An energy-efficient task scheduling for mobile devices based on cloud assistant

Abstract Mobile cloud computing is an emerging service model to extend the capability and the battery life of mobile devices. Mostly one network application can be decomposed into fine-grained tasks which consist of sequential tasks and parallel tasks. With the assistance of mobile cloud computing, some tasks could be offloaded to the cloud for speeding up executions and saving energy. However, the task offloading results in some additional cost during the communication between cloud and mobile devices. Therefore, this paper proposes an energy-efficient scheduling of tasks, in which the mobile device offloads appropriate tasks to the cloud via a Wi-Fi access point. The scheduling aims to minimize the energy consumption of mobile device for one application under the constraint of total completion time. This task scheduling problem is reconstructed into a constrained shortest path problem and the LARAC method is applied to get the approximate optimal solution. The proposed energy-efficient strategy decreases 81.93% of energy consumption and 25.70% of time at most, compared with the local strategy. Moreover, the applicability and performance of the proposed strategy are verified in different patterns of applications, where the time constraint, the workload ratio between communication and computation are various.

[1]  Xiliang Zhong,et al.  Energy-Efficient Wireless Packet Scheduling with Quality of Service Control , 2007, IEEE Transactions on Mobile Computing.

[2]  Gernot Heiser,et al.  An Analysis of Power Consumption in a Smartphone , 2010, USENIX Annual Technical Conference.

[3]  Feng Xia,et al.  BeeCup: A bio-inspired energy-efficient clustering protocol for mobile learning , 2014, Future Gener. Comput. Syst..

[4]  Yonggang Wen,et al.  Energy-efficient scheduling policy for collaborative execution in mobile cloud computing , 2013, 2013 Proceedings IEEE INFOCOM.

[5]  Jun Liu,et al.  Energy efficient scheduling of real-time tasks on multi-core processors with voltage islands , 2016, Future Gener. Comput. Syst..

[6]  Bertalan Forstner,et al.  Energy-efficient computation offloading model for mobile phone environment , 2012, 2012 IEEE 1st International Conference on Cloud Networking (CLOUDNET).

[7]  Paramvir Bahl,et al.  The Case for VM-Based Cloudlets in Mobile Computing , 2009, IEEE Pervasive Computing.

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

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

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

[11]  Xiao Ma,et al.  A Survey of Energy Efficient Wireless Transmission and Modeling in Mobile Cloud Computing , 2012, Mobile Networks and Applications.

[12]  John Sartori,et al.  Enhancing the Efficiency of Energy-Constrained DVFS Designs , 2013, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

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

[14]  N. Tapus,et al.  Cloud Computing—Task scheduling based on genetic algorithms , 2012, 2012 IEEE International Systems Conference SysCon 2012.

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

[16]  Bal Azs.  Lagrange Relaxation Based Method for the QoS Routing Problem , 2001 .

[17]  Po-Wen Cheng,et al.  Energy-efficient task scheduling for multi-core platforms with per-core DVFS , 2015, J. Parallel Distributed Comput..

[18]  Amin Vahdat,et al.  Application-specific Network Management for Energy-Aware Streaming of Popular Multimedia Formats , 2002, USENIX Annual Technical Conference, General Track.

[19]  Valérie Issarny,et al.  QoS-Aware Service Composition in Dynamic Service Oriented Environments , 2009, Middleware.

[20]  Aleksandar Kuzmanovic,et al.  Measuring serendipity: connecting people, locations and interests in a mobile 3G network , 2009, IMC '09.

[21]  Ramesh R. Rao,et al.  Improving battery performance by using traffic shaping techniques , 2001, IEEE J. Sel. Areas Commun..

[22]  Jian-Jia Chen,et al.  Energy-Efficient Scheduling in Nonpreemptive Systems With Real-Time Constraints , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[23]  Darrell D. E. Long,et al.  A dynamic disk spin-down technique for mobile computing , 1996, MobiCom '96.

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

[25]  A. Girotra,et al.  Performance Analysis of the IEEE 802 . 11 Distributed Coordination Function , 2005 .

[26]  Adam Wolisz,et al.  Primary user behavior in cellular networks and implications for dynamic spectrum access , 2009, IEEE Communications Magazine.

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

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

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

[30]  Naehyuck Chang,et al.  Accurate Modeling of the Delay and Energy Overhead of Dynamic Voltage and Frequency Scaling in Modern Microprocessors , 2013, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[31]  Danny H. K. Tsang,et al.  Performance study and system optimization on sleep mode operation in IEEE 802.16e , 2009, IEEE Transactions on Wireless Communications.

[32]  Deborah Estrin,et al.  A first look at traffic on smartphones , 2010, IMC '10.

[33]  Kyu Ho Park,et al.  A Cooperative Clustering Protocol for Energy Saving of Mobile Devices with WLAN and Bluetooth Interfaces , 2011, IEEE Transactions on Mobile Computing.