Open AccessHEALERS: a heterogeneous energy‐aware low‐overhead real‐time scheduler
Author(s) -
Moulik Sanjay,
Devaraj Rajesh,
Sarkar Arnab
Publication year - 2019
Publication title -
iet computers and digital techniques
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.219
H-Index - 46
ISSN - 1751-861X
DOI - 10.1049/iet-cdt.2019.0023
Subject(s) - computer science , frequency scaling , scheduling (production processes) , schedule , energy consumption , overhead (engineering) , distributed computing , execution time , real time computing , embedded system , heuristic , set (abstract data type) , parallel computing , mathematical optimization , engineering , operating system , electrical engineering , artificial intelligence , programming language , mathematics
Devising energy‐efficient scheduling strategies for real‐time periodic tasks on heterogeneous platforms is a challenging as well as a computationally demanding problem. This study proposes a low‐overhead heuristic strategy called, HEALERS, for dynamic voltage and frequency scaling (DVFS)‐cum‐dynamic power management (DPM) enabled energy‐aware scheduling of a set of periodic tasks executing on a heterogeneous multi‐core system. The presented strategy first applies deadline‐partitioning to acquire a set of distinct time‐slices. At any time‐slice boundary, the following three‐phase operations are applied to obtain a schedule for the next time‐slice: first, it computes the fragments of the execution demands of all tasks onto each of the different processing cores in the platform. Next, it generates a schedule for each task on one or more processing cores such that the total execution demand of all tasks is satisfied. Finally, HEALERS applies DVFS and DPM on all processing cores so that energy consumption within the time‐slice may be minimized while not jeopardising execution requirements of the scheduled tasks. Experimental results show that the proposed scheme is not only able to achieve appreciable energy savings with respect to state‐of‐the‐art (5–42% on average) but also enables a significant improvement in resource utilisation (as high as 58%).