Scavenger: A Black-Box Batch Workload Resource Manager for Improving Utilization in Cloud Environments
Scavenger: A Black-Box Batch Workload Resource Manager for Improving Utilization in Cloud Environments
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DOI：
10.1145/3357223.3362734
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发表时间：
2019-11
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通讯作者：
S. A. Javadi;Amoghavarsha Suresh;Muhammad Wajahat;Anshul Gandhi
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作者：
S. A. Javadi;Amoghavarsha Suresh;Muhammad Wajahat;Anshul Gandhi
资源利用不足在云数据中心中很常见。先前的工作已经提出通过在后台运行提供商工作负载来提高利用率，与租户工作负载共置。然而，一个尚未解决的重要挑战是将租户工作负载视为黑盒。我们提出了Scavenger，一个批处理工作负载管理器，它在黑盒租户VM旁边随机运行容器化的批处理作业，以提高利用率。Scavenger设计为无需任何离线分析或有关租户工作负载的先前信息即可工作。为了始终满足租户VM的资源需求，Scavenger动态调节批处理作业的资源使用，包括处理器使用、内存容量和网络带宽。我们在两个不同的测试床上使用与Spark作业共存的延迟敏感租户工作负载对Scavenger进行了实验评估，并表明Scavenger显着增加了资源使用率，而不会影响租户虚拟机的资源需求。
Resource under-utilization is common in cloud data centers. Prior works have proposed improving utilization by running provider workloads in the background, colocated with tenant workloads. However, an important challenge that has still not been addressed is considering the tenant workloads as a black-box. We present Scavenger, a batch workload manager that opportunistically runs containerized batch jobs next to black-box tenant VMs to improve utilization. Scavenger is designed to work without requiring any offline profiling or prior information about the tenant workload. To meet the tenant VMs' resource demand at all times, Scavenger dynamically regulates the resource usage of batch jobs, including processor usage, memory capacity, and network bandwidth. We experimentally evaluate Scavenger on two different testbeds using latency-sensitive tenant workloads colocated with Spark jobs in the background and show that Scavenger significantly increases resource usage without compromising the resource demands of tenant VMs.