A Study on Heuristic Task Scheduling Optimizing Task Deadline Violations in Heterogeneous Computational Environments
A Study on Heuristic Task Scheduling Optimizing Task Deadline Violations in Heterogeneous Computational Environments
复制标题
异构计算环境中优化任务截止日期的启发式任务调度研究
DOI：
10.1109/access.2020.3037965
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发表时间：
2020-11
期刊：
影响因子：
3.9
通讯作者：
Qin Xiaoyun
中科院分区：
文献类型：
--
作者：
Wang Bo;Song Ying;Wang Changhai;Huang Wanwei;Qin Xiaoyun
在本文中，我们专注于在各种异构计算环境中执行任务的最后期限违规优化问题。为了解决这个问题，我们制定了它作为一个二元非线性规划（BNP）模型，最大限度地提高已完成的任务的数量和优化服务器的资源利用率。为了在多项式复杂度下求解BNP模型，提出了一种启发式任务调度方法，该方法迭代地将一个任务调度到第一个核，使得所有调度任务的累积松弛时间最小，直到该核无法完成任何任务，并在每个核中首先执行截止时间最早的任务，以在一个核中执行尽可能多的任务。基于真实的世界轨迹的实验结果表明，与8种经典的启发式方法相比，该方法的任务违规率降低了100%，在资源效率优化方面具有最好的整体性能.
In this paper, we focus on the problem of optimizing deadline violations for executing tasks in various heterogeneous computational environments. To address the problem, we formulated it as a binary nonlinear programming (BNP) model, which maximize the number of completed tasks and optimize the resource utilization of servers. To solve the BNP model in a polynomial complexity, we propose a heuristic task scheduling method, which iteratively schedules a task to the first core such that the accumulated slack time of all scheduled tasks is minimum, until the core cannot finish any task, and executes tasks with the earliest deadline first in each core to execute as many task as possible in a core. Experiment results based on a real world trace show that our method has upto 100% less task violations, and has the best performance in resource efficiency optimization in overall, compared with eight classical and state-of-the-art heuristic methods.
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DOI：
10.1007/s10586-020-03151-w
发表时间：
2020-07
期刊：
Cluster Computing
影响因子：
--
作者：
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通讯作者：
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DOI：
10.1145/3357223.3362728
发表时间：
2019-11
期刊：
Proceedings of the ACM Symposium on Cloud Computing
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10.1109/tvlsi.2017.2753242
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2018
影响因子：
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DOI：
10.1016/j.future.2018.09.014
发表时间：
2019-02-01
影响因子：
7.5
作者：
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影响因子：
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