Predicting host CPU utilization in cloud computing using recurrent neural networks

Donoval, Martin; Mason, Karl; Duggan, Jim; Howley, Enda; Barrett, Enda

doi:10.23919/icitst.2017.8356348

Cited by 60 publications

(33 citation statements)

References 22 publications

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“…A similar conclusion has also been made in [34,35]. Neural network techniques have been successful in predicting cloud resource usage as seen in [36,37,38,39,40,41].…”

Section: Related Worksupporting

confidence: 68%

“…Datacenter resource prediction using neural networks ANN is a suitable method of prediction in our case because we have shown that it data has a non-Gaussian distribution. It has also shown good performance than other prediction models [29,38]. Moreover, ANN has a great ability to model a non-linear function thus able to handle complex time series [39,48,49].…”

Section: Algorithm 2: Finding the Number Of Vm Peaks That Occur Simulmentioning

confidence: 98%

See 1 more Smart Citation

Autonomous Virtual Machine Sizing and Resource Usage Prediction for Efficient Resource Utilization in Multi-Tenant Public Cloud

Kenga¹,

Omwenga²,

Ogao³

2019

IJITCS

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In recent years, the use of cloud computing has increased exponentially to satisfy computing needs in both big and small organizations. However, the high amounts of power consumed by cloud data centres have raised concern. A major cause of power wastage in cloud computing is inefficient utilization of computing resources. In Infrastructure as a Service, the inefficiency is caused when users request for more resources for virtual machines than is required. In this paper, we propose a technique for automatic virtual machine sizing and resource usage prediction using neural networks, for multi-tenant Infrastructure as a Service cloud service model. The proposed technique aims at reducing energy wastage in data centres by efficient resource utilization. An evaluation of our technique on CloudSim Plus cloud simulator and WEKA shows that effective VM sizing not only achieves energy savings but also reduces the cost of using cloud services from a customer perspective.

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“…A similar conclusion has also been made in [34,35]. Neural network techniques have been successful in predicting cloud resource usage as seen in [36,37,38,39,40,41].…”

Section: Related Worksupporting

confidence: 68%

Section: Algorithm 2: Finding the Number Of Vm Peaks That Occur Simulmentioning

confidence: 98%

Autonomous Virtual Machine Sizing and Resource Usage Prediction for Efficient Resource Utilization in Multi-Tenant Public Cloud

Kenga¹,

Omwenga²,

Ogao³

2019

IJITCS

View full text Add to dashboard Cite

show abstract

“…Zhang et al [22] proposed an RNN-based model for improving the accuracy of workload prediction. Similarly, the classic RNN architecture was adopted in [23] and [24] to forecast the future workloads in cloud data centers. It turns out that RNN can work well when coping with short-term dependencies.…”

Section: Rnn-based Approaches For Workload Predictionmentioning

confidence: 99%

Towards Accurate Prediction for High-Dimensional and Highly-Variable Cloud Workloads with Deep Learning

Chen

Min

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

115

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Resource provisioning for cloud computing necessitates the adaptive and accurate prediction of cloud workloads. However, the existing methods cannot effectively predict the high-dimensional and highly-variable cloud workloads. This results in resource wasting and inability to satisfy service level agreements (SLAs). Since recurrent neural network (RNN) is naturally suitable for sequential data analysis, it has been recently used to tackle the problem of workload prediction. However, RNN often performs poorly on learning longterm memory dependencies, and thus cannot make the accurate prediction of workloads. To address these important challenges, we propose a deep Learning based Prediction Algorithm for cloud Workloads (L-PAW). First, a top-sparse auto-encoder (TSA) is designed to effectively extract the essential representations of workloads from the original high-dimensional workload data. Next, we integrate TSA and gated recurrent unit (GRU) block into RNN to achieve the adaptive and accurate prediction for highly-variable workloads. Using realworld workload traces from Google and Alibaba cloud data centers and the DUX-based cluster, extensive experiments are conducted to demonstrate the effectiveness and adaptability of the L-PAW for different types of workloads with various prediction lengths. Moreover, the performance results show that the L-PAW achieves superior prediction accuracy compared to the classic RNN-based and other workload prediction methods for high-dimensional and highly-variable real-world cloud workloads.

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“…The ability to accurately predict future resource usage is a key challenge facing cloud resource management strategies due to the growing complexity of modern data centers . The accuracy of a prediction model has a large impact on the overall performance of live migration.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers

et al. 2018

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One of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime-ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center.

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Predicting host CPU utilization in cloud computing using recurrent neural networks

Cited by 60 publications

References 22 publications

Autonomous Virtual Machine Sizing and Resource Usage Prediction for Efficient Resource Utilization in Multi-Tenant Public Cloud

Autonomous Virtual Machine Sizing and Resource Usage Prediction for Efficient Resource Utilization in Multi-Tenant Public Cloud

Towards Accurate Prediction for High-Dimensional and Highly-Variable Cloud Workloads with Deep Learning

A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers

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