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Journal ArticleDOI

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

L. D. Dhinesh Babu, +1 more
- Vol. 13, Iss: 5, pp 2292-2303
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TLDR
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.
Abstract
Scheduling of tasks in cloud computing is an NP-hard optimization problem. Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing (HBB-LB), which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue.

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Citations
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Load-balancing algorithms in cloud computing

TL;DR: This paper study the literature on the task scheduling and load-balancing algorithms and present a new classification of such algorithms, for example, Hadoop MapReduce load balancing category, Natural Phenomena-based load balancing categories, Agent-basedLoadBalancing category, General load balancingcategory, application-oriented category, network-aware category, and workflow specific category.
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Task scheduling techniques in cloud computing: A literature survey

TL;DR: A comprehensive survey of task scheduling strategies and the associated metrics suitable for cloud computing environments is presented and the various issues related to scheduling methodologies and the limitations to overcome are discussed.
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A comprehensive survey for scheduling techniques in cloud computing

TL;DR: A systematic review as well as classification of proposed scheduling techniques along with their advantages and limitations of cloud computing are provided.
Journal ArticleDOI

Load balancing mechanisms and techniques in the cloud environments

TL;DR: A systematic literature review of the existing load balancing techniques proposed so far and the advantages and disadvantages associated with several load balancing algorithms have been discussed and the important challenges of these algorithms are addressed so that more efficientload balancing techniques can be developed in future.
Journal ArticleDOI

A belief propagation-based method for task allocation in open and dynamic cloud environments

TL;DR: The evaluation results demonstrate the desirable efficiency of PD-LBP from both the shorter problem solving time and smaller communication requirement of task allocation in dynamic environments.
References
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Journal ArticleDOI

CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

TL;DR: The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns.
Journal ArticleDOI

On the performance of artificial bee colony (ABC) algorithm

TL;DR: The simulation results show that the performance of ABC algorithm is comparable to those of differential evolution, particle swarm optimization and evolutionary algorithm and can be efficiently employed to solve engineering problems with high dimensionality.
Book

Scheduling Algorithms

Peter Brucker
TL;DR: Besides scheduling problems for single and parallel machines and shop scheduling problems, this book covers advanced models involving due-dates, sequence dependent changeover times and batching.
Book ChapterDOI

The bees algorithm, a novel tool for complex optimisation problems

TL;DR: This chapter presents a new population-based search algorithm called the Bees Algorithm, which mimics the food foraging behavior of swarms of honeybees and can be used for both combinatorial optimization and functional optimization.
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