Energy saving task scheduling for heterogeneous CMP system based on multi-objective fuzzy genetic algorithm

Miao, Lei; Qi, Yong; Hou, Di; Wu, Chang-Li; Dai, Yuehua

doi:10.1109/icsmc.2009.5346656

Cited by 2 publications

(4 citation statements)

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“…For example, [18] and [24] are one of the methods that use a Pareto approach. In [18], the authors propose a multi-objective fuzzy genetic algorithm to optimize the energy saving scheduling tasks on heterogeneous chip multi-processor (CMP) system. In this algorithm, the Pareto set includes chromosomes with the shortest task execution time and the lowest system energy.…”

Section: Pareto Approachmentioning

confidence: 99%

“…When used in this way it is commonly known as dynamic voltage and frequency scaling (DVFS). Many methods, such as [5,6,10,11,15,16,18,21,22,23,26], try to exploit this technique to reduce energy consumption.…”

Section: Hardware Levelmentioning

confidence: 99%

“…In static scheduling [5,18,21,22,26], timing information of tasks and timing information of processors are available in compile time before the deployment of tasks. This static scheduling involves most of the large-scale computational problems such as object recognition in machine vision applications, chemistry and bioinformatics.…”

Section: Hardware Levelmentioning

confidence: 99%

“…[10] and [21] addresse the task scheduling problem on distributed computing systems. [15] and [18] investigate the same problem on respectively heterogeneous computing systems and chip multi-processor systems. With the chip multi-processor being more and more widespread used in the laptop, desktop and data center area, the power-performance scheduling issues are becoming challenges.…”

Section: Computing Systemsmentioning

confidence: 99%

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Reducing energy consumption is an increasingly important issue in computing and embedded systems. In computing systems, minimizing energy consumption can significantly reduces the amount of energy bills. The demand for computing systems steadily increases and the cost of energy continues to rise. In embedded systems, reducing the use of energy allows to extend the autonomy of these systems. In addition, the reduction of energy decreases greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to consume less energy. This chapter gives an overview of the main methods used to reduce the energy consumption in computing and embedded systems. As a use case and to give an example of a method, the chapter describes our new parallel bi-objective hybrid genetic algorithm that takes into account the completion time and the energy consumption. In terms of energy consumption, the obtained results show that our approach outperforms previous scheduling methods by a significant margin. In terms of completion time, the obtained schedules are also shorter than those of other algorithms.

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Section: Pareto Approachmentioning

confidence: 99%