Use of time-series based forecasting technique for balancing load and reducing consumption of energy in a cloud data center

Shaw, Subhadra Bose; Kumar, C. M. Arun; Singh, Anil Kumar

doi:10.1109/i2c2.2017.8321782

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…Many studies have used conventional DES, which performs better than simple exponential smoothing [26], to predict cloud workloads; however, conventional DES cannot model seasonality [27]. The Seasonal Holt-Winters (SHW) model can be used to forecast trends and seasonal workloads.…”

Section: Overload Detection Algorithmmentioning

confidence: 99%

Integrated resource management pipeline for dynamic resource-effective cloud data center

2020

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Cloud computing is a popular emerging computing technology that has revolutionized information technology through flexible provisioning of computing resources. Therefore, efforts to develop an effective resource management approach have found that implementing efficient resource sharing among multiple customers that considers power saving, service-level agreements, and network traffic simultaneously is difficult. This paper proposes a practical integrated pipeline that can use various algorithms. The performance of each algorithm is evaluated independently to obtain the combination of algorithms that guarantees a resource-effective cloud data center framework. This integrated resource management pipeline approach would optimize performance based on several key performance indicators, such as power saving, network traffic, and service-level agreements, for either the whole system or the end-user. The performance of the proposed resource management framework was evaluated using a real testbed. The results demonstrated that the proactive double exponential smoothing algorithm prevents unnecessary migrations, the MMTMC2 VM selection algorithm improved the quality of service for end-users and reduced overall energy consumption and network traffic, and the medium-fit placement algorithm provided load balancing between active servers and decreased service level agreement violations. The performance comparison illustrated that the combination of these algorithms was considered to be the best solution toward a dynamic resource-effective cloud data center. Our results showed that energy consumption and the total number of migrations decreased by 16.64% and 49.44%, respectively.

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Section: Overload Detection Algorithmmentioning

confidence: 99%

Integrated resource management pipeline for dynamic resource-effective cloud data center

2020

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“…Reference [5] proposed cloud computing using a PSO-based weighted support vector machine, and the experiment results indicated that the proposed algorithm is superior to the other four prediction algorithms in terms of prediction accuracy and efficiency. Reference [6] proposed a technique that not only makes the data center more energy efficient but also balances the workload more efficiently. Reference [7] proposed a heuristic-based algorithm called the greedy-based load balance (GBLB) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Cloud Computing Resource Load Forecasting based on Bat Algorithm Optimized SVM

Li¹

2019

IJPE

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For the problem of resource load forecasting in cloud computing, the optimized bat algorithm is combined with SVM for forecasting. Firstly, the bat algorithm adopts the reverse learning strategy for population initialization, and secondly, the weighting factor in the particle swarm optimization is used for individual optimization. Finally, the individual is selected using the Gaussian mutation method. Two important parameters in the SVM are optimized using the improved algorithm. In the simulation experiment, the SVM is optimized by the particle swarm optimization and compared with the genetic algorithm optimizing SVM, and a better forecasting effect is obtained.

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Efficient VM migrations using forecasting techniques in cloud computing: a comprehensive review

Màsdàrí

Khezri

2020

Cluster Comput

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Use of time-series based forecasting technique for balancing load and reducing consumption of energy in a cloud data center

Cited by 8 publications

References 12 publications

Integrated resource management pipeline for dynamic resource-effective cloud data center

Integrated resource management pipeline for dynamic resource-effective cloud data center

Cloud Computing Resource Load Forecasting based on Bat Algorithm Optimized SVM

Efficient VM migrations using forecasting techniques in cloud computing: a comprehensive review

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