Load Balancing based on Bee Colony Algorithm with Partitioning of Public Clouds

Ehsanimoghadam, Pouneh; Effatparvar, Mehdi

doi:10.14569/ijacsa.2018.090462

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…These methods are based on meta-heuristics and are inspired mainly by nature and biological systems, such as Muth flame, Ant colony, bee colony optimization, particle swarm optimization, etc. [8], [9], [19], [20]. These methods simulate the behavior of insects or animals for food search or survival struggle [19], [20].…”

Section: Swarm Intelligence Load Balancing Methodsmentioning

confidence: 99%

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Efficient Load Balancing in Cloud Environment Using Improved Spider Monkey Optimization

Verma

2022

Preprint

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Cloud computing provides a shared resource pool in a distributed environment. Users can access these resources anywhere anytime as per the provider’s policy. Whenever tasks are submitted on the cloud for execution, they needs to be scheduled appropriately in the cloud environment as many Virtual Machines (VM) are available at the backend. The performance of the entire system depends on the scheduling algorithm. A good scheduling algorithm distributes given tasks among all VMs so that the load of each VM is balanced. This problem is called a load balancing problem and comes under the category of NP-Hard problems. This paper uses the Spider Monkey Optimization algorithm for more efficient load balancing. The proposed optimization algorithm aims to enhance the performance by selecting the minimum loaded VM for balancing. Results of simulation show that proposed algorithm performance is improved compared to the existing algorithms with respect to load balancing, response time, make span, and resource utilization.

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Section: Swarm Intelligence Load Balancing Methodsmentioning

confidence: 99%

“…[8], [9], [19], [20]. These methods simulate the behavior of insects or animals for food search or survival struggle [19], [20]. Researchers are using these methods to solve complex problems in various computational fields and try to find near-optimal solutions [8], [9], [23].…”

Section: Swarm Intelligence Load Balancing Methodsmentioning

confidence: 99%

Efficient Load Balancing in Cloud Environment Using Improved Spider Monkey Optimization

Verma

2022

Preprint

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“…In addition to solving load balancing issues in distributed systems, other efforts based on the HB algorithm have been taken as well. Paper [11] presented a load balancing method based on the BC algorithm. This method employs a resource cost model in which the cloud is partitioned into several sectors.…”

Section: Related Workmentioning

confidence: 99%

Load Balancing Based on Optimization Algorithms: An Overview

Mbarek¹,

Mosorov²

2019

JTIT

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Combinatorial optimization challenges are rooted in real-life problems, continuous optimization problems, discrete optimization problems and other significant problems in telecommunications which include, for example, routing, design of communication networks and load balancing. Load balancing applies to distributed systems and is used for managing web clusters. It allows to forward the load between web servers, using several scheduling algorithms. The main motivation for the study is the fact that combinatorial optimization problems can be solved by applying optimization algorithms. These algorithms include ant colony optimization (ACO), honey bee (HB) and multi-objective optimization (MOO). ACO and HB algorithms are inspired by the foraging behavior of ants and bees which use the process to locate and gather food. However, these two algorithms have been suggested to handle optimization problems with a single-objective. In this context, ACO and HB have to be adjusted to multiobjective optimization problems. This paper provides a summary of the surveyed optimization algorithms and discusses the adaptations of these three algorithms. This is pursued by a detailed analysis and a comparison of three major scheduling techniques mentioned above, as well as three other, new algorithms (resulting from the combination of the aforementioned techniques) used to efficiently handle load balancing issues.

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“…Each scheduling algorithm is based on one or more parameters. The most targeted objectives are the overall execution time, the cost, and the use of resources (which also indicates the quality of service (QoS)) [22,7,9]. Several task scheduling algorithms based on metaheuristic algorithms, such as BGA, HGOW-ABC, GWO and ACO algorithms have been proposed for cloud load balancing [9,14,23,25].…”

Section: Introductionmentioning

confidence: 99%

An Approach Based on Genetic and Grasshopper Optimization Algorithms for Dynamic Load Balancing in CloudIoT

Benabbes

Hemam

2023

cai

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CloudIoT is a new paradigm, which has emerged as a result of the combination of Cloud Computing (CC) and the Internet of Things (IoT). It has experienced a growing and rapid development, and it has become more popular in information and technology (IT) environments because of the advantages it offers. However, due to a strong use of this paradigm, especially in smart cities, the problem of imbalance load has emerged. Indeed, to satisfy the needs of the user, the intelligent objects send the collected data to the virtual machines (VMs) of the cloud in order to be processed. So, it is necessary to have an idea about the load of its VM. Thus, the problem of load balancing between VMs is strongly related to the technique used for the VMs selection. To tackle this problem, we propose in this paper a task scheduler called Scheduler Genetic Grasshopper Algorithm (SGGA). It allows to ensure a dynamic load balancing, as well as the optimization of the makespan and the resource usage. Our proposed SGGA is based on the combination of Genetic Algorithm (GA) and Grasshopper Optimization Algorithm (GOA). First, the tasks sent by the IoTs are mapped to the VMs in order to build the initial population, then SGGA performs the genetic algorithm, which has expressed a considerable performance. However, the weakness of the GA is marked by its heaviness caused by the mutation operator, especially when the number of tasks increases. Because of this insufficiency, we have replaced the mutation operator with the grasshopper optimization algorithm. The results of the experiments show that our approach (SGGA) is the most efficient, compared to the recent approaches, in terms of the response time to obtain the optimal solution, makespan, throughput, an average resource utilization rate and the hypervolume indicator.

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Load Balancing based on Bee Colony Algorithm with Partitioning of Public Clouds

Cited by 9 publications

References 24 publications

Efficient Load Balancing in Cloud Environment Using Improved Spider Monkey Optimization

Efficient Load Balancing in Cloud Environment Using Improved Spider Monkey Optimization

Load Balancing Based on Optimization Algorithms: An Overview

An Approach Based on Genetic and Grasshopper Optimization Algorithms for Dynamic Load Balancing in CloudIoT

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