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

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 comb...

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Published in:IEEE transactions on sustainable computing Vol. 7; no. 3; pp. 619 - 630
Main Authors: Bi, Jing, Yuan, Haitao, Zhang, Jia, Zhou, MengChu
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Back propagation networks
Clean energy
Cloud computing
Costs
Data centers
Energy
Energy costs
Errors
Evolutionary algorithms
Green clouds
Green products
intelligent optimization
Irradiance
machine learning
multi-objective optimization algorithms
Mutation
Neural networks
Optimization
recurrent neural network
Renewable energy
Revenue
Savitzky-Golay filter
Scheduling
Task analysis
Task scheduling
Time series analysis
ISSN:2377-3782, 2377-3790
Online Access:Get full text
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Summary: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.
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ISSN:2377-3782
2377-3790
DOI:10.1109/TSUSC.2021.3124893