Adaptive Approach in Handling Human Inactivity in Computer Power Management
Keywords:Reinforcement Learning, Dynamic Power Management, Human Inactivity,
AbstractHuman inactivity is handled by adapting the behavioral changes of the users. Human inactivity refers to as unpredictable workload of a complex system that is caused by increments of amount in power consumption and it can be handled automatically without the need to set a fixed time for changing the computer state. This is happens due to lack of knowledge in a software system and the software self-adaptation is one approach in dealing with this source of uncertainty. This paper observes human inactivity and Power management policy through the application of reinforcement learning approach in the computer usage and finds that computer power usage can be reduced if the idle period can be intelligently sensed from the user activities. This study introduces Control, Learn and Knowledge model that adapts the Monitor, Analyze, Planning, Execute control loop integrates with Q Learning algorithm to learn human inactivity period to minimize the computer power consumption. An experiment to evaluate this model was conducted using three case studies with same activities. The result show that the proposed model obtained those 5 out of 12 activities shows the power decreasing compared to others
Gupta, P. K. and Singh, G., "Minimizing Power Consumption by Personal Computers: A Technical Survey," Int. J. Inf. Technol. Comput. Sci. 4(10):57–66, 2012.
Irani, S. Shukla, S. and Gupta, R., "Online strategies for dynamic power management in systems with multiple power-saving states," ACM Trans. Embed. Comput. Syst. 2(3):325–346, 2003.
Sandhu, S. Rawal, A. Kaur, P. and Gupta, N., "Major components associated with green networking in information communication technology systems,” Commun. Appl. (ICCCA), 2012 Int. Conf. Comput.1–6, 2012.
Esfahani, N. and Malek, S. Uncertainty in self-adaptive software systems, Softw. Eng. Self-Adaptive Syst. II, 2013.
Shen, H. Tan, Y. Lu, J. Wu, Q.and Qiu, Q., “Achieving autonomous power management using reinforcement learning,” ACM Trans. Des. Autom. Electron. Syst.18(2):1–32, 2013.
Candrawati, R. Hashim, N. L. Mahmuddin, M. and Irwan, H., "A Model of Framework of Control , Learn , and Knowledge for Computer Power Management" In Proceeding of Knowledge Management International Conference, 2014.
Schumann, M. a. Drusinsky, D. Michael, J. B.and Wijesekera, D.,"Modeling human-in-the-loop security analysis and decision-making processes," IEEE Trans. Softw. Eng. 40(2):154–166, 2014.
Kothari, S. Deepak, A. Tamrawi, A. Holland, B. and Krishnan, S., "A ‘ Human-in-the-loop ’ Approach for Resolving Complex Software
Anomalies," in IEEE International Conference on Systems, Man, and Cybernetics, 2014,1971–1978, 2014.
Munir, S. Stankovic, J. Liang C., and Lin, S. Reducing Energy Waste for Computers by Human-in-the-Loop Control, 2013.
IBM. An architectural blueprint for autonomic computing, 2005.
Lemos, R. De Giese, H. and Müller, H., "Software engineering for selfadaptive systems: A second research roadmap," Softw. Eng.1–32, 2013.
Brun, Y. Di, G. Serugendo, M. Gacek, C. Giese, H.and Shaw, M. Engineering Self-Adaptive Systems through Feedback Loops.. 48–70, 2009.
Chedid W. and Yu, C. Survey on power management techniques for energy efficient computer systems, 2002.
Lu Y.-H. and Micheli, G. De, "Comparing system level power
management policies," IEEE Des. Test Comput. 18(2):10–19, 2001.
Srivastava, M. B. Chandrakasan, A. P. and Brodersen, R. W.,
"Predictive System Shutdown and Other Architectural Techniques for Energy Efficient Programmable Computation," IEEE Trans. VERY
LARGE SCALE Integr. Syst. 4(I):42–55, 1996.
Benini, L. Bogliolo, A. and De Micheli, G., "A survey of design techniques for system-level dynamic power management," IEEE Trans. Very Large Scale Integr. Syst., 8(3):299–316, 2000.
Barto, A. and Dietterich, T. Reinforcement learning and its relationship to supervised learning, Handb. Learn. Approx. Dyn. Program, 2004.
Gurumurthi, S. Sivasubramaniam, A. Irwin, M. J. Vijaykrishnan, N. and Kandemir, M. Using complete machine simulation for software power estimation: The softwatt approach, High-Performance Comput. Arch, 2002.
Tan, Y. and Qiu, Q., "A Framework of Stochastic Power
Management Using Hidden Markov Model," 92–97, 2008.
Dhiman, G. and Rosing, T., "Dynamic Power Management Using Machine Learning," in 2006 IEEE/ACM International Conference on Computer Aided Design, 2006. 747–754, 2006.
Khan U. A.and Rinner, B., "A Reinforcement Learning Framework for Dynamic Power Management of a Portable, Multi-camera Traffic
Monitoring System," IEEE Int. Conf. Green Comput. Commun. Nov.
Khan, U.and Rinner, B., "Online learning of timeout policies for dynamic power management," ACM Trans. Embed. Comput. 13(4), 2014.
Higgs, T. Energy Efficient Computing, IEEE,210–215, 2007.
Moshnyaga, V. G. How to Really Save Computer Energy?, Computer (Long. Beach. Calif).3, 2008.
Moshnyaga, V. G. he use of eye tracking for PC energy management, Proc. 2010 Symp. Eye-Tracking Res. Appl. - ETRA ’10, 113, 2010.
CPUID, “HWMONITOR-PRO,” 2016. [Online]. Available:
http://www.cpuid.com/softwares/hwmonitor-pro.html. [Accessed: 31-Dec-2015].
How to Cite
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.