A Decentralized Multi-agent Approach to Job Scheduling in Cloud Environment

Gąsior, Jakub; Seredyński, Franciszek

doi:10.1007/978-3-319-11313-5_36

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…b) Generic agent kinds are proven to be effective for the suitable functional decomposition of e-Learning systems. c) Dynamic and interoperability features of agents are very appropriate for supporting extensibility and maintainability of e-Learning systems [26,27].…”

Section: Intelligent Agents (Ias) Approach For Smart Learning Systemsmentioning

confidence: 99%

Exploiting the knowledge engineering paradigms for designing smart learning systems

Salem

Parusheva

2018

EEJET

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Section: Intelligent Agents (Ias) Approach For Smart Learning Systemsmentioning

confidence: 99%

Exploiting the knowledge engineering paradigms for designing smart learning systems

Salem

Parusheva

2018

EEJET

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“…Several works covering the application of multi‐agent systems to the scheduling of distributed system resources can be found in the literature . Example is the work presented in Qiang et al .…”

Section: Research Problem Description and Related Workmentioning

confidence: 99%

“…Several works covering the application of multi-agent systems to the scheduling of distributed system resources can be found in the literature [19][20][21][22]. Example is the work presented in Qiang et al [23]; for instance, a mobile agented based prototype system, referred to as the Geographic Information Grid System (GIGS), was proposed.…”

Section: Related Workmentioning

confidence: 99%

Scheduling multi‐component applications with mobile agents in heterogeneous distributed systems

Ezugwu

Junaidu

Frîncu

et al. 2015

Concurrency and Computation

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Summary In grid computing environment, several classes of multi‐component applications exist. These types of applications may often require additional resources of different types that go beyond what is available in any of the sites making up the grid resource composition. The heterogeneity nature of both the user application and the computing environment makes this a challenging problem. However, the current off‐the‐shelf scheduling software can hardly cope with these diversities in distributed computing application frameworks. Therefore, there is the need for an adequate scheduling system that would grant simultaneous or coordinated access to application of multi‐component nature that requires resources of possibly multiple types, in multiple locations, managed by different resource providers. The main focus of this paper is to develop a mobile agent scheduling model that addresses the aforementioned challenge. A scheduling policy that pertains to job scheduling and resource allocation is proposed. The scheduling policy treats different multi‐component applications requiring diverse heterogeneous resources fairly. The policy is used by mobile agents to schedule user applications and to also find available and suitable distributed resource that are capable of executing user application at a very minimal time. Copyright © 2015 John Wiley & Sons, Ltd.

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“…To overcome lot of the problem in scheduling [3, 4,5,6,7] a new workflow model was introduced in this paper. However the simple scheduling and resource allocation is not important, but the optimization of resource allocation [8,9,10] is important.…”

Section: Introductionmentioning

confidence: 99%

Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms in Cloud Environment

Muthu¹,

Enoch²

2017

IJIES

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Cloud computing is a powerful computing technology, which render a flexible services at anywhere to the user. One of the major issue of cloud computing was scheduling. In this work, a bacterial foraging optimization algorithm with genetic algorithm (GABFO) was combined to find out trustworthy scheduling problems in cloud workflow. Generally job scheduling and resource allocation in cloud is a tedious optimization problem at the time of considering QoS requirements. Lot of existing works under scheduling only concentrates on cost optimization and deadline problems, and it ignores the importance of reliability, availability and robustness. The main subscription of my work is to state a new optimized approach to schedule the jobs efficiently and allocate the resources in a efficient manner by introducing GABFO algorithm. Experiments were done in PSO, Genetic, BFO and then Genetic and BFO was combined to generate a hybrid optimized result, and the work was compared with above mentioned algorithms. The algorithms were executed for 52 iterations and totally 10 runs are calculated. The size of the job as well as virtual machines was varied for each iteration to calculate performance variation. We considered the optimization parameter as time and cost, and throughput. The work is implemented in cloudsim to create a simulated cloud environment. Final result shows better performance and maximum resource utilization in GABFO when compared to PSO, GA, BFO.

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A Decentralized Multi-agent Approach to Job Scheduling in Cloud Environment

Cited by 5 publications

References 8 publications

Exploiting the knowledge engineering paradigms for designing smart learning systems

Exploiting the knowledge engineering paradigms for designing smart learning systems

Scheduling multi‐component applications with mobile agents in heterogeneous distributed systems

Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms in Cloud Environment

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