An elastic energy-aware scheduling strategy for heterogeneous computing systems

Energy saving has become a major issue for heterogeneous computing systems to minimize electricity cost, improve system reliability and protect environment. Conventional energy-aware scheduling strategies developed on heterogeneous computing systems concentrated on energy savings regardless of the user expected finish times of tasks while making scheduling decisions. As a result, the user expectations by such strategies will be affected greatly especially when the systems are heavily loaded, which results in inferior system adaptivity or, in some situations, (e. g., emergency service) it is even not tolerated. In this paper, we developed a novel dynamic scheduling strategy named Elastic Energy-Aware Scheduling (EEAS) for aperiodic, and independent tasks on heterogeneous computing systems with dynamic voltage scaling. The EEAS strategy aims at adaptively adjusting voltages according to the system workload, thereby making trade-offs between energy conservation and user expectation. i. e., when the system is under heavy workload, to meet user expectations, EEAS not only considers the voltage for a new task, but also takes the voltages to run tasks waiting in local queues into account; in contrast, EEAS degrades voltage levels to reduce energy consumption while holding higher user satisfaction rate in terms of user expected finish time. We conducted extensive experiments to compare our EEAS with three schemes - GEA, HVEA and LVEA. Experimental results show that EEAS significantly improves the scheduling quality of others, and is able to effectively enhance the system elasticity.