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

Cloud computing is a new form of network technology, it has been through the grid computing, distributed computing and parallel computing three stages. Cloud computing and Internet technology are combined to form a new business service model, and its extensive use to the traditional computing model has a huge impact. However, the task scheduling is a key problem in the research of cloud computing.Faced with the continuous expansion of the data and continuing increase of users, how to not only makes the data resources in a highly efficient use,but also to ensure that the user's QoS to be raised,become a problem of cloud computing research tasks in the task scheduling problem. Therefore, in the present stage of the development of cloud computing,it is a great significance to study the problem of task scheduling in cloud computing.In this paper,the problem of task scheduling is to combine the characteristics of ant colony algorithm with the could computation of the scheduling strategy in the cloud environment, and design a task scheduling strategy based on the improvement of basic ant colony algorithm and guaranteed the resource load balancing. First, LB-AACO creates a QoS model of the task scheduling in cloud computing environment whose the main factors such as time, reliability,cost,and are designed to make the system achieve load balancing in some degree. Secondly, the LB-AACO is to avoid the algorithm's search stagnation by actively adjusting the pheromone of ant colony algorithm.After the research on the feature of task scheduling and ant colony algorithm, the LB-AACO scheduling strategy in this paper is based on the simulation platform named CloudSim, and the results are compared and analyzed, the feasibility and effectiveness of LB-AACO is verified.