On accurate prediction of cloud workloads with adaptive pattern mining

Liang Bao, Jin Yang, Zhengtong Zhang, Wenjing Liu, Junhao Chen, Chase Wu

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalJournal of Supercomputing
DOIs
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Software
  • Information Systems
  • Hardware and Architecture

Keywords

  • Cloud computing
  • Ensemble method
  • Pattern mining
  • Workload prediction

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