Task Scheduling for Energy Consumption Constrained Parallel Applications on Heterogeneous Computing Systems

Abstract

Power-aware task scheduling on processors has been a research hotspot in computing systems. Given an application $G$ G containing a set $N$ N of tasks $\lbrace n_1,\ldots, n_{|N|}\rbrace$ { n 1 , ... , n | N | } , and a system containing a set $U$ U of processors $\lbrace u_1,\ldots, u_{|U|}\rbrace$ { u 1 , ... , u | U | } , the power-aware task scheduling generally refers to finding the appropriate processor and frequency for each task $n_i$ n i , so as to make sure that all the tasks can be finished efficiently and the overall energy consumption is guaranteed. In this article, we study the problem of minimizing the schedule length for energy consumption constrained parallel applications on heterogeneous computing systems, where the schedule length refers to the time interval between starting the first task and finishing the last task. For this problem, existing work adopts a policy that preassigns the minimum energy consumption for each unassigned task. Nevertheless, our analysis reveals that, such a preassignment policy could be unfair for the low priority tasks, and it may not achieve an optimistic schedule length . Thereby, we propose a new task scheduling algorithm that suggests a weight-based mechanism to preassign energy consumption for unassigned tasks, and we provide the rigorous proof to show its feasibility. Further, we show that this idea can be extended to solve reliability maximization problems with energy consumption constraint or with both deadline and energy consumption constraints, where the reliability refers to the probability of executing application $G$ G without failures, and the deadline constraint refers to the “allowable” maximum schedule length. We have conducted extensive experiments based on real parallel applications. The experimental results consistently demonstrate that our proposed algorithms can achieve favourable performance, compared to state-of-the-art algorithms.