Criticality-cognizant Energy-efficient Task Scheduling on Heterogeneous Multicore Processor

Criticality-cognizant Energy-efficient Task Scheduling on Heterogeneous Multicore Processor

© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : N. Gomathi, K. Nagalakshmi
DOI :  10.14445/22315381/IJETT-V70I4P217

How to Cite?

N. Gomathi, K. Nagalakshmi, "Criticality-cognizant Energy-efficient Task Scheduling on Heterogeneous Multicore Processor," International Journal of Engineering Trends and Technology, vol. 70, no. 4, pp. 203-214, 2022. Crossref,

Recently, scheduling mixed-criticality tasks on a common computational system has become an imperative study in academia and engineering proposals. Since multicore processors are the main paradigm in mixed-criticality systems (MCS), reliability and energy consumption are vital concerns. In modern MCS, increased peak power dissipation, particularly in critical scenarios, may cause temperature issues, disturbing the system`s consistency and timeliness. This work proposes a criticality-cognizant energy-efficient scheduling approach (CESA) that concurrently provides reliability, power management, and failsafe service level in MCS. The proposed approach decreases the system power dissipation as far as achievable at runtime through the dynamic voltage and frequency scaling (DVFS) approach with laxity allocation. CESA simultaneously accepts a number of tasks (i.e., workloads) and creates clusters with one high-criticality workload and a set of low-criticality workloads. It calculates the available laxities effectively and finds the most suitable task cluster to utilize that available laxity by considering its effect on the instantaneous power consumption and thermal issues. At the same time, varying the core speed, assigning an appropriate cluster for remaining laxity, and selecting a suitable core for task migration at runtime are arduous endeavors and lead to deadline defilement which is not acceptable for high-level workloads. Hence, we propose an online scheduling approach with DVFS and task migration during runtime whenever there is laxity. A cost function is defined as finding out the most suitable cluster to allot the laxities to reduce its V/F level or transfer the task to a new processing element. We assess the performance of our approach in an asymmetric multicore platform (i.e., ARM big. LITTLE processor) with several benchmark task sets. Empirical results demonstrate that the proposed algorithm realizes up to a 6.76% drop in maximum power and a 26.17% drop in core temperature related to the state-of-the-art method

ARM big.LITTLE, DVFS, Energy efficiency, Task scheduling, Mixed-criticality system, Multicore processors, Laxity utilization.

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