article

Towards a multi-QoS human-centric cloud computing load balance resource allocation method

Authors:

Bing XieAuthors Info & Claims

The Journal of Supercomputing, Volume 72, Issue 7

Pages 2488 - 2501

https://doi.org/10.1007/s11227-015-1472-2

Published: 01 July 2016 Publication History

Abstract

In the large-scale clustering resource pool as human-centric cloud computing, peer load balance not only improves overall system efficiency, but also saves energy. As various factors should be considered in resource scheduling and each has different emphasis, resource allocation method adapted by different scene also has respective criteria. Based on resource allocation techniques, the multi-QoS load balance resource allocation method (MQLB-RAM) was proposed in the paper. It combines needs of users and service providers to constitute multi-QoS indexes. The needs from cost, system and network were met by quantitative analysis on load balancing using real-time load of peers. The algorithm also compares weight of each index in peer to match need and resource, so as to achieve the target of ensuring load balance, making full use of resources and saving money. Simulation experiment with CloudSim shows that the MQLB-RAM can achieve balance among load, resource access performance and cost.

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Jami SMunir S(2021)Current Trends in Cloud Computing for Data Science ExperimentsInternational Journal of Cloud Applications and Computing10.4018/IJCAC.202110010511:4(80-99)Online publication date: 1-Oct-2021
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Towards a multi-QoS human-centric cloud computing load balance resource allocation method

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Published In

cover image The Journal of Supercomputing

The Journal of Supercomputing Volume 72, Issue 7

July 2016

473 pages

ISSN:0920-8542

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Copyright © Copyright © 2016 Springer Science+Business Media New York.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2016

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Cited By

Jami SMunir S(2021)Current Trends in Cloud Computing for Data Science ExperimentsInternational Journal of Cloud Applications and Computing10.4018/IJCAC.202110010511:4(80-99)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.4018/IJCAC.2021100105
Ren CLuo QZhao JHuang YGarcía Guirao J(2020)Agricultural information resource scheduling algorithm based on firefly algorithm in cloud computingJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-17981738:6(7437-7448)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/JIFS-179817
Hosseini LTang SMookerjee VSriskandarajah C(2020)A Switch in Time Saves the DimeInformation Systems Research10.1287/isre.2019.091231:3(753-775)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1287/isre.2019.0912
Ma Hda Silva AKuang W(2019)NSGA-II with Local Search for Multi-objective Application Deployment in Multi-Cloud2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790006(2800-2807)Online publication date: 10-Jun-2019
https://dl.acm.org/doi/10.1109/CEC.2019.8790006
Panda SJana P(2019)Load balanced task scheduling for cloud computing: a probabilistic approachKnowledge and Information Systems10.1007/s10115-019-01327-461:3(1607-1631)Online publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1007/s10115-019-01327-4
Panda SJana P(2017)SLA-based task scheduling algorithms for heterogeneous multi-cloud environmentThe Journal of Supercomputing10.1007/s11227-016-1952-z73:6(2730-2762)Online publication date: 1-Jun-2017
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Madni SLatiff MCoulibaly YAbdulhamid S(2017)Recent advancements in resource allocation techniques for cloud computing environmentCluster Computing10.1007/s10586-016-0684-420:3(2489-2533)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s10586-016-0684-4

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