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Issue No.09 - September (2009 vol.8)
pp: 1189-1204
Savvas Gitzenis , University of Thessaly, Athens
Nicholas Bambos , Stanford University, Stanford
We investigate a wireless computing architecture, where mobile terminals can execute their computation tasks either 1) locally, at the terminal's processor, or 2) remotely, assisted by the network infrastructure, or even 3) combining the former two options. Remote execution involves: 1) sending the task to a computation server via the wireless network, 2) executing the task at the server, and 3) downloading the results of the computation back to the terminal. Hence, it results to energy savings at the terminal (sparing its processor from computations) and execution speed gains due to (typically) faster server processor(s), as well as overheads due to the terminal server wireless communication. The net gains (or losses) are contingent on network connectivity and server load. These may vary in time, depending on user mobility, network, and server congestion (due to the concurrent sessions/connections from other terminals). In local execution, the wireless terminal faces the dilemma of power managing the processor, trading-off fast execution versus low energy consumption. We model the system within a Markovian dynamic control framework, allowing the computation of optimal execution policies. We study the associated energy versus delay trade-off and assess the performance gains attained in various test cases in comparison to conventional benchmark policies.
Task migration, power management, processor power management, mobile computing, energy efficiency.
Savvas Gitzenis, Nicholas Bambos, "Joint Task Migration and Power Management in Wireless Computing", IEEE Transactions on Mobile Computing, vol.8, no. 9, pp. 1189-1204, September 2009, doi:10.1109/TMC.2009.34
[1] D. Bertsekas, Dynamic Programming: Deterministic and Stochastic Models. Prentice Hall, 1987.
[2] M. Weiser, B. Welch, A. Demers, and S. Shenker, “Scheduling for Reduced CPU Energy,” Proc. First USENIX Symp. Operating Systems Design and Implementation, pp. 13-23, 1994.
[3] P. Pilai and K.G. Shin, “Real-Time Dynamic Voltage Scaling for Low-Power Embedded Operating Systems,” Proc. 18th ACM Symp. Operating Systems Principles, pp. 89-102, 2001.
[4] R. Xu, C. Xi, R. Melhem, and D. Mosse, “Practical PACE for Embedded Systems,” Proc. Conf. Embedded Software, pp.54-63, 2004.
[5] E.-Y. Chung, L. Benini, and G. De Michelli, “Source Code Transformation Based on Software Cost Analysis,” Proc. ACM Int'l Symp. System Synthesis, vol. 4, pp. 153-158, 2001.
[6] J. Seo, S. Lee, and J. Kim, “Synchronous Load Balancing in Hypercube Multicomputers with Faulty Nodes,” Proc. IEEE Int'l Conf. Parallel and Distributed Systems, pp. 414-421, Dec. 1997.
[7] A. Rudenko, P. Reiher, G. Popek, and G. Kuenning, “Saving Portable Computer Battery Power through Remote Process Execution,” Mobile Computing and Comm. Rev., vol. 2, no. 1, pp.19-26, 1998.
[8] X. Gu, K. Nahrstedt, A. Messer, I. Greenberg, and D. Milojicic, “Adaptive Offloading Inference for Delivering Applications in Pervasive Computing,” Proc. IEEE Int'l Conf. Pervasive Computing, pp. 107-114, 2003.
[9] S. Mohapatra and N. Venkatasubramanian, “PARM: Power Aware Reconfigurable Middleware,” Proc. IEEE Int'l Conf. Distributed Computing Systems, pp. 312-319, 2003.
[10] P. Rong and M. Pedram, “Extending the Lifetime of a Network of Battery-Powered Mobile Devices by Remote Processing: A Markovian Decision-Based Approach,” Proc. ACM/IEEE Design Automation Conf., pp. 906-911, 2003.
[11] D. Qiao, S. Choi, A. Soomro, and K.G. Shin, “MiSer: An Optimal Low-Energy Transmission Strategy for IEEE 802.11a/h,” Proc. ACM Int'l Conf. Mobile Computing and Networking, pp. 161-175, Sept. 2003.
[12] G. Leuzzi and C. Micheli, “Variable-Load Constant-Efficiency Power Amplifier for Mobile Communications Applications,” Proc. Gallium Arsenide (GaAs) Applications Symp., pp. 481-484, Oct. 2003.
[13] Y.G. Li, A.F. Molisch, and J. Zhang, “Channel Estimation and Signal Detection for UWB,” Proc. Int'l Symp. Wireless Personal Multimedia Comm. (WPMC '03), Oct. 2003.
[14] L. Yuan and G. Qu, “Design Space Exploration for Energy-Efficient Secure Sensor Network,” Proc. IEEE Int'l Conf. Application-Specific Systems, Architectures and Processors, pp. 88-97, 2002.
[15] M. Zuniga and B. Krishnamachari, “Analyzing the Transitional Region in Low Power Wireless Links,” Proc. IEEE Sensor and AdHoc Comm. and Networks, pp. 517-526, Oct. 2004.
[16] http:/, 2009.
[17] http:/, 2009.
[18] N. Bambos and S. Kandukuri, “Power-Controlled Multiple Access Schemes for Next-Generation Wireless Packet Networks,” IEEE Wireless Comm., vol. 9, no. 3, pp. 58-64, June 2002.
[19] B. Delaney, T. Simunic, and N. Jayant, “Power Aware Distributed Speech Recognition for Wireless Mobile Devices,” IEEE Design & Test, vol. 22, no. 1, pp. 39-49, Jan. 2005.
[20] “Speech Processing, Transmission and Quality Aspects (STQ); Distributed Speech Recognition; Front-End Feature Extraction Algorithm; Compression Algorithms,” ETSI Standard ES 201 108 v.1.1.3, Sept. 2003.
[21] M. Othman and S. Hailes, “Power Conservation Strategy for Mobile Computers Using Load Sharing,” SIGMOBILE Mobile Computing and Comm. Rev., vol. 2, no. 1, pp. 44-51, Jan. 1998.
[22] Z. Li, C. Wang, and R. Xu, “Computation Offloading to Save Energy on Handheld Devices: A Partition Scheme,” Proc. ACM Int'l Conf. Compilers, Architecture, and Synthesis for Embedded Systems, pp. 238-246, Nov. 2001.
[23] L. Shang, R.P. Dick, and N.K. Jha, “An Economics-Based Power-Aware Protocol for Computation Distribution in Mobile Ad-Hoc Networks,” Proc. IASTED Int'l Conf. Parallel and Distributed Computing and Systems, pp. 344-349, Nov. 2002.
[24] S. Gitzenis and N. Bambos, “Mobile-to-Base Task Migration in Wireless Computing,” Proc. IEEE Pervasive Comm. Conf. (PerCom '04), pp. 187-196, Mar. 2004.
[25] S. Gitzenis and N. Bambos, “Power-Managed Block Level File Decryption in Wireless Network Computing,” Proc. ACM Int'l Symp. Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (Wiopt '06), Apr. 2006.
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