# Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing

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## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. SWIM Load Balancing Requirements and Related Definitions

#### 3.1. SWIM Load Balancing Requirements

#### 3.2. Relevant Definitions

## 4. Ant Colony Optimization Algorithm Based on Load Balancing

#### 4.1. Ant Colony Optimization Algorithm Analysis

#### 4.2. Ant Colony Optimization Task Scheduling Algorithm Rule

#### 4.3. Ant Colony Task Scheduling Algorithm Optimization Process

## 5. Experiment and Results Analysis

#### 5.1. Experimental Environment

#### 5.2. Experimental Parameter Settings

#### 5.3. Experimental Results and Analysis

#### 5.4. ACTS-LB Algorithm Shortcomings

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Traditional mesh communication structure (left) and System Wide Information Management (SWIM) bus communication structure (right) comparison chart.

**Figure 2.**The flow chart of the ant colony optimization task scheduling algorithm based on load balancing.

Serial Number | Reference Number | Author | Algorithm Name | Advantages | Disadvantages |
---|---|---|---|---|---|

1 | [4] | Baatar, D | Proposed a new approach for solving the shift minimization personnel task scheduling problem. | Create an efficient solution procedure. | Do not confer benefits from the perspective of system load balancing. |

2 | [5] | Domenico C | Presented a novel distributed implementation of Multiple Hypothesis Tracking (MHT). | Can enable fast operations on local trees and also allow sharing of hypotheses between local and remote nodes. | Do not confer benefits from the perspective of system load balancing. |

3 | [6] | Romano, G | Proposed a general algorithm for fast estimation of probability. | Can reduce to a single simulation run that can estimate. | Do not confer benefits from the perspective of system load balancing. |

4 | [7] | Liu, C. | Presented a multi-objective task scheduling algorithm based on the fusion of a genetic algorithm and a particle swarm algorithm. | It can improve the global search ability and convergence speed. | Do not confer benefits from the perspective of system load balancing. |

5 | [8] | Guoning, G | Presented 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. | Do not confer benefits from the perspective of system load balancing. |

6 | [9] | Babukarthik, R.G. | Proposed a hybrid algorithm based on ant colony optimization (ACO) and Cuckoo. | It was used to reduce task execution time. | Do not confer benefits from the perspective of system load balancing. |

7 | [10] | Tong, Z. | 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. | Do not confer benefits from the perspective of system load balancing. |

8 | [11] | Wang, X. | Proposed a task scheduling strategy that improves the greedy algorithm. | It can improve the overall scheduling efficiency. | Do not confer benefits from the perspective of system load balancing. |

9 | [12] | V. M, Arul Xavier. | Proposed an algorithm for the task scheduling problems in various heterogeneous virtual machines. | In the research of task scheduling algorithms aimed at load balancing. | The task scheduling is not efficient. |

10 | [13] | Zhao, M. | Proposed a task load balancing scheduling algorithm based on ant colony optimization (WLB-ACO). | The algorithm task completion efficiency is good. | The task scheduling quality is difficult to guarantee. |

11 | [14] | Jiang, W. | Presented a particle swarm optimization with the random forest classifier algorithm. | In order to balance the utilization of virtual machine resources. | This method relies too much on intermediate nodes. |

12 | [15] | Chen, J. | Presented a resource scheduling method based on a parallel genetic algorithm. | It can reduce the overall execution time of scheduling tasks to a certain extent. | It easily falls into local solutions. |

13 | [16] | Ren, J. | Introduced the concept of virtual machine relative fitness. | It can obtain greater variation. | It speeds up the convergence of the algorithm. |

Serial Number | Symbol | Notation |
---|---|---|

1 | ${P}_{ij}^{k}(t)$ | The transition probability value |

2 | ${\tau}_{ij}(t)$ | The pheromone value |

3 | ${\eta}_{ij}(t)$ | The heuristic function |

4 | $\alpha $ | The information heuristic factor |

5 | $\beta $ | The expect heuristic factor |

6 | $Mip({s}_{j})$ | The computing power |

7 | $Bandwidth({s}_{j})$ | The communication bandwidth |

8 | $Performance({s}_{j})$ | The hardware performance |

9 | $L{B}_{j}$ | The load balancing value |

10 | $C({s}_{j})$ | The CPU utilization rate |

11 | $M({s}_{j})$ | The memory usage |

12 | $B({s}_{j})$ | The bandwidth occupancy |

13 | $LA$ | The average value of the load |

14 | $LS$ | The load standard deviation |

15 | $\mu $ | The information residual factor |

16 | $\Delta {\tau}_{ij}$ | The pheromone increment value |

Serial Number | $\mathit{\mu}$ | $\mathit{\alpha}$ | $\mathit{\beta}$ | Optimal Path | Running Time (S) |
---|---|---|---|---|---|

1 | 0.5 | 1.0112 | 2.7375 | 568.10969 | 39.98 |

2 | 0.5 | 1.0351 | 2.6856 | 568.19446 | 40.37 |

3 | 0.5 | 1.0323 | 2.7685 | 568.19446 | 40.56 |

4 | 0.5 | 1.0289 | 2.7386 | 568.10969 | 40.33 |

5 | 0.5 | 1.0156 | 2.7235 | 568.10969 | 40.25 |

6 | 0.5 | 4.3589 | 1.5195 | 568.19446 | 41.79 |

7 | 0.5 | 5.2368 | 2.4978 | 568.10969 | 41.65 |

8 | 0.5 | 5.3646 | 2.5568 | 568.10969 | 41.15 |

9 | 0.5 | 6.3567 | 4.2556 | 568.19446 | 42.48 |

10 | 0.5 | 6.3551 | 4.2550 | 568.19446 | 42.68 |

11 | 0.6 | 1.0253 | 2.7526 | 568.10969 | 39.86 |

12 | 0.6 | 1.0362 | 2.6898 | 568.19446 | 40.29 |

13 | 0.6 | 1.0387 | 2.7365 | 568.19446 | 40.53 |

14 | 0.6 | 1.0236 | 2.7472 | 568.10969 | 40.25 |

15 | 0.6 | 1.0187 | 2.7356 | 568.10969 | 40.27 |

16 | 0.6 | 4.3789 | 1.5387 | 568.19446 | 41.58 |

17 | 0.6 | 5.2468 | 2.4998 | 568.10969 | 41.31 |

18 | 0.6 | 5.3846 | 2.5368 | 568.10969 | 41.02 |

19 | 0.6 | 6.3985 | 4.2673 | 568.19446 | 42.29 |

20 | 0.6 | 6.3653 | 4.2672 | 568.19446 | 42.37 |

Parameter Name | $\mathit{\alpha}$ | $\mathit{\beta}$ | $\mathit{\mu}$ | Bandwidth | Delay | ${\mathit{\delta}}_{1}$ | ${\mathit{\delta}}_{2}$ | ${\mathit{\delta}}_{3}$ | Population Size | $\mathit{N}{\mathit{C}}_{\mathbf{max}}$ |
---|---|---|---|---|---|---|---|---|---|---|

Parameter value | 1.0 | 2.7 | 0.6 | 150 Mbps | 10 ms | 0.4 | 0.3 | 0.3 | 50 | 150 |

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**MDPI and ACS Style**

Li, G.; Wu, Z.
Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing. *Future Internet* **2019**, *11*, 90.
https://doi.org/10.3390/fi11040090

**AMA Style**

Li G, Wu Z.
Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing. *Future Internet*. 2019; 11(4):90.
https://doi.org/10.3390/fi11040090

**Chicago/Turabian Style**

Li, Gang, and Zhijun Wu.
2019. "Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing" *Future Internet* 11, no. 4: 90.
https://doi.org/10.3390/fi11040090