedasticity and noise, having little or even a negative effect on model accuracy improvement. In our model, trend and seasonal components are seen as macro workload changes, and the micro workload changes are obtained by an adaptive sliding window algorithm. Therefore, we propose an ensembling model named FAST for Forecasting workloads with Adaptive Sliding window and Time locality integration. Notably, we propose an adaptive sliding window algorithm that considers trend correlation, time correlation, and random fluctuations of workload for online regression to achieve higher accuracy with lower overhead; and for the error-based integration strategy, we propose a time locality concept for local-predictor behavior and develop a multi-class regression algorithm for model integration. Finally, we conduct experiments on Google cluster trace datasets which show FAST has better accuracy than all other state-of-the-art models for dynamic workloads.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Flexible Offloading of Service Function Chains to Programmable Switches

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      Authors: Junte Ma;Sihao Xie;Jin Zhao;
      Pages: 1198 - 1211
      Abstract: A Service Function Chain (SFC) is an ordered sequence of network functions (NFs). Though cost-effective, software-based NFs could introduce a significant performance penalty. In this paper, we present P4SFC, a high-performance and flexible SFC system that leverages the capability of emerging programmable switches. We seek to accelerate packet processing in SFC by offloading proper NFs to P4-capable switches. First, considering the current limitations of P4, we analyze the offloadability of NFs at different granularities in detail, and enable P4SFC to generate offloading strategies for both partially and fully offloadable SFCs. Second, to deploy new SFCs at runtime, we design a dynamic P4 data plane, of which the execution logic can be reconfigured at runtime without interrupting the existed execution logic. Third, to efficiently utilize the limited memory in programmable switches, we propose a state allocator to dynamically offload those NF states that bring the highest performance profits according to the recent flow distribution. We demonstrate the feasibility and practicality of P4SFC with our implementation on a commodity Tofino-based programmable switch. Experimental results show that P4SFC achieves significant performance improvement for real SFC implementations.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • H3Rec: Higher-Order Heterogeneous and Homogeneous Interaction Modeling for
           Group Recommendations of Web Services

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      Authors: Zhixiang He;Chi-Yin Chow;Jia-Dong Zhang;Kam-Yiu Lam;
      Pages: 1212 - 1224
      Abstract: Recommendations are important web services in the era of information explosion. Particularly, group recommendations aim to suggest new items to groups such that the members of groups are likely interested in. However, existing works still suffer from sparsity and cold-start issues (e.g., cold-start groups or items) for groups with few interactions on items. Most of them model the preferences or features of entities (i.e., users, items and groups) from heterogeneous interactions (i.e., user-item, group-item and user-group interactions) between two distinct types of entities, while ignoring the homogeneous interactions (i.e., user-user, item-item and group-group interactions) between entities of one type. To this end, we propose a new model, called H3Rec, which learns the representations of entities by developing two graph embedding layers based on an interaction graph of all entities. Specifically, the two graph embedding layers make full use of the hidden information in the Higher-order Heterogeneous and Homogeneous interactions of the graph. Therefore, H3Rec can alleviate the sparsity and cold-start issues and improve the performance of group recommendations. The experimental results on two real world datasets in different domains show the superiority of H3Rec in group recommendations, especially for cold-start groups and items.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Heterogeneous Computational Resource Allocation for NOMA: Toward Green
           Mobile Edge-Computing Systems

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      Authors: Amin Mohajer;Mahya Sam Daliri;A. Mirzaei;A. Ziaeddini;M. Nabipour;Maryam Bavaghar;
      Pages: 1225 - 1238
      Abstract: Mobile Edge Computing (MEC) is a viable solution in response to the growing demand for broadband services in the new-generation heterogeneous systems. The dense deployment of small cell networks is a key feature of next-generation radio access networks aimed at providing the necessary capacity increase. Nonetheless, the problem of green networking and service computing will be of great importance in the downlink, because the uncontrolled installation of too many small cells may increase operational costs and emit more carbon dioxide. In addition, given the resource and computational limitation of the user layer, energy efficiency (EE) and fairness assurance are critical issues in MEC-based cellular systems. Considering the user fairness criteria, this paper proposes a dynamic optimization model which maximizes the total UL/DL EE along with satisfying the necessary QoS constraints. Based on the non-convex characteristics of the EE maximization problem, the mathematical model can be divided into two separate subproblems, i.e., computational carrier scheduling and resource allocation. So that, a subgradient method is applied for the computational resource allocation and also successive convex approximation (SCA) and dual decomposition methods are adopted to solve the max-min fairness problem. The simulation results exhibit considerable EE improvement for various traffic models in addition to guaranteeing the fairness requirements. It also proved that the proposed computational partitioning scheme managed to significantly improve the total throughput for mobile computing services.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Intelligent and Collaborative Orchestration of Network Slices

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      Authors: Ying Wang;Naling Li;Peng Yu;Wenjing Li;Xuesong Qiu;Shangguang Wang;Mohamed Cheriet;
      Pages: 1239 - 1253
      Abstract: 5G and beyond network will support vertical industry applications, and the resource requirements of each service vary widely. The introduction of network slices provides great flexibility to the network, which can realize the differentiated customization requirements of service. However, while determining how to intelligently orchestrate the network slices is an important challenge, current solutions rarely treat multiple customized requirements of delay, bandwidth, load balancing, and slice isolation. In this article, network slice orchestration is considered from the perspective of slice isolation and cloud-edge collaboration. First, differentiated isolation level requirements are restricted to constraints, the customized isolation is realized. Second, bandwidth is saved and network latency is reduced via the collaboration of cloud and edge data centers. In addition, exclusive orchestration optimization objectives that match various service needs are proposed to distinguish the specific requirements of different slices. Finally, two deep reinforcement learning-based algorithms are proposed. The experimental results demonstrate that the proposed algorithms can optimize the objectives while ensuring differentiated isolation levels. For typical slices, the proposed algorithms respectively reduce bandwidth consumption by about 29% and 64%, reduce slice delay by about 14% and 70%, and optimize load balancing by about 17% and 23%.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Joint Optimization of Request Assignment and Computing Resource Allocation
           in Multi-Access Edge Computing

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      Authors: Haolin Liu;Xiaoling Long;Zhetao Li;Saiqin Long;Rong Ran;Hui-Ming Wang;
      Pages: 1254 - 1267
      Abstract: With the development of multi-access edge computing (MEC), the cloudlet at the edge of the network can provide nearby high-performance computing services, thus reducing the computational consumption of user equipments (UEs). To provide more real-time computing services to UEs, service providers face the challenge of optimizing the assignment of requests and the allocation of cloudlets’ computing resources to achieve low latency while dealing with the large number of offloaded requests from UEs. Therefore, in this paper, we study the problem of minimizing the total latency to complete the requests in the MEC network by jointly optimizing request assignment and computing resource allocation. The problem is formulated as a mixed integer nonlinear programming (MINLP) problem which is NP-hard. To solve the problem, we decompose the problem into two subproblems which respectively optimize the request assignment and the computing resource allocation. We first deal with the computing resource allocation problem by utilizing the Lagrangian multiplier method, and the resulting solution is applied for the request assignment problem. Then a novel primal-dual based approximation algorithm is devised to address the request assignment problem. Finally, to verify the efficiency of the proposed algorithm, we provide an upper bound on the approximation ratio. The experiment results show that the proposed algorithm outperforms baseline algorithms in terms of total latency, loading balancing, and computational speed.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Multi-Use Trust in Crowdsourced IoT Services

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      Authors: Mohammed Bahutair;Athman Bouguettaya;Azadeh Ghari Neiat;
      Pages: 1268 - 1281
      Abstract: We introduce the concept of adaptive trust in crowdsourced IoT services. It is a customized fine-grained trust tailored for specific IoT consumers. Usage patterns of IoT consumers are exploited to provide an accurate trust value for service providers. A novel adaptive trust management framework is proposed to assess the dynamic trust of IoT services. The framework leverages a novel detection algorithm to obtain trust indicators that are likely to influence the trust level of a specific IoT service type. Detected trust indicators are then used to build service-to-indicator model to evaluate a service’s trust at each indicator. Similarly, a usage-to-indicator model is built to obtain the importance of each trust indicator for a particular usage scenario. The per-indicator trust and the importance of each trust indicator are utilized to obtain an overall value of a given service for a specific consumer. We conduct a set of experiments on a real dataset to show the effectiveness of the proposed framework.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • MU-TEIR: Traceable Encrypted Image Retrieval in the Multi-User Setting

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      Authors: Tengfei Yang;Jianfeng Ma;Yinbin Miao;Yue Wang;Ximeng Liu;Kim-Kwang Raymond Choo;Bin Xiao;
      Pages: 1282 - 1295
      Abstract: The encrypted image retrieval technique allows users to retrieve images in an encrypted manner without decrypting images. However, most of the existing schemes still are vulnerable to security threats and inefficiency, caused by malicious users and inefficient feature extraction methods, respectively. To this end, we propose a traceable encrypted image retrieval in the multi-user setting in this article, termed as MU-TEIR. First, MU-TEIR employs a convolutional neural network VGG16 to extract image feature vectors and calculate the mean and variance of the feature vectors to construct the index, then encrypts index with the distributed two trapdoors public-key cryptosystem. After that, MU-TEIR protects image content by encrypting each image pixel with a standard stream cipher. Furthermore, MU-TEIR utilizes a watermark-based mechanism to prevent malicious query users from maliciously distributing images. Detailed security analysis shows that MU-TEIR protects the outsourced images and indexes security as well as query privacy, and can track malicious users. Experimental results verify effectiveness of MU-TEIR.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Optimizing Data Center Energy Efficiency via Event-Driven Deep
           Reinforcement Learning

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      Authors: Yongyi Ran;Xin Zhou;Han Hu;Yonggang Wen;
      Pages: 1296 - 1309
      Abstract: To reduce the skyrocketing energy consumption of data centers, the prevailing approaches adopt the time-driven manner to control IT and cooling subsystems. These methods suffer from highly dynamic system states, complex action spaces and the risk of instability caused by frequent and unnecessary control operations. To tackle these problems, we propose a novel event-driven control paradigm and an optimization algorithm, under the deep reinforcement learning (DRL) framework. The principle is to make decisions based on certain critical events (e.g., overheating), rather than fixed periodic control. Specifically, we design an event-driven optimization framework to trigger control operations. Then, we present several models to describe IT and cooling subsystems, and mathematically define events to capture four types of prior factors that impact system performance. Furthermore, we develop an event-driven DRL (E-DRL) optimization algorithm to dispatch jobs and regulate cooling facilities for energy efficiency. Using two different types of real workload traces, we conduct extensive experiments to demonstrate that: 1) E-DRL reduces the number of regulating decisions by 70%$sim$∼95% while achieving a comparable or even better energy efficiency in comparison with the state-of-the-art algorithm; and 2) E-DRL can adapt the control frequency to the changing operational conditions and diverse workloads.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Optimizing Energy Efficiency for Data Center via Parameterized Deep
           Reinforcement Learning

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      Authors: Yongyi Ran;Han Hu;Yonggang Wen;Xin Zhou;
      Pages: 1310 - 1323
      Abstract: The rapid advancements in cloud computing, Big Data and their related applications have led to a skyrocketing increase in data center energy consumption year by year. The prior approaches for improving data center energy efficiency mostly suffer from high system dynamics or the complexity of data centers. In this paper, we propose an optimization framework based on deep reinforcement learning, named DeepEE, to jointly optimize energy consumption from the perspectives of task scheduling and cooling control. In DeepEE, a PArameterized action space based Deep Q-Network (PADQN) algorithm is proposed to tackle the hybrid action space problem. Then, a dynamic time factor mechanism for adjusting cooling control interval is introduced into PADQN (PADQN-D) to achieve more accurate and efficient coordination of IT and cooling subsystems. Finally, in order to train and evaluate the proposed algorithms safely and quickly, a simulation platform is built to model the dynamics of IT and cooling subsystems. Extensive real-trace based experiments illustrate that: 1) the proposed PADQN algorithm can save up to 15% and 10% energy consumption compared with the baseline siloed and joint optimization approaches respectively; 2) the proposed PADQN-D algorithm with dynamic cooling control interval can better adapt to the change of IT workload; 3) our proposed algorithms achieve more stable performance gain in terms of power consumption by adopting the parameterized action space.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Privacy-Preserving Fast Three-Factor Authentication and Key Agreement for
           IoT-Based E-Health Systems

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      Authors: Liping Zhang;Yue Zhu;Wei Ren;Yixin Zhang;Kim-Kwang Raymond Choo;
      Pages: 1324 - 1333
      Abstract: Electronic healthcare (e-health) systems have received renewed interest, particularly in the current COVID-19 pandemic (e.g., lockdowns and changes in hospital policies due to the pandemic). However, ensuring security of both data-at-rest and data-in-transit remains challenging to achieve, particularly since data is collected and sent from less insecure devices (e.g., patients’ wearable or home devices). While there have been a number of authentication schemes, such as those based on three-factor authentication, to provide authentication and privacy protection, a number of limitations associated with these schemes remain (e.g., (in)security or computationally expensive). In this study, we present a privacy-preserving three-factor authenticated key agreement scheme that is sufficiently lightweight for resource-constrained e-health systems. The proposed scheme enables both mutual authentication and session key negotiation in addition to privacy protection, with minimal computational cost. The security of the proposed scheme is demonstrated in the Real-or-Random model. Experiments using Raspberry Pi show that the proposed scheme achieves reduced computational cost (of up to 89.9% in comparison to three other related schemes).
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • PuzzleMesh: A Puzzle Model to Build Mesh of Agnostic
           Services for Edge-Fog-Cloud

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      Authors: Dante D. Sanchez-Gallegos;J. L. Gonzalez-Compean;Jesus Carretero;Heidy M. Marin-Castro;Andrei Tchernykh;Raffaele Montella;
      Pages: 1334 - 1345
      Abstract: This paper presents the design, development, and evaluation of PuzzleMesh, an agnostic service mesh composition model to process large volumes of data in edge-fog-cloud environments. This model is based on a puzzle metaphor where pieces, puzzles, and metapuzzles represent self-contained autonomous and reusable software artifacts encapsulated into containers and published as microservices. A piece represents the integration of apps with I/O interfaces (loops/sockets), parallel processing, and management software. A puzzle represents a processing structure (e.g., workflows) built coupling pieces through loops and sockets. Puzzles integrate structures with a microservice architecture, implicit continuous dataflows, and transparent data exchange management software. A metapuzzle represents a recursive assemble of puzzles. A mesh represents a pool of pieces, puzzles, and metapuzzles available for designers to choose artifacts to build services. A prototype developed using PuzzleMesh model was evaluated through case studies about the automatic construction of processing services for the acquisition, pre-processing, manufacturing, preserving, and visualizing of satellite imagery. A qualitative comparison revealed that PuzzleMesh provides a flexible way to build reusable and portable services and to improve the usability of the services. The case study also revealed that PuzzleMesh yielded better performance results than other state-of-the-art tools.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • QoS-Aware Cloud Resource Prediction for Computing Services

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      Authors: Patryk Osypanka;Piotr Nawrocki;
      Pages: 1346 - 1357
      Abstract: Computing services are increasingly located in computing clouds, which allows for on-demand scalability but may also increase operating costs. It is believed that cloud expenses constitute a significant budget item in companies of all sizes. There is a considerable body of work dedicated to reducing the costs of cloud computing, which is mainly focused on optimizing the use of cloud resources. Such optimization, however, tends to result in the deterioration of computing service responsiveness and, as a result, quality of service parameters, especially when applied to real-world, noisy data which include anomalies. This article presents a novel approach which involves a six-stage optimization process incorporating load prediction supported by machine learning, the discovery of computing service characteristics and long-term planning of resource usage alongside anomaly detection and continuous monitoring with a self-adapting ability. The solution proposed works autonomously, builds knowledge about the optimized system and its load patterns, calculates cost-optimal resource provisioning plans and adapts to rapid environmental changes. Our evaluation using Microsoft’s Azure cloud environment demonstrates savings ranging from 31% to 89% depending on the test scenario; cost reductions for other cloud computing providers were estimated as well.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Real-Time Scheduling on Hierarchical Heterogeneous Fog Networks

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      Authors: Amanjot Kaur;Nitin Auluck;Omer Rana;
      Pages: 1358 - 1372
      Abstract: Cloud computing is widely used to support offloaded data processing for various applications. However, latency constrained data processing has requirements that may not always be suitable for cloud-based processing. Fog computing brings processing closer to data generation sources, by reducing propagation and data transfer delays. It is a viable alternative for processing tasks with real-time requirements. We propose a scheduling algorithm $RTH^{2}S$RTH2S (Real Time Heterogeneous Hierarchical Scheduling) for a set of real-time tasks on a heterogeneous integrated fog-cloud architecture. We consider a hierarchical model for fog nodes, with nodes at higher tiers having greater computational capacity than nodes at lower tiers, though with greater latency from data generation sources. Tasks with various profiles have been considered. For the regular profile jobs, we use least laxity first (LLF) to find the preferred fog node for scheduling. In case of “tagged” profiles, based on their tag values, the jobs are split in order to finish execution before the deadline, or the LLF heuristic is used. Using HPC2N workload traces across 3.5 years of activity, the real-time performance of $RTH^{2}S$RTH2S versus comparable algorithms is d-monstrated. We also consider Microsoft Azure-based costs for the proposed algorithm. Our proposed approach is validated using both simulation (to demonstrate scale up) as well as a lab-based testbed.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • ReMatch: An Efficient Virtual Data Center Re-Matching Strategy Based on
           Matching Theory

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      Authors: Anurag Satpathy;Manmath Narayan Sahoo;Lucky Behera;Chittaranjan Swain;
      Pages: 1373 - 1386
      Abstract: A virtual data center (VDC) comprises multiple virtual machines (VMs) with communication dependencies represented as virtual links (VLs). These virtual components, i.e., VMs and VLs, often experience fluctuating demands across different resource types. In this article, we focus on addressing the issue of dynamic resource expansion that leads to the relocation of solution components (SCs), where a SC comprises a VM and its attached VLs, with either the VM and/or at least one of the VLs facing resource expansion. This is challenging because of the complexity involved in frequently relocating multiple dependent virtual components across the substrate network. This article presents a model called ReMatch that aims at building an efficient remapping plan with reduced remapping cost and improved resource utilization for service providers (SPs) in polynomial time. The overall relocation problem is formulated as a one-to-many matching game with heterogeneous VM demands. Owing to the inapplicability of the classical deferred acceptance algorithm (DAA) and revised DA (RDA), we propose a modified version of the RDA (MRDA) to obtain a weakly stable assignment. Thorough simulation and analysis show that ReMatch outperforms the baseline algorithms considering multiple evaluation metrics.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Robust Anomaly Clue Localization of Multi-Dimensional Derived Measure for
           Online Video Services

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      Authors: Yongqian Sun;Daguo Cheng;Pengxiang Jin;Quan Ding;Shenglin Zhang;Xu Chen;Yuzhi Zhang;Minghan Liang;Dan Pei;Jianyan Zheng;Sen Luo;Xinyu Tang;
      Pages: 1387 - 1401
      Abstract: Anomaly clue localization of multi-dimensional derived measure is vitally important for the reliability of online video services. In this paper, we propose RobustSpot, an end-to-end framework for localizing the clues to anomalous multi-dimensional derived measures. RobustSpot integrates two novel indicators, i.e., “Anomaly Degree” and “Contribution Ability”, with a simple yet effective method, weighted association rule mining (WARM), to automatically mine the hidden relationships across data dimensions for localizing the most likely clues to the root cause. Using 135 real-world cases collected from a top-tier global online video service provider $H$H with 170+ million monthly active users, we demonstrate that RobustSpot achieves high accuracy (Top-5 accuracy of 98%), significantly outperforming state-of-the-art methods. The average localization time of RobustSpot is 1.83s, which is satisfying in our scenario. We have open-sourced the implementation of RobustSpot as well as the data used in the evaluation experiments.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Scaling and Placing Distributed Services on Vehicle Clusters in Urban
           Environments

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      Authors: Kanika Sharma;Bernard Butler;Brendan Jennings;
      Pages: 1402 - 1416
      Abstract: Many vehicles spend a significant amount of time in urban traffic congestion. Due to the evolution of autonomous vehicles, driver assistance systems, and in-vehicle entertainment, these vehicles have plentiful computational and communication capacity. How can we deploy data collection and processing tasks on these (slowly) moving vehicles to productively use any spare resources' To answer this question, we study the efficient placement of distributed services on a moving vehicle cluster. We present a macroscopic flow model for an intersection in Dublin, Ireland, using real vehicle density data. We show that such aggregate flows are highly predictable (even though the paths of individual vehicles are not known in advance), making it viable to deploy services harnessing vehicles’ sensing capabilities. After studying the feasibility of using these vehicle clusters as infrastructure, we introduce a detailed mathematical specification for a task-based, distributed service placement model. The distributed service scales according to the resource requirements and is robust to the changes caused by the mobility of the cluster. We formulate this as a constrained optimization problem, with the objective of minimizing overall processing and communication costs. Our results show that jointly scaling tasks and finding a mobility-aware, optimal placement results in reduced processing and communication costs compared to the two schemes in the literature. We compare our approach to an autonomous vehicular edge computing-based naive solution and a clustering-based solution.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Service Home Identification of Multiple-Source IoT Applications in Edge
           Computing

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      Authors: Jing Li;Weifa Liang;Wenzheng Xu;Zichuan Xu;Yuchen Li;Xiaohua Jia;
      Pages: 1417 - 1430
      Abstract: The real-time communication requirement of the Internet of Things (IoT) applications promotes the convergence of IoT and Mobile Edge Computing (MEC). The MEC paradigm greatly shortens the IoT service delay by leveraging cloudlets (edge servers) of MEC in the proximity of IoT devices. Considering limited computing and storage resources in an MEC network, it is challenging to provide efficient IoT-enabled service provisioning in such a network. In this article, we study the service home identification problem of service provisioning for multi-source IoT applications in an MEC network, by identifying a service home (cloudlet) of each multi-source IoT application for its data processing, querying and storage. Each multi-source IoT application consists of multiple sources located at different geographical locations and each source uploads its data stream via a gateway (its nearby access point) to the MEC network and the uploaded data then is aggregated with the stream data of the other sources of the IoT application at the service home. We here focus on two novel service home identification problems: the service operational cost minimization problem with the aim to minimize the total service operational cost by admitting as many multi-source IoT applications as possible, and the online throughput maximization problem with the aim to maximize the number of multi-source IoT application requests admitted. We first show that both the problems are NP-hard. We then formulate an Integer Linear Programming (ILP) solution to the service operational cost minimization problem, and propose a randomized algorithm with high probability and a deterministic approximation algorithm respectively, at moderate resource capacity violations. We third develop an efficient heuristic algorithm for the problem without any resource violation. Furthermore, we deal with the online throughput maximization problem under an assumption that multi-source IoT application requests arrive one by one withou- the knowledge of future arrivals, for which we formulate an Integer Linear Programming (ILP) solution to its offline version, followed by devising an online algorithm with competitive ratio. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising, and outperform their comparison counterparts.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Shrinking the Kernel Attack Surface Through Static and Dynamic Syscall
           Limitation

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      Authors: Dongyang Zhan;Zhaofeng Yu;Xiangzhan Yu;Hongli Zhang;Lin Ye;
      Pages: 1431 - 1443
      Abstract: Linux Seccomp is widely used by the program developers and the system maintainers to secure the operating systems, which can block unused syscalls for different applications and containers to shrink the attack surface of the operating systems. However, it is difficult to configure the whitelist of a container or application without the help of program developers. Docker containers block about only 50 syscalls by default, and lots of unblocked useless syscalls introduce a big kernel attack surface. To obtain the dependent syscalls, dynamic tracking is a straight-forward approach but it cannot get the full syscall list. Static analysis can construct an over-approximated syscall list, but the list contains many false positives. In this paper, a systematic dependent syscall analysis approach, sysverify, is proposed by combining static analysis and dynamic verification together to shrink the kernel attack surface. The semantic gap between the binary executables and syscalls is bridged by analyzing the binary and the source code, which builds the mapping between the library APIs and syscalls systematically. To further reduce the attack surface at best effort, we propose a dynamic verification approach to intercept and analyze the security of the invocations of indirect-call-related or rarely invoked syscalls with low overhead.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Time Controlled Expressive Predicate Query With Accountable Anonymity

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      Authors: Yang Yang;Chunming Rong;Xianghan Zheng;Hongju Cheng;Victor Chang;Xiangyang Luo;Zuoyong Li;
      Pages: 1444 - 1457
      Abstract: Many existing searchable encryption schemes are inflexible in retrieval patterns. The data usage authorization is almost permanent valid as long as the user is not revoked. This “all-or-nothing” authorization mode is not compatible with the “pay-as-you-use” commercial billing model. In this article, we propose a new notion called time controlled expressive predicate query with accountable anonymity. It realizes time controlled data query, where a time server issues time token to authorize search privilege in designated time period. The data users can anonymously query on encrypted data and the anonymity is accountable in a way that the trusted authority is able to deanonymize data users if they misbehave in the system. The underlying techniques are anonymous credential, Pederson commitment and non-interactive zero-knowledge proof. We firstly design an efficient expressive predicate query (EPQ) scheme, which is proved secure to protect the privacy of expressive search predicate. Based on EPQ, we present a concrete system instantiation, which realizes key-escrow free and time token nontransferability. The formal definition and security models are given out. The system is formally proved indistinguishable against chosen keyword-set attacks, unforgeable of time tokens and accountable of anonymous users. The comparison and experiment results demonstrate its scalability and efficiency.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Utility-Aware and Privacy-Preserving Mobile Query Services

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      Authors: Emre Yigitoglu;M. Emre Gursoy;Ling Liu;
      Pages: 1458 - 1472
      Abstract: Location-based queries enable fundamental services for mobile users. While the benefits of location-based services (LBS) are numerous, exposure of mobile users’ locations to untrusted LBS providers may lead to privacy concerns. This article proposes StarCloak, a utility-aware and attack-resilient location anonymization service for privacy-preserving LBS usage. StarCloak combines several desirable properties. First, unlike conventional approaches which are indifferent to underlying road network structure, StarCloak uses the concept of stars and proposes cloaking graphs for effective location cloaking on road networks. Second, StarCloak supports user-specified $k$k-user anonymity and $l$l-segment indistinguishability, for enabling personalized privacy protection and for serving users with varying privacy preferences. Third, StarCloak achieves strong attack-resilience against replay and query injection attacks through randomized star selection and pruning. Finally, to enable efficient query processing with high throughput and low bandwidth overhead, StarCloak makes cost-aware star selection decisions by considering query evaluation and network communication costs. We evaluate StarCloak on two datasets using real-world road networks, under various privacy and utility constraints. Results show that StarCloak achieves improved query success rate and throughput, reduced anonymization time and network usage, and higher attack-resilience in comparison to XStar, its -ost relevant competitor.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Where is the Traffic Going' A Comparative Study of Clouds Following
           Different Designs

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      Authors: Qinkai Wang;Ye Tian;Xin Yu;Lan Ding;Xinming Zhang;
      Pages: 1473 - 1484
      Abstract: Cloud computing is critical for today's information society. In this paper, we shed light on two radically different cloud design philosophies: the DC-cloud built around massive data centers, and the ISP-cloud built upon a large ISP. With extensive measurements on Alibaba, Tencent, and CTYun, we find that both designs have strengths and weaknesses: the ISP-cloud of CTYun has less inflated paths to users within the same ISP, but its paths to external users are more inflated comparing with the DC-clouds of Alibaba and Tencent. By analyzing the clouds’ routing policies, we reveal the reasons behind the path inflations: Alibaba and Tencent adopt an early-exit policy to use more inflated public Internet paths as early as possible; while CTYun follows a global and location-agnostic policy to detour traffic to remote PoPs, leading to highly inflated paths. Based on the insights, we suggest alternative policies and averagely reduce 11.0% latency to 30.5% destinations for Alibaba, 9.8% latency to 18.6% destinations for Tencent, and 54.1% latency to external destinations for CTYun. The results suggest that both cloud designs have rooms for improvement, and an ISP-cloud has the potential to achieve a superior performance, thanks to its inherited advantages from the ISP infrastructure.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • AI-Enabled Secure Microservices in Edge Computing: Opportunities and
           Challenges

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      Authors: Firas Al-Doghman;Nour Moustafa;Ibrahim Khalil;Nasrin Sohrabi;Zahir Tari;Albert Y. Zomaya;
      Pages: 1485 - 1504
      Abstract: The paradigm of edge computing has formed an innovative scope within the domain of the Internet of Things (IoT) through expanding the services of the cloud to the network edge to design distributed architectures and securely enhance decision-making applications. Due to the heterogeneous, distributed and resource-constrained essence of edge Computing, edge applications are required to be developed as a set of lightweight and interdependent modules. As this concept aligns with the objectives of microservice architecture, effective implementation of microservices-based edge applications within IoT networks has the prospective of fully leveraging edge nodes capabilities. Deploying microservices at IoT edge faces plenty of challenges associated with security and privacy. Advances in Artificial Intelligence (AI) (especially Machine Learning), and the easy access to resources with powerful computing providing opportunities for deriving precise models and developing different intelligent applications at the edge of network. In this study, an extensive survey is presented for securing edge computing-based AI Microservices to elucidate the challenges of IoT management and enable secure decision-making systems at the edge. We present recent research studies on edge AI and microservices orchestration and highlight key requirements as well as challenges of securing Microservices at IoT edge. We also propose a Microservices-based edge computing framework that provides secure edge AI algorithms as Microservices utilizing the containerization technology to offer automated and secure AI-based applications at the network edge.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Load Balancing Algorithms in Fog Computing

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      Authors: Mostafa Haghi Kashani;Ebrahim Mahdipour;
      Pages: 1505 - 1521
      Abstract: Recently, fog computing has been introduced as a modern distributed paradigm and complement to cloud computing to provide services. The fog system extends storing and computing to the edge of the network, which can remarkably solve the problem of service computing in delay-sensitive applications besides enabling location awareness and mobility support. Load balancing is an important aspect of fog networks that avoids a situation with some under-loaded or overloaded fog nodes. Quality of service parameters such as resource utilization, throughput, cost, response time, performance, and energy consumption can be improved by load balancing. In recent years, some research in load balancing algorithms in fog networks has been carried out, but there is no systematic study to consolidate these works. This article investigates the load-balancing algorithms systematically in fog computing in four classifications, including approximate, exact, fundamental, and hybrid algorithms. Also, this article investigates load balancing metrics with all advantages and disadvantages related to chosen load balancing algorithms in fog networks. The evaluation techniques and tools applied for each reviewed study are explored as well. Additionally, the essential open challenges and future trends of these algorithms are discussed.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
  • Serverless Computing: State-of-the-Art, Challenges and Opportunities

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      Authors: Yongkang Li;Yanying Lin;Yang Wang;Kejiang Ye;Chengzhong Xu;
      Pages: 1522 - 1539
      Abstract: Serverless computing is growing in popularity by virtue of its lightweight and simplicity of management. It achieves these merits by reducing the granularity of the computing unit to the function level. Specifically, serverless allows users to focus squarely on the function itself while leaving other cumbersome management and scheduling issues to the platform provider, who is responsible for striking a balance between high-performance scheduling and low resource cost. In this article, we conduct a comprehensive survey of serverless computing with a particular focus on its infrastructure characteristics. Whereby some existing challenges are identified, and the associated cutting-edge solutions are analyzed. With these results, we further investigate some typical open-source frameworks and study how they address the identified challenges. Given the great advantages of serverless computing, it is expected that its deployment would dominate future cloud platforms. As such, we also envision some promising research opportunities that need to be further explored in the future. We hope that our work in this article can inspire those researchers and practitioners who are engaged in related fields to appreciate serverless computing, thereby setting foot in this promising area and making great contributions to its development.
      PubDate: March-April 1 2023
      Issue No: Vol. 16, No. 2 (2023)
       
 
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