• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

A Task Scheduling Strategy in Cloud Computing with Service Differentiation

Vol. 12, No.11, November 30, 2018
10.3837/tiis.2018.11.006, Download Paper (Free):

Abstract

Task scheduling is one of the key issues in improving system performance and optimizing resource management in cloud computing environment. In order to provide appropriate services for heterogeneous users, we propose a novel task scheduling strategy with service differentiation, in which the delay sensitive tasks are assigned to the rapid cloud with high-speed processing, whereas the fault sensitive tasks are assigned to the reliable cloud with service restoration. Considering that a user can receive service from either local SaaS (Software as a Service) servers or public IaaS (Infrastructure as a Service) cloud, we establish a hybrid queueing network based system model. With the assumption of Poisson arriving process, we analyze the system model in steady state. Moreover, we derive the performance measures in terms of average response time of the delay sensitive tasks and utilization of VMs (Virtual Machines) in reliable cloud. We provide experimental results to validate the proposed strategy and the system model. Furthermore, we investigate the Nash equilibrium behavior and the social optimization behavior of the delay sensitive tasks. Finally, we carry out an improved intelligent searching algorithm to obtain the optimal arrival rate of total tasks and present a pricing policy for the delay sensitive tasks.


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Cite this article

[IEEE Style]
Yuanzheng Xue, Shunfu Jin and Xiushuang Wang, "A Task Scheduling Strategy in Cloud Computing with Service Differentiation," KSII Transactions on Internet and Information Systems, vol. 12, no. 11, pp. 5269-5286, 2018. DOI: 10.3837/tiis.2018.11.006

[ACM Style]
Xue, Y., Jin, S., and Wang, X. 2018. A Task Scheduling Strategy in Cloud Computing with Service Differentiation. KSII Transactions on Internet and Information Systems, 12, 11, (2018), 5269-5286. DOI: 10.3837/tiis.2018.11.006