Cloudlet dynamic server selection policy for mobile task off-loading in mobile cloud computing using soft computing techniques

Cloudlet dynamic server selection policy for mobile task off-loading in mobile cloud computing... Wide acceptance of mobile phones and their resource hungry applications have highlighted resource limitations of mobile devices. In this regard, cloud computing has provided mobile phones with unlimited resources in order to help them overcome their constraints and enable them to support wider range of applications; so, mobile devices can outsource their tasks to public or local clouds. To accommodate to exponential growth of requests, user requests should be distributed to different cloudlets and then transparently and dynamically redirected to the servers according to the latest network and server status. Therefore, finding the best place to off-load is vital and crucial to both functionality and performance of the system. However, accurate and timely parameters of network and servers’ status are improbable to achieve, so the traditional algorithms cannot perform effectively and fully efficient. As a solution in this paper, an adaptive neuro-fuzzy inference system is proposed and trained to assign tasks to the servers efficiently. The trained system is robust to imprecise context information and is tolerable measurement noise and errors. We have considered improving both system performance and user quality of service parameters in this paper. Simulation results demonstrate that, compared with other server selection schemes, the proposed scheme can achieve higher resource utilization (utilization is a percentage of time that a server is busy doing something), provide better user-perceived quality of service, and efficiently deal with network dynamics. Simulation results show that our proposed algorithm excels over the compared works in terms of performance, at the best case about 30% and at the worst case about 8.93%. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Supercomputing Springer Journals

Cloudlet dynamic server selection policy for mobile task off-loading in mobile cloud computing using soft computing techniques

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Programming Languages, Compilers, Interpreters; Processor Architectures; Computer Science, general
ISSN
0920-8542
eISSN
1573-0484
D.O.I.
10.1007/s11227-017-1983-0
Publisher site
See Article on Publisher Site

Abstract

Wide acceptance of mobile phones and their resource hungry applications have highlighted resource limitations of mobile devices. In this regard, cloud computing has provided mobile phones with unlimited resources in order to help them overcome their constraints and enable them to support wider range of applications; so, mobile devices can outsource their tasks to public or local clouds. To accommodate to exponential growth of requests, user requests should be distributed to different cloudlets and then transparently and dynamically redirected to the servers according to the latest network and server status. Therefore, finding the best place to off-load is vital and crucial to both functionality and performance of the system. However, accurate and timely parameters of network and servers’ status are improbable to achieve, so the traditional algorithms cannot perform effectively and fully efficient. As a solution in this paper, an adaptive neuro-fuzzy inference system is proposed and trained to assign tasks to the servers efficiently. The trained system is robust to imprecise context information and is tolerable measurement noise and errors. We have considered improving both system performance and user quality of service parameters in this paper. Simulation results demonstrate that, compared with other server selection schemes, the proposed scheme can achieve higher resource utilization (utilization is a percentage of time that a server is busy doing something), provide better user-perceived quality of service, and efficiently deal with network dynamics. Simulation results show that our proposed algorithm excels over the compared works in terms of performance, at the best case about 30% and at the worst case about 8.93%.

Journal

The Journal of SupercomputingSpringer Journals

Published: Feb 20, 2017

References

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