Energy-Efficient Mapping and Scheduling of Task Interaction Graphs for Code Offloading in Mobile Cloud Computing

To reduce the energy consumption in mobile devices, intricate applications are divided into several interconnected partitions like Task Interaction Graph (TIG) and are of floaded to cloud resources or nearby surrogates. Dynamic Voltage and Frequency Scaling (DVFS) is an effective technique to reduce the power consumption during mapping and scheduling stages. Most of the existing research works proposed several task scheduling solutions by considering the voltage/frequency scaling at the scheduling stage alone. But, the efficacy of these solutions can be improved by applying the DVFS in both mapping as well as scheduling stages. This research work attempts to apply DVFS in mapping as well as scheduling stages by combining both the task-resource and resource-frequency assignments in a single problem. The idea is to estimate the worst-case global slack time for each task-resource assignment, distributes it over the TIG and slowing down the execution of tasks using dynamic voltage and frequency scaling. This optimal slowdown increases the computation time of TIG without exceeding its worst-case completion time. Further, the proposed work models the code offloading as a Quadratic Assignment Problem (QAP) in Matlab-R2012b and solves it using two-level Genetic Algorithm (GA) of the global optimization toolbox. The effectiveness of the proposed model is assessed by a simulation and the results conclude that there is an average energy savings of 35% in a mobile device.

[1]  Petru Eles,et al.  Scheduling and mapping of conditional task graphs for the synthesis of low power embedded systems , 2003, 2003 Design, Automation and Test in Europe Conference and Exhibition.

[2]  Wang Yi,et al.  Minimizing Multi-resource Energy for Real-Time Systems with Discrete Operation Modes , 2010, 2010 22nd Euromicro Conference on Real-Time Systems.

[3]  Clayton W. Commander,et al.  A Survey of the Quadratic Assignment Problem, with Applications , 2003 .

[4]  Ralf Klamma,et al.  Mobile Cloud Computing: A Comparison of Application Models , 2011, ArXiv.

[5]  Massoud Pedram,et al.  Fine-Grained Dynamic Voltage and Frequency Scaling for Precise Energy and Performance Trade-Off Based on the Ratio of Off-Chip Access to On-Chip Computation Times , 2004, DATE.

[6]  Xiaobo Sharon Hu,et al.  Task scheduling and voltage selection for energy minimization , 2002, DAC '02.

[7]  F. Frances Yao,et al.  A scheduling model for reduced CPU energy , 1995, Proceedings of IEEE 36th Annual Foundations of Computer Science.

[8]  Petru Eles,et al.  Energy-efficient mapping and scheduling for DVS enabled distributed embedded systems , 2002, Proceedings 2002 Design, Automation and Test in Europe Conference and Exhibition.

[9]  Jan Kuper,et al.  Optimal DPM and DVFS for frame-based real-time systems , 2013, TACO.

[10]  Gang Quan,et al.  Energy efficient fixed-priority scheduling for real-time systems on variable voltage processors , 2001, DAC '01.

[11]  Vinay Devadas,et al.  On the Interplay of Voltage/Frequency Scaling and Device Power Management for Frame-Based Real-Time Embedded Applications , 2012, IEEE Transactions on Computers.

[12]  Gernot Heiser,et al.  Dynamic voltage and frequency scaling: the laws of diminishing returns , 2010 .

[13]  Petru Eles,et al.  Scheduling and mapping of conditional task graph for the synthesis of low power embedded systems , 2003 .

[14]  Hiroto Yasuura,et al.  Voltage scheduling problem for dynamically variable voltage processors , 1998, Proceedings. 1998 International Symposium on Low Power Electronics and Design (IEEE Cat. No.98TH8379).

[15]  Bashir M. Al-Hashimi,et al.  Considering power variations of DVS processing elements for energy minimisation in distributed systems , 2001, International Symposium on System Synthesis (IEEE Cat. No.01EX526).

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

[17]  Massoud Pedram,et al.  Dynamic voltage and frequency scaling under a precise energy model considering variable and fixed components of the system power dissipation , 2004, IEEE/ACM International Conference on Computer Aided Design, 2004. ICCAD-2004..