A predictive workload balancing algorithm in cloud services

Mahdee Jodayree, Mahmoud Abaza, Qing Tan

Research output: Contribution to journalConference articlepeer-review

15 Citations (Scopus)

Abstract

Performance of dynamic clouds depends on the efficiency of its load balancing and resource allocation. This paper is an exploratory study on the predictive approach for dynamic resource distribution of cloud services. Efficient cloud resource management can be achieved by simulating cloud services based on the predictions of incoming workloads, which can be more efficient than static allocation methods. This paper introduces a rule-based workload-balancing algorithm based on the predictions of an end-to-end system called Cicada. A simulation of cloud services can be achieved by a cloud service simulator called CloudSim and it will be used to achieve an algorithm with lower computational demand and a faster workload balancing. The final result will demonstrate the effectiveness of a predictive workload balancing approach that can achieve faster workload balancing with a lower computational power usage.

Original languageEnglish
Pages (from-to)902-912
Number of pages11
JournalProcedia Computer Science
Volume159
DOIs
Publication statusPublished - 2019
Event23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, KES 2019 - Budapest, Hungary
Duration: 4 Sep. 20196 Sep. 2019

Keywords

  • algorithm
  • dynamic cloud
  • load balance
  • predictive
  • workload

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