Improving the Performance of Data Sharing in Dynamic Peer-to-Peer Mobile Cloud

Mobile cloud computing has become an emerging computing paradigm to extend the capability of the mobile devices and it has gained increasing popularity in recent years. Existing studies mainly focus on how to leverage the computing capability of the individual device by employing the capability from remote cloud datacenters or local mobile cloud formed by nearby devices. Different from these studies, we investigate how to improve the performance of data sharing in the peer-to-peer mobile cloud, with the limited bandwidth and the presence of dynamic and unpredictable wireless channel state. Specifically, we first formulate the data transmission among devices as a utility maximization problem with the consideration of limited bandwidth, incentive participation and the QoE (Quality of Experience) heterogeneity, based on incorporating publish/subscribe component into the base station. Then, a dynamic online algorithm, which does not need the future context (e.g., channel state) of the mobile cloud, is developed to simultaneously make the decision of data transmission and communication interface selection. Rigorously theoretical analysis shows the optimality and the effectiveness of the proposed algorithm. Extensive experiments are conducted to verify the analysis results and the superiority of the proposed algorithm over existing strategies.

[1]  Michael J. Neely,et al.  Optimal Peer-to-Peer Schedulingfor Mobile Wireless Networkswith Redundantly Distributed Data , 2014, IEEE Transactions on Mobile Computing.

[2]  J. Lofberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004, 2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508).

[3]  Wei Gao,et al.  Code offload with least context migration in the mobile cloud , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[4]  Bhaskar Krishnamachari,et al.  Hermes: Latency Optimal Task Assignment for Resource-constrained Mobile Computing , 2017, IEEE Transactions on Mobile Computing.

[5]  Feng Xia,et al.  Phone2Cloud: Exploiting computation offloading for energy saving on smartphones in mobile cloud computing , 2013, Information Systems Frontiers.

[6]  Wei Zhou,et al.  DistressNet: a wireless ad hoc and sensor network architecture for situation management in disaster response , 2010, IEEE Communications Magazine.

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

[8]  Ramesh Govindan,et al.  Odessa: enabling interactive perception applications on mobile devices , 2011, MobiSys '11.

[9]  T. V. Lakshman,et al.  Online Allocation of Virtual Machines in a Distributed Cloud , 2017, IEEE/ACM Transactions on Networking.

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

[11]  Yoshitaka Shibata,et al.  Mobile Cloud Computing for Distributed Disaster Information System in Challenged Communication Environment , 2015, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops.

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

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

[14]  Wei Gao Opportunistic Peer-to-Peer Mobile Cloud Computing at the Tactical Edge , 2014, 2014 IEEE Military Communications Conference.

[15]  Karan Mitra,et al.  A Mobile Cloud Computing System for Emergency Management , 2014, IEEE Cloud Computing.

[16]  Ellen W. Zegura,et al.  Serendipity: enabling remote computing among intermittently connected mobile devices , 2012, MobiHoc '12.

[17]  Yu-Wei Su,et al.  A Comparative Study of Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi , 2007, IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society.

[18]  Xu Chen,et al.  COMET: Code Offload by Migrating Execution Transparently , 2012, OSDI.

[19]  Michael J. Neely,et al.  Distributed Stochastic Optimization via Correlated Scheduling , 2016, IEEE/ACM Transactions on Networking.