Distributed System Task Assignment Considering Ant Colony Algorithm

Abstract

In a company of a certain scale, a large number of tasks need to be allocated every day. It is particularly important to provide a comprehensive and efficient distributed system for resource scheduling. Ant colony algorithm (ACO) has great advantages in resource scheduling, so In this paper, the problem of task assignment in distributed systems is studied considering the ant colony optimization algorithm. The structure of this paper can be divided into three parts, including related theoretical overview, system design and system analysis. In the system design and system analysis, through the comparative analysis of the ant colony algorithm (ACO), the ant colony optimization algorithm based on load balancing (LBACO) and the improved ant colony algorithm (IACO), the optimal algorithm is found. Assign tasks. 1.IntroductionWith the rapid development of computer and information technology and its in-depth integration with traditional technologies in various industries, the data transmitted by task allocation is huge, which will inevitably increase the load pressure of distributed systems. An efficient and reasonable resource scheduling scheme will meet the needs of users At the same time of demand, resources can also be used to the fullest, avoiding the situation that resources are wasted due to idleness or time-consuming due to waiting [1][2]. ACO was initially used to solve the traveling salesman problem by virtue of its good performance in finding optimal paths, and was gradually used in solving task scheduling problems [3].At present, many researchers have carried out in-depth research on ACO and task allocation in distributed systems, and have achieved good results. For example, scholars such as Dnmez E proposed an intelligent planning (IP) system for scenic routes based on ACO. The software part of the system uses ACO to convert the landscape route planning problem into the shortest feasible route problem, and calculates the transition probability of ants to scenic spots (SS) by calculating the transition probability of ants to SS. Construct the weight matrix path search used by ants in this process, build an IP model, and realize the IP of SS. The results show that the system can solve the