Adaptive Grid Resource Selection Based on Job History Analysis Using Plackett-Burman Designs

Management Enabling the Future Internet for Changing Business and New Computing Services - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04492-2_14 ◽

2009 ◽

pp. 133-142 ◽

Cited By ~ 2

Author(s):

Cinyoung Hur ◽

Yoonhee Kim

Keyword(s):

Resource Selection ◽

Adaptive Grid ◽

Grid Resource ◽

History Analysis

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On Evaluating JARS: Job history-based Adaptive grid resource selection

2010 IEEE Network Operations and Management Symposium - NOMS 2010 ◽

10.1109/noms.2010.5488368 ◽

2010 ◽

Author(s):

Cinyoung Hur ◽

Yoonhee Kim

Keyword(s):

Resource Selection ◽

Adaptive Grid ◽

Grid Resource

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A Self-Adaptive Grid Resource Selection Algorithm

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.474-476.1955 ◽

2011 ◽

Vol 474-476 ◽

pp. 1955-1960

Author(s):

Ning Qi ◽

Xiao Jun Zhang ◽

Bin Qiang Wang ◽

Jia Guo

Keyword(s):

Resource Selection ◽

Load Condition ◽

Adaptive Grid ◽

Performance Ratio ◽

Selection Algorithm ◽

Grid Resource ◽

Allocation Policy ◽

System A ◽

High Load Condition ◽

Self Adaptive

In order to provide high Quality of Service (QoS), rational scheduling and resource allocation are needed when a great deal of tasks requesting grid resources. By quantifying some important properties of Grid QoS and maximizing cost-performance ratio of Grid system, a QoS-guaranteed self-adaptive grid resource selection algorithm based on SAGA which is named QoS-SGRSA is proposed, and the flow of the algorithm is introduced. In the low load condition, traditional resource selection algorithm is adopted, so an allocation policy is found rapidly. While in the high load condition, by means of using complementary advantages of GA and SA, it can guarantee that the search is on the optimization direction. Finally, we evaluated the effectiveness of QoS-SGRSA by emulation experiments.

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Grid Resource Allocation with Genetic Algorithm Using Population Based on Multisets

Journal of Intelligent Systems ◽

10.1515/jisys-2015-0089 ◽

2017 ◽

Vol 26 (1) ◽

pp. 169-184 ◽

Cited By ~ 3

Author(s):

Absalom E. Ezugwu ◽

Nneoma A. Okoroafor ◽

Seyed M. Buhari ◽

Marc E. Frincu ◽

Sahalu B. Junaidu

Keyword(s):

Genetic Algorithm ◽

Resource Allocation ◽

Grid Computing ◽

Resource Selection ◽

Computing System ◽

Population Based ◽

Fine Tuning ◽

Grid Resource ◽

Performance Results ◽

Grid Resources

AbstractThe operational efficacy of the grid computing system depends mainly on the proper management of grid resources to carry out the various jobs that users send to the grid. The paper explores an alternative way of efficiently searching, matching, and allocating distributed grid resources to jobs in such a way that the resource demand of each grid user job is met. A proposal of resource selection method that is based on the concept of genetic algorithm (GA) using populations based on multisets is made. Furthermore, the paper presents a hybrid GA-based scheduling framework that efficiently searches for the best available resources for user jobs in a typical grid computing environment. For the proposed resource allocation method, additional mechanisms (populations based on multiset and adaptive matching) are introduced into the GA components to enhance their search capability in a large problem space. Empirical study is presented in order to demonstrate the importance of operator improvement on traditional GA. The preliminary performance results show that the proposed introduction of an additional operator fine-tuning is efficient in both speed and accuracy and can keep up with high job arrival rates.

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