Research On Cloud Computing Task Scheduling Based On Improved Ant Colony Algorithm

In recent years,due to the high scalability,reliability,information sharing and low cost of distributed computing,cloud computing has developed rapidly.The cloud is a huge pool of shared resources that provides a pay-as-you-go business model that provides different services to users.Users use the resource services in the cloud in a personalized way according to their actual needs.Task scheduling problem has always been a hot issue in cloud computing research.The core of cloud task scheduling lies in how to manage and schedule user tasks and computing resources reasonably.How to reasonably allocate coud resources for tasks is a complex issue that deserves to be explored in depth.Considering that the task attributes are different,and the requirements of the task for the resource types are different,the mapping of tasks and resources will vary widely.At the same time,the state and attributes of cloud resources will also affect the final allocation results.The reason why cloud computing systems can operate efficiently and stably is whether or not to use a scheduling algorithm with excellent performance.When designing the scheduling algorithm,it is necessary to meet the needs of cloud service providers and users at the same time.How to schedule dynamic heterogeneous cloud resources is a problem that researchers should pay close attention to.Ant Colony Optimization is a combinatorial optimization algorithm that simulates the foraging behavior of ants.The ants communicate with each other through pheromones,and the pheromone released from the ant colony to find the food source will remain on the path that the ant has traveled.The shorter the path,the more ants will pass through the path per unit time.More,then the higher the concentration of pheromone released,the more attractive the path to later ants,and eventually all ants choose this path,which is to determine a shorter path between the nest and the food source.Positive feedback loop.Since the ant colony algorithm performs well in the field of combinatorial optimization,it is often used in cloud task scheduling,but it also has certain limitations.In the process of task scheduling,the ant colony algorithm may have low resource utilization and imbalance of resource load.To solve this problem,this paper improves the pheromone update method of ant colony algorithm,and closely combines load balancing factor with state transition probability.A cloud computing task scheduling strategy based on Load Balancing Ant Colony Optimization(LBACO).This article details the cloud simulation platform CloudSim.After the experimental environment is configured,the improved ant colony algorithm designed in this paper is simulated.Compared with the the basic ant colony algorithm,the load balancing ant colony algorithm designed in this paper performs well in task execution time,and improves the resource utilization to a certain extent,effectively ensuring the load balancing of the system.