improving completion time and execution time using fsmpia: a case study

Cloud computing (cc) has facilitated the use, access, and storage of resources via sharing them with customers of different organizations. to shorten the completion time and execution time, we introduced the fuzzy k-means (fkm) clustering method, which is based on the new fuzzy entropy. this method was combined with greedy smpia, max-min smpia, min-min smpia, ga smpia and pso smpia to make virtual machines (vms) smarter. the fkm clustering method was implemented for both non-smpia and smpia. the simulation results in matlab showed that the improvement of completion time of tasks with ga smpia was up to 88.43% more than other studied methods. execution time was improved also further improved to 55.37% compared to the other methods studied. the fuzzy smart mpi approach (fsmpia) performs better than non-fsmpia. also, a comparison of both methods shows that the fsmpia performance is 32.49% and 11.26% higher than that of the smpia in terms of competition time and resource utilization (ru), respectively.