Mobile-Edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling

The incorporation of dynamic voltage scaling technology into computation offloading offers more flexibilities for mobile edge computing. In this paper, we investigate partial computation offloading by jointly optimizing the computational speed of smart mobile device (SMD), transmit power of SMD, and offloading ratio with two system design objectives: energy consumption of SMD minimization (ECM) and latency of application execution minimization (LM). Considering the case that the SMD is served by a single cloud server, we formulate both the ECM problem and the LM problem as nonconvex problems. To tackle the ECM problem, we recast it as a convex one with the variable substitution technique and obtain its optimal solution. To address the nonconvex and nonsmooth LM problem, we propose a locally optimal algorithm with the univariate search technique. Furthermore, we extend the scenario to a multiple cloud servers system, where the SMD could offload its computation to a set of cloud servers. In this scenario, we obtain the optimal computation distribution among cloud servers in closed form for the ECM and LM problems. Finally, extensive simulations demonstrate that our proposed algorithms can significantly reduce the energy consumption and shorten the latency with respect to the existing offloading schemes.

[1]  Gang Qu,et al.  What is the limit of energy saving by dynamic voltage scaling? , 2001, IEEE/ACM International Conference on Computer Aided Design. ICCAD 2001. IEEE/ACM Digest of Technical Papers (Cat. No.01CH37281).

[2]  Minho Kang,et al.  Delay-guaranteed Energy Saving Algorithm for the Delay-sensitive Applications in IEEE 802.16e Systems , 2007, IEEE Transactions on Consumer Electronics.

[3]  Jukka K. Nurminen,et al.  Energy Efficiency of Mobile Clients in Cloud Computing , 2010, HotCloud.

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

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

[6]  Zhigang Cao,et al.  ARCOR: Agile Rateless Coded Relaying for Cognitive Radios , 2011, IEEE Transactions on Vehicular Technology.

[7]  Yongbin Wei,et al.  A survey on 3GPP heterogeneous networks , 2011, IEEE Wireless Communications.

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

[9]  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.

[10]  Preben E. Mogensen,et al.  LTE UE Power Consumption Model: For System Level Energy and Performance Optimization , 2012, 2012 IEEE Vehicular Technology Conference (VTC Fall).

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

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

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

[14]  Sergio Barbarossa,et al.  Joint Optimization of Radio Resources and Code Partitioning in Mobile Cloud Computing , 2013, ArXiv.

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

[16]  Katinka Wolter,et al.  Tradeoff between performance improvement and energy saving in mobile cloud offloading systems , 2013, 2013 IEEE International Conference on Communications Workshops (ICC).

[17]  Sanon Chimmanee,et al.  PACS metric based on regression for evaluating end-to-end QoS capability over the Internet for telemedicine , 2013, The International Conference on Information Networking 2013 (ICOIN).

[18]  Sergio Barbarossa,et al.  Joint allocation of computation and communication resources in multiuser mobile cloud computing , 2013, 2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[19]  Massoud Pedram,et al.  A semi-Markovian decision process based control method for offloading tasks from mobile devices to the cloud , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).

[20]  Yuan Zhao,et al.  When mobile terminals meet the cloud: computation offloading as the bridge , 2013, IEEE Network.

[21]  J. Wenny Rahayu,et al.  Mobile cloud computing: A survey , 2013, Future Gener. Comput. Syst..

[22]  Sergio Barbarossa,et al.  Small cell clustering for efficient distributed cloud computing , 2014, 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC).

[23]  Sergio Barbarossa,et al.  On the impact of backhaul network on distributed cloud computing , 2014, 2014 IEEE Wireless Communications and Networking Conference Workshops (WCNCW).

[24]  Yu Cheng,et al.  CONCERT: a cloud-based architecture for next-generation cellular systems , 2014, IEEE Wireless Communications.

[25]  Sergio Barbarossa,et al.  Computation offloading strategies based on energy minimization under computational rate constraints , 2014, 2014 European Conference on Networks and Communications (EuCNC).

[26]  Antonio Pascual-Iserte,et al.  Joint scheduling of communication and computation resources in multiuser wireless application offloading , 2014, 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC).

[27]  Sergio Barbarossa,et al.  The Fog Balancing: Load Distribution for Small Cell Cloud Computing , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[28]  Xu Chen,et al.  Decentralized Computation Offloading Game for Mobile Cloud Computing , 2014, IEEE Transactions on Parallel and Distributed Systems.

[29]  Yonggang Wen,et al.  Collaborative Task Execution in Mobile Cloud Computing Under a Stochastic Wireless Channel , 2015, IEEE Transactions on Wireless Communications.

[30]  Mohamed Kamoun,et al.  Joint resource allocation and offloading strategies in cloud enabled cellular networks , 2015, 2015 IEEE International Conference on Communications (ICC).

[31]  Jiannong Cao,et al.  Multi-User Computation Partitioning for Latency Sensitive Mobile Cloud Applications , 2015, IEEE Transactions on Computers.

[32]  Antonio Pascual-Iserte,et al.  Optimization of Radio and Computational Resources for Energy Efficiency in Latency-Constrained Application Offloading , 2014, IEEE Transactions on Vehicular Technology.

[33]  Sergio Barbarossa,et al.  Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing , 2014, IEEE Transactions on Signal and Information Processing over Networks.

[34]  Yan Shi,et al.  Energy-optimal partial computation offloading using dynamic voltage scaling , 2015, 2015 IEEE International Conference on Communication Workshop (ICCW).

[35]  R. Krishnaveni,et al.  Toward Transcoding As A Service In A Multimedia Cloud Energy-Efficient Job Dispatching Algorithm , 2016 .