Cloud offloading for multi-radio enabled mobile devices

The advent of 5G networking technologies has increased the expectations from mobile devices, in that, more sophisticated, computationally intense applications are expected to be delivered on the mobile device which are themselves getting smaller and sleeker. This predicates a need for offloading computationally intense parts of the applications to a resource strong cloud. Parallely, in the wireless networking world, the trend has shifted to multi-radio (as opposed to multi-channel) enabled communications. In this paper, we provide a comprehensive computation offloading solution that uses the multiple radio links available for associated data transfer, optimally. Our contributions include: a comprehensive model for the energy consumption from the perspective of the mobile device; the formulation of the joint optimization problem to minimize the energy consumed as well as allocating the associated data transfer optimally through the available radio links and an iterative algorithm that converges to a locally optimal solution. Simulations on an HTC phone, running a 14-component application and using the Amazon EC2 as the cloud, show that the solution obtained through the iterative algorithm consumes only 3% more energy than the optimal solution (obtained via exhaustive search).

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

[2]  Stephen P. Boyd,et al.  Subgradient Methods , 2007 .

[3]  Athanasios V. Vasilakos,et al.  A Survey of Green Mobile Networks: Opportunities and Challenges , 2012, Mob. Networks Appl..

[4]  Laura Vasiliu,et al.  CloneCloud: Elastic Execution between Mobile Device and Cloud , 2012 .

[5]  Xinwen Zhang,et al.  Towards an Elastic Application Model for Augmenting Computing Capabilities of Mobile Platforms , 2010, MOBILWARE.

[6]  Geng Wu,et al.  M2M: From mobile to embedded internet , 2011, IEEE Communications Magazine.

[7]  Sergio Barbarossa,et al.  Computation offloading for mobile cloud computing based on wide cross-layer optimization , 2013, 2013 Future Network & Mobile Summit.

[8]  Kai Hong,et al.  SpiderRadio: A Cognitive Radio Implementation Using IEEE 802.11 Components , 2013, IEEE Transactions on Mobile Computing.

[9]  Narseo Vallina-Rodriguez,et al.  Energy Management Techniques in Modern Mobile Handsets , 2013, IEEE Communications Surveys & Tutorials.

[10]  Byung-Gon Chun,et al.  CloneCloud: elastic execution between mobile device and cloud , 2011, EuroSys '11.

[11]  Nitin H. Vaidya,et al.  Resource Allocation in Multi-Radio Multi-Channel Multi-Hop Wireless Networks , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[12]  Nitin H. Vaidya,et al.  Scheduling in Multi-Channel Wireless Networks , 2010, ICDCN.

[13]  Dominik Kaspar,et al.  Multipath aggregation of heterogeneous access networks , 2012, ACMMR.

[14]  Pan Hui,et al.  ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading , 2012, 2012 Proceedings IEEE INFOCOM.

[15]  Haiyun Luo,et al.  Energy-optimal mobile application execution: Taming resource-poor mobile devices with cloud clones , 2012, 2012 Proceedings IEEE INFOCOM.

[16]  Haiyun Luo,et al.  Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel , 2013, IEEE Transactions on Wireless Communications.

[17]  Dusit Niyato,et al.  A Dynamic Offloading Algorithm for Mobile Computing , 2012, IEEE Transactions on Wireless Communications.

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

[19]  Alan Messer,et al.  Adaptive offloading for pervasive computing , 2004, IEEE Pervasive Computing.

[20]  Alec Wolman,et al.  MAUI: making smartphones last longer with code offload , 2010, MobiSys '10.

[21]  Tian Yu,et al.  Adaptive Computation Offloading from Mobile Devices into the Cloud , 2012, 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications.