Cuckoo Genetic Optimization Algorithm for Efficient Job Scheduling with Load Balance in Grid Computing

Grid computing incorporates dispersed resources to work out composite technical, industrial, and business troubles. Thus a capable scheduling method is necessary for obtaining the objectives of grid. The disputes of parallel computing are commencing with the computing resources for the number of jobs and intricacy, craving, resource malnourishment, load balancing and efficiency. The risk stumbling upon parallel computing is the enthusiasm to scrutinize different optimization techniques to achieve the tasks without unsafe surroundings. Here Cuckoo Genetic Optimization Algorithm (CGOA) is established that was motivated from cuckoo optimization algorithm (COA) and genetic algorithm (GA) for task scheduling in parallel environment (grid computing system). This CGOA is implemented on parallel dealing out for effective scheduling of multiple tasks with less schedule length and load balance. Here transmission time is evaluated with number of job set. This is computed with the help of job-processor relationship. This technique handles the issues well and the results show that complexity, load balance and resource utilization are finely managed.