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Energy-efficient Real-time Scheduling on Multicores

Energy-efficient Real-time Scheduling on Multicores With the increasing demand for higher performance, the adoption of multicores has been a major stepping stone in the evolution of hard real-time systems. Though the computational bandwidth is increased due to parallel processing, the indispensable interactivity between the hierarchical memory sub-system and multiple cores has further aggravated the already complex worst case execution time (WCET) analysis of tasks. Furthermore, caches have the biggest influence on task execution time, and the inclusion of shared caches further increases the unpredictability of the system. Cache partitioning techniques have been proposed as a counter-measure to decouple the shared cache latency from the WCET. However, existing energy-efficient scheduling algorithms are oblivious to the unpredictable nature of shared caches or cache partitioning techniques, thus, diminishing their applicability to real-world systems. Without considering inter-task cache contention, directly using existing algorithms or attempting to allocate and schedule a taskset with cache-partition assignments can result in cache violations. To overcome this dilemma, we propose a novel approach to model inter-task cache contention as a dependency graph to be used by well-established algorithms to minimize energy consumption. Extensive simulations demonstrate the effectiveness of our approach to minimize energy consumption while also avoiding cache violations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Embedded Computing Systems (TECS) Association for Computing Machinery

Energy-efficient Real-time Scheduling on Multicores

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References (78)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2020 ACM
ISSN
1539-9087
eISSN
1558-3465
DOI
10.1145/3399413
Publisher site
See Article on Publisher Site

Abstract

With the increasing demand for higher performance, the adoption of multicores has been a major stepping stone in the evolution of hard real-time systems. Though the computational bandwidth is increased due to parallel processing, the indispensable interactivity between the hierarchical memory sub-system and multiple cores has further aggravated the already complex worst case execution time (WCET) analysis of tasks. Furthermore, caches have the biggest influence on task execution time, and the inclusion of shared caches further increases the unpredictability of the system. Cache partitioning techniques have been proposed as a counter-measure to decouple the shared cache latency from the WCET. However, existing energy-efficient scheduling algorithms are oblivious to the unpredictable nature of shared caches or cache partitioning techniques, thus, diminishing their applicability to real-world systems. Without considering inter-task cache contention, directly using existing algorithms or attempting to allocate and schedule a taskset with cache-partition assignments can result in cache violations. To overcome this dilemma, we propose a novel approach to model inter-task cache contention as a dependency graph to be used by well-established algorithms to minimize energy consumption. Extensive simulations demonstrate the effectiveness of our approach to minimize energy consumption while also avoiding cache violations.

Journal

ACM Transactions on Embedded Computing Systems (TECS)Association for Computing Machinery

Published: Jul 4, 2020

Keywords: DAG

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