Evolution-Based Real-Time Job Scheduling for Co-Optimizing Processor and Memory Power Savings

Abstract

With the recent advances in battery-based mobile computing technologies, power-saving techniques in real-time embedded devices are becoming increasingly important. This paper presents a novel job scheduling policy for real-time systems, which aims at minimizing the power consumption of processor and memory without missing the deadline constraints of real-time jobs. To do so, we formulate the power saving techniques of processor voltage/frequency scaling and memory job placement as a unified measure, and show that it is a complex search problem that has the exponential time complexity. Thus, an efficient heuristic based on evolutionary computation is performed to cut down the huge searching space and find a reasonable schedule within the feasible time budget. To evaluate the proposed scheduling policy, we conduct experiments under various workload conditions. Our experimental results show that the proposed policy significantly reduces the energy consumption of real-time systems. Specifically, the average reduction in the energy consumption is 41.7% without deadline misses.

Keywords

• Real-time job scheduling
• evolutionary computation
• power saving
• genetic algorithm
• dynamic voltage/frequency scaling
• deadline