<scp>PGWO‐AVS‐RDA</scp>: An intelligent optimization and clustering based load balancing model in cloud

Laxman, Raghavender Reddy Kothi; Lathigara, Amit; Aluvalu, Rajanikanth; Viswanadhula, Uma Maheswari

doi:10.1002/cpe.7136

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…Kothi Laxman et al 30 proposed a probabilistic gray wolf optimization algorithm (PGWO)‐based load balancing scheme for effective and efficient allocation of tasks to the available VMs in the clouds. This PGWOA mechanism adopted the parameters of scheduling and identified the superior tasks during the process of load balancing.…”

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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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: 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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“…Where the L best of a particle is the best result reached by the particle so far, and the G best is the best result among the whole population. Intelligent PSO optimization technique works on global search (8) . Every single particle controls its own independent course according to the local and global best in every generation.…”

Section: Scheduling Techniquementioning

confidence: 99%

Intelligent Particle Swarm Optimization Based Resource Provisioning Technique in Cloud Computing

Reddy¹,

Indiramma²,

Nagarathna³

2023

IJST

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Objectives: To find the optimal allocation of resources and minimize the overall cost while meeting the performance requirements of the applications. Methods: The proposed Intelligent PSO-based resource optimization in cloud computing evaluates the quality of the solutions based on their resource allocation parameters. Cloud Sim software is used as a simulation tool for testing and evaluating new solutions and strategies in the cloud. The Closest Data Center Service Broker Policy is implemented in Cloud Simulation. Findings: The proposed technique assigns workloads effectively on available resources with an improvement of 10.46% in electricity consumption. Novelty:The algorithm can further be employed for identifying the unused VM's in the Data Center to reduce the cost.

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“…CC allows a large sum of people to share a single set of resources, lowering the overall cost for everyone. CC service patterns that can be broken down into three categories: as Infrastructure as a Service, Platform as a Service and Software as a Service (Laxman et al , 2022).…”

Section: Introductionmentioning

confidence: 99%

Clustering based EO with MRF technique for effective load balancing in cloud computing

N.,

Lathigara,

Aluvalu

et al. 2023

IJPCC

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Purpose Cloud computing (CC) refers to the usage of virtualization technology to share computing resources through the internet. Task scheduling (TS) is used to assign computational resources to requests that have a high volume of pending processing. CC relies on load balancing to ensure that resources like servers and virtual machines (VMs) running on real servers share the same amount of load. VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. Design/methodology/approach VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. With a large number of VM or jobs, this method has a long makespan and is very difficult. A new idea to cloud loads without decreasing implementation time or resource consumption is therefore encouraged. Equilibrium optimization is used to cluster the VM into underloaded and overloaded VMs initially in this research. Underloading VMs is used to improve load balance and resource utilization in the second stage. The hybrid algorithm of BAT and the artificial bee colony (ABC) helps with TS using a multi-objective-based system. The VM manager performs VM migration decisions to provide load balance among physical machines (PMs). When a PM is overburdened and another PM is underburdened, the decision to migrate VMs is made based on the appropriate conditions. Balanced load and reduced energy usage in PMs are achieved in the former case. Manta ray foraging (MRF) is used to migrate VMs, and its decisions are based on a variety of factors. Findings The proposed approach provides the best possible scheduling for both VMs and PMs. To complete the task, improved whale optimization algorithm for Cloud TS has 42 s of completion time, enhanced multi-verse optimizer has 48 s, hybrid electro search with a genetic algorithm has 50 s, adaptive benefit factor-based symbiotic organisms search has 38 s and, finally, the proposed model has 30 s, which shows better performance of the proposed model. Originality/value User’s request or data transmission in a cloud data centre may cause the VMs to be under or overloaded with data. To identify the load on VM, initially EQ algorithm is used for clustering process. To figure out how well the proposed method works when the system is very busy by implementing hybrid algorithm called BAT–ABC. After the TS process, VM migration is occurred at the final stage, where optimal VM is identified by using MRF algorithm. The experimental analysis is carried out by using various metrics such as execution time, transmission time, makespan for various iterations, resource utilization and load fairness. With its system load, the metric gives load fairness. How load fairness is worked out depends on how long each task takes to do. It has been added that a cloud system may be able to achieve more load fairness if tasks take less time to finish.

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PGWO‐AVS‐RDA: An intelligent optimization and clustering based load balancing model in cloud

Cited by 8 publications

References 32 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

Intelligent Particle Swarm Optimization Based Resource Provisioning Technique in Cloud Computing

Clustering based EO with MRF technique for effective load balancing in cloud computing

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