Research Area: Metaheuristic Computing
High energy consumption is one of the biggest obstacles to the rapid development of computing systems, and reducing energy consumption is quite urgent and necessary for sustainable computing. Low-energy scheduling based on Dynamic Voltage and Frequency Scaling (DVFS) is one of the most commonly used energy optimization techniques. Recent survey works have reviewed some low-energy scheduling algorithms, but there is currently no systematic review in low-energy parallel scheduling algorithms. With the increasing complexity of function requirements, many parallel applications have been executed in various sustainable computing systems. In this paper, we survey recent advances in low-energy parallel scheduling algorithms according to three scheduling styles, namely, 1) energy-efficient parallel scheduling algorithms; 2) energy-aware parallel scheduling algorithms; and 3) energy-conscious parallel scheduling algorithms. Low-energy parallel scheduling algorithms basically involve five categories of 1) heuristic algorithms; 2) meta-heuristic algorithms; 3) integer programming algorithms; 4) machine learning algorithms; and 5) game theory algorithms. Further, we introduce the future trends in low-energy parallel scheduling algorithms from the perspectives of new requirements and future developments. By surveying the recent advances and introducing the future trends, we expect to provide researchers with a systematic reference and development directions in low-energy parallel scheduling for sustainable computing systems.
Keywords:
Energy-aware
Energy-conscious
Energy-efficient
Parallel scheduling
Optimization techniques
Sustainable computing systems.
Author(s) Name: Guoqi Xie, Xiongren Xiao, Hao Peng, Renfa Li, Keqin Li
Journal name: IEEE Transactions on Sustainable Computing
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/TSUSC.2021.3057983
Volume Information: Volume: 7
