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Adaptive Job-Scheduling Algorithm based on Queuing Theory in a Hybrid Cloud Environment

Volume 15, Number 6, June 2019, pp. 1580-1590
DOI: 10.23940/ijpe.19.06.p9.15801590

Yanpei Liua, Xiaoni Chena, Ying Hua, and Qiang Caib

aSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
bBeijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 102488, China


(Submitted on March 20, 2019; Revised on April 3, 2019; Accepted on June 5, 2019)


To resolve the problem of unreasonable resource allocations caused by the continuous arrival of different types of jobs in a hybrid cloud environment, an adaptive job-scheduling algorithm based on queuing theory is proposed. This paper analyses job load types, and the jobs are classified according to the logistic regression method. A resource utility is used to classify the nodes in a private cloud cluster by considering the heterogeneity of the private cloud resources. Based on the job classification and the resource classification, a queuing model is established, and an adaptive genetic algorithm is used to manage the job queue's arrival rate that becomes the basis of the resource allocation. The proposed algorithm is compared with some existed similar algorithms to verify its performance in terms of job response times and throughput.


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