A Novel Approach of Particle Swarm and ANT Colony Optimization for Task Scheduling in Cloud

“The cloud” is shorthand for a cloud-based computer infrastructure that helps make other technologies possible. The workload is split among several cloud-based, virtual computers according to some factors. However, cloud computing (CC) provides options for dealing with issues like job scheduling, such as improved task scheduling. To begin, we present a Dynamic Weighted Round-Robin strategy for more efficient cloud-based task allocation. Because of its efficiency in optimizing schedules in light of finite resources, jobs' relative importance, and available time, the SPMOA approach comes highly recommended. Second, a heuristic method called Hybrid Particle Swarm Parallel Ant Colony Optimization is introduced to solve the problem of task execution latency in SPMOA-based task scheduling. In conclusion, we enhance cloud-based task scheduling by creating a fuzzy logic framework for MLOA. Both the inertial mass of the PSO and the pheromone trails left by the P ACO are modified using fuzzy methods. Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization is the optimal method for scheduling tasks in the cloud. The strategy reduces downtime due to waiting and procedures while simultaneously increasing output and minimizing inefficiency.