On accurate prediction of cloud workloads with adaptive pattern mining

Document Type

Article

Publication Date

1-1-2023

Abstract

Resource provisioning for cloud computing requires adaptive and accurate prediction of cloud workloads. However, existing studies in workload prediction have faced significant challenges in predicting time-varying cloud workloads of diverse trends and patterns, and the lack of accurate prediction often results in resource waste and violation of Service-Level Agreements (SLAs). We propose a bagging-like ensemble framework for cloud workload prediction with Adaptive Pattern Mining (APM). Within this framework, we first design a two-step method with various models to simultaneously capture the “low frequency” and “high frequency” characteristics of highly variable workloads. For a given workload, we further develop an error-based weights aggregation method to integrate the prediction results from multiple pattern-specific models into a final result to predict a future workload. We conduct experiments to demonstrate the efficacy of APM in workload prediction with various prediction lengths using two real-world workload traces from Google and Alibaba cloud data centers, which are of different types. Extensive experimental results show that APM achieves above 19.62% improvement over several classic and state-of-the-art workload prediction methods for highly variable real-world cloud workloads.

Identifier

85133450907 (Scopus)

Publication Title

Journal of Supercomputing

External Full Text Location

https://doi.org/10.1007/s11227-022-04647-5

e-ISSN

15730484

ISSN

09208542

First Page

160

Last Page

187

Issue

1

Volume

79

Grant

20310102D

Fund Ref

National Natural Science Foundation of China

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