A performance analysis of load Balancing algorithms in Cloud environment

Garala, Kunjal; Goswami, Namrata; Maheta, Prashant D.

doi:10.1109/iccci.2015.7218063

Cited by 17 publications

(3 citation statements)

References 5 publications

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“…Nature inspired in designing the efficient load balancing algorithm in cloud domain. Yao et al [24] suggested a load balancing technique by concerning an artificial bee colony (ABC) algorithm and presented an enhanced artificial bee colony algorithm for increasing the system's throughput. A load balancing technique using modified PSO task scheduling (LBMPSO) [54] considered the execution time, and starting time variation of the tasks and assigned them to the ideal VMs.…”

Section: Related Workmentioning

confidence: 99%

A Double Threshold-Based Power-Aware Honey Bee Cloud Load Balancing Algorithm

et al. 2021

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Present-day advancement in cloud computing provides ICT infrastructure as a service on a pay per use. Cloud computing provides this infrastructure as a service and as service demand increases, service providers organize large-scale data centers with a lot of resources, and cause of huge greenhouse gases' emission. This data center's huge power demand necessitates the balancing of cloud load. To attain the optimum resource utilization, least processing time of CPU, minimal average response time, and avoiding over-load, cloud load balancing algorithms distributes workload across virtual machines. The key challenge here is to develop such a load balancing algorithm which consumes the least resources to fulfill the service demands. In this paper, a double threshold-based power-aware honey bee load balancing algorithm is proposed for the fair and even distribution of the incoming task requests to all the virtual machines. This paper compares the proposed algorithm with five widely used existing load balancing algorithms. Moreover, we have done the performance analysis using the popular CloudAnalyst simulation toolkit. Results of simulation showed that the proposed algorithm gives a note-worthy outcome for average response time, CPU cost, storage cost, memory cost, and energy consumption in cloud computing to show the resource utilization.

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Section: Related Workmentioning

confidence: 99%

A Double Threshold-Based Power-Aware Honey Bee Cloud Load Balancing Algorithm

et al. 2021

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“…The authors consider CPU utilization of the running instances of EC2 carried out load balancing and also provide elasticity while serving the requests [16]. Ms. Kunjal Garala et al [17] presented a decentralized technique for energy efficiently managing the VMs along with scalability aspect while provisioning the resources in large enterprise clouds. Each node perform its operation autonomously.…”

Section: Related Workmentioning

confidence: 99%

Untitled

2020

IJCSENET

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Cloud Computing is a technique involving multiple resources being requested by versatile cloud users for allocation of shared resources. The tasks of application requests are allotted to virtual machines (VMs). In different situation different machines get different load. So, load balancing becomes necessary among different VMs. A decentralized load balancer is used to identify best number of tasks to be allocated to each VM. During load allocation to VM, for execution of tasks, the most optimal VM is identified for the load which is best capable of handling the task. The cloud user's application task is them mapped onto that VM to reduce the energy consumption and total execution time. In this paper, decentralized load balancing technique is used to distribute the load on each virtual machine which is enhanced using particle swarm optimization (PSO) which is a swarm based heuristic optimization technique. Moreover, the results are analyzed and compared with centralized load balancer for energy efficiency and throughput parameters.

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“…In order to assess the performance of algorithms, a list of indicators is needed to compare the performances in different conditions. Various Key Performance Indicators (KPIs) have been defined in [28], [78], [27], [77], [54], [54], [72], [37], [63], [80] and [46]. However, these KPIs were either not adequate or not precise enough for the presented work.…”

Section: Performance Assessmentmentioning

confidence: 99%

A Runtime Adaptation Concept to reinforce Versatility in Industrial Automation

Elfahaam¹

2018

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Unter dem Stichwort Wandelbarkeit wird die Fähigkeit verstanden, Industrieanlagen in die Lage zu versetzen, auf ungeplante Änderungen zu reagieren. Daher muss das Automatisierungssystem bereit sein auf Änderungen auf allen Ebenen in der Automatisierungspyramide reagieren zu können. In dieser Dissertation wird ein Konzept zur Laufzeitadaption vorgestellt. Das Konzept adressiert die Prozessleitebene und stellt ein Adaptionssystem vor, das die Softwarekomponenten im Netzwerk nach den verschiedenen Optimierungskriterien stabil verteilt und damit das Automatisierungssystem zur Wandelbarkeit befähigt. Kurzfassung Die Prozessleittechnik als ein Teil der Automatisierungstechnik erfährt durch Initiativen wie „Industrie 4.0\. In der Prozessleittechnik sind verschiedene Initiativen auf der ganzen Welt (z. B. “ Industrie 4.0\ in Deutschland) derzeit in Forschung und Entwicklung richtungsweisend. Die Auflösung der Kommunikationshierarchie in der Automatisierungspyramide wird al...

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A performance analysis of load Balancing algorithms in Cloud environment

Cited by 17 publications

References 5 publications

A Double Threshold-Based Power-Aware Honey Bee Cloud Load Balancing Algorithm

A Double Threshold-Based Power-Aware Honey Bee Cloud Load Balancing Algorithm

Untitled

A Runtime Adaptation Concept to reinforce Versatility in Industrial Automation

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