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Using Kestrel and XMPP to Support the STAR Experiment in the Cloud

机译:使用Kestrel和XMPP支持云中的STAR实验

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摘要

This paper presents the results and experiences of adapting and improving the Many-Task Computing (MTC) framework Kestrel for use with bag of tasks applications and the STAR experiment in particular. Kestrel is a lightweight, highly available job scheduling framework for Virtual Organization Clusters (VOCs) constructed in the cloud. Kestrel uses the Extensible Message and Presence Protocol (XMPP) for increasing MTC platform scalability and mitigating faults in Wide Area Network (WAN) communications. Kestrel's architecture is based upon pilot job frameworks used extensively in Grid computing, with fault-tolerant communications inspired by command-and-control botnets. The extensibility of XMPP has allowed development of protocols for identifying manager nodes, discovering the capabilities of worker agents, and for distributing tasks. Presence notifications provided by XMPP allow Kestrel to monitor the global state of the pool and to perform task dispatching based on worker availability. Since its inception, Kestrel has been modified based on its performance managing operational scientific workloads from the STAR group at Brookhaven National Laboratories. STAR provided a virtual machine image with applications for simulating proton collisions using PYTHIA and GEANT3. A Kestrel-based Virtual Organization Cluster, created on top of Clemson University's Palmetto cluster, CERN, and Amazon EC2, was able to provide over 400,000 CPU hours of computation over the course of a month using an average of 800 virtual machine instances every day, generating nearly seven terabytes of data and the largest PYTHIA production run that STAR has achieved to date.
机译:本文介绍了适应和改进用于任务包应用程序(尤其是STAR实验)的多任务计算(MTC)框架Kestrel的结果和经验。 Kestrel是一种轻量级,高度可用的作业调度框架,适用于在云中构建的虚拟组织集群(VOC)。 Kestrel使用可扩展消息和状态协议(XMPP)来提高MTC平台的可伸缩性并减轻广域网(WAN)通信中的故障。 Kestrel的体系结构基于在网格计算中广泛使用的试点工作框架,其容错通信受到命令和控制僵尸网络的启发。 XMPP的可扩展性允许开发协议来识别管理器节点,发现工作人员代理的功能以及分发任务。 XMPP提供的状态通知使Kestrel可以监视池的全局状态,并根据工作人员的可用性执行任务调度。自成立以来,Kestrel已根据其在Brookhaven国家实验室STAR集团的绩效管理操作科学工作量的绩效进行了修改。 STAR为虚拟机映像提供了使用PYTHIA和GEANT3模拟质子碰撞的应用程序。在克莱姆森大学的Palmetto集群,CERN和Amazon EC2之上创建的基于Kestrel的虚拟组织集群能够在一个月的时间内平均每天使用800个虚拟机实例,提供超过40万个CPU小时的计算,生成了将近7 TB的数据,这是STAR迄今为止实现的最大的PYTHIA生产运行。

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