Earticle

초록

영어

Cloud computing is a new technology, instead of all computer hardware and software that used on desktop, or somewhere within company's network, it's presented as a service by cloud service providers and accessed via the Internet. Exactly where hardware and software are located and how everything works does not matter. In cloud computing there are many jobs that requires to be executed on the available resources to achieve best minimal execution time. Several optimization methods are available for cloud job scheduling. However, the job scheduling process is still need to be optimized. This paper proposes a new job scheduling mechanism using Firefly Algorithm to minimize the execution time of jobs. The proposed mechanism based on information of jobs and resources such as length of job speed of resource and identifiers. The scheduling function in the proposed job scheduling mechanism firstly creates a set of jobs and resources to generate the population by assigning the jobs to resources randomly and evaluates the population using a fitness value which represents the execution time of jobs. Secondly the function used iterations to regenerate populations based on firefly behavior to produce the best job schedule that gives the minimum execution time of jobs. Several scenarios are implemented using Java Language and CloudSim simulator. Different settings have been considered in the evaluation and experimentation phase to examine the proposed mechanism in different workloads. The first phase of the evaluation process describes how the proposed mechanism can be used to minimize the execution time of jobs. The second phase of the evaluation process compares the proposed mechanism with First Come First Serves (FCFS) algorithm. The results revealed that the proposed mechanism minimizes the execution time significantly. Furthermore, the proposed mechanism outperformed the FCFS algorithm.

목차

Abstract
 1. Introduction
 2. Cloud Job Scheduling
 3. Job Scheduling Methods
  3.1. Bees Life Algorithm
  3.2. RSDC (Reliable Scheduling Distributed In Cloud Computing)
  3.3 Deadline and Budget Distribution based Scheduling
  3.4 Improved Genetic Algorithm
  3.5 Ant Colony Optimization
  3.6 ACO-LB Algorithm
  3.7 Improved Particle Swarm Optimization (PSO) algorithm
 4. Firefly Algorithm
 5. Proposed Firefly Algorithm for Cloud Job Scheduling
  5.1 Pseudo Code for the Proposed Firefly Algorithm Begin
 6. Evaluation and Experimentation
  6.1 The First Scenario
  6.2 The Second Scenario
  6.3 The Third Scenario
  6.4 The Fourth Scenario
 7. Conclusion and Future Work
 References

키워드

저자

• Demyana Izzat Esa [ University of Science and Technology-Omdurman-Sudan ]
• Adil Yousif [ University of Science and Technology-Omdurman-Sudan ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605