HEARS: A heterogeneous energy-aware real-time scheduler

Abstract

Developing energy-efficient schedulers for real-time heterogeneous platforms executing periodic tasks is an onerous as well as a computationally challenging problem. As a result, today we are confronting a scarcity of real-time energy-aware scheduling techniques which are applicable to heterogeneous platforms. Hence, this research proposes a heuristic strategy called, HEARS, for DVFS enabled energy-aware scheduling of a set of periodic tasks executing on a heterogeneous multicore system having an arbitrary number of core types. The presented scheme first applies deadline-partitioning to acquire a set of distinct time-intervals called frames. At any frame boundary, the following two-phase hierarchical operation is applied to obtain schedule for the next frame: First, it computes execution requirements for each task on every processing core of the platform. For any task, its execution requirement on a core within a frame depends upon the following criteria: (i) Length of the ensuing frame, (ii) Total execution requirements of the instance of the task on different cores (as a single task may have different execution requirement on different cores) and, (iii) Deadline of the instance of the task. Next, it simultaneously allocates each task on one or more cores and selects operating frequencies for the concerned cores such that the total execution demand of all the allocated tasks are satisfied as well as there is minimum change in energy consumption for the system. Experimental results show that our proposed strategy is not only able to achieve appreciable energy savings with respect to state-of-the-art MaxMin (2% to 37% on average) but also enables significant improvement in resource utilization (as high as 57%).