ENHANCED BAT OPTIMIZATION ALGORITHM AND LOW LATENCY FAULT TOLERANCE MODEL FOR EFFICIENT RESOURCE ALLOCATION IN GRID COMPUTING
Author(s):
R.Ananthi Lakshmi, Dr.S.Vidhya
Keywords:
Grid computing, Max-Min Heuristic (MMH), Enhanced Bat Optimization (EBO) algorithm, load balancing, resource allocation, fault tolerance, Low Latency Fault Tolerance (LLFT) model
Abstract
Effectual allocation of resources with fault tolerance is one of the important targets in any computational grid environment to accomplish the task execution on time. In the existing system, computational complexity and error rates are still an issue. Also the requirements and grid services are not satisfied effectively. Hence, the overall grid computing performance is reduced prominently. To overcome the above mentioned issues, in this work, Enhanced Bat Optimization (EBO) algorithm and Low Latency Fault Tolerance (LLFT) model is proposed to improve the optimal resource allocation and fault tolerance over grid computing environment. The proposed system includes main phases are such as system model, load balancing, resource allocation and fault tolerance system. Initially, consider the number of resources, number of tasks, Virtual Machine (VM) and number of grid users over the grid computing. In this work, load balancing is done by using MMH algorithm which is used to equalize the total workloads over grid. Load balancing is achieved by transferring tasks from over-loaded nodes to under-loaded nodes. Then the resource allocation is done by using EBO algorithm which is used to select more optimal resources effectively. The best fitness values are used to choose the available optimal resources. The fault tolerance is performed using LLFT which provides fault tolerance for distributed applications deployed within a grid computing using the leader/follower replication. The simulation result concludes that the proposed EBO+LLFT algorithm provides better performance by means of higher accuracy, lower error rate, cost complexity and time complexity than the existing algorithms
Article Details
Unique Paper ID: 154086

Publication Volume & Issue: Volume 8, Issue 10

Page(s): 131 - 141
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