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Job allocation and balancing of load is major issue in public cloud network. The allocation of job and resource used queening principle, the queueing principle creates the job wanting condition and finally job allocation process is failed. For the improvement of job allocation process used meta-heuristic function and dynamic load balancing technique. the dynamic load balancing technique offers various methods for the job allocation and resource management. In this paper evaluate the performance of job allocation technique in cloud network based on different heuristic function such as genetic algorithm, ant colony optimization algorithm and particle swarm optimization. The heuristic function increases the performance of cloud network in interdisciplinary environments. The evaluation process used MATLAB software and design different scenario of load over public cloud network.

Performance Evaluation of Job Allocation and Load Balancing Methods Using Meta-Heuristic Function in Public Cloud Network