Energy-efficient application assignment in profile-based data center management through a Repairing Genetic Algorithm

Vasudevan, Meera; Tian, Yu-Chu; Tang, Maolin; Kozan, Erhan; Zhang, Xueying

doi:10.1016/j.asoc.2018.03.016

Cited by 20 publications

(8 citation statements)

References 21 publications

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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%

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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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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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show abstract

“…Every population generates its scheme concurrently for each iteration, and every ant builds a scheme in the population (line 1-3), where nI is the number of iterations, nA is the number of ants in the population and nC is the number of populations. If there are virtual machines to be migrated, we randomly select one of them to allocate destination host (line [4][5][6][7][8][9][10][11][12][13][14][15]. If the source host of the virtual machine is overloaded, we use equation (13) to compute heuristic.…”

Section: Local Search Strategy and Pheromone Exchange Rulementioning

confidence: 99%

“…Because Extreme Learning Machine (ELM) is accurate, fast, and generalized [13], we use ELM to predict host usage. The VMP problem is NP-hard [14], [15]. Some researchers put forward linear programming to solve virtual machine consolidation [16], [17].…”

Section: Introductionmentioning

confidence: 99%

A Virtual Machine Consolidation Algorithm Based on Ant Colony System and Extreme Learning Machine for Cloud Data Center

Liu

Wang

et al. 2020

IEEE Access

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The energy consumption issue of large-scale data centers is attracting more and more attention. Virtual machine consolidation can significantly reduce energy consumption by migrating virtual machines from one physical machine to another. However, excessive virtual machine consolidation can lead to dangerous Service Level Agreement (SLA) violations. Therefore, how to balance between effective energy consumption and SLA violations avoidance effectively is a paradox to be mediated. The virtual machine consolidation problem is NP-hard. The traditional heuristic algorithm is easy to fall into the local optimal and some meta-heuristic algorithms can help to avoid it. However, the existing meta-heuristic algorithms are with high complexity. Therefore, we propose a lower complexity multi-population ant colony system algorithm with the Extreme Learning Machine (ELM) prediction (ELM_MPACS). The algorithm firstly predicts the host state employing ELM and then the virtual machine on the overloaded host will be migrated to the normal host, while the virtual machine on the underloaded host will be consolidated to another underloaded host with higher utilization. Multiple populations concurrently construct migration plans and local search further optimizes the results obtained by each population to reduce SLA violations. We compare ELM_MPACS with the benchmark, heuristic and meta-heuristic algorithms. The experimental results have shown that compared with these algorithms, our algorithm reduces energy consumption, migration times and SLA violations effectively.INDEX TERMS Cloud computing, ant colony system, virtual machine consolidation, energy consumption, SLA.

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“…Massive deployment of cloud datacenter comes with the cost of more energy consumption and excessive carbon footprint. This requires the green initiative in the datacenters for efficient energy consumption strategy [3]. Allocation of the task in the different Virtual Machine (VM) helps to reduce the energy consumption and effective resource utilization in the cloud datacentre.…”

Section: Introductionmentioning

confidence: 99%

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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Energy-efficient application assignment in profile-based data center management through a Repairing Genetic Algorithm

Cited by 20 publications

References 21 publications

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

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

A Virtual Machine Consolidation Algorithm Based on Ant Colony System and Extreme Learning Machine for Cloud Data Center

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

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