# Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing
## 2 Related Work  
At present, experts and scholars both at home and abroad have conducted much research on task scheduling in large-scale network systems, such as cloud computing, grid and other network systems. Among them, the min-min algorithm, max-min algorithm and other task scheduling algorithms are the early classic methods. There are also exact algorithms to solve the task scheduling problems, such as branch-and-bound algorithms, linear programming and cross-entropy methods. The heuristic algorithms were used to solve optimization problems, such as genetic algorithm (GA), simulated annealing (SA), ant colony (AC) algorithm, particle swarm optimization (PSO) and greedy algorithm. These algorithms have different characteristics, which are based on the idea of optimizing minimizing scheduling objectives. They have been applied in the task scheduling of these network systems and have become the reference object for subsequent research on scheduling algorithms. In [4], the authors proposed a new approach for solving the shift minimization personnel task scheduling problem. These properties are used to develop a new branch and bound scheme, which is used in conjunction with two column generation based approaches and a heuristic algorithm to create an efficient solution procedure. In [5], the authors presented a novel distributed implementation of multiple hypothesis tracking (MHT). Based on hash-tree distributed content storing approach to enable fast operations on local trees and also allow sharing of hypotheses between local and remote nodes. In [6], the authors proposed a general algorithm for fast estimation of probability of error of linear block codes on BSC channels based on the importance sampling and the cross-entropy  
Figure 1: Traditional mesh communication structure (left) and System Wide Information Management (SWIM) bus communication structure (right) comparison chart. method for rare-events that can be employed for any hard-decision decoder. When optimal decoding is used the algorithm reduces to a single simulation run that can estimate, with a given accuracy, performances for a whole range of sufficiently high signal-to-noise ratios. In [7], a multi-objective task scheduling algorithm was proposed based on the fusion of a genetic algorithm and a particle swarm algorithm to improve the global search ability and convergence speed. In [8], this paper introduced an optimized algorithm for task scheduling based on genetic simulated annealing algorithm in cloud computing and its implementation. Algorithm considers the QoS requirements of different type tasks, the QoS parameters are dealt with dimensionless. In addition, a hybrid algorithm based on ant colony optimization (ACO) and Cuckoo was used to reduce task execution time, as proposed by [PERSON] [9]. In [10], the authors proposed a new heuristic algorithm combined with the particle swarm optimization (PSO) algorithm. It has the characteristics of strong optimization search ability, fast convergence speed and high solving quality, providing a new direction for solving task scheduling problems in a cloud computing environment. In [11], the authors comprehensively considered the characteristics of tasks and virtual machine resources in the cloud environment and proposed a task scheduling strategy that improves the greedy algorithm to improve the overall scheduling efficiency of the system in the cloud computing environment. These algorithms have good performance in processing task scheduling, but they seldom confer benefits from the perspective of system load balancing. In the research of task scheduling algorithms aimed at load balancing, [PERSON] and others proposed a cloud computing load balancing task aware scheduling algorithm for the task scheduling problems in various heterogeneous virtual machines [12]. In [13], by analyzing and comparing some cluster load balancing algorithms, a task load balancing scheduling algorithm based on ant colony optimization (WLB-ACO) was proposed.