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Open Access

Application-Oriented Cloud Workload Prediction: A Survey and New Perspectives

Key Laboratory of Embedded System and Service Computing, Ministry of Education, and also with Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Department of Computer Science and Technology, Tongji University, Shanghai 201804, China, and also with Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
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Abstract

Workload prediction is critical in enabling proactive resource management of cloud applications. Accurate workload prediction is valuable for cloud users and providers as it can effectively guide many practices, such as performance assurance, cost reduction, and energy consumption optimization. However, cloud workload prediction is highly challenging due to the complexity and dynamics of workloads, and various solutions have been proposed to enhance the prediction behavior. This paper aims to provide an in-depth understanding and categorization of existing solutions through extensive literature reviews. Unlike existing surveys, for the first time, we comprehensively sort out and analyze the development landscape of workload prediction from a new perspective, i.e., application-oriented rather than prediction methodologies per se. Specifically, we first introduce the basic features of workload prediction, and then analyze and categorize existing efforts based on two significant characteristics of cloud applications: variability and heterogeneity. Furthermore, we also investigate how workload prediction is applied to resource management. Finally, open research opportunities in workload prediction are highlighted to foster further advancements.

Keywords

cloud computingworkload predictionresource managementartificial intelligence for IT operations (AIOps)

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Tsinghua Science and Technology
Volume 30 Issue 1,
February 2025
Pages 34-54
DOI: 10.26599/TST.2024.9010024
Cite this article:
Feng B, Ding Z. Application-Oriented Cloud Workload Prediction: A Survey and New Perspectives. Tsinghua Science and Technology, 2025, 30(1): 34-54. https://doi.org/10.26599/TST.2024.9010024

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Received: 25 October 2023
Revised: 19 January 2024
Accepted: 24 January 2024
Published: 11 September 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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