Multi-Mode Instance-Intensive Workflow Task Batch Scheduling in Containerized Hybrid Cloud

IF 5.3 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Transactions on Cloud Computing Pub Date : 2023-12-19 DOI:10.1109/TCC.2023.3344194

An Liu;Ming Gao;Jiafu Tang

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引用次数: 0

Abstract

The migration of containerized microservices from virtual machines (VMs) to cloud data centers has become the most advanced deployment technique for large software applications in the cloud. This study investigates the scheduling of instance-intensive workflow (IWF) tasks to be executed in containers on a hybrid cloud when computational resources are limited. The process of scheduling these IWF tasks becomes complicated when considering the deployment time of containers, inter-task communication time, and their dependencies simultaneously, particularly when the task can choose multi-mode executions due to the flexible computational resource allocation of the container. We propose a batch scheduling strategy (BSS) for the IWF task scheduling problem. The BSS prioritizes the execution of IWF tasks with high repetition rates with a certain probability and records the virtual machines and modes selected for task execution, which can reduce the data transfer time and the randomness of computation. Based on this, we use an improved hybrid algorithm combined with BSS to solve the multi-mode IWF task scheduling problem. The experimental results demonstrate that employing the BSS can reduce the scheduling time by 6% when the number of workflows increases to 80. Additionally, we tested the effectiveness of all operators in the algorithm, and the results show that each step of the algorithm yields good performance. Compared to similar algorithms in related studies, the overall algorithm can achieve a maximum reduction of approximately 18% in the target value.

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容器化混合云中的多模式实例密集型工作流任务批量调度

将容器化微服务从虚拟机（VM）迁移到云数据中心已成为云中大型软件应用程序最先进的部署技术。本研究探讨了在计算资源有限的情况下，混合云上容器执行的实例密集型工作流（IWF）任务的调度问题。当同时考虑容器的部署时间、任务间的通信时间以及它们之间的依赖关系时，这些 IWF 任务的调度过程就变得复杂了，尤其是当任务由于容器灵活的计算资源分配而可以选择多模式执行时。我们针对 IWF 任务调度问题提出了一种批量调度策略（BSS）。BSS 以一定概率优先执行重复率高的 IWF 任务，并记录任务执行所选择的虚拟机和模式，从而减少数据传输时间和计算的随机性。在此基础上，我们采用改进的混合算法结合 BSS 解决多模式 IWF 任务调度问题。实验结果表明，当工作流数量增加到 80 个时，采用 BSS 可以减少 6% 的调度时间。此外，我们还测试了算法中所有算子的有效性，结果表明算法的每个步骤都能产生良好的性能。与相关研究中的类似算法相比，整个算法的目标值最多可减少约 18%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Cloud Computing Computer Science-Software

CiteScore

9.40

自引率

6.20%

发文量

167

期刊介绍： The IEEE Transactions on Cloud Computing (TCC) is dedicated to the multidisciplinary field of cloud computing. It is committed to the publication of articles that present innovative research ideas, application results, and case studies in cloud computing, focusing on key technical issues related to theory, algorithms, systems, applications, and performance.