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Computer Journal
Journal Prestige (SJR): 0.319
Citation Impact (citeScore): 1
Number of Followers: 9  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0010-4620 - ISSN (Online) 1460-2067
Published by Oxford University Press Homepage  [411 journals]
  • Faulty Node Detection in HMM-Based Cooperative Spectrum Sensing For
           Cognitive Radio Networks
    • Authors: Das S; Acharya T, Chen I.
      Pages: 1468 - 1478
      Abstract: AbstractSpectrum sensing (SS) is often considered to be one of the highly challenging issues in cognitive radio networks (CRNs), which is being extensively investigated to solve the growing problem RF spectrum scarcity in the next-generation wireless networks. Recently, spectrum prediction assisted cooperative spectrum sensing (CSS) is emerging as an effective way of SS, enabling efficient utilization of two critical resources of SS: time and energy. In this paper, we evaluate the reliability of hidden Markov model (HMM)-based CSS in CRNs, in the presence of random malfunctioning of secondary user (SU) nodes participating in the process. In view of the poor performance of the CSS, especially at low signal-to-noise ratio (SNR) values, we propose a new scheme to detect the possible presence of faulty nodes in the CSS system with high accuracy and quarantine them to maintain the reliability of the spectrum prediction process. The proposed scheme suggests a novel integration of the forward algorithm of HMM with fuzzy-C means clustering technique to design a robust spectrum prediction assisted CSS in CRNs. Simulation results confirm that our scheme delivers a significantly improved receiver operating characteristics compared to a prominent scheme even in the presence of high percentage of failure of SU nodes.
      PubDate: Sat, 06 Jan 2018 00:00:00 GMT
      DOI: 10.1093/comjnl/bxx127
      Issue No: Vol. 61, No. 10 (2018)
       
  • Performability-Based Workflow Scheduling in Grids
    • Authors: Entezari-Maleki R; Trivedi K, Sousa L, et al.
      Pages: 1479 - 1495
      Abstract: AbstractIn this paper, the performance of a grid resource is modeled and evaluated using stochastic reward nets (SRNs), wherein the failure–repair behavior of its processors is taken into account. The proposed SRN is used to compute the blocking probability and service time of a resource for two different types of tasks: grid and local tasks. After modeling a grid resource and evaluating the performability measures, an algorithm is presented to find the probability mass function (pmf) of the service time of the grid resource for a program which is composed of grid tasks. The proposed algorithm exploits the universal generating function to find the pmf of service time of a single grid resource for a given program. Therefore, it can be used to compute the pmf of the service time of entire grid environment for a workflow with several dependent programs. Each possible scheduling of programs on grid resources may result in different service times and successful execution probabilities. Due to this fact, a genetic-based scheduling algorithm is proposed to appropriately dispatch programs of a workflow application to the resources distributed within a grid computing environment. Numerical results obtained by applying the proposed SRN model, the algorithm to find the pmf of grid service time, and the genetic-based scheduling algorithm to a comprehensive case study demonstrate the applicability of the proposed approach to real systems.
      PubDate: Wed, 10 Jan 2018 00:00:00 GMT
      DOI: 10.1093/comjnl/bxx125
      Issue No: Vol. 61, No. 10 (2018)
       
  • Cache Freshness in Named Data Networking for the Internet of Things
    • Authors: Meddeb M; Dhraief A, Belghith A, et al.
      Pages: 1496 - 1511
      Abstract: AbstractThe Information-Centric Networking (ICN) paradigm is shaping the foreseen future Internet architecture by focusing on the data itself rather than its hosting location. It is a shift from a host-centric communication model to a content-centric model supporting among others unique and location-independent content names, in-network caching and name-based routing. By leveraging the easy data access, and reducing both the retrieval delay and the load on the data producer, the ICN can be a viable framework to support the Internet of Things (IoT), interconnecting billions of heterogeneous constrained objects. Among several ICN architectures, the Named Data Networking (NDN) is considered as a suitable ICN architecture for IoT systems. However, its default caching approach lacks a data freshness mechanism, while IoT data are transient and frequently updated by the producer which imposes stringent requirements in terms of information freshness. Furthermore, IoT devices are usually resource-constrained with harsh limitations on energy, memory and processing power. We propose in this paper a caching strategy and a novel cache freshness mechanism to monitor the validity of cached contents in an IoT environment while minimizing the caching process cost. We compared our solution to several relevant schemes using the ccnSim simulator. Our solution exhibits the best system performances in terms of hop reduction ratio, server hit reduction ratio and response latency, yet it provides the lowest cache cost and significantly improves the content validity.
      PubDate: Sat, 27 Jan 2018 00:00:00 GMT
      DOI: 10.1093/comjnl/bxy005
      Issue No: Vol. 61, No. 10 (2018)
       
  • BOUQUET—Aggregating Network Paths in Trees to Reduce Data-Plane
           Forwarding State
    • Authors: Mamede M; Martins J, Horta J, et al.
      Pages: 1512 - 1522
      Abstract: AbstractFlexible network management requires explicit control of the exact paths taken by different network flows. Whatever the way this endeavour is achieved (e.g. Multiprotocol Label Switching, Virtual Local Area Networks and OpenFlow), this need may lead to an explosion of entries in the forwarding tables of network equipment. In this article, we present an algorithm that aggregates many network paths in a reduced number of trees, thus allowing shrinking the forwarding state in switching devices. Path aggregation algorithms are often deployed to reduce data-plane state with different routing approaches and the presented algorithm achieves better results than existent algorithms with similar goals. Additionally, we show that most types of popular routing and switching equipment, even using off-the-shelf routing software, may be used to implement multi-path routing with trees. This highlights the applicability of the proposed algorithm and its significance in light of the current trend of separating the data- and the control-planes in modern networks.
      PubDate: Wed, 21 Feb 2018 00:00:00 GMT
      DOI: 10.1093/comjnl/bxy015
      Issue No: Vol. 61, No. 10 (2018)
       
  • A New Multi-Objective Optimal Programming Model for Task Scheduling using
           Genetic Gray Wolf Optimization in Cloud Computing
    • Authors: Gobalakrishnan N; Arun C, Marshall A.
      Pages: 1523 - 1536
      Abstract: AbstractNowadays, the cloud computing has emerged as the advanced form of distributed computing, grid computing, utility computing and virtualization. Efficient task scheduling algorithms would help reduce the number of virtual machines used and in turn reduce the cost and increase the fitness function. According to this, a new multi-objective function is proposed combining load utilization, energy consumption, migration cost and time. Using this objective function, we proposed a hybrid algorithm namely Genetic Gray Wolf Optimization Algorithm (GGWO) by combining Gray Wolf Optimizer (GWO) and Genetic Algorithm (GA). The performance of the algorithm is analyzed based on the different evaluation measures. The algorithm such as GWO and GA algorithm is compared with proposed GGWO and it is taken for the comparative analysis. To improve the performance analysis the work has been computed with five common scientific workflows such as LIGO, Montage, Epigenomics, SIPHT and Cybershake. Experiments show that GGWO can improve task scheduling when compared with standard GWO and GA with minimum computation time, migration cost, energy consumption and maximum load utilization.
      PubDate: Sat, 24 Mar 2018 00:00:00 GMT
      DOI: 10.1093/comjnl/bxy009
      Issue No: Vol. 61, No. 10 (2018)
       
  • Energy-Aware Routing in Software-Defined Network using Compression
    • Authors: Giroire F; Huin N, Moulierac J, et al.
      Pages: 1537 - 1556
      Abstract: AbstractSoftware-defined Network (SDN) is a new networking paradigm enabling innovation through network programmability. Over past few years, many applications have been built using SDN such as server load balancing, virtual-machine migration, traffic engineering and access control. In this paper, we focus on using SDN for energy-aware routing (EAR). Since traffic load has a small influence on the power consumption of routers, EAR allows putting unused links into sleep mode to save energy. SDN can collect traffic matrix and then computes routing solutions satisfying QoS while being minimal in energy consumption. However, prior works on EAR have assumed that the SDN forwarding table switch can hold an infinite number of rules. In practice, this assumption does not hold since such flow tables are implemented in Ternary Content Addressable Memory (TCAM) which is expensive and power hungry. We consider the use of wildcard rules to compress the forwarding tables. In this paper, we propose optimization methods to minimize energy consumption for a backbone network while respecting capacity constraints on links and rule space constraints on routers. In details, we present two exact formulations using Integer Linear Program (ILP) and introduce efficient heuristic algorithms. Based on simulations on realistic network topologies, we show that using this smart rule space allocation, it is possible to save almost as much power consumption as the classical EAR approach.
      PubDate: Sat, 24 Mar 2018 00:00:00 GMT
      DOI: 10.1093/comjnl/bxy029
      Issue No: Vol. 61, No. 10 (2018)
       
  • Impact FD: An Unreliable Failure Detector Based on Process Relevance and
           Confidence in the System
    • Authors: Rossetto A; Geyer C, Arantes L, et al.
      Pages: 1557 - 1576
      Abstract: AbstractThis paper presents a new unreliable failure detector, called the Impact failure detector (FD), that, contrarily to the majority of traditional FDs, outputs a trust level value which expresses the degree of confidence in the system. An impact factor is assigned to each process and the trust level is equal to the sum of the impact factors of the processes not suspected of failure. Moreover, a threshold parameter defines a lower bound value for the trust level, over which the confidence in the system is ensured. In particular, we defined a flexibility property that denotes the capacity of the Impact FD to tolerate a certain margin of failures or false suspicions, i.e. its capacity of considering different sets of responses that lead the system to trusted states. The Impact FD is suitable for systems that present node redundancy, heterogeneity of nodes, clustering feature and allow a margin of failures which does not degrade the confidence in the system. The paper also includes a timer-based distributed algorithm which implements an Impact FD, as well as its proof of correctness, for systems whose links are lossy asynchronous or for those whose all (or some) links are eventually timely. Performance evaluation results, based on PlanetLab (Planetlab. http://www.planet-lab.org. ‘Online. Access date: 16 September 2016’) traces, confirm the degree of flexible applicability of our FD and that, due to the accepted margin of failure, both failures and false suspicions are more tolerated when compared to traditional unreliable FDs.
      PubDate: Mon, 30 Apr 2018 00:00:00 GMT
      DOI: 10.1093/comjnl/bxy041
      Issue No: Vol. 61, No. 10 (2018)
       
  • Efficient Privacy-Preserving Data Sanitization over Cloud Using Optimal
           GSA Algorithm
    • Authors: Renuga S; Jagatheeshwari S, Liu J.
      Pages: 1577 - 1588
      Abstract: AbstractData sharing is one of the important tasks in cloud computing application. When large volume of data is stored in the cloud, there is a chance for malicious attacks on the sensitive information stored in the cloud due to traditional poor privacy-preserving methods in the cloud. In this paper, an efficient data transform in the cloud with privacy-preserving method using sanitization algorithm is proposed. The optimal key is chosen here to process the sanitization algorithm effectively and gravitational search algorithm (GSA) is used to select the optimal key. The optimal key value is difficult to predict while using GSA algorithm. So this work is more secure compared to the existing privacy-preserving approach. The performance of the proposed technique is compared with artificial bee colony algorithm (ABC), elliptic curve cryptography (ECC), and RSA (Rivest–Shamir–Adleman). From the experimental result, this proposed method serves more secure with minimum execution time and hiding failure, maximum dissimilarity value when compared to the existing method. Finally, the result obtained shows that the proposed system has improved the privacy performance in cloud computing. The implementation will be done in JAVA using cloud simulator.
      PubDate: Fri, 13 Jul 2018 00:00:00 GMT
      DOI: 10.1093/comjnl/bxy067
      Issue No: Vol. 61, No. 10 (2018)
       
  • Optimized Service Level Agreement Establishment in Cloud Computing
    • Authors: de Azevedo L; Estrella J, Nakamura L, et al.
      Pages: 1429 - 1442
      Abstract: Nowadays, the access to a cloud computing environment is provided on-demand, offering transparent services to clients. Although the cloud allows an abstraction of the behavior of the infrastructure in the service providers (involving logical and physical resources), the Service Level Agreements (SLAs) fulfilment remain a challenge, because depending on the service demand and the system configuration, the providers may not be able to meet the clients requirements. In this way, mechanisms that take account of load balancing and resource provisioning algorithms to provide an efficient load distribution in the available resources are necessary. However, the studies in the literature do not effectively address the problem of the resource provisioning to meet clients requirements using optimization techniques, restricting the analysis to a limited set of objectives. This paper proposes algorithms to address the computational resource provisioning problem using optimization techniques on-the-fly. The techniques optimize the use of the resources available in the cloud infrastructure, aiming to fulfill the clients requirements defined in the SLAs, and ensuring the efficient use of resources.
      PubDate: Thu, 21 Sep 2017 00:00:00 GMT
      DOI: 10.1093/comjnl/bxx087
      Issue No: Vol. 61, No. 10 (2017)
       
  • Optimal Distributed Auction for Mobile Crowd Sensing
    • Authors: Feng Z; Zhu Y, Cai H, et al.
      Pages: 1443 - 1459
      Abstract: AbstractMobile crowd sensing, also called crowd sensing over smartphones, has been an appealing paradigm for collecting sensory data over a vast urban area, due to advantages of low deployment cost and widely spatial coverage of geographically distributed smartphones or other smart devices. In the paper, we focus on a nontrivial problem of making an agreement between crowdsourcers and smartphone users to find the most efficient assignment of sensing tasks to smartphone users. However, there exist several technical challenges such as the incentive issue to encourage participation of smartphone users, preserving private information and distributed implementation in practice. Existing approaches usually have several limitations, e.g. the absence of proper incentives, the assumption of a centralized auctioneer or platform. To this end, we propose a distributed auction framework that explicitly models the interaction between crowdsourcers and smartphone users, achieving the optimal social profit and providing proper incentives to entities without disclosing their privacy as well. We demonstrate that the proposed distributed auction algorithm satisfies a lot of good properties, including optimality of social profit, computation efficiency, convergence, individual rationality through both solid theoretical analysis and extensive experiments.
      PubDate: Mon, 11 Dec 2017 00:00:00 GMT
      DOI: 10.1093/comjnl/bxx115
      Issue No: Vol. 61, No. 10 (2017)
       
  • urgMAC: A New Traffic and QoS-aware Cross-Layer MAC protocol for Wireless
           Multimedia Sensor Networks
    • Authors: Ozen Y; Bayilmis C, Chen I.
      Pages: 1460 - 1467
      Abstract: AbstractWireless Multimedia Sensor Networks (WMSNs) transmit heterogeneous data having different quality of service and urgency constraints. WMSNs focus on offering QoS for multimedia transmission while Wireless Sensor Networks focus on minimizing energy consumption. To overcome urgency challenges, a new traffic and QoS-aware cross-layer MAC protocol for WMSNs namely urgMAC is proposed in this paper. The urgMAC aims to provide continuous QoS support with video quality tradeoff at the application layer dynamically for applications such as habitat monitoring, military border surveillance and border monitoring containing specific urgency challenges. To this end, the urgMAC includes new mechanisms called Two Tiered Service Differentiation Mechanism, Adaptive Data Rate Adjustment Mechanism, Urgency-based Contention Window Size Adaptation, Traffic Type Adaptive Duty Cycle and Multimedia Message Passing. The urgMAC has been modeled and simulated by Riverbed Modeling and Simulation Software. In addition, the urgMAC is compared with the recent protocols in the literature, and it achieves better results in terms of end-to-end delay and channel utilization.
      PubDate: Fri, 29 Dec 2017 00:00:00 GMT
      DOI: 10.1093/comjnl/bxx126
      Issue No: Vol. 61, No. 10 (2017)
       
 
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