A Genetic Algorithm Scheduling Approach for Virtual Machine Resources in a Cloud Computing Environment

Sawant, Shailesh S.

doi:10.31979/etd.et9e-73fz

Cited by 20 publications

(26 citation statements)

References 9 publications

(4 reference statements)

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“…We also made a comparison with other works, such as Sawant et al's scheme, Wei et al's scheme, Rodriguez et al's scheme, Fernando et al's scheme, and Pandey et al's scheme, to demonstrate the difference and outperform the proposed system, as shown in Table . As mentioned previously, we can summarize that the proposed agent‐based workflow scheduling scheme can meet the requirements of deadline constraint, dynamic job dispatching, real‐time scheduling, multi job scheduling, and optimal resource utilization.…”

Section: Implementation and Evaluationmentioning

confidence: 99%

“…It is increasingly business and scientific‐oriented jobs with certain workflow (short for workflow) composed of multiple tasks with dependency are migrated to and run on a variety of cloud environments . Workflow scheduling is a complex problem, and it has been proven that is a NP‐complete problem .…”

Section: Introductionmentioning

confidence: 99%

“…An excellent workflow scheduling mechanism could solve many issues in cloud computing, such as resource utilization, execution cost, public resource cost, and deadline‐constraint issues. Recently, many heuristic approaches for workflow scheduling and optimization have been developed for cloud environment . Some researchers used genetic algorithm, particle swarm optimization (PSO) or other approach as their workflow scheduling algorithms to optimize the execution time, execution cost, or resource utilization.…”

Section: Introductionmentioning

confidence: 99%

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An agent‐based workflow scheduling mechanism with deadline constraint on hybrid cloud environment

Chang

Fan

Sheu

et al. 2017

Int J Communication

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Summary: With the advances of cloud computing, business and scientific-oriented jobs with certain workflows are increasingly migrated to and run on a variety of cloud environments. These jobs are often with the property of deadline constraint and have to be completed within limited time. Therefore, to schedule a job with workflow (short for workflow) with deadline constraint is increasingly becoming a crucial research issue. In this paper, we, based on previous work, propose an agent-based workflow scheduling mechanism to schedule workflows that are with deadline constraint into federated cloud environment.Design and Methods: We add a workflow agent into the original framework to schedule the deadline-constraint workflow. The workflow agent can smoothly schedule workflows to the cloud system according to their required resource and automatically monitor their execution. In order to accurately predict the execution time of each task to meet deadline constraint on certain VM with given resource, we inherit the use of rough set theory to estimate execution time of task in our previous work.Result and Discussion: A heuristic algorithm that is embedded into the workflow agent is also proposed because the problem had been shown to be NP-complete. The mechanism also adopts dynamic job dispatching method to reduce the usage of VM and to improve the resource utilization. We also conducted experiments to evaluate the efficiency and effectiveness. Conclusion:The experimental results show that the prediction time is very close to the real execution time and can efficiently schedule multiple scientific workflows to meet the deadline constraints simultaneously.

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Section: Implementation and Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An agent‐based workflow scheduling mechanism with deadline constraint on hybrid cloud environment

Chang

Fan

Sheu

et al. 2017

Int J Communication

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“…For instance, s 1 = 2 denotes that task 1 is assigned to the second resource (also called machine or node). To represent the mapping relationship between the virtual machines, Sawant [70] and Gu et al [71] used used the tree structure to encode the scheduling solutions of the GA. The transition operator, of course, needs to be reconsidered to guarantee the legality of chromosomes.…”

Section: B Metaheuristic Scheduling Algorithmsmentioning

confidence: 99%

“…The orthogonal crossover and mutation operators to transit the chromosomes are designed for representing agent grid [77]. For the mutation operator, swapping the genes of a chromosome is commonly used in the GA. Also, for the tree representation [70], the crossover and mutation operators need to be redesigned or adopted to make them applicable. Instead of fixing the crossover rate, some researchers [76] used the fitness ratio to dynamically adjust the crossover rate between chromosomes to retain chromosomes with high fitness values.…”

Section: B Metaheuristic Scheduling Algorithmsmentioning

confidence: 99%

Metaheuristic Scheduling for Cloud: A Survey

Tsai

Rodrigues

2014

IEEE Systems Journal

207

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Cloud computing has become an increasingly important research topic given the strong evolution and migration of many network services to such computational environment. The problem that arises is related with efficiency management and utilization of the large amounts of computing resources. This paper begins with a brief retrospect of traditional scheduling, followed by a detailed review of metaheuristic algorithms for solving the scheduling problems by placing them in a unified framework. Armed with these two technologies, this paper surveys the most recent literature about metaheuristic scheduling solutions for cloud. In addition to applications using metaheuristics, some important issues and open questions are presented for the reference of future researches on scheduling for cloud.

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Deadline‐constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing

Liu

Zhang

Buyya

et al. 2016

Concurrency and Computation

100

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Summary The cloud infrastructures provide a suitable environment for the execution of large‐scale scientific workflow application. However, it raises new challenges to efficiently allocate resources for the workflow application and also to meet the user's quality of service requirements. In this paper, we propose an adaptive penalty function for the strict constraints compared with other genetic algorithms. Moreover, the coevolution approach is used to adjust the crossover and mutation probability, which is able to accelerate the convergence and prevent the prematurity. We also compare our algorithm with baselines such as Random, particle swarm optimization, Heterogeneous Earliest Finish Time, and genetic algorithm in a WorkflowSim simulator on 4 representative scientific workflows. The results show that it performs better than the other state‐of‐the‐art algorithms in the criterion of both the deadline‐constraint meeting probability and the total execution cost.

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A Genetic Algorithm Scheduling Approach for Virtual Machine Resources in a Cloud Computing Environment

Cited by 20 publications

References 9 publications

An agent‐based workflow scheduling mechanism with deadline constraint on hybrid cloud environment

An agent‐based workflow scheduling mechanism with deadline constraint on hybrid cloud environment

Metaheuristic Scheduling for Cloud: A Survey

Deadline‐constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing

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