文章基本信息

标题：GoCJ: Google Cloud Jobs Dataset for Distributed and Cloud Computing Infrastructures
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作者：Altaf Hussain ; Muhammad Aleem
期刊名称：Data
印刷版ISSN：2306-5729
出版年度：2018
卷号：3
期号：4
页码：38-49
DOI：10.3390/data3040038
出版社：MDPI Publishing
摘要：Developers of resource-allocation and scheduling algorithms share test datasets (i.e., benchmarks) to enable others to compare the performance of newly developed algorithms. However, mostly it is hard to acquire real cloud datasets due to the users’ data confidentiality issues and policies maintained by Cloud Service Providers (CSP). Accessibility of large-scale test datasets, depicting the realistic high-performance computing requirements of cloud users, is very limited. Therefore, the publicly available real cloud dataset will significantly encourage other researchers to compare and benchmark their applications using an open-source benchmark. To meet these objectives, the contemporary state of the art has been scrutinized to explore a real workload behavior in Google cluster traces. Starting from smaller- to moderate-size cloud computing infrastructures, the dataset generation process is demonstrated using the Monte Carlo simulation method to produce a Google Cloud Jobs (GoCJ) dataset based on the analysis of Google cluster traces. With this article, the dataset is made publicly available to enable other researchers in the field to investigate and benchmark their scheduling and resource-allocation schemes for the cloud. The GoCJ dataset is archived and available on the Mendeley Data repository.
关键词：GoCJ dataset; meta-task dataset; HPC dataset; scientific dataset GoCJ dataset ; meta-task dataset ; HPC dataset ; scientific dataset