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.

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.

Modified Ant Colony Optimization for Workflow Scheduling in Cloud Enviornment

2015 ◽  
Vol 14 (10) ◽  
pp. 6176-6183
Author(s):  
S.J. Mohana ◽  
Dr.M. Saroja ◽  
Dr.M. Venkatachalam
Keyword(s):  
Cloud Computing ◽  
Ant Colony Optimization ◽  
Heuristic Algorithms ◽  
Ant Colony ◽  
Workflow Scheduling ◽  
Ant Colony Optimization Algorithm ◽  
Computing Environment ◽  
Shared Resources ◽  
Cloud Computing Environment ◽  
Computing Model

Cloud computing is a type of parallel and distributed system consisting of a collection of interconnected and virtual computers. This technological trend has enabled the realization of a new computing model called cloud computing, in which shared resources, information,software & other devices are provided according to client requirement at specific time, are provided as general utilities that can be leased and released by users through the Internet in an on-demand fashion.Cloud workflow scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it.Allocation of resources to a large number of workflows in a cloud computing environment presents more difficulty than in network computational environments.A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this work, modified ant colony optimization for cloud task scheduling is proposed. The goal of modification is to enhance the performance of the basic ant colony optimization algorithm and optimize the task execution time in view of minimizing the makespan of a given tasks set.

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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