Providing a load balancing method based on dragonfly optimization algorithm for resource allocation in cloud computing

Amini, Zahra; Maeen, Mehrdad; Jahangir, Mohammad Reza

doi:10.2991/ijndc.2018.6.1.4

Cited by 22 publications

(10 citation statements)

References 8 publications

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“…DA and its hybrids have also been applied for optimal resource allocation. In [69], DA is used for the optimal resource allocation in cloud computing by optimizing parameters like load balance, execution time and response time for optimal allocation of resources to tasks and to establish load balance. In [70], it has been used to optimally allocate generators and capacitors in a distribution system, and in [71], it has been used for the distributed generation (DG) placement in distributed networks to obtain the optimal DG units size and placement.…”

Section: Resource Allocationmentioning

confidence: 99%

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Emambocus

Jasser

Amphawan

2021

Sensors

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Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.

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Section: Resource Allocationmentioning

confidence: 99%

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Emambocus

Jasser

Amphawan

2021

Sensors

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“…Pseudocode of Standard DAO [104] 1. Initialization of data centers, host machines, and virtual machines 2.…”

Section: Pseudocode Of Standard Dao [104]mentioning

confidence: 99%

“…Resource allocation-based dragonfly optimization algorithm (RDOA): Amini et al [104] proposed the dragonfly optimization technique to manage resources so that load balancing becomes possible. This technique targets the task scheduling issue in the datacenters of the cloud.…”

Section: Doa and Its Variationsmentioning

confidence: 99%

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

Elmagzoub¹,

Syed²,

Shaikh³

et al. 2021

Electronics

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Cloud computing offers flexible, interactive, and observable access to shared resources on the Internet. It frees users from the requirements of managing computing on their hardware. It enables users to not only store their data and computing over the internet but also can access it whenever and wherever it is required. The frequent use of smart devices has helped cloud computing to realize the need for its rapid growth. As more users are adapting to the cloud environment, the focus has been placed on load balancing. Load balancing allocates tasks or resources to different devices. In cloud computing, and load balancing has played a major role in the efficient usage of resources for the highest performance. This requirement results in the development of algorithms that can optimally assign resources while managing load and improving quality of service (QoS). This paper provides a survey of load balancing algorithms inspired by swarm intelligence (SI). The algorithms considered in the discussion are Genetic Algorithm, BAT Algorithm, Ant Colony, Grey Wolf, Artificial Bee Colony, Particle Swarm, Whale, Social Spider, Dragonfly, and Raven roosting Optimization. An analysis of the main objectives, area of applications, and targeted issues of each algorithm (with advancements) is presented. In addition, performance analysis has been performed based on average response time, data center processing time, and other quality parameters.

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“…The methods using static and dynamic swarming behaviors of dragonflies for load balancing in the cloud is being considered as the reference for performance modeling. The methods considered are Deadline Aware Multi-Objective Dragonfly Optimization (DAMO), Dragonfly Optimization Algorithm (DOA), dragonfly algorithm with dragonfly parameters, and constraint measure based dragonfly optimization [29][30][31][32][33]. The interaction among the dragonflies while navigating, their food search procedure and even the steps followed by them to avoid enemies while swarming is used to provide best load balancing solution in crucial situations especially when the ratio of incoming jobs and available resources in the nodes are inappropriate.…”

Section: Dragonfly Lbtmentioning

confidence: 99%

Performance Modeling of Load Balancing Techniques in Cloud: Some of the Recent Competitive Swarm Artificial Intelligence-based

Bhargavi¹,

Babu²,

Pitt

2020

Journal of Intelligent Systems

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AbstractCloud computing deals with voluminous heterogeneous data, and there is a need to effectively distribute the load across clusters of nodes to achieve optimal performance in terms of resource usage, throughput, response time, reliability, fault tolerance, and so on. The swarm intelligence methodologies use artificial intelligence to solve computationally challenging problems like load balancing, scheduling, and resource allocation at finite time intervals. In literature, sufficient works are being carried out to address load balancing problem in the cloud using traditional swarm intelligence techniques like ant colony optimization, particle swarm optimization, cuckoo search, bat optimization, and so on. But the traditional swarm intelligence techniques have issues with respect to convergence rate, arriving at the global optimum solution, complexity in implementation and scalability, which limits the applicability of such techniques in cloud domain. In this paper, we look into performance modeling aspects of some of the recent competitive swarm artificial intelligence based techniques like the whale, spider, dragonfly, and raven which are used for load balancing in the cloud. The results and analysis are presented over performance metrics such as total execution time, response time, resource utilization rate, and throughput achieved, and it is found that the performance of the raven roosting algorithm is high compared to other techniques.

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Providing a load balancing method based on dragonfly optimization algorithm for resource allocation in cloud computing

Cited by 22 publications

References 8 publications

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

Performance Modeling of Load Balancing Techniques in Cloud: Some of the Recent Competitive Swarm Artificial Intelligence-based

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