Improved Cuckoo Search with Artificial Bee Colony for Efficient Load Balancing in Cloud Computing Environment

Kumar, Rakesh; Chaturvedi, Abhay

doi:10.1007/978-981-15-5345-5_11

Cited by 10 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This algorithm transfers extra tasks from an overloaded virtual machine. In [28], the authors have used a hybrid algorithm combining cuckoo search with ACO to balance load in a cloud environment. The authors in [29] have used the BAT optimization algorithm for balancing load in the cloud environment.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Load Balancing Algorithm for Cloud Computing Using Rock Hyrax Optimization

Singhal,

Sharma,

Anushree

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Cloud computing offers dynamic, scalable, and virtualized computing resources to end users over the internet. Load balancing is crucial for efficient resource use, distributing workloads across multiple resources to prevent overloading. Load balancing is crucial for resource utilization and processing time reduction, but traditional algorithms are often stuck at local maxima, leading to unequal allocation and performance decline. A metaheuristic based algorithm is proposed to dynamically adjust load distribution, ensuring resilience and sensitivity to changing workloads while managing energy consumption. This research presents a Rock Hyrax-based load balancing algorithm that addresses local maxima and power efficiency issues using QoS parameters. The algorithm's performance is evaluated qualitatively and statistically, considering both static and dynamic modes of jobs and virtual machines. Comparing it with existing scheduling algorithms, the algorithm reduces makespan by 10%-15% and total energy consumption in data centers by 8%-13%. These results demonstrate the effectiveness of the Rock Hyrax-based load balancing algorithm in improving performance and energy efficiency in data centers, highlighting its potential impact on optimizing resource allocation and enhancing overall system performance. INDEX TERMSCloud computing, Energy consumption, Load balancing, Makespan, Rock Hyrax I. INTRODUCTION

show abstract

Section: Related Workmentioning

confidence: 99%

Energy Efficient Load Balancing Algorithm for Cloud Computing Using Rock Hyrax Optimization

Singhal,

Sharma,

Anushree

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…For IoT-based devices, a comparative analysis is presented in [ 25 ] while [ 26 ] discussed the security of IoT devices using blockchain. Kumar et al [ 27 ] used the BAT algorithm for managing load in the cloud environment, while cuckoo and artificial bee colonies were combined for managing the load on the cloud [ 28 ].…”

Section: Related Workmentioning

confidence: 99%

Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing

Singhal

Athithan

Alomar

et al. 2023

Sensors

View full text Add to dashboard Cite

Data centers are producing a lot of data as cloud-based smart grids replace traditional grids. The number of automated systems has increased rapidly, which in turn necessitates the rise of cloud computing. Cloud computing helps enterprises offer services cheaply and efficiently. Despite the challenges of managing resources, longer response plus processing time, and higher energy consumption, more people are using cloud computing. Fog computing extends cloud computing. It adds cloud services that minimize traffic, increase security, and speed up processes. Cloud and fog computing help smart grids save energy by aggregating and distributing the submitted requests. The paper discusses a load-balancing approach in Smart Grid using Rock Hyrax Optimization (RHO) to optimize response time and energy consumption. The proposed algorithm assigns tasks to virtual machines for execution and shuts off unused virtual machines, reducing the energy consumed by virtual machines. The proposed model is implemented on the CloudAnalyst simulator, and the results demonstrate that the proposed method has a better and quicker response time with lower energy requirements as compared with both static and dynamic algorithms. The suggested algorithm reduces processing time by 26%, response time by 15%, energy consumption by 29%, cost by 6%, and delay by 14%.

show abstract

“…In short, any remaining or new task in any given period will choose the most powerful of the available processing resources, depending on the selection power it has, and this process will continue until all tasks are completed. If we show the periods of allocating tasks by m, each bee's selection power is (6).…”

Section: The Efficiency Formulationmentioning

confidence: 99%

“…MOALO algorithm has performed better than other algorithms. In [6], a new load balancing was also, which proposed by combining a cuckoo search algorithm with a bee colony called a self-adaptive artificial bee colony (SABC). It increases the utilization of resources, and execution time is reduced.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective tasks scheduling using artificial bee colony algorithm based on cellular automata in cloud computing environment

Babadi

Shiri

Goudarzi

et al. 2022

IJECE

View full text Add to dashboard Cite

<span>Due to the development of communication device technology and the need to use up-to-date infrastructure ready to respond quickly and in a timely manner to computational needs, the competition for the use of processing resources is increasing nowadays. The scheduling tasks in the cloud computing environment have been remained a challenge to access a quick and efficient solution. In this paper, the aim is to present a new tactic for allocating the available processing resources based on the artificial bee colony (ABC) algorithm and cellular automata for solving the task scheduling problem in the cloud computing network. The results show the performance of the proposed method is better than its counterparts.</span>

show abstract

Improved Cuckoo Search with Artificial Bee Colony for Efficient Load Balancing in Cloud Computing Environment

Cited by 10 publications

References 8 publications

Energy Efficient Load Balancing Algorithm for Cloud Computing Using Rock Hyrax Optimization

Energy Efficient Load Balancing Algorithm for Cloud Computing Using Rock Hyrax Optimization

Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing

Multi-objective tasks scheduling using artificial bee colony algorithm based on cellular automata in cloud computing environment

Contact Info

Product

Resources

About