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An intelligent hybrid approach for task scheduling in cluster computing environments as an infrastructure for biomedical applications

机译：群集计算环境中任务调度的智能混合方法作为生物医学应用程序的基础架构

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摘要

Nowadays, increase in time complexity of applications and decrease in hardware costs are two major contributing drivers for the utilization of high-performance architectures such as cluster computing systems. Actually, cluster computing environments, in the contemporary sophisticated data centres, provide the main infrastructure to process various data, where the biomedical one is not an exception. Indeed, optimized task scheduling is key to achieve high performance in such computing environments. The most distractive assumption about the problem of task scheduling, made by the state-of-the-art approaches, is to assume the problem as a whole and try to enhance the overall performance, while the problem is actually consisted of two disparate-in-nature subproblems, that is, sequencing subproblem and assigning one, each of which needs some special considerations. In this paper, an efficient hybrid approach named ACO-CLA is proposed to solve task scheduling problem in the mesh-topology cluster computing environments. In the proposed approach, an enhanced ant colony optimization (ACO) is developed to solve the sequence subproblem, whereas a cellular learning automata (CLA) machine tackles the assigning subproblem. The utilization of background knowledge about the problem (i.e., tasks' priorities) has made the proposed approach very robust and efficient. A randomly generated data set consisting of 125 different random task graphs with various shape parameters, like the ones frequently encountered in the biomedicine, has been utilized for the evaluation of the proposed approach. The conducted comparison study clearly shows the efficiency and superiority of the proposed approach versus traditional counterparts in terms of the performance. From our first metric, that is, the NSL (normalized schedule length) point of view, the proposed ACO-CLA is 2.48% and 5.55% better than the ETF (earliest time first), which is the second-best approach, and the average performance of all other competing methods. On the other hand, from our second metric, that is, the speedup perspective, the proposed ACO-CLA is 2.66% and 5.15% better than the ETF (the second-best approach) and the average performance of all the other competitors.
机译：如今，应用程序的时间复杂程度和硬件成本的减少是用于利用群集计算系统等高性能架构的两个主要贡献驱动因素。实际上，在当代复杂的数据中心中，群集计算环境提供了主要基础架构来处理各种数据，其中生物医学载体不是例外。实际上，优化的任务调度是在这种计算环境中实现高性能的关键。由最先进的方法制作的任务调度问题最具分发的假设是占据整个问题，并尝试提高整体性能，而问题实际上是由两个不同的-nature子问题，即排序子问题并分配一个，每个子问题都需要一些特殊的考虑因素。在本文中，提出了一种名为ACO-CLA的有效的混合方法，以解决网格拓扑集群计算环境中的任务调度问题。在所提出的方法中，开发了增强的蚁群优化（ACO）以解决序列子问题，而蜂窝学习自动机（CLA）机器解决分配子问题。利用背景知识了关于问题的背景（即，任务的优先事项）使提出的方法非常强大和高效。随机生成的数据集由具有各种形状参数的125个不同的随机任务图组成，例如生物医学中经常遇到的各种形状参数，已被利用用于评估所提出的方法。所进行的比较研究清楚地表明了所提出的方法与传统对应物的效率和优势。从我们的第一个公制，即NSL（规范化的时间表长度）的观点，所提出的ACO-CLA比ETF（最早的时间）更好地为2.48％和5.55％，这是第二次最佳方法，以及所有其他竞争方法的平均性能。另一方面，从我们的第二次公制，即加速度的透视图，所提出的ACO-CLA比ETF（第二代方法）和所有其他竞争对手的平均性能更好。

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