首页|Public Datasets for Cloud Computing: A Comprehensive Survey

Public Datasets for Cloud Computing: A Comprehensive Survey

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  • NETL
  • NSTL
  • Assoc Computing Machinery
Publicly available datasets are vital to researchers because they permit the testing of new algorithms under a variety of conditions and ensure the verifiability and reproducibility of scientific experiments. In cloud computing research, there is a particular dependence on obtaining load traces and network traces from real cloud computing clusters, which are used for designing energy efficiency prediction, workload analysis, and anomaly detection solutions. To address the current lack of a comprehensive overview and thorough analysis of cloud computing datasets and to gain insight into their current status and future trends, in this article, we provide a comprehensive survey of existing publicly cloud computing datasets. First, we utilize a systematic mapping approach to analyze 968 scientific papers from 6 scientific databases, resulting in the retrieval of 42 datasets related to cloud computing. Second, we categorize these datasets based on 11 characteristics to assist researchers in quickly finding datasets suitable for their specific needs. Third, we provide detailed descriptions of each dataset to assist researchers in gaining a clearer understanding of their characteristics. Fourth, we select 12 mainstream datasets and conduct a comprehensive analysis and comparison of their characteristics. Finally, we discuss the weaknesses of existing datasets, identify challenges, provide recommendations for long-term dataset maintenance and updates, and outline directions for the future creation of new cloud computing datasets.

Cloud computingdatasetsworkloadsecurityenergy efficiencyresource allocationtask schedulinganomaly detection

GUOZHI LIU、WEIWEI LIN、HAOTONG ZHANG、JIANPENG LIN、SHAOLIANG PENG、KEQIN LI

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School of Computer Science and Technology, South China University of Technology, Guangzhou, China

South China University of Technology, Guangzhou, China and Pengcheng Laboratory, Shenzhen, China

South China University of Technology, Guangzhou, China

Hunan University, Changsha, China

Department of Computer Science, State University of New York, New Paltz, United States

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2025

10.1145/3719003

ACM Computing Surveys

ACM Computing Surveys

ISSN：0360-0300
年,卷(期)：2025.57(8)
  • 137