Maximizing quality of experience through context‐aware mobile application scheduling in cloudlet infrastructure. (27th January 2016)

• Record Type: Journal Article
• Authors: Mahmud, Md. Redowan; Afrin, Mahbuba; Razzaque, Md. Abdur; Hassan, Mohammad Mehedi; Alelaiwi, Abdulhameed; Alrubaian, Majed
• Is Part Of: Software, practice & experience. Volume 46:Number 11(2016)
• Page Start: 1525
• Page End: 1545
• Publication Date: 2016-01-27
• Subjects: context‐aware scheduling -- mobile application software -- mobile cloud computing -- quality of experience; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2392
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Application software execution requests, from mobile devices to cloud service providers, are often heterogeneous in terms of device, network, and application runtime contexts. These heterogeneous contexts include the remaining battery level of a mobile device, network signal strength it receives and quality‐of‐service (QoS) requirement of an application software submitted from that device. Scheduling such application software execution requests (from many mobile devices) on competent virtual machines to enhance user quality of experience (QoE) is a multi‐constrained optimization problem. However, existing solutions in the literature either address utility maximization problem for service providers or optimize the application QoS levels, bypassing device‐level and network‐level contextual information. In this paper, a multi‐objective nonlinear programming solution to the context‐aware application software scheduling problem has been developed, namely, QoE and context‐aware scheduling (QCASH) method, which minimizes the application execution times (i.e., maximizes the QoE) and maximizes the application execution success rate. To the best of our knowledge, QCASH is the first work in this domain that inscribes the optimal scheduling problem for mobile application software execution requests with three‐dimensional context parameters. In QCASH, the context priority of each application is measured by applying min–max normalization and multiple linear regression models on three context parameters—battery level, network signal strength, and application QoS. Experimental results, found from simulation runs on CloudSim toolkit, demonstrate that the QCASH outperforms the state‐of‐the‐art works well across the success rate, waiting time, and QoE. Copyright © 2016 John Wiley & Sons, Ltd.