Improved LSTM-based Prediction Method for Highly Variable Workload and Resources in Clouds

Shuang Li, Jing Bi, Haitao Yuan, Meng Chu Zhou, Jia Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A large number of services provided by cloud/edge computing systems have become the most important part of Internet services. In spite of their numerous benefits, cloud/edge providers face some challenging issues, e.g., inaccurate prediction of large-scale workload and resource usage traces. However, due to the complexity of cloud computing environments, workload and resource usage traces are highly-variable, thus making it difficult for traditional models to predict them accurately. Traditional models fail to deal with nonlinear characteristics and long-term memory dependencies. To solve this problem, this work proposes an integrated prediction method that combines Bi-directional and Grid Long Short-Term Memory network (BG-LSTM) models to predict workload and resource usage traces. In this method, workload and resource usage traces are first smoothed by a Savitzky-Golay filter to eliminate their extreme points and noise interference. Then, an integrated prediction model is established to achieve accurate prediction for highly-variable traces. Using real-world workload and resource usage traces from Google cloud data centers, we have conducted extensive experiments to show the effectiveness and adaptability of BG-LSTM for different traces. The performance results well demonstrate that BG-LSTM achieves better prediction results than some typical prediction methods for highly-variable real-world cloud systems.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1206-1211
Number of pages6
Volume2020-October
ISBN (Electronic)9781728185262
DOIs
StatePublished - Oct 11 2020
Event2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada
Duration: Oct 11 2020Oct 14 2020

Conference

Conference2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
Country/TerritoryCanada
CityToronto
Period10/11/2010/14/20

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • BG-LSTM
  • Cloud computing systems
  • Savitzky-Golay filter
  • artificial intelligence
  • deep learning
  • hybrid prediction
  • resource provisioning

Fingerprint

Dive into the research topics of 'Improved LSTM-based Prediction Method for Highly Variable Workload and Resources in Clouds'. Together they form a unique fingerprint.

Cite this