A Strategy of Cloud Resource Load Balancing Enhancement Based on Ant Colony Optimization

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.

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An innovative approach of Ant Colony optimization for load balancing in peer to peer grid environment

2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) ◽

10.1109/icicict.2014.6781242 ◽

2014 ◽

Cited By ~ 2

Author(s):

Anamika Jain ◽

Ravinder Singh

Keyword(s):

Load Balancing ◽

Ant Colony Optimization ◽

Peer To Peer ◽

Ant Colony ◽

Innovative Approach ◽

Grid Environment

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Dynamic Load Balancing of Software-Defined Networking Based on Genetic-Ant Colony Optimization

Sensors ◽

10.3390/s19020311 ◽

2019 ◽

Vol 19 (2) ◽

pp. 311 ◽

Cited By ~ 5

Author(s):

Hai Xue ◽

Kyung Kim ◽

Hee Youn

Keyword(s):

Load Balancing ◽

Ant Colony Optimization ◽

Network Performance ◽

Optimal Path ◽

Optimal Solution ◽

Software Defined Networking ◽

Ant Colony ◽

Centralized Control ◽

Trip Time ◽

Overall Performance

Load Balancing (LB) is one of the most important tasks required to maximize network performance, scalability and robustness. Nowadays, with the emergence of Software-Defined Networking (SDN), LB for SDN has become a very important issue. SDN decouples the control plane from the data forwarding plane to implement centralized control of the whole network. LB assigns the network traffic to the resources in such a way that no one resource is overloaded and therefore the overall performance is maximized. The Ant Colony Optimization (ACO) algorithm has been recognized to be effective for LB of SDN among several existing optimization algorithms. The convergence latency and searching optimal solution are the key criteria of ACO. In this paper, a novel dynamic LB scheme that integrates genetic algorithm (GA) with ACO for further enhancing the performance of SDN is proposed. It capitalizes the merit of fast global search of GA and efficient search of an optimal solution of ACO. Computer simulation results show that the proposed scheme substantially improves the Round Robin and ACO algorithm in terms of the rate of searching optimal path, round trip time, and packet loss rate.

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