DLB3M : A noble load balancing algorithm for cloud computing services

Hossain, Md. Alam; Roy, Saumendu; Sen, Sujit Kumar; Hossain, Nazmul

doi:10.1080/02522667.2020.1864937

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…The comparative investigation of the proposed HDWOA‐LBM is conducted with the baseline HHHPOA, 25 HABC‐MBOA 24 and MMMA‐DLB 23 schemes, since they are the recently proposed hybrid metaheuristic optimization algorithms‐based load balancing mechanisms contributed towards the objective of achieving load balancing with balanced exploration and exploitation in the search space. They were contributed as the multi‐objective constraints‐based optimization solutions which achieved load balancing using the difference between the number of submitted tasks to the number of processing unit, total energy consumption, and deviation of load between each host and mean load of the entire network.…”

Section: Resultsmentioning

confidence: 99%

“…The results of MrLBA‐ACO confirmed efficient utilization of available resources by balancing the load among resources with reduced cost and execution time. Furthermore, Hossain et al 23 proposed Max‐min and Max algorithm‐based dynamic load balancing (MMMA‐DLB) scheme was proposed for confirmed best resource utilization and overall response time reduction during task processing in clouds. It was contributed to estimate the condition of VMs to enforce better allocation of tasks to underloaded VMs without any time delay in real time.…”

Section: Related Workmentioning

confidence: 99%

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Hybrid dingo and whale optimization algorithm‐based optimal load balancing for cloud computing environment

Ramya

Ayothi

2023

Trans Emerging Tel Tech

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Cloud computing is the most rapidly growing technology that facilitates virtualized computer resources to the users through its service providers. Load balancing and task scheduling is the important concern in the cloud computing scenario from the service provider point of view. The process of load balancing is responsible for allocating the dynamic workload among the cloud systems, such that equal sharing of the resources without the virtual machines (VMs) being overloaded or underloaded. This load balancing process not only needs to allocate the tasks to suitable VMs but also need to maintain trade‐off among VMs. Thus, a potential dynamic and optimized load balancing strategy is essential for improving resource utilization, services, reliability, and throughput by maintaining equilibrium between VMs with minimized makespan. In this article, hybrid dingo and whale optimization algorithm‐based load balancing mechanism (HDWOA‐LBM) is proposed for effective load balancing those aids in maximized throughput, reliability, and resource utilization in the clouds. This HDWOA‐LBM is proposed by mimicking the hunting characteristics of dingo (equivalent to tasks) and VMs as their prey to be hunted. It inherited the process of exploration and exploitation through the chasing and approaching, encircling, and harassing inherent with dingo optimization algorithm (DOA) to determine the optimal allocation of incoming tasks to the suitable VM. In specific, it utilized the merits of whale optimization for improving the exploitation phase of DOA to balance the trade‐off between local and global search. The simulation experiments of the proposed HDWOA‐LBM conducted using CloudSim confirmed better throughput of 21.28%, maximized reliability of 25.42%, minimized makespan of 22.98%, and improved resource allocation of 20.86%, on par with the competitive intelligent load balancing schemes used for investigation.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Hybrid dingo and whale optimization algorithm‐based optimal load balancing for cloud computing environment

Ramya

Ayothi

2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…To obtain more desirable cloud load balancing, the performance is analyzed based on a few metrics [47][48][49].…”

Section: Implementation Resultsmentioning

confidence: 99%

An Implementation of Modified Blowfish Technique with Honey Bee Behavior Optimization for Load Balancing in Cloud System Environment

Rani

Singh

Verma

et al. 2022

Wireless Communications and Mobile Computing

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Cloud computing is a system that allows data to be saved in the cloud on a virtual worker. Outsiders and virtual machines in the cloud worker supplier played a critical part in efficiently storing and accessing information. Security, access control, and load balancing are critical challenges in cloud engineering. In the past, various solutions for adjusting cloud load have been proposed. Operator-based burden adjustment calculation surpassed all other offered CPU use, cost, and idle time strategies. The productivity of the specialist-based load adjustment computation decreased when any of the client hubs changed regions. Experimental outcomes show that Modified-HBB-LB performs better than the existing load balancing strategies such as HBB-LB, DLB, FCFS, WRR, HDLB, and FIFO by achieving the load balance of the complete system. The Modified-HBB-LB technique reduces the number of migrations tasks (30%, 25% and 20%) as compared to HDLB, DLB, and HBB-LB. The proposed Modified-HBB-LB technique maintains the 3-5% higher performance levels on makespan, completion, and response time as compared to existing comparative techniques.

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Challenges and issues in energy efficient load balancing in the cloud computing environment

Tyagi

Bansal

Singhal

et al. 2022

2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON)

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DLB3M : A noble load balancing algorithm for cloud computing services

Cited by 3 publications

References 15 publications

Hybrid dingo and whale optimization algorithm‐based optimal load balancing for cloud computing environment

Hybrid dingo and whale optimization algorithm‐based optimal load balancing for cloud computing environment

An Implementation of Modified Blowfish Technique with Honey Bee Behavior Optimization for Load Balancing in Cloud System Environment

Challenges and issues in energy efficient load balancing in the cloud computing environment

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