RVLBPNN: A Workload Forecasting Model for Smart Cloud Computing

Lu, Yao; Panneerselvam, John; Liu, Lu; Wu, Yunnan

doi:10.1155/2016/5635673

Cited by 41 publications

(20 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%

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%

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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show abstract

“…Recently, a workload prediction model named RVLBPNN [23] has been proposed in our earlier work. This model can forecast the future workload trend by exploiting historical data based on Neural Networks.…”

Section: Related Workmentioning

confidence: 99%

Latency-Based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacenters

Liu

Panneerselvam

et al. 2020

IEEE Trans. Sustain. Comput.

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Cloud datacentres are turning out to be massive energy consumers and environment polluters, which necessitate the need for promoting sustainable computing approaches for achieving environment-friendly datacentre execution. Direct causes of excess energy consumption of the datacentre include running servers at low level of workloads and over-provisioning of server resources to the arriving workloads during execution. To this end, predicting the future workload demands and their respective behaviours at the datacentres are being the focus of recent research in the context of sustainable datacentres. But prediction analytics of Cloud ds suffer various limitations imposed by the dynamic and unclear characteristics of Cloud workloads. This paper proposes a novel forecasting model named K-RVLBPNN (K-means based Rand Variable Learning Rate Backpropagation Neural Network) for predicting the future workload arrival trend, by exploiting the latency sensitivity characteristics of Cloud workloads, based on a combination of improved K-means clustering algorithm and BPNN (Backpropagation Neural Network) algorithm. Experiments conducted on real-world Cloud datasets exhibit that the proposed model exhibits better prediction accuracy, outperforming traditional Hidden Markov Model, Naïve Bayes Classifier and our earlier RVLBPNN model respectively.

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“…Shyam and Manvi [26] proposed a shortand long-term prediction model of virtual resource requirements for CPU/memory-intensive applications based on Bayesian networks, where the relationships and dependencies between variables are identified to facilitate resource prediction. Lu et al [27] proposed a workload prediction model RVLBPNN (Rand Variable Learning Rate Backpropagation Neural Network) based on BPNN algorithm, which achieves higher prediction accuracy than the hidden Markov model and the naive Bayes classifier. is method not only predicts CPU-intensive and memoryintensive workloads but also improves prediction accuracy by using the intrinsic relations among the arriving cloud workloads.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

Jing

Wang

2019

Journal of Electrical and Computer Engineering

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Dynamic resource scheduling is a critical activity to guarantee quality of service (QoS) in cloud computing. One challenging problem is how to predict future host utilization in real time. By predicting future host utilization, a cloud data center can place virtual machines to suitable hosts or migrate virtual machines in advance from overloaded or underloaded hosts to guarantee QoS or save energy. However, it is very difficult to accurately predict host utilization in a timely manner because host utilization varies very quickly and exhibits strong instability with many bursts. Although machine learning methods can accurately predict host utilization, it usually takes too much time to ensure rapid resource allocation and scheduling. In this paper, we propose a hybrid method, EEMD-RT-ARIMA, for short-term host utilization prediction based on ensemble empirical mode decomposition (EEMD), runs test (RT), and autoregressive integrated moving average (ARIMA). First, the EEMD method is used to decompose the nonstationary host utilization sequence into relatively stable intrinsic mode function (IMF) components and a residual component to improve prediction accuracy. Then, efficient IMF components are selected and then reconstructed into three new components to reduce the prediction time and error accumulation due to too many IMF components. Finally, the overall prediction results are obtained by superposing the prediction results of three new components, each of which is predicted by the ARIMA method. An experiment is conducted on real host utilization traces from a cloud platform. We compare our method with the ARIMA model and the EEMD-ARIMA method in terms of error, effectiveness, and time-cost analysis. The results show that our method is a cost-effective method and is more suitable for short-term host utilization prediction in cloud computing.

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RVLBPNN: A Workload Forecasting Model for Smart Cloud Computing

Cited by 41 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

Latency-Based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacenters

A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

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