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首页> 外文会议>IEEE/ACM International Conference on Utility and Cloud Computing Companion >Exploring Potential for Resource Request Right-Sizing via Estimation and Container Migration in Apache Mesos
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Exploring Potential for Resource Request Right-Sizing via Estimation and Container Migration in Apache Mesos

机译：通过Apache Mesos中的估计和容器迁移探索资源请求权限调整的潜力

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摘要

Both commercial clouds and academic campus clusters suffer from low resource utilization and long wait times as the resource estimates for jobs, provided by users, is often inaccurate. Incorrect resource estimation poses challenges in the overall cluster and cloud management. Under allocation can cause significant slowdown or termination of applications. Over-allocation of resources for applications causes increased wait times for pending tasks in the queue, reduced throughput, and underutilization of the cluster. For end users that pay for resource allocations, incorrect estimation of resources (CPU, Memory, etc.) that are needed for each job can significantly increase the overall cost of running applications. Also, for academic cloud managers, resource fragmentation is unacceptable as they need to keep the utilization high to maximize the return on investment for the funding sponsors. We address the resource estimation problem for commercial and academic clouds/clusters that use the Apache Mesos resource management system. Our vision is a resource management system for Apache Mesos that can: (1) dynamically right-size the resources required for each application, thus improving overall utilization; and (2) incorporate migration of containerized jobs within the Mesos cluster.
机译：商业云和学术园区集群都受到资源利用率低和等待时间长的困扰，因为用户提供的工作资源估算通常不准确。不正确的资源估算给整个集群和云管理带来了挑战。分配不足会导致应用程序严重减速或终止。应用程序资源的过度分配会导致队列中待处理任务的等待时间增加，吞吐量降低以及群集利用率不足。对于为资源分配付费的最终用户，每个作业所需的资源（CPU，内存等）估计不正确会大大增加运行应用程序的总体成本。同样，对于学术云管理者来说，资源分散是不可接受的，因为他们需要保持较高的利用率以使资助方最大化投资回报。我们针对使用Apache Mesos资源管理系统的商业和学术云/集群的资源估计问题。我们的愿景是针对Apache Mesos的资源管理系统，该系统可以：（1）动态调整每个应用程序所需资源的大小，从而提高整体利用率； （2）在Mesos集群中合并容器化作业的迁移。

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