Virtual resource prediction in cloud environment: A Bayesian approach

Shyam, Gopal K.; Manvi, Sunilkumar S.

doi:10.1016/j.jnca.2016.03.002

Cited by 74 publications

(16 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, a primary component sequence is extracted from a sequence with N historical data points ASTPA exhibits a high time cost, as depicted in Figure 20. In our previous work [38], we also compared the time costs of ARIMA, EEMD-ARIMA, and EEMD-RT-ARIMA. The EEMD-RT-ARIMA method uses the selection and reconstruction of efficient components to reduce its time cost and achieve a cost-effective trade-off.…”

Section: Discussionmentioning

confidence: 99%

“…An NN-based regression prediction has been conducted on the energy usage and power source output [37]. A Bayesianbased prediction model of virtual resources has also been proposed, in which the correlations among the parameter variables have been identified to improve the resource prediction accuracy [38]. In addition, a novel Dendritic Neutron Model (DNM) has been proposed to solve the classification, approximation, and prediction problems, which considers the nonlinearity of the synapses and uses effective learning algorithms to train the DNM [39].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Short-Term Prediction Algorithm for Resource Demands in Cloud Computing

Chen

Wang

2020

IEEE Access

View full text Add to dashboard Cite

Cloud computing has been widely applied in various fields with the development of big data and artificial intelligence. The associated resource demands exhibit characteristics such as diversity, large scale, burst and uncertainty. This paper analyzes these characteristics of cloud resource demands based on Alibaba cluster data, and proposes an adaptive short-term prediction algorithm for those demands. The proposed algorithm uses a principal component analysis method to extract the primary types of container demands from a time series of resource demands, and executes outlier detection and replacement to obtain a more stationary sequence. An adaptive short-term prediction strategy is proposed to adaptively select a higher-accuracy short-term prediction method to implement the prediction. Further, an error adjustment factor is proposed to reduce the prediction error. Thus, the short-term prediction accuracy of cloud resource demands is improved via outlier detection and replacement, an adaptive selection strategy and an error adjustment. We evaluated the effectiveness of these improvements, and compared our algorithm with existing algorithms in terms of effectiveness and time cost. The experimental results demonstrate that the proposed algorithm improves short-term prediction accuracy effectively.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Adaptive Short-Term Prediction Algorithm for Resource Demands in Cloud Computing

Chen

Wang

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Tseng et al [25] proposed a prediction method for CPU and memory utilization of VMs and physical machines based on a genetic algorithm (GA), which precedes the gray model under stable tendency and unstable tendency in terms of prediction accuracy. 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.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Different machine learning techniques have been utilised for supporting decision making in cloud resource usage, which is the main focus of this paper. Earlier studies reported the use of regression [30][31][32], Markov chain [33], decision trees [6], neural networks [31], Bayesian network [34], or polynomial approximation [22]. Deep RL refers to learning through interaction [35].…”

Section: Related Workmentioning

confidence: 99%

Online architecture for predicting live video transcoding resources

2019

View full text Add to dashboard Cite

End users stream video increasingly from live broadcasters (via YouTube Live, Twitch etc.). Adaptive live video streaming is realised by transcoding different representations of the original video content. Management of transcoding resources creates costs for the service provider, because transcoding is a CPU-intensive task. Additionally, the content must be transcoded within real time with the transcoding resources in order to provide satisfying Quality of Service. The contribution of this paper is validation of an online architecture for enabling live video transcoding with Docker in a Kubernetes-based cloud environment. Particularly, online cloud resource allocation has been focused on by executing experiments in several configurations. The results indicate that Random Forest regressor provided the best overall performance in terms of precision regarding transcoding speed and CPU consumption on resources, and the amount of realised transcoding tasks. Reinforcement Learning provided lower performance, and required more effort in terms of training.

show abstract

Virtual resource prediction in cloud environment: A Bayesian approach

Cited by 74 publications

References 15 publications

An Adaptive Short-Term Prediction Algorithm for Resource Demands in Cloud Computing

An Adaptive Short-Term Prediction Algorithm for Resource Demands in Cloud Computing

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

Online architecture for predicting live video transcoding resources

Contact Info

Product

Resources

About