Energy Efficient Cloud Task Scheduling Research Based On Immunity-ant Colony Algorithm

Nowadays cloud computing has become one of the infrastructure to support many high-tech developments.However,the high energy consumption of the data center greatly increases the cost of cloud computing provi ders,in sharp contrast to high energy consumption is the low energy efficiency of data center generally.Therefore,the energy consumption of cloud computing is an important research direction.Cloud computing faces the mass of users,as a business model;personal developers,corporate companies and research institutions have developed applications on the cloud platform to reduce the early infrastructure investment,follow-up expansion and maintenance costs.Cloud service providers should also ensure that the cloud platform for the quality of service,which is a widely studied in the cloud computing aspects.Data center energy-saving technology mainly through hardware and software.This paper proposes a task scheduling strategy considering Qo S of cloud service and the energy consumption of data center,allocates different computing resources efficiently for cloud tasks.The goal of the strategy is making the execution time of the cloud task and the energy consumption of the data center less.The strategy is to ensure the Qo S of the cloud service and the energy consumption of the data center can be reduced,What’s more,to meet the different needs of the optimization,we can adjust the optimization weight to select the optimal focus of the scheduling strategy.The proposed scheduling strategy dynamically combines the immune algorithm and the ant colony algorithm,In the early stage,the immune algorithm is used to quickly and globally converge the pheromone concentration distribution required by the ant colony algorithm.When the evolution rate of the immune algorithm is lower than the threshold value,the improved ant colony algorithm can accelerate the convergence speed of the algorithm,and can make a quick response when the data center server changes,The improved ant colony algorithm considers the particularity of the cloud environment and maps the task assignment problem to the node search problem in the process of ant foraging and ensures the effectiveness of the task scheduling algorithm in the cloud environment.Finally,this paper uses Cloudsim-4.0 simulation simulation tool to construct the cloud computing environment.In optimization of task execution time,reduce the task execution process data center energy consumption,server load balancing rate,We used immune ant colony energy saving scheduling algorithm,the sequential scheduling,Min-Min algorithm,the original ant colony algorithm and other scheduling model for experimental comparison.The experimental results show that the immune ant colony algorithm has advantages in terms of task execution time,data center energy consumption and server load balancing,and the advantages of the immune ant colony algorithm are more obvious as the task amount increases.