A new agent based load balancing model for improving the grid performance

Wided, Ali; Kazar, Okba

doi:10.3233/mgs-200326

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…The current methods address both load balancing and fault tolerance in grid environments separately [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. The method proposed in this paper deals with each of them within one framework by combining some classical methods based on fractal transform and entropy computation for reducing the complexity of data to obtain an optimal method for rendering the grid structure.…”

Section: Related Workmentioning

confidence: 99%

The Application of Fractal Transform and Entropy for Improving Fault Tolerance and Load Balancing in Grid Computing Environments

Khorsheed

Zainel

Hassen

et al. 2020

Entropy

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This paper applies the entropy-based fractal indexing scheme that enables the grid environment for fast indexing and querying. It addresses the issue of fault tolerance and load balancing-based fractal management to make computational grids more effective and reliable. A fractal dimension of a cloud of points gives an estimate of the intrinsic dimensionality of the data in that space. The main drawback of this technique is the long computing time. The main contribution of the suggested work is to investigate the effect of fractal transform by adding R-tree index structure-based entropy to existing grid computing models to obtain a balanced infrastructure with minimal fault. In this regard, the presented work is going to extend the commonly scheduling algorithms that are built based on the physical grid structure to a reduced logical network. The objective of this logical network is to reduce the searching in the grid paths according to arrival time rate and path’s bandwidth with respect to load balance and fault tolerance, respectively. Furthermore, an optimization searching technique is utilized to enhance the grid performance by investigating the optimum number of nodes extracted from the logical grid. The experimental results indicated that the proposed model has better execution time, throughput, makespan, latency, load balancing, and success rate.

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Section: Related Workmentioning

confidence: 99%

The Application of Fractal Transform and Entropy for Improving Fault Tolerance and Load Balancing in Grid Computing Environments

Khorsheed

Zainel

Hassen

et al. 2020

Entropy

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Agent Based Load Balancing in Grid Computing

Wided¹,

Bouakkaz²

2021

Artificial Intelligence

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Load-Balancing is an important problem in distributed heterogeneous systems. In this paper, an Agent-based load-balancing model is developed for implementation in a grid environment. Load balancing is realized via migration of worker agents from overloaded resources to underloaded ones. The proposed model purposes to take benefit of the multi-agent system characteristics to create an autonomous system. The Agent-based load balancing model is implemented using JADE (Java Agent Development Framework) and Alea 2 as a grid simulator. The use of MAS is discussed, concerning the solutions adopted for gathering information policy, location policy, selection policy, worker agents migration, and load balancing.

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A new agent based load balancing model for improving the grid performance

Cited by 2 publications

References 5 publications

The Application of Fractal Transform and Entropy for Improving Fault Tolerance and Load Balancing in Grid Computing Environments

The Application of Fractal Transform and Entropy for Improving Fault Tolerance and Load Balancing in Grid Computing Environments

Agent Based Load Balancing in Grid Computing

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