Earticle

초록

영어

Task scheduling is an important and challenging issue of Cloud computing. Existing solutions to task scheduling problems are unsuitable for Cloud computing because they only focus on a specific purpose like the minimization of execution time or workload and do not use characteristics of Cloud computing for task scheduling. A task scheduler in Cloud computing has to satisfy cloud users with the agreed QoS and improve profits of cloud providers. In order to solve task scheduling problems in Cloud computing, this paper proposes a task scheduling model based on the genetic algorithm. In the proposed model, the task scheduler calls the GA scheduling function every task scheduling cycle. This function creates a set of task schedules and evaluates the quality of each task schedule with user satisfaction and virtual machine availability. The function iterates genetic operations to make an optimal task schedule. Experimental results show effectiveness and efficiency of the genetic algorithm-based task scheduling model in comparison with existing task scheduling models, which are the round-robin task scheduling model, the load index-based task scheduling model, and the ABC based task scheduling model.

목차

Abstract
 1. Introduction
 2. GA-based Task Scheduling for Cloud Computing
  2.1. Encoding and Initiation
  2.2. Fitness Function and Selection
  2.3. Crossover and Mutation
  2.4. Restart & Stop Condition
 3. Simulation Results
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Sung Ho Jang [ School of Information Engineering Inha University ]
• Tae Young Kim [ School of Information Engineering Inha University ]
• Jae Kwon Kim [ School of Information Engineering Inha University ]
• Jong Sik Lee [ School of Information Engineering Inha University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605