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Open AccessJournal Article

A Survey Of Various Load Balancing Algorithms In Cloud Computing

Dharmesh Kashyap, +1 more
- 25 Nov 2014 - 
- Vol. 3, Iss: 11, pp 115-119
TLDR
A survey on load balancing algorithms in cloud computing environment along with their corresponding advantages, disadvantages and performance metrics are discussed in detail.
Abstract
Cloud computing is emerging as a new paradigm for manipulating, configuring, and accessing large scale distributed computing applications over the network. Load balancing is one of the main Challenges in cloud computing which is required to distribute the workload evenly across all the nodes. Load is a measure of the amount of work that a computation system performs which can be classified as CPU load, network load, memory capacity and storage capacity. It helps to achieve a high user satisfaction and resource utilization ratio by ensuring an efficient and fair allocation of every computing resource. Proper load balancing aids in implementing fail-over, enabling scalability, overprovisioning, minimizing resource consumption and avoiding bottlenecks etc. This paper describes a survey on load balancing algorithms in cloud computing environment along with their corresponding advantages, disadvantages and performance metrics are discussed in detail.

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Citations
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Toward cloud computing QoS architecture: analysis of cloud systems and cloud services

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Load balancing in cloud computing environment using the Grey wolf optimization algorithm based on the reliability: performance evaluation

TL;DR: Grey wolf optimization (GWO) algorithm has been used based on the resource reliability capability to maintain proper load balancing and the results of simulation in CloudSim showed that the costs and response time in the proposed method are less than the other methods, and the obtained solutions are ideal.
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An improved load balanced metaheuristic scheduling in cloud

TL;DR: A proposal for cloud scheduling by means of a balanced load using both firefly algorithm (FA) and particle swarm optimization (PSO) heuristics has been made to balance the load of the entire system while at the same time bring down the makespan of a set of tasks.
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DDMTS: A novel dynamic load balancing scheduling scheme under SLA constraints in cloud computing

TL;DR: Compared with three other task scheduling algorithms applied to randomly generated benchmark and Google real user workload trace benchmark, the proposed algorithm exhibits the best performance in balancing VMs load and reducing the task rejection rate, improving the overall level of cloud computing services.
References
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Journal ArticleDOI

Honey bee behavior inspired load balancing of tasks in cloud computing environments

TL;DR: An algorithm named honey bee behavior inspired load balancing (HBB-LB) is proposed, which aims to achieve well balanced load across virtual machines for maximizing the throughput and compared with existing load balancing and scheduling algorithms.

Cloud Load Balancing Techniques : A Step Towards Green Computing

TL;DR: The existing load balancing techniques in cloud computing are discussed and further compares them based on various parameters like performance, scalability, associated overhead etc that are considered in different techniques.
Journal ArticleDOI

Ant colony Optimization: A Solution of Load balancing in Cloud

TL;DR: This paper is proposing a method based on Ant Colony optimization to resolve the problem of load balancing in cloud environment, which has many methods to resolve this problem.

Enhanced Max-min Task Scheduling Algorithm in Cloud Computing

TL;DR: The algorithm is built based on comprehensive study of the impact of Improved Max-min task scheduling algorithm in cloud computing and based on the expected execution time instead of completion time as a selection basis, it produces Minimum completion time (Slowest Resource).
Journal Article

A Survey Of Various Load Balancing Techniques And Challenges In Cloud Computing

TL;DR: This paper has discussed many different load balancing techniques used to solve the issue in cloud computing environment and suggested ways to improve the performance of these techniques.
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