Green Energy Forecast-based Bi-objective Scheduling of Tasks across Distributed Clouds

Jing Bi, Haitao Yuan, Jia Zhang, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

15 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.

Original languageEnglish (US)
JournalIEEE Transactions on Sustainable Computing
StatePublished - 2022

All Science Journal Classification (ASJC) codes

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


  • Cloud computing
  • Costs
  • Green clouds
  • Green products
  • Optimization
  • Savitzky-Golay filter
  • Scheduling
  • Task analysis
  • Time series analysis
  • intelligent optimization
  • machine learning
  • multi-objective optimization algorithms
  • recurrent neural network
  • task scheduling


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