Energy-minimized Scheduling of Real-time Parallel Workflows on Heterogeneous Distributed Computing Systems
| Autor: | Biao Hu, MengChu Zhou, Zhengcai Cao |
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| Rok vydání: | 2022 |
| Předmět: |
Schedule
Information Systems and Management Computer Networks and Communications Least slack time scheduling Computer science Heuristic (computer science) 020209 energy Reliability (computer networking) Distributed computing 020208 electrical & electronic engineering 02 engineering and technology Energy consumption Computer Science Applications Scheduling (computing) Workflow Hardware and Architecture 0202 electrical engineering electronic engineering information engineering Integer programming |
| Zdroj: | IEEE Transactions on Services Computing. 15:2766-2779 |
| ISSN: | 2372-0204 |
| DOI: | 10.1109/tsc.2021.3054754 |
| Popis: | Todays large-scale parallel workflows are often processed on heterogeneous distributed computing platforms. From an economic perspective, computing resource providers should minimize the cost while offering high service quality. It has become well-organized that energy consumption accounts for a large part of a computing systems total cost, and timeliness and reliability are two important service indicators. This article studies the problem of scheduling a parallel workflow that minimizes the system energy consumption under the constraints of response time and reliability. We first mathematically formulate this problem as a Non-linear Mixed Integer Programming problem. Since this problem is hard to solve directly, we present some highly-efficient heuristic solutions. Specifically, we first develop an algorithm that minimizes the schedule length while meeting reliability requirement, on top of which we propose a processor-merging algorithm and a slack time reclamation algorithm using a dynamic voltage frequency scaling (DVFS) technique to reduce energy consumption. The processor-merging algorithm tries to turn off some energy-inefficient processors such that energy consumption can be minimized. The DVFS technique is applied to scale down the processor frequency at both processor and task levels to reduce energy consumption. Experimental results on two real-life workflows and extensive synthetic parallel workflows demonstrate their effectiveness. |
| Databáze: | OpenAIRE |
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