Ph.D - Dynamic Power Management
Intelligent power management in portable, embedded devices
PhD Thesis Tittle: Online Learning of Timeout Policies for Dynamic Power Management
Thesis Defense Presentation [Download]
In this PhD thesis, I present the development of a mobile, embedded heterogeneous visual sensing platform for traffic surveillance, named MobiTrick. I also propose a novel machine learning-based approach for Dynamic Power Management (DPM) of a computing system, particularly MobiTrick, to reduce power consumption during runtime while maintaining optimal overall performance. My work primarily focuses on two key aspects of dynamic power management: (i) obtaining various solutions corresponding to power consumption and performance based on user-selected criteria, and (ii) dynamically reconfiguring the system during operation to achieve user-specified power consumption or performance constraints.
Given the dynamic nature of real environments, I employ a Reinforcement Learning (RL) based approach for the DPM technique, which adapts to the environment and adjusts the DPM decisions online during the system's operation. This learning framework, called Online Learning of Timeout Policies (OLTP), optimally selects timeout values in different device states. Unlike widely used static timeout policies, OLTP learns to dynamically change timeout decisions in various device states, including non-operational ones.
Furthermore, I introduce an Online Adaptation of Power/Performance (OAPP) framework, which allows the proposed DPM approach to adapt user-specified power/performance constraints online. I also demonstrate the compatibility and effectiveness of the OLTP/OAPP framework for systems with a higher number of power/performance states. The proposed techniques have been implemented and evaluated on the embedded traffic surveillance platform, MobiTrick.
Keywords: Reinforcement learning. dynamic power management, timeout policies, non-stationary workload, power-performance trade-off.
MobiTrick's Sensing Platform and OLTP/OAPP Performance Evaluation
High level view of MobiTrick's heterogeneous sensing platform under power management
Traffic surveillance with MobiTrick's power managed prototype
OAPP: Convergence to the user specified latency and power constraints
OLTP: Power-performance Pareto-front for various workloads
Power profile for different latency constraints with changing workload
U. A. Khan and B. Rinner. "Online Learning of Timeout Policies for Dynamic Power Management". ACM Transactions on Embedded Computing Systems, Vol. 13, No. 4, Article 96, 25 pages, February 2014. (DOI: http://dx.doi.org/10.1145/2529992) [Download]
U.A.Khan, F.A.Jokhio, I.H.Sadhayo, “Reinforcement Learning for Dynamic Power Management of Embedded Visual Sensor Nodes”, Mehran University Research Journal of Engineering & Technology, vol. 33, No.2, 2014. [Download]
U.A.Khan, B.Rinner, “A Reinforcement Learning Framework For Dynamic Power Management of a Portable, Multi-Camera Traffic Monitoring System”, In Proceedings of IEEE conference on Green Computing & Communication (GreenCom), pp. 557-564, Besancon, France, 2012. [Download]
U.A.Khan, M. Godec, M. Quaritsch, M. Hennecke, H. Bischof, B. Rinner, “MobiTrick – Mobile Traffic Checker”, In Proceedings of 19th Intelligent Transportation Systems (ITS) World Congress, pp. 1-10, Vienna, Austria, 2012. [Download]
U.A.Khan, B.Rinner, “Dynamic Power Management for Portable, Multi-Camera Traffic Monitoring”, In Proceedings of 18th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pp.37-40, Beijing, China, 2012. [Download]
U.A.Khan, M.Quaritsch, B.Rinner, “Design of a Heterogeneous, Energy-Aware, Stereo-Vision Based Sensing Platform for Traffic Surveillance”, In Proceedings of IEEE Ninth Workshop on Intelligent Solutions in Embedded Systems (WISES), pages 47–52, Regensberg, Germany, 2011. [Download]