Load Balancing of Nodes in Cloud Using Ant Colony Optimization

Nishant, Kumar; Sharma, Pratik; Krishna, Vishal; Gupta, Chanchal; Singh, Kuwar Pratap; Nitin, Nitin; Rastogi, Ravi

doi:10.1109/uksim.2012.11

Cited by 192 publications

(100 citation statements)

References 8 publications

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“…Kumar Nishant et al [6] have implemented a modified Ant Colony Optimization (ACO) Algorithm for the purpose of load balancing suitable for both cloud and grid computing environment. In contrast to regular ACO, in this modified algorithm each ant updates its own result set after each iteration rather than updating the result set given by head ant.…”

Section: Related Workmentioning

confidence: 99%

A Proposed Approach for Efficient Energy Utilization in Cloud Data Center

Goyal¹,

Chahal²

2015

IJCA

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Cloud computing has transmuted the IT industry on the whole and made easy to access the resources from anywhere and at anytime. These resources can be accessed as a service rather than as a product. Clients pay for the service used as per their requirement. The huge demand of cloud computing has given rise to the creation of hefty amount of data centers. These data centers consist of different types of resources and each resource is bound to its own power consumption scenario. Besides a number of benefits; Cloud data centers also pose the problem of large amount of power consumption. Lots of techniques and methods have been developed to solve this issue out of which load balancing is one such technique. This paper discusses load balancing using a nature inspired algorithm known as Particle Swarm Optimization (PSO) algorithm. A different nature inspired algorithm known as Firefly Algorithm (FA) is proposed to solve the above said issue.

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Section: Related Workmentioning

confidence: 99%

A Proposed Approach for Efficient Energy Utilization in Cloud Data Center

Goyal¹,

Chahal²

2015

IJCA

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“…Kumar Nishant, Pratik Sharma, Vishal Krishna, Nitin and Ravi Rastogi [6] have developed the ACO algorithm for load distribution of workloads among nodes of a cloud by the use of Ant Colony Optimization (ACO).ACO is inspired from the ant colonies that work together in foraging behavior. The ants work together in search of new sources of food and simultaneously use the existing food sources to shift the food back to the nest.…”

Section: Figure1 Cloud Computingmentioning

confidence: 99%

Efficient Cost Scheduling algorithm with Load Balancing in a Cloud Computing Environment

Chawla¹

2015

IJIRCCE

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Cloud computing is a very popular paradigm in the field of computer science. It provides on demand, ubiquitous access as a service to different people via the internet. In spite of offering many benefits over the traditional computing, there are numerous issues in cloud computing . Load balancing is one of the issue in cloud computing that distributes the workload to the nodes in such a way no one node is overloaded or under -loaded. In this paper, cost scheduling algorithm is applied for load balancing by considering the cost of service provider and user. In the cost scheduling algorithm, efficient mapping of tasks to available packages in cloud so it beneficial to user and service provider.

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“…The ants work together in search of new sources of food and simultaneously use the existing food sources to shift the food back to the nest. The ant use two types of pheromone for its movement these are [8] …”

Section: Aco(ant Colony Optimization)mentioning

confidence: 99%

“…In our algorithm the ant would lay down foraging pheromone after encountering under loaded nodes for searching overloaded nodes. Therefore, after an ant comes up to an under loaded node it will try to find the next path through foraging pheromone [8].…”

Section: Foraging Pheromone (Fp)mentioning

confidence: 99%