A pareto-based GA for scheduling HPC applications on distributed cloud infrastructures

Kessaci, Yacine; Melab, Nouredine; Talbi, El‐Ghazali

doi:10.1109/hpcsim.2011.5999860

Cited by 28 publications

(17 citation statements)

References 9 publications

(10 reference statements)

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“…This algorith m incorporates the reliability analysis into the active replication schema, and explo its a dynamic number of replicas for different tasks. Scheduling based genetic algorith m is proposed in [23][24][25]. Th is algorith ms optimizes the energy consumption, carbon dio xide emissions and the generated profit of a geographically distributed cloud computing infrastructure.…”

Section: Related Workmentioning

confidence: 99%

Cloud Task Scheduling for Load Balancing based on Intelligent Strategy

Keshk¹,

El-Sisi²,

Tawfeek³

2014

IJISA

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Abstract-Cloud computing is a type of parallel and distributed system consisting of a co llect ion of interconnected and virtual computers. With the increasing demand and benefits of cloud computing infrastructure, different co mputing can be performed on cloud environment. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorith ms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this paper a cloud task scheduling policy based on ant colony optimizat ion algorith m for load balancing compared with different scheduling algorith ms has been proposed. Ant Co lony Optimization (ACO) is random optimization search approach that will be used for allocating the incoming jobs to the virtual mach ines. The main contribution of our work is to balance the system load while t rying to min imizing the make span of a given tasks set. The load balancing factor, related to the job fin ishing rate, is proposed to make the job fin ishing rate at different resource being similar and the ability of the load balancing will be improved. The proposed scheduling strategy was simu lated using Cloudsim toolkit package. Experimental results showed that, the proposed algorith m outperformed scheduling algorith ms that are based on the basic ACO or Modified Ant Colony Optimization (MACO).

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Section: Related Workmentioning

confidence: 99%

Cloud Task Scheduling for Load Balancing based on Intelligent Strategy

Keshk¹,

El-Sisi²,

Tawfeek³

2014

IJISA

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“…In addition, our approach satisfies also the provider by using a mechanism that gives him/her the ability to make a dynamic choice among the Pareto set of the proposed solutions according to his/her real time needs in order to improve the results. This paper is an extended version of the work presented in [15]. Indeed, in this paper the related work, the considered model and the algorithm features are more detailed, a deeper evaluation process is applied and more experiments with new instances and different comparison aspects are realized.…”

Section: Introductionmentioning

confidence: 99%

A Pareto-based metaheuristic for scheduling HPC applications on a geographically distributed cloud federation

2012

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“…In this paper algorithm is being proposed to initiate the service load distribution.  R.Vijindra et al (October-2012) have analysed that resources is one of the most important issues [9]. In cloud computing energy consumption has been an issue not only on the basis of cost but also for improving system reliability .This paper focuses on minimizing the energy consumption, minimizing the completion time and allocation.…”

Section: Objective Of Researchmentioning

confidence: 99%

Hybrid Scheduling Method for Cloud Computing Using ACO

Inder¹

2017

IJARCSSE

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Cloud computingis a model where services are provided to users as per their demands and requirements. The model allowed infrastructure provider to provide pay per use model and the guarantee to the service is maintained through Service Level Agreements. Task scheduling is one of the major issue in cloud computing and it is expected by scheduler to perform task scheduling in an efficient and optimized way. Different task scheduling algorithms are First Come First Serve (FCFS), Round Robin (RR) and Ant colony optimization method. The impact of ACO increases with passage of time as it increases the value of pheromone on the path. In this study we proposes to use ACO after a passage of time so that the strong value of pheromone provides a better and efficient path to implement the scheduling in efficient way.

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A pareto-based GA for scheduling HPC applications on distributed cloud infrastructures

Abstract: Reducing energy consumption is an increasingly important issue in cloud computing

Cited by 28 publications

References 9 publications

Cloud Task Scheduling for Load Balancing based on Intelligent Strategy

Cloud Task Scheduling for Load Balancing based on Intelligent Strategy

A Pareto-based metaheuristic for scheduling HPC applications on a geographically distributed cloud federation

Hybrid Scheduling Method for Cloud Computing Using ACO

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