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A Survey on Load Balancing in Cloud Systems for Big Data Applications

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High-Performance Computing and Big Data Analysis (TopHPC 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 891))

Abstract

Today’s ever-growing information world, in which we witness the juggernaut of information explosion stemming from social networks, medical records, diverse medias, IoT, and so forth, has called for a solution—encompassing boundless resources for this voluminous information’s storing as well as processing in a distributed manner. To do so, although cloud computing has come up with an applicable remedy, it has overwhelmingly required a well-defined load-balancing mechanism, lifeblood of any given distributed system; a load-balancing algorithm has consistently strove to pinpoint overloaded nodes so as to disseminate and shift the burden of extra workload towards the under-loaded ones—by which the overall system performance in terms of resource utilization, throughput, cost, and response time will be guaranteed after all. In the interests of placing a high premium on load-balancing issue in distributed systems, in this study, we have provided a review concerning load-balancing algorithms in cloud environment for Big Data environment.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Kharazmi University, Tehran, Iran

    Arman Aghdashi & Seyedeh Leili Mirtaheri

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  1. Arman Aghdashi
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  2. Seyedeh Leili Mirtaheri
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Correspondence to Arman Aghdashi .

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  1. University of Calabria, Rende, Italy

    Lucio Grandinetti

  2. Kharazmi University, Tehran, Iran

    Seyedeh Leili Mirtaheri

  3. University of Calabria, Rende, Italy

    Reza Shahbazian

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Aghdashi, A., Mirtaheri, S.L. (2019). A Survey on Load Balancing in Cloud Systems for Big Data Applications. In: Grandinetti, L., Mirtaheri, S., Shahbazian, R. (eds) High-Performance Computing and Big Data Analysis. TopHPC 2019. Communications in Computer and Information Science, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-33495-6_13

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  • DOI: https://doi.org/10.1007/978-3-030-33495-6_13

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