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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Research Article

The Fuzzy AHP Based Intelligent Middleware for Load Balancing in Grid Computing Environment

Author(s): Sunita Yadav and Jay Kant Pratap Singh Yadav*

Volume 15, Issue 3, 2022

Published on: 04 September, 2020

Article ID: e180322185586 Pages: 10

DOI: 10.2174/2666255813999200904163853

Price: $65

Abstract

Background: In grid computing, several computing nodes work together to accomplish a common goal. During computation some nodes get overloaded and some nodes remain idle without any job, which degrades the overall grid performance. For better resource utilization, the load balancing strategy of a grid must be improved.

Objective: A good load balancing strategy intelligently perceives grid information and finds the best node to transfer jobs from an overloaded node. In our study, we found that the good load balancing strategies have two prominent needs while decision making, i.e., considering multiple parameters and handling uncertainty present in the grid environment.

Methods: This paper proposed a model, an intelligent fuzzy middleware for load balancing in a grid computing environment (IFMLBG) which fulfilled both the needs. The processing of IFMLBG is based on Chang’s extent analysis for the fuzzy analytical hierarchy process (FAHP). FAHP hierarchically structured the load-balancing problem and used the non-crisp input to handle the uncertainty of the grid environment. Chang’s analysis is performed to generate weights to prioritize nodes and find the best one.

Results: The results show that the IFMLBG Model assigned more weight to the best-selected node as compared to the AHP model and performs well with prudent nodes and criteria.

Conclusion: This paper comprehensively described the design of an Intelligent Fuzzy middleware for Load Balancing in Grid computing (IFMLBG) which used Chang’s extent analysis for FAHP and implemented using four parameters and four computing nodes. The Chang’s extent analysis for FAHP takes triangular fuzzy numbers as input and generates weights for nodes. We compared IFMLBG with the classical AHP model on thirteen datasets and concluded that IFMLBG gives more weight to select the node as compared to the AHP model. The results also show that IFMLBG would work better with the number of parameters and computing nodes.

Keywords: Grid computing, Load balancing, IFMLBG, Analytical hierarchy process, Fuzzy analytical hierarchy process, Multi- criteria decision-making problem, Chang's extent analysis.

Graphical Abstract

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