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• W4319782489 abstract "Energy efficiency is an important issue in designing portable and battery-limited Cyber-Physical Systems (CPSs). Correspondingly, processor is a major component that consumes large percentage of energy of the whole system. To achieve the energy efficiency in multiprocessor systems, various energy aware task scheduling approaches are proposed in the literature. This paper presents a survey on energy-aware task scheduling approaches utilizing Genetic Algorithms (GA) to exploit slack time and Dynamic Voltage and Frequency Scaling (DVFS) mechanism proposed for multiprocessor and real-time systems. Our aim is to give a full view of the problem. Although energy efficiency in other domains such as cloud and grid computing has been broadly investigated, energy efficiency in CPSs is largely left behind due to the complicated interactions between computational component and the physical component that forms the whole system. The analysis part of this paper presents the main results starting from early 2000s until recently, showing how the proposed approaches evolved to address the evolution of the platform features and needs. The discussion highlights the platform model, the application model, the key idea, and results of each energy-aware task scheduling approach. In addition, the survey discusses the approaches proposed to deal with additional problems related to the evolution of computing platforms." @default.
• W4319782489 created "2023-02-11" @default.
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• W4319782489 date "2022-12-15" @default.
• W4319782489 modified "2023-09-23" @default.
• W4319782489 title "A Survey on Energy-Aware Scheduling using Genetic Algorithms" @default.
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• W4319782489 doi "https://doi.org/10.1109/icicyta57421.2022.10038154" @default.
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