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Multi-User Computation Partitioning for Latency Sensitive Mobile Cloud Applications

Yang, L and Cao, J and Cheng, H and Ji, Y (2014) Multi-User Computation Partitioning for Latency Sensitive Mobile Cloud Applications. IEEE TRANSACTIONS ON COMPUTERS, 64 (8). pp. 2253-2266. ISSN 0018-9340

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Abstract

Elastic partitioning of computations between mobile devices and cloud is an important and challenging research topic for mobile cloud computing. Existing works focus on the single-user computation partitioning, which aims to optimize the application completion time for one particular single user. These works assume that the cloud always has enough resources to execute the computations immediately when they are offloaded to the cloud. However, this assumption does not hold for large scale mobile cloud applications. In these applications, due to the competition for cloud resources among a large number of users, the offloaded computations may be executed with certain scheduling delay on the cloud. Single user partitioning that
does not take into account the scheduling delay on the cloud may yield significant performance degradation. In this paper, we study, for the first time, Multi-user Computation Partitioning Problem (MCPP), which considers the partitioning of multiple users’ computations together with the scheduling of offloaded computations on the cloud resources. Instead of pursuing the minimum application completion time for every single user, we aim to achieve minimum average completion time for all the users, based on
the number of provisioned resources on the cloud. We show that MCPP is different from and more difficult than the classical job scheduling problems. We design an offline heuristic algorithm, namely SearchAdjust, to solve MCPP. We demonstrate through benchmarks that SearchAdjust outperforms both the single user partitioning approaches and classical job scheduling approaches by 10% on average in terms of application delay. Based on SearchAdjust, we also design an online algorithm for MCPP that can be easily deployed in practical systems. We validate the effectiveness of our online algorithm using real world load traces.
Index Terms—mobile cloud computing; offloading; computation partitioning; job scheduling

Item Type: Article
Additional Information: (c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
Uncontrolled Keywords: 0803 Computer Software, 0805 Distributed Computing, 1006 Computer Hardware
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science
Publisher: IEEE COMPUTER SOC
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Date Deposited: 14 Mar 2016 13:08
Last Modified: 14 Mar 2016 13:08
DOI or Identification number: 10.1109/TC.2014.2366735
URI: http://researchonline.ljmu.ac.uk/id/eprint/3201

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