JournalTOCs

Service Oriented Computing and Applications
Journal Prestige (SJR): 0.339

Citation Impact (citeScore): 1
Number of Followers: 2

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1863-2394 - ISSN (Online) 1863-2386
Published by Springer-Verlag

[2469 journals]

A deep reinforcement learning-based approach for pricing in the competing
auction-based cloud market
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  Abstract: Abstract In the cloud market, there exist multiple cloud providers adopting auction-based mechanisms to offer cloud resources to users. These auction-based cloud providers need to compete against each other to maximize the profits by setting the cloud resource prices effectively. In this paper, we analyze how an auction-based cloud provider sets the auction price effectively when competing against other cloud providers in the evolutionary market where the amount of participated cloud users is changing. The pricing strategy is affected by many factors, such as the auction price of its opponents, the prices charged to users in the previous round, the bidding behavior of cloud users, and so on. Therefore, we model this problem as a partially observable Markov game and adopt a gradient-based multi-agent deep reinforcement learning algorithm to generate the competing pricing strategy. We also run extensive experiments to evaluate our pricing strategy against other five benchmark pricing strategies in the auction-based cloud market. The experimental results show that our generated pricing strategy can beat other pricing strategies in terms of long-term profits and the amount of participated users, and it can also learn cloud users’ marginal values and their choice of cloud providers effectively.
  PubDate: 2022-04-13
Community-based service ecosystem evolution analysis
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  Abstract: Abstract Services are flourishing dramatically, and continuously increasing interactions among them are resulting in a new phenomenon called “service ecosystems,” which has become a focus of academia and industry. Driven by technology innovation, changes in regulations, and changes in the competitive strategies of individual businesses, service ecosystems are constantly evolving. Service ecosystem evolution analysis is an emerging research problem of great significance. By analyzing service ecosystem evolution history, common evolution patterns can be identified, underlying driving forces can be discovered, and future evolution trends can be predicated so that service providers can adjust their competitive strategies in a timely manner to adapt to evolution trends. In this paper, a framework for identifying service ecosystem evolution patterns from the service community perspective is presented. First, following the approaches of community detection and community evolution analysis, time-series community evolution traces are identified from historical service ecosystem evolution, and a service community evolution prediction model is trained in accordance with such traces. Second, the prediction model is explained to show how different factors affect the evolution of service communities. Finally, an approach for assisting service providers in making business decisions is presented according to interpretable prediction results and prior domain knowledge. Experiments on a real-world dataset showed that this work can indeed provide business-level insights on service ecosystem evolution. Additionally, all the data and source code have been made fully open-source for service ecosystem researchers.
  PubDate: 2022-04-11
Publisher Correction to: A third-party replication service for dynamic
hidden databases
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  Abstract: In the original publication of the article, the figure legends of Figs. 2 and 4 were published with an error, due to two missed corrections. The corrections had been provided by the authors during the proofing process, but were missed by the typesetters.
  PubDate: 2022-03-01
  DOI: 10.1007/s11761-021-00316-2
Access control based on entity matching for secure data sharing
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  Abstract: Abstract Useful information for analysis and learning purposes is often located in heterogeneous and autonomous sources. We consider the problem where data owners want to share data that has access control policies associated with it. Data sources share information relying on entity matching rules (Conditions for which two records from different sources are considered as a match, i.e., represent the same real-world object) between their contents. In this paper, we propose an entity matching-oriented and policy-oriented methodology to provide a secure data sharing framework. We present an algorithm for translating a query submitted against one schema into an augmented query for the other schema to capture concerned tuples, based on entity matching rules. Then, we provide a methodology to answer queries while preserving local access control policies and also avoiding any inference leakage that could result from entity matching. Furthermore, we adduce details on our implementation and describe the key findings of the experiments.
  PubDate: 2022-01-07
  DOI: 10.1007/s11761-021-00331-3
Data-driven method for mobile game publishing revenue forecast
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  Abstract: Abstract Games as a service is similar to software as a service, which provides players with game content on a continuous monetization model. Game revenue forecast is vital to game developers to make the right business decisions, such as determining the marketing budget, controlling the development cost, and setting up benchmarks for evaluating game publishing performance. How to make the revenue forecast and integrate it with the game publishing process is hard for small and medium-sized independent (indie) game developers. This includes all steps of the process, from forecasting to decision-making based on the results. This paper provides a data-driven method that uses the mobile game revenue forecast based on different time-series prediction models to drive the game publishing. We demonstrate how to use the data-driven method to guide an indie game studio to forecast revenue and then set the revenue forecast as the internal benchmark to drive game publishing. In practice, we involve a real game project from an indie game studio and provide guidance for one of their casual game projects. Then, based on the revenue forecast, we discuss how to set the revenue forecast as an internal benchmark and drive the actions for mobile game publishing. Finally, we make a conclusion on how our data-driven method can be used to drive mobile game publishing and also discuss future research work.
  PubDate: 2021-12-23
  DOI: 10.1007/s11761-021-00332-2
A third-party replication service for dynamic hidden databases
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  Abstract: Abstract Much data on the web is available in hidden databases. Users browse their contents by sending search queries to form-based interfaces or APIs. Yet, hidden databases just return the top-k result entries and limit the number of queries per time interval. Such access restrictions constrict those tasks that require many/specific queries or need to access many/all data entries. For a temporary solution, an unrestricted local snapshot can be created by crawling the hidden database. Yet, keeping the snapshot permanently consistent is challenging due to the access restrictions of its origin. In this paper, we propose a replication approach providing permanent unrestricted access to the local copy of a hidden database with dynamic changes. To this end, we present an algorithm to effectively crawl hidden databases that outperforms the state of the art. Furthermore, we propose a new way to continuously control the consistency of the replicated database in an efficient manner. We also introduce the cloud-based architecture of a replication service for hidden databases. We show the effectiveness of the approach through a variety of reproducible experimental evaluations.
  PubDate: 2021-12-01
  DOI: 10.1007/s11761-020-00313-x
Multi-objective Scheduling Policy for Workflow Applications in Cloud Using
Hybrid Particle Search and Rescue Algorithm
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  Abstract: Abstract Cloud has been developed as a prominent distributed computing model over the last few years because of its wide array of resources and services that are virtualized, scalable, and on demand. In a distributed environment, coordination of workflow applications is an accepted NP-complete problem; hence, it is hard to derive exact solutions. Because of its dynamic and heterogeneous properties, this happens to be even more difficult in cloud environment. The intention of this work is to improve multi-objective optimization of scientific workflow scheduling based on proposed multi-objective hybrid particle search optimization algorithm (MOHPSO) in cloud computing platform and to propose an effective framework for workflow execution. For initial stage, fuzzy Manhattan distance-based clustering is performed to cluster the cloud resources. After that, enhanced chaotic neural network technique is applied to encrypt the task details for security purpose. In this article, the recent search and rescue optimization algorithm (SAR) is hybridized with popular particle swarm optimization algorithm (PSO) to enhance the exploration as well as search ability of optimization algorithm to create best schedules for workflow requests in cloud environment. Moreover, the scientific workflows like Epigenomics, Montage, and Cybershake with varying amount of task sizes are utilized to perform the scheduling process. CloudSim tool is utilized for the simulation of workflow scheduling problem in cloud. Performance enhancement of proposed methodology in terms of load balance, makespan, and cost is validated by comparison with various state-of-the-art algorithms. .
  PubDate: 2021-11-10
  DOI: 10.1007/s11761-021-00330-4
SPReaD: service-oriented process for reengineering and DevOps
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  Abstract: Abstract The reengineering of systems into a microservice-based architecture can be seen as an implementation of a service-oriented architecture (SOA). However, the deployment of SOA into an enterprise is a challenging task, as it may involve the modernization of mission-critical systems with high technical debt and high maintenance costs. To this end, a process is required to provide an appropriate set of steps and techniques that minimize risks and at the same time ensure the quality of the systems during the migration process. Thus, this work presents the Service-oriented Process for Reengineering and DevOps—SPReaD, an instantiation of the mainstream SOA methodology focusing on the reengineering of legacy systems integrating DevOps aspects for developing microservices systems. This process has been defined during a real software reengineering project of legacy systems from a Brazilian State Department of Taxation. The results obtained include a substantial improvement in the quality of the main taxation system used by the state, including not only code-related metrics but also performance improvements of the services offered, and a change in the methodology adopted by the software development team.
  PubDate: 2021-10-20
  DOI: 10.1007/s11761-021-00329-x
Security and privacy in the Internet of Things: threats and challenges
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  Abstract: Abstract In the past few years, the Internet of Things (IoT) has emerged, grown and gradually affected the daily lives of human beings in many new application domains, ranging from wearable devices, smart manufacturing, to smart homes and ambient intelligence just to mention a few. However, realizing the full potential of IoT while ensuring user security and privacy remains an open research challenge. Existing security solutions and techniques are mainly conceived for centralized and distributed information systems and are not directly applicable to IoT-based systems. In fact, IoT systems have unconventional characteristics such as intermittent connectivity, high scalability, dynamic changes and limited resources and thus require a paradigm shift to develop innovative security and privacy solutions. In this survey, we firstly give an overview of security and privacy in IoT. After defining the context of IoT systems, we identify four main characteristics, which imply unprecedented threats and challenges to existing security solutions and techniques. From the perspective of these characteristics and IoT security requirements, we identify and elaborate specific threats and challenges related to the radio-frequency identification (RFID), wireless sensor networks (WSNs) and mobile delay tolerant networks (MDTNs), which are building blocks in many IoT-based systems. In addition, we discuss potential countermeasures to handle IoT threats and challenges.
  PubDate: 2021-10-01
  DOI: 10.1007/s11761-021-00327-z
Cloud services description ontology used for service selection
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  Abstract: Abstract Cloud computing has emerged as a tremendous opportunity for both industry and academia as it introduces a fundamental shift in Cloud service delivery. This situation has led to more competitive Cloud providers giving end-users greater freedom to choose the best Cloud service. However, each Cloud provider uses its techniques to describe its Cloud services. As a result, it is increasingly difficult for users to find and access Cloud services with similar functionality. The variety of these techniques is due to the lack of Cloud services description standardization. To deal with such issues and due to the vast search space, we propose in this paper a Cloud services description ontology, “CSDO,” that assists the Cloud service publication, discovery, and selection processes. The proposed description is based on the Linked-USDL language to describe Cloud services thanks to its expressiveness.
  PubDate: 2021-09-28
  DOI: 10.1007/s11761-021-00328-y
Privacy preservation of genome data analysis using homomorphic encryption
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  Abstract: Abstract Very few organizations can afford the necessary infrastructure for genomic data analytic, which requires a very large amount of storage and computational resources. In this context, cloud computing platforms can be adopted as a practical solution for storage and computations. However, sharing such sensitive information with cloud providers can lead to violating privacy preservation and public regulations. To resolve this problem, homomorphic encryption (HE) can be used. Homomorphic encryption enables computation over encrypted data, which helps tackling the problem of privacy preservation. Despite this advantage, existing HE schemes suffer from high computational complexity and storage overhead; designing a practical HE scheme that provides simultaneously the efficiency and the required level of security still remains an open question. In this paper, we propose a secure cloud based scheme for storing and analyzing classified genetic data using homomorphic encryption (HE). In our scheme, we adopted an optimization technique based on decomposing a homomorphic cipher text into a number of independent cipher texts with lower storage overhead using the Chinese remainder theorem (CRT). Optimization is then accomplished by applying parallel processing over the independent cipher texts. The presented technique is applied to improve the efficiency of two well-known HE schemes: the Domingo Ferrer (DF) and the Paillier cryptosystems. After examining the correctness of the proposed optimization, it is used to design a secure cloud-based application dedicated for genome data analysis. A virtual cloud environment was created for this purpose. Different performance and security analyses have shown the efficiency of such solution and its compatibility with real-world applications. The execution time is reduced to more than half while maintaining a high security level.
  PubDate: 2021-09-14
  DOI: 10.1007/s11761-021-00326-0
Service-enabled systems and applications: current and future trends
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  PubDate: 2021-09-01
  DOI: 10.1007/s11761-021-00323-3
( $$k,\varepsilon ,\delta $$ k , ε , δ )-Anonymization:
privacy-preserving data release based on k-anonymity and differential
privacy
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  Abstract: Abstract The General Data Protection Regulation came into effect on May 25, 2018, and has rapidly become a touchstone model for modern privacy law. It empowers consumers with unprecedented control over the use of their personal information. However, new guarantees of consumer privacy adversely affect data sharing and data application markets because service companies (e.g., Apple, Google, Microsoft) cannot provide immediate and optimized services through analysis of collected consumer experiences. Therefore, data de-identification technology (e.g., k-anonymity and differential privacy) is a candidate solution to protect sharing data privacy. Various workarounds based on existing methods such as k-anonymity and differential privacy technologies have been proposed. However, they are limited in data utility, and their data sets have high dimensionality (the so-called curse of dimensionality). In this paper, we propose the ( $k,\varepsilon ,\delta $ )-anonymization synthetic data set generation mechanism (called ( $k,\varepsilon ,\delta $ )-anonymization for short) to protect data privacy before releasing data sets to be analyzed. Synthetic data sets generated by ( $k,\varepsilon ,\delta $ )-anonymization satisfy the definitions of k-anonymity and differential privacy by applying KD-tree and random sampling mechanisms. Moreover, ( $k,\varepsilon ,\delta $ )-anonymization uses principle component analysis to rationally replace high-dimensional data sets with lower-dimensional data sets for consideration of efficient computation. Finally, we confirm the relationships between parameters k, $\varepsilon $ , and $\delta $ for k-anonymity and ( $\varepsilon ,\delta $ )-differential privacy and estimate the utility of ( $k,\varepsilon ,\delta $ )-anonymization synthetic data sets. We report a privacy analysis and a series of experiments that prove that ( $k,\varepsilon ,\delta $ )-anonymization is feasible and efficient.
  PubDate: 2021-09-01
  DOI: 10.1007/s11761-021-00324-2
Modified Chebyshev polynomial-based access control mechanism for secured
data access in cloud computing environment
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  Abstract: Abstract Cloud computing is a popular model for offering infinite storage resources for the users. In the present scenario, extremely sensitive data is stored in the third-party cloud service provider’s infrastructure. For instance, patients who have direct access to the healthcare system may access the cloud-based health care data at any time based on the needs. Such sensitive data is subjected to various security issues because of the entities from multiple hosts connecting to cloud storage. In the proposed work, an optimized secured data access control mechanism named as modified Chebyshev polynomial-based access control (MCPAC) is presented. In MCPAC scheme, multiple levels of verification and authentications are carried out to provide resilience against widely recognized attacks. The proposed access control scheme is evaluated in a real private cloud infrastructure using the metrics, such as precision, recall and detection rate. It is proved that the proposed MCPAC offers better privacy protection with the precision of 0.8947, recall of 0.8983, and detection rate of 85.63%, which is high as compared to the conventional state-of-the-art methods. Security analysis is also done for the MCPAC scheme, and it indicates that the system efficiently handles the well-known attacks. The performance analysis shows that MCPAC meets the essential security mandates and excels in computational efficiency by making it suitable for the realistic applications hosted in a cloud computing environment.
  PubDate: 2021-09-01
  DOI: 10.1007/s11761-020-00307-9
QoS Prediction based on temporal information and request context
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  Abstract: Abstract Due to the complex and dynamic nature of the Internet, the status of services and their qualities (QoS) change frequently. It is thus important to predict the service quality accurately at runtime from the user’s perspective. Traditional service quality prediction methods either rarely utilize the context data or ignore the request temporal information. As a result, these methods are unable to well capture the depending factors and predict the QoS values accurately. To address this issue, we propose in this paper a novel method, QSPC, to predict service quality concerning both the context data and the temporal information. By mapping raw data to low-dimensional manifold space and fit the real dataset more effectively, our model can greatly utilize the context data to predict the QoS values. Moreover, a sequence-to-sequence layer is proposed to fit the temporal information in the dataset to capture the implicit factors of QoS. The experimental results show that our model outperforms the baseline solutions for service QoS prediction under a benchmark dataset.
  PubDate: 2021-09-01
  DOI: 10.1007/s11761-021-00322-4
An Event-B formal model for a system reconfiguration pattern and its
instantiation: application to Web services compensation
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  Abstract: Abstract System substitution can be defined as the capability to replace a system by another one that preserves the specification of the original one. It may be used for reconfiguration in various situations like failure management, maintenance or Web services compensation. When substituting a system at runtime, a key requirement is to correctly restore the state of the substituted one. This paper proposes a correct-by-construction generic model for system reconfiguration defined using formal methods, based on a system substitution operator we define. This model provides a formal semantics for Web services compensation seen as a particular case of system substitution. The originality of the proposed approach relies on the fact that it is defined on a family of systems and it provides instantiation mechanisms for particular systems using witnesses. Systems are seen as state transition systems, and the system substitution operation is formalized as a state recovery operation. This proposal is supported by a formal model relying on stepwise refinements and proofs. A generic formal model is developed using Event-B. Specific systems instantiate this generic model using a particular use of refinement based on the definition of witnesses for existential proof obligations. A specific case study, borrowed from an electronic commerce application, is used as a particular instance of the defined generic model.
  PubDate: 2021-09-01
  DOI: 10.1007/s11761-021-00314-4
Guarded attribute grammars and publish/subscribe for implementing
distributed collaborative business processes with high data availability
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  Abstract: Abstract With the ever-increasing development of the Internet and the diversification of communication media, business processes of companies are increasingly collaborative and distributed. This contrasts with traditional solutions deployed for their management which are usually centralized, based either on the control and coordination of the flow of activities, or on the documents exchanged or artifacts. Moreover, the users who are usually the main actors in collaborations are often relegated to second place. Recently, a distributed, data-driven and user-centric approach called guarded attribute grammar (GAG) has been proposed for the modeling of such processes; it thus provides an answer to most of these limitations. In this paper, we present an approach for implementing business processes modeled using GAG in which communications are done by publish/subscribe with redirection of subscription (pub/sub-RS). In fact, the pub/sub-RS—which we propose—is a new variant of the publish/subscribe protocol particularly adapted to the asynchronous and incremental exchange of semi-structured data. It allows to guarantee high data availability during the process execution by ensuring that an actor, perceived as a subscriber, will always receive a data he needs to perform a task as soon as it is produced. Moreover, if the data are semi-structured and produced collaboratively and incrementally by several actors, its subscribers will be notified as soon as one of its components (a prefix) is produced, simultaneously as they will be subscribed in a transparent way to the remaining components (the suffix).
  PubDate: 2021-09-01
  DOI: 10.1007/s11761-021-00319-z
Exposing safe correlations in transactional datasets
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  Abstract: Abstract A particularly challenging problem for data anonymization is dealing with transactional data. Most anonymization methods assume homogeneous, independent and identically distributed (i.i.d.) data; “flattening” transactional data to satisfy this model results in wide, sparse data that does not anonymize well with traditional techniques. While there have been some approaches for generalization-based anonymization, bucketization techniques (e.g., anatomy) pose new challenges. In particular, bucketization provides the opportunity to learn correlations between data items, but also a risk of identifying individuals because of dependencies inferred from such correlations. We present a method that balances these issues, retaining the ability to discover correlations in the data, while hiding dependencies that would enable correlations to be used to link specific values to individuals. We introduce a correlation anonymization constraint that ensures correlations do not allow data to be linked to a specific individual, and an elastic safe grouping algorithm that meets this constraint while preserving data correlations. We evaluate the utility loss on a transactional rental dataset.
  PubDate: 2021-08-21
  DOI: 10.1007/s11761-021-00325-1
An evolution model of composed service based on global dependence net
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  Abstract: Abstract Web service composition is a major way of constructing SOA-based applications. However, as uses’ requirements change, web services have to be recomposed correspondingly once again from the scratch. It will be rather time-consuming, error-prone and mostly fussy. To tackle the widespread requirements changes, we propose a novel approach that can make an existing composed service automatically grade to reach another new composed service in an evolutionary manner according to user’s requirements. An evolution model called control structure net is built to formally represent composition structure of a certain composed service based on interface dependence. Furthermore, a global dependence net, which provides an evolution knowledge base, is constructed by modeling all available web services. Evolution process is presented in detail and evolution reasoning algorithms are given to automatically remove invalid paths and make up necessary paths. Experimental results show that our proposed approach can correctly evolve to target composed service, and its performance also greatly surpasses that of classic service composition approach.
  PubDate: 2021-04-07
  DOI: 10.1007/s11761-021-00318-0
Privacy protection in government data sharing: an improved LDP-based
approach
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  Abstract: Abstract Governments own various types and large amounts of individual data. One governmental department manages specific areas of data. To develop smart government, data need to be shared among the governmental departments. However, how to prevent potential attackers from getting private information in data sharing is a challenging problem. To protect private information while sharing statistics among government departments, an improved LDP-based (local differential privacy) approach is proposed. This approach combines the data binning technique with the count mean sketch (CMS) algorithm. Equi-width binning is adopted to divide the data records into smaller data domains to overcome the problem of large statistical errors in the current privacy protection algorithms with large data domain size and small amounts of data. Then, the proposed algorithm is compared with the CMS and HCMS algorithms from different aspects such as frequency estimation, data size, privacy budget, and data domain size. Experimental results show that the proposed algorithm effectively reduces statistical errors and enhances the utility of data after privacy protection with both various distributions and data domain sizes.
  PubDate: 2021-03-01
  DOI: 10.1007/s11761-021-00315-3