论文标题
用于工作负载的可扩展基础架构集群轨迹的表征
Scalable Infrastructure for Workload Characterization of Cluster Traces
论文作者
论文摘要
在最近的过去，已经尝试在新兴的无服务器云市场中获得工艺，尤其是在Google，AWS等大型生产云集群中。在分析和表征来自大型生产云集群中的实际工作负载的同时，使云提供商，研究人员和日常用户分析这些集群的工作负载痕迹，这是由于数据的异质性质而成为一项艰巨的任务。本文提出了一个基于Google的数据Proc的可扩展基础架构，用于分析云环境的工作负载轨迹。我们使用Google Cloud Cloud cluster-usage-usage-traces-v3评估了所提出的基础架构的功能。我们在此数据集上执行工作负载表征，重点介绍工作负载的异质性，工作持续时间的变化，资源消耗各个方面以及集群提供的资源的总体可用性。本文报告的发现将对云基础架构提供商和用户有益，同时管理云计算资源，尤其是无服务器平台。
In the recent past, characterizing workloads has been attempted to gain a foothold in the emerging serverless cloud market, especially in the large production cloud clusters of Google, AWS, and so forth. While analyzing and characterizing real workloads from a large production cloud cluster benefits cloud providers, researchers, and daily users, analyzing the workload traces of these clusters has been an arduous task due to the heterogeneous nature of data. This article proposes a scalable infrastructure based on Google's dataproc for analyzing the workload traces of cloud environments. We evaluated the functioning of the proposed infrastructure using the workload traces of Google cloud cluster-usage-traces-v3. We perform the workload characterization on this dataset, focusing on the heterogeneity of the workload, the variations in job durations, aspects of resources consumption, and the overall availability of resources provided by the cluster. The findings reported in the paper will be beneficial for cloud infrastructure providers and users while managing the cloud computing resources, especially serverless platforms.
