ADAPTIVE POWER MANAGEMENT FOR COMPUTERS AND MOBILE DEVICES

Power consumption has become a major concern in the design of computing systems today. High power consumption increases cooling cost, degrades the system reliability and also reduces the battery life in portable devices. Modern computing/communication devices support multiple power modes which enable power and performance tradeoff. Dynamic power management (DPM), dynamic voltage and frequency scaling (DVFS), and dynamic task migration for workload consolidation are system level power reduction techniques widely used during runtime. In the first part of the dissertation, we concentrate on the dynamic power management of the personal computer and server platform where the DPM, DVFS and task migrations techniques are proved to be highly effective. A hierarchical energy management framework is assumed, where task migration is applied at the upper level to improve server utilization and energy efficiency, and DPM/DVFS is applied at the lower level to manage the power mode of individual processor. This work focuses on estimating the performance impact of workload consolidation and searching for optimal DPM/DVFS that adapts to the changing workload. Machine learning based modeling and reinforcement learning based policy optimization techniques are investigated. Mobile computing has been weaved into everyday lives to a great extend in recent years. Compared to traditional personal computer and server environment, the mobile computing environment is obviously more context-rich and the usage of mobile computing device is clearly imprinted with user\u27s personal signature. The ability to learn such signature enables immense potential in workload prediction and energy or battery life management. In the second part of the dissertation, we present two mobile device power management techniques which take advantage of the context-rich characteristics of mobile platform and make adaptive energy management decisions based on different user behavior. We firstly investigate the user battery usage behavior modeling and apply the model directly for battery energy management. The first technique aims at maximizing the quality of service (QoS) while keeping the risk of battery depletion below a given threshold. The second technique is an user-aware streaming strategies for energy efficient smartphone video playback applications (e.g. YouTube) that minimizes the sleep and wake penalty of cellular module and at the same time avoid the energy waste from excessive downloading. Runtime power and thermal management has attracted substantial interests in multi-core distributed embedded systems. Fast performance evaluation is an essential step in the research of distributed power and thermal management. In last part of the dissertation, we present an FPGA based emulator of multi-core distributed embedded system designed to support the research in runtime power/thermal management. Hardware and software supports are provided to carry out basic power/thermal management actions including inter-core or inter-FPGA communications, runtime temperature monitoring and dynamic frequency scaling

Power Management Techniques for Data Centers: A Survey

With growing use of internet and exponential growth in amount of data to be stored and processed (known as 'big data'), the size of data centers has greatly increased. This, however, has resulted in significant increase in the power consumption of the data centers. For this reason, managing power consumption of data centers has become essential. In this paper, we highlight the need of achieving energy efficiency in data centers and survey several recent architectural techniques designed for power management of data centers. We also present a classification of these techniques based on their characteristics. This paper aims to provide insights into the techniques for improving energy efficiency of data centers and encourage the designers to invent novel solutions for managing the large power dissipation of data centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy Efficiency, Green Computing, DVFS, Server Consolidatio

Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering

Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering

Dynamic Energy Management for Chip Multi-processors under Performance Constraints

We introduce a novel algorithm for dynamic energy management (DEM) under performance constraints in chip multi-processors (CMPs). Using the novel concept of delayed instructions count, performance loss estimations are calculated at the end of each control period for each core. In addition, a Kalman filtering based approach is employed to predict workload in the next control period for which voltage-frequency pairs must be selected. This selection is done with a novel dynamic voltage and frequency scaling (DVFS) algorithm whose objective is to reduce energy consumption but without degrading performance beyond the user set threshold. Using our customized Sniper based CMP system simulation framework, we demonstrate the effectiveness of the proposed algorithm for a variety of benchmarks for 16 core and 64 core network-on-chip based CMP architectures. Simulation results show consistent energy savings across the board. We present our work as an investigation of the tradeoff between the achievable energy reduction via DVFS when predictions are done using the effective Kalman filter for different performance penalty thresholds