Real-Time Multiple-Workflow Scheduling in Cloud Environments
Real-Time Multiple-Workflow Scheduling in Cloud Environments
复制标题
云环境中的实时多工作流调度
DOI：
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发表时间：
2021
影响因子：
5.3
通讯作者：
Minjie Bian
中科院分区：
文献类型：
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作者：
Xiaojin Ma;Huahu Xu;Honghao Gao;Minjie Bian
随着云计算的发展，越来越多的不同领域的应用被部署到云上。在此过程中，由这些不同应用程序的任务组成的多个工作流的实时调度必须考虑各种影响调度性能的影响因素。提出了一种实时多工作流调度（RMWS）方案，在不同的工期约束下以最小的成本动态调度工作流。由于工作流到达时间和规格的不确定性，RMWS动态分配任务，并将调度过程分为三个阶段。首先，当有新的工作流到达时，根据截止日期计算每个任务的最迟开始时间和最迟完成时间，并通过概率向上排序获得每个任务的子截止日期。然后，根据每个就绪任务的子期限和虚拟机（VM）的增加成本分配每个就绪任务。同时，每个虚拟机只能分配一个等待任务，以减少延迟波动。最后，当任务在分配的虚拟机上完成后，相关任务的所有参数都会更新，然后再分配给相应的虚拟机。基于四个实际工作流轨迹的实验结果表明，在不同条件下，该算法在总租赁成本、资源利用率、成功率和截止日期偏差等方面都优于两种最先进的算法。
With the development of cloud computing, an increasing number of applications in different fields have been deployed to the cloud. In this process, the real-time scheduling of multiple workflows composed of tasks from these different applications must consider various influencing factors that strongly affect scheduling performance. This paper proposes a real-time multiple-workflow scheduling (RMWS) scheme to schedule workflows dynamically with minimum cost under different deadline constraints. Due to the uncertainty of workflow arrival time and specification, RMWS dynamically allocates tasks and divides the scheduling process into three stages. First, when a new workflow arrives, the latest start time and the latest finish time of each task are calculated according to the deadline, and the subdeadline of each task is obtained by probabilistic upward ranking. Then, each ready task is allocated according to its subdeadline and the increased cost of the virtual machine (VM). Meanwhile, only one waiting task can be assigned to each VM to reduce delay fluctuations. Finally, when the task is completed on the assigned VM, all the parameters of the relevant tasks are updated before allocating them to appropriate VMs. The experimental results based on four real-world workflow traces show that the proposed algorithm is superior to two state-of-the-art algorithms in terms of total rental cost, resource utilization, success rate and deadline deviation under different conditions.