Power Consumption Constrained Task Scheduling Using Enhanced Genetic Algorithms

2013 ◽  
pp. 139-159 ◽  
Author(s):  
Gang Shen ◽  
Yanqing Zhang
Keyword(s):  
Genetic Algorithms ◽  
Power Consumption ◽  
Task Scheduling

Using Genetic Algorithm for Scheduling Tasks of Computational Grids in the Gridsim Simulator

2014 ◽  
pp. 521-535
Author(s):  
João Phellipe ◽  
Carla Katarina ◽  
Francisco das Chagas ◽  
Dario Aloise
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Distributed System ◽  
Task Scheduling ◽  
Computational Grid ◽  
Computer Processing ◽  
Computational Grids ◽  
Processing Power

Computer processing power has evolved considerably in recent years. However, there are problems that still require many machines to perform a large amount of processing in a parallel and distributed way. In this context, the task scheduling in a distributed system present many algorithms. In this chapter, the authors present a scheduler based on genetic algorithms in order to distribute tasks more efficiently in a computational grid; it has been implemented in GRIDSIM, a computational grid simulator with the features and attributes of a real grid.

POPULATION SIZE MODELING FOR GA IN TIME-CRITICAL TASK SCHEDULING

2011 ◽  
Vol 22 (03) ◽  
pp. 603-620 ◽  
Author(s):  
WEI SUN
Keyword(s):  
Genetic Algorithms ◽  
Population Size ◽  
Task Scheduling ◽  
Scheduling Problems ◽  
Success Ratio ◽  
Critical Task ◽  
Time Critical

Genetic algorithms (GAs) have been well applied in solving scheduling problems and their performance advantages have also been recognized. However, practitioners are often troubled by parameters setting when they are tuning GAs. Population Size (PS) has been shown to greatly affect the efficiency of GAs. Although some population sizing models exist in the literature, reasonable population sizing for task scheduling is rarely observed. In this paper, based on the PS deciding model proposed by Harik, we present a model to represent the relation between the success ratio and the PS for the GA applied in time-critical task scheduling, in which the efficiency of GAs is more necessitated than in solving other kinds of problems. Our model only needs some parameters easy to know through proper simplifications and approximations. Hence, our model is applicable. Finally, our model is verified through experiments.

scholarly journals Genetic algorithms and multiprocessor task scheduling: A systematic literature review

2019 ◽  
Author(s):  
Eduardo Silva ◽  
Paulo Gabriel
Keyword(s):  
Systematic Review ◽  
Genetic Algorithms ◽  
Literature Review ◽  
Impact Factor ◽  
Task Scheduling ◽  
Systematic Literature Review ◽  
Meta Analysis ◽  
Scheduling Problem ◽  
Review Of The Literature ◽  
Multiprocessor Task Scheduling

This paper reports a systematic review of the literature about genetic algorithms applied to the multiprocessor task scheduling problem. After defining a protocol with the main rules of this review, the research was performed considering journal papers published between 1990 and 2018. At the end of this process, 37 works were recovered and analyzed. By performing a meta-analysis, a variety of information was extracted and summarized, including impact factor, Eigenfactor score, scenarios considered, optimization metrics, volume of citations, and others.

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