An enhanced ordinal optimization with lower scheduling overhead based novel approach for task scheduling in cloud computing environment

Abstract Efficient utilization of available computing resources in Cloud is one the most challenging problems for cloud providers. This requires design an efficient and optimal task-scheduling strategy that can play a vital role functioning overall performance system. Optimal Schedules are specifically needed scheduling virtual machines fluctuating & unpredictable dynamic scenario. Although there exist numerous approaches enhancing task environment, it still open issue. The paper focuses on improved enhanced ordinal optimization technique to reduce large search space minimum time achieve goal makespan. To meet current requirement schedule makespan, uses horse race conditions selection rules applied reiterative manner low overhead by smartly allocating load promising schedule. proposed linear regression generate schedules help Furthermore, mathematical equation, derived using regression, predicts any future workload makespan period target.