Bi-objective Intelligent Task Scheduling for Green Clouds with Deep Learning-based Prediction

Heng Liu, Xiaofen Zhang, Jing Bi, Haitao Yuan, Meng Chu Zhou

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

Abstract

The ever-increasing deployment of cloud data centers causes high energy consumption, high cost, and harmful environmental pollution. To solve above problems, cloud service providers are actively exploring to use green cloud data centers (GCDCs) by using green energy. Yet it is challenging to accurately predict the future wind and solar energy before making intelligent task scheduling decisions. In addition, it is difficult to jointly optimize cost and revenue. In this work, to make optimal task scheduling, various types of applications, service level agreements, service rates, task loss probability, electricity prices and green energy in different GCDCs are considered. First, this work employs a long short-term memory network to predict wind and solar energy. Then, it adopts a bi-objective optimization algorithm to achieve a better trade-off between cost and revenue of GCDCs. Finally, it adopts real-world data including workload trace, wind energy, solar energy and electricity prices to demonstrate the effectiveness of the proposed energy prediction and task scheduling methods. It's shown that the proposed methods achieve higher performance than other neural network methods.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728168531
DOIs
StatePublished - Oct 30 2020
Event2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020 - Nanjing, China
Duration: Oct 30 2020Nov 2 2020

Publication series

Name2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

Conference

Conference2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020
CountryChina
CityNanjing
Period10/30/2011/2/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Statistics, Probability and Uncertainty
  • Control and Optimization
  • Sensory Systems

Keywords

  • Bi-objective optimization
  • green clouds
  • green energy prediction
  • long short-term memory network
  • task scheduling

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