Greening Duplication-Based Dependent-Tasks Scheduling on Heterogeneous Large-Scale Computing Platforms

Hagras, Tarek

doi:10.1007/s10723-021-09554-2

2021-03-13

PRE

Hagras, Tarek *

Asmaa Atef

Yousef B. Mahdy

DOI：10.1007/s10723-021-09554-2

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摘要

En 中文

Low-cost and high-performance execution of nowadays computing-intensive applications will not be possible without large-scale heterogeneous computing platforms. The huge computing power of such platforms raises the problem of the electrical energy consumed by such platforms. One of the key issues to achieve high-performance in such platforms is task-scheduling. Among the heuristics-based compile-time dependent-task scheduling heuristics, duplication-based list scheduling heuristics give the earliest finish time of the application tasks. Unfortunately, due to the additional computing cost required by duplication, these heuristics consume more computing power that leads to more electrical energy consumption. Energy-efficiency and green-computing turn the attention to the need for new generations of energy-aware task-scheduling algorithms. This paper presents a duplication reduction mechanism that can be applied to any schedule produced by a duplication-based scheduling algorithm. The aims of the proposed mechanism are to keep the same finish time of the scheduled application tasks, to keep the lower-bound time-complexity of the heuristics-based dependent task scheduling algorithms, and to significantly reduce the energy consumed by task-duplication. The mechanism is called Green. Green was applied to four of the most-recent and well-known duplication-based list-scheduling algorithms. The experimental results based on computer simulation utilizing C# language for large sets of both randomly generated and three real-world applications graphs show that Green can significantly reduce the energy consumed by each algorithm.

Keyword:

Energy-aware scheduling

Heuristics-based scheduling

Duplication-based scheduling

Heterogeneous large-scale computing platforms

Green computing