{"title":"Cotask scheduling in cloud computing","authors":"Yangming Zhao, Shouxi Luo, Yi Wang, Sheng Wang","doi":"10.1109/ICNP.2017.8117587","DOIUrl":null,"url":null,"abstract":"Computing frameworks have been widely deployed to support global-scale services. A job typically has multiple sequential stages, where each stage is further divided into multiple parallel tasks. We call the set of all the tasks in a stage of a job a cotask. In this paper, we aim to minimize the average Cotask Completion Time (CCT) in cotask scheduling. To the best of our knowledge, there is no prior work on cotask scheduling for cloud computing. We propose the Cotask Scheduling Scheme (CSS), and take MapReduce as a representative of computing frameworks. CSS schedules cotasks following the Minimum Completion Time First (MCTF) policy, and we prove this problem is NP-hard. We formulate the model using the Integer Linear Programming (ILP), and solve it through an efficient heuristics based on ILP relaxation. Through real trace based simulations, we show that CSS is able to reduce the average CCT by up to 62.20% and 69.93% with traces from our testbed and from a large production cluster respectively.","PeriodicalId":6462,"journal":{"name":"2017 IEEE 25th International Conference on Network Protocols (ICNP)","volume":"68 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 25th International Conference on Network Protocols (ICNP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNP.2017.8117587","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Computing frameworks have been widely deployed to support global-scale services. A job typically has multiple sequential stages, where each stage is further divided into multiple parallel tasks. We call the set of all the tasks in a stage of a job a cotask. In this paper, we aim to minimize the average Cotask Completion Time (CCT) in cotask scheduling. To the best of our knowledge, there is no prior work on cotask scheduling for cloud computing. We propose the Cotask Scheduling Scheme (CSS), and take MapReduce as a representative of computing frameworks. CSS schedules cotasks following the Minimum Completion Time First (MCTF) policy, and we prove this problem is NP-hard. We formulate the model using the Integer Linear Programming (ILP), and solve it through an efficient heuristics based on ILP relaxation. Through real trace based simulations, we show that CSS is able to reduce the average CCT by up to 62.20% and 69.93% with traces from our testbed and from a large production cluster respectively.
计算框架已被广泛部署以支持全球规模的服务。一个作业通常有多个连续的阶段,其中每个阶段又进一步划分为多个并行任务。我们把作业的一个阶段中所有任务的集合称为协同任务。在本文中,我们的目标是最小化协同任务调度中的平均协同任务完成时间(CCT)。据我们所知,目前还没有关于云计算协同任务调度的研究。提出了协同任务调度方案(CSS),并以MapReduce为代表的计算框架。CSS调度协同任务遵循最小完成时间优先(Minimum Completion Time First, MCTF)策略,我们证明了这个问题是np困难的。我们使用整数线性规划(ILP)来建立模型,并通过基于ILP松弛的高效启发式求解。通过基于真实轨迹的模拟,我们表明CSS能够将平均CCT分别降低62.20%和69.93%,分别来自我们的测试平台和大型生产集群的轨迹。