Optimal scheduling for data transmission between mobile devices and cloud

Mobile cloud computing has emerged as a new computing paradigm promising to extend the capabilities of resource-constrained mobile devices. In this new paradigm, mobile devices are enabled to offload computing tasks, report sensing records, and store large files on the cloud through wireless networks. Therefore, efficient data transmission has become an important issue affecting user experiences on mobile cloud. Considering the limited battery energy of mobile devices and different application requirements on transmission delay, this study presents an online control algorithm (OPERA) based on the Lyapunov optimization theory for optimally scheduling data transmission between mobile devices and cloud. The OPERA algorithm is able to make control decisions on application scheduling, interface selection and packet dropping to minimize a joint utility of network energy cost and packet dropping penalty, without requiring any statistical information of traffic arrivals and link throughputs. Rigorous analysis and extensive simulations have demonstrated its distinguished performance in terms of utility optimality, system stability and service delay.

[1]  Anand Sivasubramaniam,et al.  Optimal power cost management using stored energy in data centers , 2011, PERV.

[2]  Longbo Huang,et al.  Optimal Sleep-Wake Scheduling for Energy Harvesting Smart Mobile Devices , 2013, IEEE Transactions on Mobile Computing.

[3]  Alexandros G. Dimakis,et al.  Efficient Algorithms for Renewable Energy Allocation to Delay Tolerant Consumers , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[4]  Ahmad Rahmati,et al.  Context-for-wireless: context-sensitive energy-efficient wireless data transfer , 2007, MobiSys '07.

[5]  Prasant Mohapatra,et al.  Dynamic speed scaling for energy minimization in delay-tolerant smartphone applications , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[6]  Bo Li,et al.  Ready, Set, Go: Coalesced offloading from mobile devices to the cloud , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[7]  Wei Cai,et al.  Next Generation Mobile Cloud Gaming , 2013, 2013 IEEE Seventh International Symposium on Service-Oriented System Engineering.

[8]  Zongpeng Li,et al.  Dynamic pricing and profit maximization for the cloud with geo-distributed data centers , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[9]  Naixue Xiong,et al.  A Bare-Metal and Asymmetric Partitioning Approach to Client Virtualization , 2014, IEEE Transactions on Services Computing.

[10]  Jie Wu,et al.  Geocommunity-Based Broadcasting for Data Dissemination in Mobile Social Networks , 2012 .

[11]  Xi Fang,et al.  Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing , 2012, Mobicom '12.

[12]  Arun Venkataramani,et al.  Energy consumption in mobile phones: a measurement study and implications for network applications , 2009, IMC '09.

[13]  Ramesh Govindan,et al.  Energy-delay tradeoffs in smartphone applications , 2010, MobiSys '10.

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

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

[16]  Jiming Chen,et al.  Trapping Mobile Targets in Wireless Sensor Networks: An Energy-Efficient Perspective , 2013, IEEE Transactions on Vehicular Technology.

[17]  Igor Chikalov,et al.  Dynamic programming approach to optimization of approximate decision rules , 2013, Inf. Sci..

[18]  Rajesh K. Gupta,et al.  CoolSpots: reducing the power consumption of wireless mobile devices with multiple radio interfaces , 2006, MobiSys '06.

[19]  Feng Qian,et al.  A close examination of performance and power characteristics of 4G LTE networks , 2012, MobiSys '12.

[20]  Longbo Huang,et al.  Power Cost Reduction in Distributed Data Centers: A Two-Time-Scale Approach for Delay Tolerant Workloads , 2015, IEEE Transactions on Parallel and Distributed Systems.

[21]  Ashok K. Agrawala,et al.  Local Adjustment and Global Adaptation of Control Periods for QoC Management of Control Systems , 2012, IEEE Transactions on Control Systems Technology.

[22]  Yuguang Fang,et al.  Energy and Network Aware Workload Management for Sustainable Data Centers with Thermal Storage , 2014, IEEE Transactions on Parallel and Distributed Systems.

[23]  Randy H. Katz,et al.  Measuring and Reducing Energy Consumption of Network Interfaces in Hand-Held Devices (Special Issue on Mobile Computing) , 1997 .

[24]  Jiming Chen,et al.  Load scheduling with price uncertainty and temporally-coupled constraints in smart grids , 2015, 2015 IEEE Power & Energy Society General Meeting.

[25]  Jean C. Walrand,et al.  Optimal demand response with energy storage management , 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).

[26]  Narseo Vallina-Rodriguez,et al.  Exhausting battery statistics: understanding the energy demands on mobile handsets , 2010, MobiHeld '10.

[27]  Michael J. Neely,et al.  Opportunistic scheduling with worst case delay guarantees in single and multi-hop networks , 2011, 2011 Proceedings IEEE INFOCOM.

[28]  Xin Xu,et al.  Reinforcement learning algorithms with function approximation: Recent advances and applications , 2014, Inf. Sci..

[29]  Rajkumar Buyya,et al.  Cloud-Based Augmentation for Mobile Devices: Motivation, Taxonomies, and Open Challenges , 2013, IEEE Communications Surveys & Tutorials.

[30]  Shaolei Ren,et al.  Online capacity provisioning for carbon-neutral data center with demand-responsive electricity prices , 2013, PERV.

[31]  Naixue Xiong,et al.  On the throughput-energy tradeoff for data transmission between cloud and mobile devices , 2014, Inf. Sci..

[32]  Ness B. Shroff,et al.  Heterogeneous Delay Tolerant Task Scheduling and Energy Management in the Smart Grid with Renewable Energy , 2013, IEEE Journal on Selected Areas in Communications.