Adaptive regressive holt–winters workload prediction and firefly optimized lottery scheduling for load balancing in cloud

Prassanna, J.; Neelanarayanan, V.

doi:10.1007/s11276-019-02090-8

Cited by 21 publications

(13 citation statements)

References 29 publications

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“…The proposed and existing method is simulated in CloudSim to evaluate the performance. The proposed ES-MPSO method is compared with the existing firefly based optimization method [14] and standard PSO method. Various metrics like energy consumption, makespan, memory usage and SLA violation were measured and compared with existing methods.…”

Section: Resultsmentioning

confidence: 99%

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Eagle Strategy with Cauchy Mutation Particle Swarm Optimization for Energy Management in Cloud Computing

Kollu¹,

Vadlamudi²

2020

IJIES

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Energy management of the cloud datacentre is a challenging task, especially when the cloud server receives a number of the user’s request simultaneously. This requires an efficient method to optimally allocate the resources to the users. Resource allocation in cloud data centers need to be done in optimized manner for conserving energy keeping in view of Service Level Agreement (SLA). We propose, Eagle Strategy (ES) based Modified Particle Swarm Optimization (ES-MPSO) to minimize the energy consumption and SLA violation. The Eagle Strategy method is applied due to its efficient local optimization technique. The Cauchy Mutation method which schedules the task effectively and minimize energy consumption, is applied to the proposed ES-MPSO method for improving the convergence performance. The simulation result shows that the energy consumption of ES-MPSO is 42J and Particle Swarm Optimization (PSO) is 51J. The proposed method ES-MPSO achieves higher efficiency compared to the PSO method in terms of energy management and SLA.

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Section: Resultsmentioning

confidence: 99%

“…Prassanna and Venkataraman [14] developed Nature-inspired Meta-heuristic Threshold based Firefly Optimized Lottery Scheduling (NMT-FOLS) for the VM consolidation. The adaptive regression method was used for the prediction of the workload state.…”

Section: Literature Reviewmentioning

confidence: 99%

Eagle Strategy with Cauchy Mutation Particle Swarm Optimization for Energy Management in Cloud Computing

Kollu¹,

Vadlamudi²

2020

IJIES

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“…The SLA is used to define QoS in the cloud datacenters and this measures the services provided to the users [16,19,20]. If a user places excessive demands on the data centers, an SLA violation occurs.…”

Section: Service Level Agreement (Sla) Violationmentioning

confidence: 99%

“…Part of this research is focused on the analysis of the relation between the cloud resources (CPU, RAM and disk) to energy consumption [16][17][18], and SLA violation [19,20]. A mathematical model describes the network activity that helps to apply the optimization methods and provide an effective solution to reduce the energy consumption.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning

Jayaprakash¹,

Nagarajan

Prado

et al. 2021

Energies

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Nowadays, many organizations and individual users are employing cloud services extensively due to their efficiency, reliability and low cost. A key aspect for cloud data centers is to achieve management methods to reduce energy consumption, increasing the profit and reducing the environmental impact, which is critical in the deployment of leading-edge technologies today such as blockchain and digital finances, IoT, online gaming and video streaming. In this review, various clustering, optimization, and machine learning methods used in cloud resource allocation to increase the energy efficiency and performance are analyzed, compared and classified. Specifically, on the one hand, we discuss how clustering methods and optimization techniques are widely applied in energy management due to their capacity to provide solutions for energy consumption reduction. On the other hand, we study how multi-objective optimization methods focus on reducing energy consumption as well as service level agreement (SLA) violation, and improving quality of services (QoS) simultaneously. Also, we discuss how optimization methods such as the firefly algorithm, whale optimization algorithm (WOA), particle swarm optimization (PSO) and genetic algorithm (GA) provide the highest performance in the field. Moreover, we analyze how machine learning methods such as deep neural network (DNN), random forest, and support vector machine (SVM) are applied to the prediction of energy consumption in the cloud, showing an accurate performance in this prediction. Nevertheless, we study how the existing methods still have limitations of low convergence, trap into local optima and overfitting.

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“…Conventional scheduling techniques do not minimize Service Level Agreement (SLA) violations, resource wastage and energy consumption. Therefore, the next paper [15] presents multi objective scheduling algorithm. The proposed Nature-inspired Meta-heuristic Threshold based Firefly Optimized Lottery Scheduling (NMT-FOLS) Technique outperforms existing state of art and conventional scheduling algorithms.…”

mentioning

confidence: 99%

Special issue on the technologies and applications of big data

et al. 2021

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Adaptive regressive holt–winters workload prediction and firefly optimized lottery scheduling for load balancing in cloud

Cited by 21 publications

References 29 publications

Eagle Strategy with Cauchy Mutation Particle Swarm Optimization for Energy Management in Cloud Computing

Eagle Strategy with Cauchy Mutation Particle Swarm Optimization for Energy Management in Cloud Computing

A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning

Special issue on the technologies and applications of big data

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