Optimized resource allocation in grid networks using genetic algorithm with error rate factor

Grid computing is an emerging computing paradigm that will have significant impact on the next generation information infrastructure. Due to the largeness and complexity of grid system, its quality of service, performance and reliability are difficult to model, analyze and evaluate. In real time evaluation, various noises will influence the model and which in turn accounts for increase in packet loss and Bit Error Rate (BER). Therefore, a novel optimization model for maximizing the expected grid service profit is mandatory. In our work, to achieve the improvement in the end to end grid network performance, an optimizer, which is based on Genetic Algorithm (GA) with Fitness Evaluation parameters considers BER and Service Execution Time, is designed in the RMS. This paper presents the novel tree structured model, is better than other existing models for grid computing performance and reliability analysis by not only considering data dependence and failure correlations, but also takes link failure, packet loss & BER real time parameters in account. The algorithm based on the Graph theory and Probability theory.