A Contemporary Access of Improved BEE Colony Optimization for Scheduling Problems in Cloud Environment

Today's competitive environment improves interoperability across many devices, illuminating resource distribution for user demands and preferences. In cloud computing, task scheduling triggers the goal of increasing performance by leveraging resource distribution efficiently. Many approaches that lead to task optimization for resource utilization were carried out with an emphasis on static and dynamic models. Today's computing systems are dynamic, and meta-heuristic algorithms can be useful in solving problems. Evolutionary algorithms with swarm intelligence help provide ideal solutions to today's difficult problems. This research work has implemented an improvement of Bee colony optimization for scheduling the tasks in cloud environment.