Notice of Retraction: An improved resource scheduling approach using Job Grouping strategy in grid computing

Sharma, Rahul; Soni, Vishnu Kant; Mishra, Manoj Kumar

doi:10.1109/icent.2010.5532135

Cited by 16 publications

(19 citation statements)

References 8 publications

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“…Some of the existing resource discovery and brokering systems that are related to this research include condor ( Raman et al, 1998;Han and Berry, 2008), ARMS (Cao et al, 2001;Ding et al, 2005;Dominiak et al, 2007), ACG (Chunlin and Layuan, 2003), UWAgents (Fukuda and Smith, 2006;Khanli and Kargar, 2010;Sharma et al, 2010;Li et al, 2006).…”

Section: Related Workmentioning

confidence: 99%

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Ontology-enhanced agent-based cloud service discovery

Kang

Sim

2016

IJCC

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Cloud computing has attracted great interest from both industrial and academic communities. However, only a few efforts have been devoted to building tools for supporting Cloud service discovery. Therefore, we present a four-stage, agent-based Cloud service discovery protocol. Additionally, two Cloud ontologies (CO-1 and CO-2) are designed to semantically define the relationship among Cloud services. Whereas CO-1 contains only Cloud concepts, CO-2 contains a set of Cloud concepts, individuals of those concepts, and the relationship among those individuals. The similarity among Cloud services is determined by concept, object property, and data type property similarity reasoning. In addition, two kinds of recommendation approaches (R1 and R2) based on attribute value prediction are presented. R1 is based on the maximum and R2 on the average similarity between the provided and the requested requirements. Empirical results show that our system achieved the best performance in finding the appropriate Cloud services with CO-2 and R2.

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Section: Related Workmentioning

confidence: 99%

“…In Sharma et al (2010), an agent-based dynamic resource scheduling model using a first-come-first-serve-Job grouping strategy is introduced. In this paper, the Grid resource selection strategy is based on max heap tree (MHT) that is appropriate for large scale application and the root node of MHT is selected for job submission.…”

Section: Related Workmentioning

confidence: 99%

Ontology-enhanced agent-based cloud service discovery

Kang

Sim

2016

IJCC

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“…Job grouping strategy [7] is used to improve that increase the utilization of the resource and decrease the processing time of jobs in grid computing environment. The resource and job scheduling model is divided into three levels user level, global level and local level (cluster level).Grid computing is a combination of different clusters and a combination of nodes is called as cluster.…”

Section: Job Grouping Strategy In Grid Computingmentioning

confidence: 99%

Survey on Job Scheduling Algorithms in Grid Computing

Kumari¹,

Kumar²

2015

IJCA

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Grid computing is a type of distributed computing that distributes the tasks to a group of network computers where computer are linked together to provide a better computational resource. Scheduling plays a vital role in gaining high consumption of resources in the field of grid computing and achieving utilization of resources in grid environments. The use of the scheduling is to increase the throughput, efficiency and decrease the average waiting time. In this paper we have been surveyed various job scheduling algorithm in grid computing that will benefit for the researchers to carry out the future work in that area and develop a better algorithm. It is found that the resource allocation and job scheduling plays a vital role to improve performance and can proficiently and efficiently allocate workloads to proper resources.

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“…So, the FCFS should consider all these possibilities and identify which node will complete the task earliest [8] [9]. As a result, the aim of FCFS is to find the earliest possible time for each task to complete, according to the sequence of the task arrivals.…”

Section: Scheduling Algorithms For the Grid Environment 31 First Comentioning

confidence: 99%

Comparative Analysis of Scheduling Algorithms in Computational Grid Environment

Bhattacharya¹,

Dey²

2014

IJCA

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This paper tracks the development of Grid Computing since its inception in the late 1990s to its dominance in today's world of Distributed Computing and Information Technology. It focuses on the recent developments that spurred our interest to take up this field of research with emphasis on the algorithms we are researching for job scheduling and load balancing in the Grid Environment. The entire structure of the Grid Environment is dynamic and hybrid by nature, changing with the availability and the capability of resources or hosts that perform user tasks and the Quality of Service requirements of the tasks themselves. This makes the problem of developing an optimal task-toresource schedule that ensures proper load balancing and also produces the minimum overall makespan (time to complete scheduled tasks) an NP-Hard Problem. Our attempt in this research is not to find the optimal solution for this problem, but to analyze and test various algorithms that produce acceptable performance in the commonly occurring practical scenarios.

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Notice of Retraction: An improved resource scheduling approach using Job Grouping strategy in grid computing

Cited by 16 publications

References 8 publications

Ontology-enhanced agent-based cloud service discovery

Ontology-enhanced agent-based cloud service discovery

Survey on Job Scheduling Algorithms in Grid Computing

Comparative Analysis of Scheduling Algorithms in Computational Grid Environment

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