Efficient Computation Offloading Strategies for Mobile Cloud Computing

Development of cloud computing and mobile wireless technologies has given rise to mobile cloud computing (MCC). The limitations of battery capacity and computing capability of mobile devices can be alleviated by offloading some tasks from mobile devices to the cloud. In this paper, we focus on the computation offloading problem in mobile cloud computing. In particular, for a given set of computational components which constitute a mobile application, we attempt to decide which components should be offloaded to the cloud such that the application can be completed at the minimal execution cost. We formulate the mobile computation offloading problem as an optimization problem. Then we propose two optimal offloading algorithms to solve the problem. The efficiency of the proposed algorithms is evaluated using numerical experiments.

[1]  Chonho Lee,et al.  A survey of mobile cloud computing: architecture, applications, and approaches , 2013, Wirel. Commun. Mob. Comput..

[2]  Khaled A. Harras,et al.  Towards resource sharing in mobile device clouds: power balancing across mobile devices , 2013, MCC '13.

[3]  Qinru Qiu,et al.  A game theoretic resource allocation for overall energy minimization in mobile cloud computing system , 2012, ISLPED '12.

[4]  Bharat K. Bhargava,et al.  A Survey of Computation Offloading for Mobile Systems , 2012, Mobile Networks and Applications.

[5]  Tao Li,et al.  A Framework for Partitioning and Execution of Data Stream Applications in Mobile Cloud Computing , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[6]  Jean-Luc Gaudiot,et al.  An efficient heuristic for code partitioning , 2000, Parallel Comput..

[7]  Jörg Ott,et al.  Optimizing Offloading Strategies in Mobile Cloud Computing , 2013 .

[8]  Sivan Toledo,et al.  Wishbone: Profile-based Partitioning for Sensornet Applications , 2009, NSDI.

[9]  Bo Li,et al.  Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications , 2013, IEEE Wireless Communications.

[10]  Bo Li,et al.  eTime: Energy-efficient transmission between cloud and mobile devices , 2013, 2013 Proceedings IEEE INFOCOM.

[11]  Takayuki Nishio,et al.  Service-oriented heterogeneous resource sharing for optimizing service latency in mobile cloud , 2013, MobileCloud '13.

[12]  Yung-Hsiang Lu,et al.  Cloud Computing for Mobile Users: Can Offloading Computation Save Energy? , 2010, Computer.

[13]  Guang R. Gao,et al.  Source Code Partitioning in Program Optimization , 2011, 2011 IEEE 17th International Conference on Parallel and Distributed Systems.