A Novel Approach of Load Balancing and Task Scheduling Using Ant Colony Optimization Algorithm

2019 ◽  
Vol 7 (2) ◽  
pp. 9-20 ◽  
Author(s):  
Selvakumar A. ◽  
Gunasekaran G.
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Novel Approach ◽  
Productive Asset ◽  
Reduce Energy Consumption ◽  
Set Up

Cloud computing is a model for conveying data innovation benefits in which assets are recovered from the web through online devices and applications, instead of an immediate association with a server. Clients can set up and boot the required assets and they need to pay just for the required assets. Subsequently, later on giving a component to a productive asset administration and the task will be a vital target of Cloud computing. Load balancing is one of the major concerns in cloud computing, and the main purpose of it is to satisfy the requirements of users by distributing the load evenly among all servers in the cloud to maximize the utilization of resources, to increase throughput, provide good response time and to reduce energy consumption. To optimize resource allocation and ensure the quality of service, this article proposes a novel approach for load-balancing based on the enhanced ant colony optimization.

Computing Resource Allocation for Enterprise Information Management Based on Cloud Platform Ant Colony Optimization Algorithm

2013 ◽  
Vol 791-793 ◽  
pp. 1232-1237 ◽  
Author(s):  
Ying Liang ◽  
Qiu Peng Rui ◽  
Jie Xu
Keyword(s):  
Cloud Computing ◽  
Response Time ◽  
Ant Colony Optimization ◽  
Information Management ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Enterprise Information ◽  
Cloud Computing Environment ◽  
Set Up

The paper allocated a method to optimize the management pattern under cloud computing environment according to the current situation of Enterprise Information Management. The method used Ant Colony Optimization (ACO) as basic foundation to satisfy the property of Cloud Computing. CloudSim was also set up as the simulation to imitate the cloud environment and the test of computing. The algorithm prognosticated the capability of the potential available resource node when being allocated and analyzed the usage of bandwidth, the quality of networks and the response time. This algorithm met the needs of limitation of resource with better performance and has shorter response time.

Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining

2012 ◽  
Vol 433-440 ◽  
pp. 3577-3583
Author(s):  
Yan Zhang ◽  
Hao Wang ◽  
Yong Hua Zhang ◽  
Yun Chen ◽  
Xu Li
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Computing Power ◽  
Slow Convergence ◽  
The Impact

To overcome the defect of the classical ant colony algorithm’s slow convergence speed, and its vulnerability to local optimization, the authors propose Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining to solve Traveling Salesman Problem according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm, combined with OpenMP parallel programming idea. The new algorithm combines three different pheromone updating methods to make a new declining pheromone updating method. It effectively reduces the impact of pheromone on the non-optimal path in the ants parade loop to subsequent ants and improves the parade quality of subsequent ants. It makes full use of multi-core CPU's computing power and improves the efficiency significantly. The new algorithm is compared with ACO through experiments. The results show that the new algorithm has faster convergence rate and better ability of global optimization than ACO.

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