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  • 1.
    Anwar, Adnan
    et al.
    UNSW, Canberra, ACT 2600, Australia..
    Mahmood, A. N.
    La Trobe Univ, Bundoora, Vic 3086, Australia..
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). Karlstad Univ, Dept Comp Sci, S-65188 Karlstad, Sweden..
    Tari, Zahir
    RMIT Univ, Distributed Syst, Melbourne, Vic 3001, Australia..
    Zomaya, Albert Y.
    Univ Sydney, Sch Informat Technol, Sydney, NSW 2006, Australia..
    HPC-Based Intelligent Volt/VAr Control of Unbalanced Distribution Smart Grid in the Presence of Noise2017In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061, Vol. 8, no 3, p. 1446-1459Article in journal (Refereed)
    Abstract [en]

    The performance of Volt/VAr optimization has been significantly improved due to the integration of measurement data obtained from the advanced metering infrastructure of a smart grid. However, most of the existing works lack: 1) realistic unbalanced multi-phase distribution system modeling; 2) scalability of the Volt/VAr algorithm for larger test system; and 3) ability to handle gross errors and noise in data processing. In this paper, we consider realistic distribution system models that include unbalanced loadings and multi-phased feeders and the presence of gross errors such as communication errors and device malfunction, as well as random noise. At the core of the optimization process is an intelligent particle swarm optimization-based technique that is parallelized using high performance computing technique to solve Volt/VAr-based power loss minimization problem. Extensive experiments covering the different aspects of the proposed framework show significant improvement over existing Volt/VAr approaches in terms of both the accuracy and scalability on IEEE 123 node and a larger IEEE 8500 node benchmark test systems.

  • 2.
    Boru, Dejene
    et al.
    Create-Net, Italy.
    Kliazovich, Dzmitry
    University Luxembourg, Luxembourg.
    Granelli, Fabrizio
    DISI University Trento, Italy..
    Bouvry, Pascal
    University Luxembourg, Luxembourg.
    Zomaya, Albert Y.
    University Sydney, Australia.
    Energy-Efficient Data Replication in Cloud Computing Datacenters2013In: 2013 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), IEEE , 2013, p. 446-451Conference paper (Refereed)
    Abstract [en]

    Cloud computing is an emerging paradigm that provides computing resources as a service over a network. Communication resources often become a bottleneck in service provisioning for many cloud applications. Therefore, data replication, which brings data (e.g., databases) closer to data consumers (e.g., cloud applications), is seen as a promising solution. It allows minimizing network delays and bandwidth usage. In this paper we study data replication in cloud computing data centers. Unlike other approaches available in the literature, we consider both energy efficiency and bandwidth consumption of the system, in addition to the improved Quality of Service (QoS) as a result of the reduced communication delays. The evaluation results obtained during extensive simulations help to unveil performance and energy efficiency tradeoffs and guide the design of future data replication solutions.

  • 3.
    Boru, Dejene
    et al.
    Create-Net, Italy.
    Kliazovich, Dzmitry
    University Luxembourg, Luxembourg.
    Granelli, Fabrizio
    University Trento, Italy.
    Bouvry, Pascal
    University Luxembourg, Luxembourg.
    Zomaya, Albert Y.
    University Sydney, Australia.
    Models for Efficient Data Replication in Cloud Computing Datacenters2015In: 2015 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), Institute of Electrical and Electronics Engineers (IEEE), 2015, p. 6056-6061Conference paper (Refereed)
    Abstract [en]

    Cloud computing is a computing model where users access ICT services and resources without regard to where the services are hosted. Communication resources often become a bottleneck in service provisioning for many cloud applications. Therefore, data replication which brings data (e.g., databases) closer to data consumers (e.g., cloud applications) is seen as a promising solution. In this paper, we present models for energy consumption and bandwidth demand of database access in cloud computing datacenter. In addition, we propose an energy efficient replication strategy based on the proposed models, which results in improved Quality of Service (QoS) with reduced communication delays. The evaluation results obtained with extensive simulations help to unveil performance and energy efficiency tradeoffs and guide the design of future data replication solutions.

  • 4.
    Casas, Israel
    et al.
    University of Sydney, Australia; CSIRO, Data61, Canberra, ACT, Australia.
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Ranjan, Rajiv
    Newcastle University, England ; CSIRO, Data61, Canberra, ACT, Australia.
    Zomaya, Albert Y.
    University of Sydney, Australia.
    PSO-DS: a scheduling engine for scientific workflow managers2017In: Journal of Supercomputing, ISSN 0920-8542, E-ISSN 1573-0484, Vol. 73, no 9, p. 3924-3947Article in journal (Refereed)
    Abstract [en]

    Cloud computing, an important source of computing power for the scientific community, requires enhanced tools for an efficient use of resources. Current solutions for workflows execution lack frameworks to deeply analyze applications and consider realistic execution times as well as computation costs. In this study, we propose cloud user-provider affiliation (CUPA) to guide workflow's owners in identifying the required tools to have his/her application running. Additionally, we develop PSO-DS, a specialized scheduling algorithm based on particle swarm optimization. CUPA encompasses the interaction of cloud resources, workflow manager system and scheduling algorithm. Its featured scheduler PSO-DS is capable of converging strategic tasks distribution among resources to efficiently optimize makespan and monetary cost. We compared PSO-DS performance against four well-known scientific workflow schedulers. In a test bed based on VMware vSphere, schedulers mapped five up-to-date benchmarks representing different scientific areas. PSO-DS proved its efficiency by reducing makespan and monetary cost of tested workflows by 75 and 78%, respectively, when compared with other algorithms. CUPA, with the featured PSO-DS, opens the path to develop a full system in which scientific cloud users can run their computationally expensive experiments.

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