Minimizing expected energy in real-time embedded systems

Abstract

We study the problem of minimizing energy consumption in realtime embedded systems that execute variable workloads and are equipped with processors having dynamic voltage scaling (DVS) capabilities. This problem is about how to decide tasks' running speeds (speed schedule) before they are scheduled to execute. In this paper, we show that it is possible to incorporate the dynamic behavior of the tasks into the speed schedule to, along with the dynamic slack reclamation technique, minimize the expected (total) energy consumption in the system.

Energy-Aware Task Scheduling for Real-Time Systems with Discrete Frequencies

“…Since the static power consumption is a constant, the power consumption function P( f i ) could be rescaled by substituting P i , while not affecting the analysis results. Distinct from many previous work [8], [10], [14], [15], [17] which assumes P( f i ) = f α i (α > 2), this paper can cope with P( f i ) as an arbitrary function.…”

“…Gruian and Kuchcinski [16] and Leung et al [20] develop heuristics for task ordering to reduce the expected energy consumption. Zhang et al [14] and Xu et al [15] consider distribution information and slacks concurrently and obtain the optimal speed schedule on the assumption of continuous processor frequency tuning. However, processors, now, can only provide discrete frequencies.…”

“…However, processors, now, can only provide discrete frequencies. Xian and Lu [17] and Xu et al [15] deal with this problem by applying the research result from Ishihara and Yasuura [21] which uses two adjacent frequencies to execute task. Berten et al [19] also present a technique to adapt a continuous-speed-based method to a discrete-speed system.…”

Energy efficient multiprocessor task scheduling under input-dependent variation

“…The work in [7][9] [12] [14][28] models the execution time of a task as a random variable and minimizes expected energy consumption on a single processor system. A heuristic is provided in [7] for obtaining a low-energy schedule.…”

“…A heuristic is provided in [7] for obtaining a low-energy schedule. In [9] [12], exact solutions are provided using convex optimization techniques; however, many of their assumptions, such as the ability to change the voltage to any arbitrary value at any point during the execution of a task, are not valid for practical systems. Many of these issues are addressed in [14] for uniprocessor systems.…”