A Hybrid Meta-Heuristic for Optimal Load Balancing in Cloud Computing

Princess, G. Annie Poornima; Radhamani, A. S.

doi:10.1007/s10723-021-09560-4

Cited by 45 publications

(14 citation statements)

References 45 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 experimental validation of HABC‐MBOA confirmed its excellence in maximizing throughput, mean imbalance degree, SD in connections, makespan, load variance minimization independent to the number of VMs and tasks in the clouds. Annie Poornima Princess and Radhamani 25 proposed a hybrid Harris Hawk and Pigeon optimization algorithm (HHHPOA) was proposed for dynamic workload allocation among the cloud systems. It verified the overloaded and underloaded condition of database server during load balancing to derived maximized utilization of resources with improved reliability and services.…”

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

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“…In the planning phase, the Harris hawks optimization (HHO) [14,15] meta-heuristic technique is employed to determine an appropriate placement solution. HHO is an evolutionary algorithm that explores and exploits solutions within a search space to find economical solutions, and has been successful in various domains, including cloud computing [16,17], image processing [18,19], Medical [20,21], and IoT [22][23][24]. Finally, the solution achieved using HHO is executed in the execution phase, to solve the fog service placement problem.…”

Section: Our Approach and Contributionmentioning

confidence: 99%

A Cost and Energy-Aware Deployment of IoT Applications scheme in Fog Systems

Faraji-Mehmandar

Ghobaei‐Arani

Shakarami

2023

Preprint

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The number of Internet-connected devices is constantly increasing due to the growth of IoT. However, this results in a large volume of data transmission, which can cause issues with cloud-based storage and data processing due to inadequate bandwidth. This could lead to inadequacy of IoT; therefore, managing and storing data in such a way as not to cause the slightest delay in processing has become a major challenge in IoT. Both fog and cloud computing offer storage space, applications, and data for users, but fog computing is more geographically distributed and closer to the end-user, which increases system efficiency and reduces data transmission distance. Various QoS requirements of IoT services, distributed and heterogeneous nature of fog node computational capabilities make the application placement in Fog a challenging task. This paper proposes a solution that utilizes the Harris hawks optimization technique to monitor QoS requirements and available fog node capabilities to determine an efficient service placement plan. The proposed mechanism considers throughput, cost, and energy consumption as objective functions while meeting the QoS requirements of each IoT service. The simulation results obtained demonstrate that the proposed solution increases the resource usage and service acceptance ratio by 4.5% and 3.8%, respectively and reduces the service delay and the energy consumption by 2.95% and 1.62%, respectively compared with other state-of-the-art works.

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“…The results of this FIMPSO approach confirm its efficacy in achieving potential outcomes in terms of average response time, CPU utilization, memory utilization, reliability, throughput and makespan compared to the benchmarked schemes. Then, Annie Poornima Princess and Radhamani 20 have proposed a hybrid meta‐heuristic scheme that integrates Harries Hawks Optimization (HHO) with Pigeon Inspired Optimization (PIA) Algorithm to support effective LB and ensure ideal resource utilization along with reduced task response time. It aids in dynamic sharing of workload amid cloud systems and equal sharing of resources.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Guided Differential Evolution‐based Slime Mould Algorithm‐based efficient Multi‐objective Task Scheduling for Cloud Computing Environments

Nithiavathy,

Janakiraman,

Deva Priya

2023

Trans Emerging Tel Tech

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In a Cloud Computing (CC) environment, users are charged based on on‐demand resource utilization and expected Quality of Service (QoS). Multi‐Objective‐based Task Scheduling (MOTS) problem formulated for achieving the expected QoS in a CC scenario is always NP‐complete. The majority of existing Task Scheduling (TS) algorithms are not efficient in determining global optimal solutions due to huge space offered by large‐scale problem instances and characteristics inherent in NP‐complete problems. In this article, an Adaptive Guided Differential Evolution‐based Slime Mould Algorithm (AGDESMA) is proposed for dealing with the problem of Multi‐Objective large‐scale TS optimization over Infrastructure‐as‐a‐Service (IaaS) CC environment. The proposed AGDESMA is formulated with the merits of Slime Mould Algorithm (SMA) and Adaptive Guided Differential Evolution (AGDE) for achieving a proper balance amid exploitation and exploration phases and preventing the solutions from getting trapped into minimum local regions. It specifically adopts the method of AGDE for preventing premature convergence with maximized diversity of population and local search of swarm agents. The experimental validation of the proposed AGDESMA is conducted using CloudSim based on the synthesized and standard workload traces with larger instances up to a maximum of 5000. The results confirm the predominance of the proposed AGDESMA scheme over benchmarked schemes based on computational overhead and trade‐off between makespan and financial cost.

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A Hybrid Meta-Heuristic for Optimal Load Balancing in Cloud Computing

Cited by 45 publications

References 45 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

A Cost and Energy-Aware Deployment of IoT Applications scheme in Fog Systems

Adaptive Guided Differential Evolution‐based Slime Mould Algorithm‐based efficient Multi‐objective Task Scheduling for Cloud Computing Environments

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