Green Energy Forecast-Based Bi-Objective Scheduling of Tasks Across Distributed Clouds

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Current large-scale green cloud data centers (GCDCs) tend to consume a huge amount of energy and generate enormous carbon emissions. Existing studies have tried to solve this problem by either realizing prediction of green energy, or optimizing task scheduling. In contrast, this work seamlessly combines green energy prediction and task scheduling to jointly optimize revenue and energy cost of GCDCs. Specifically, this work designs a prediction method, named Savitzky-Golay and Long Short-Term Memory network (SG-LSTM), to realize noise filtering and forecast green energy. Based on such prediction, a bi-objective optimization method, named Decomposition-based Multi-objective evolutionary algorithm with Gaussian mutation and Crowding distance (DMGC), is developed to optimize the revenue and energy cost of GCDCs. Its performance is demonstrated over real-life datasets including Google cluster traces, wind speeds, solar irradiance and prices of electricity. Experimental results show that SG-LSTM outperforms its two peers, back propagation neural network and gated recurrent unit, in terms of root mean square errors and mean absolute errors. In addition, DMGC surpasses its such peers as NSGA-II, SPEA2, and MOEA/D in terms of revenue, energy cost and average execution time. Particularly, DMGC's revenue is 18%, 20% and 13.1% higher, energy cost is 16%, 19.8% and 15.2% lower, and average execution time is 60.02%, 38.47% and 24.17% lower than those of NSGA-II, SPEA2, and MOEA/D, respectively.

Original languageEnglish (US)
Pages (from-to)619-630
Number of pages12
JournalIEEE Transactions on Sustainable Computing
Issue number3
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Renewable Energy, Sustainability and the Environment
  • Hardware and Architecture
  • Control and Optimization
  • Computational Theory and Mathematics


  • Green clouds
  • Savitzky-Golay filter
  • intelligent optimization
  • machine learning
  • multi-objective optimization algorithms
  • recurrent neural network
  • task scheduling


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