Power and Thermal Aware Scheduling for Real-time Computing Systems

Abstract

Over the past few decades, we have been enjoying tremendous benefits thanks to the revolutionary advancement of computing systems, driven mainly by the remarkable semiconductor technology scaling and the increasingly complicated processor architecture. However, the exponentially increased transistor density has directly led to exponentially increased power consumption and dramatically elevated system temperature, which not only adversely impacts the system's cost, performance and reliability, but also increases the leakage and thus the overall power consumption. Today, the power and thermal issues have posed enormous challenges and threaten to slow down the continuous evolvement of computer technology. Effective power/thermal-aware design techniques are urgently demanded, at all design abstraction levels, from the circuit-level, the logic-level, to the architectural-level and the system-level.In this dissertation, we present our research efforts to employ real-time scheduling techniques to solve the resource-constrained power/thermal-aware, design-optimization problems. In our research, we developed a set of simple yet accurate system-level models to capture the processor's thermal dynamic as well as the interdependency of leakage power consumption, temperature, and supply voltage. Based on these models, we investigated the fundamental principles in power/thermal-aware scheduling, and developed real-time scheduling techniques targeting at a variety of design objectives, including peak temperature minimization, overall energy reduction, and performance v maximization.The novelty of this work is that we integrate the cutting-edge research on power and thermal at the circuit and architectural-level into a set of accurate yet simplified system-level models, and are able to conduct system-level analysis and design based on these models. The theoretical study in this work serves as a solid foundation for the guidance of the power/thermal-aware scheduling algorithms development in practical computing systems.

A holistic energy optimization framework for cloud-assisted mobile computing

“…The energy consumption for running an application at a mobile device mainly depends on the workload and multi-core scheduling strategy, as well as the configuration of the cores like voltage and frequency. When a core is selected to execute a task, its computation energy can be optimized through configuring the clock frequency of the chip via the dynamic voltage scaling (DVS) technology [9]. Particularly, the energy consumption for each operation is proportional to v 2 , and v is approximately linearly proportional to f, that is, v = af, where a is a constant coefficient [10].…”

“…Decreasing clock frequency and supply voltage can result in reduced energy consumption, but may incur longer execution time beyond its deadline. Moreover, the thermal effect Th dev greatly depends on the supply voltage v when the operating temperature remains constant, that is, Th dev = c · kfv 2 , where c is the coefficient relative to thermal conductivity, and typically, c = 0.8 [9]. The increase in clock frequency and supply voltage will also lead to the rising thermal that directly affects the quality of experience (QoE) perceived by mobile users.…”