Cloud environment task scheduling method based on improved ant colony algorithm

Abstract

The invention discloses a cloud environment task scheduling method based on an improved ant colony algorithm. On the basis of the conventional ant colony algorithm, a rest-life-pheromone (RLP) concept is introduced; by comparing the RLPs of ant brothers in the searching process, repeated search can be effectively eliminated, and searching accuracy is improved; and by adoption of a method for computing the quantity of the ant brothers, the quantity of ants can be dynamically changed under the specific conditions of loads and tasks of cloud environment, so a system is not overloaded, and the searching accuracy is ensured. Compared with the prior art, the cloud environment task scheduling method based on the improved ant colony algorithm has the advantages that requirements of a user on task execution time can be met to the greatest extent, and the allocation efficiency and execution efficiency of the tasks are improved.

Description

technical field [0001] The invention relates to a task scheduling method in a cloud environment, in particular to a cloud environment task scheduling method based on an improved ant colony algorithm, which belongs to the field of distributed computing and computer network applications. Background technique [0002] Cloud computing is a computing model that uses the Internet to access shared resource pools (such as computing facilities, storage devices, applications, etc.) anytime, anywhere, on demand, and conveniently. It is the development and commercial realization of grid computing. It distributes computing tasks in a data center composed of a large number of computers, so that various applications can obtain computing power, storage space and information services as needed. At present, the cloud computing environment is composed of large-scale cheap computing nodes, and how to efficiently use resources such as computing, storage, and broadband of these nodes is particula...

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Problems solved by technology

Xin Lu et al. used the ant colony algorithm to migrate the tasks being executed when some nodes in the cloud environment were overloaded, so as to ensure the load balance of the cloud environment, but when there were many tasks, the cost of task migration was high (Xin Lu, Zilong Gu.A load-adapative cloud resource scheduling model based on antcolony algorithm[C],IEEE International Conference on Cloud Computing and Intelligence e Systems 2011:296-300); Wang Yonggui and Han Ruilian combined the ant colony algorithm and genetic algorithm in the process of task allocation, which effectively shortened the task allocation time, but the interaction between the ant colonies was not strong, and too many invalid searches would be carried out (Wang Yonggui, Han Ruilian. Research on Task Sch

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