Improved Multi-Core Real-Time Task Scheduling of Reconfigurable Systems With Energy Constraints
| Autor: | MengChu Zhou, Hamza Chniter, Mohamed Khalgui, Olfa Mosbahi, Zhiwu Li |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Green computing
020203 distributed computing Multi-core processor General Computer Science Linear programming Computer science Distributed computing General Engineering Control reconfiguration 02 engineering and technology Energy consumption multi-core processor reconfigurable systems reconfiguration 020202 computer hardware & architecture Scheduling (computing) 0202 electrical engineering electronic engineering information engineering General Materials Science lcsh:Electrical engineering. Electronics. Nuclear engineering integer linear program low power consumption lcsh:TK1-9971 Execution model optimization real-time scheduling |
| Zdroj: | IEEE Access, Vol 8, Pp 95698-95713 (2020) |
| ISSN: | 2169-3536 |
| Popis: | This paper deals with the scheduling of real-time periodic tasks executed on heterogeneous multicore platforms. Each processor is composed of a set of multi-speed cores with limited energy resources. A reconfigurable system is sensible to unpredictable reconfiguration events from related environment, such as the activation, removal or update of tasks. The problem is to handle feasible reconfiguration scenarios under energy constraints. Since any task can finish execution before achieving its worst-case execution time (WCET), the idea is to distribute this execution on different processor cores for meeting related deadlines and reducing energy consumption. The methodology consists in using lower processor speeds first to consume less energy. If the system is still non-feasible after reconfiguration, then we adjust the task periods as a flexible solution or migrate some of them to the least loaded processors. Accordingly, an integer linear program (ILP) is formulated to encode the execution model that assigns tasks to different cores with optimal energy consumption, thereby realizing energy-efficient computing/green computing. The potency and effectiveness of the proposed approach are rated through simulation studies. By measuring the energy consumption cost, our solution offers better than 11% of gain than recently published methods and improves by 85% the overall number of adjusted periods. |
| Databáze: | OpenAIRE |
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