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Dynamic Load balancing in Cloud Computing Using Ant Colony Optimization

Authors(3):

Samriddhi Gupta, Abhishek Bajpai, Dr. Neeraj Kumar Tiwari
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As the cloud computing is a new style of computing over internet. It has many advantages along with some crucial issues to be resolved in order to improve reliability of cloud environment. These issues are related with the load management, fault tolerance and different security issues in cloud environment. In this paper the main concern is to prevent bottleneck in cloud computing. The load can be CPU load, memory capacity, delay or network load. Load balancing is the process of distributing the load among various nodes of a distributed system to improve both resource utilization and job response time while also avoiding a situation where some of the nodes are heavily loaded while other nodes are idle or doing very little work. Load balancing ensures that all the processor in the system or every node in the network does approximately the equal amount of work at any instant of time. Many methods to resolve this problem has been came into existence like Particle Swarm Optimization, hash method, genetic algorithms and several scheduling based algorithms are there. In this paper we are proposing a method based on Ant Colony optimization to resolve the problem of load balancing in cloud environment

Samriddhi Gupta, Abhishek Bajpai, Dr. Neeraj Kumar Tiwari

Cloud computing, Advantages and Disadvantages , Ant Colony Optimization, Swarm intelligence

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Publication Details

Published in : Volume 3 | Issue 3 | May-June - 2017
Date of Publication Print ISSN Online ISSN
2017-06-30 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
145-150 IJSRSET173343   Technoscience Academy

Cite This Article

Samriddhi Gupta, Abhishek Bajpai, Dr. Neeraj Kumar Tiwari, "Dynamic Load balancing in Cloud Computing Using Ant Colony Optimization", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 3, pp.145-150, May-June-2017.
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