Cross-Correlation Prediction of Resource Demand for Virtual Machine Resource Allocation in Clouds

Minarolli, Dorian; Freisleben, Bernd

doi:10.1109/cicsyn.2014.36

Cited by 16 publications

(7 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, in this case, an accurate prediction is difficult owing to the randomness and uncertainty. Machine-or deep-learning methods are generally used to solve the unpredictability of nonlinear problems, such as Genetic Algorithm (GA) [28], Neural Network (NN) [29], Support Vector Machine (SVM) [30], Support Vector Regression (SVR) [31], Deep Belief Network (DBN) [32], and BackPropagation Neural Network (BPNN) [33]. A GA-based prediction method has been proposed for resource utilization of VMs and PMs [28].…”

Section: Related Workmentioning

confidence: 99%

“…A GA-based prediction method has been proposed for resource utilization of VMs and PMs [28]. A cross-correlation prediction approach is presented based on SVM, which uses the cross relation of VMs running the same application to improve prediction accuracy [30]. In addition, a long short-term memory (LSTM) method has been used to predict the dynamic network traffic to obtain a low transmission latency and power consumption [34].…”

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: Related Workmentioning

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

“…There are also other studies that examined forecasting of host resources usage [29]- [31], prediction of VM usage [32]- [35] and prediction of web application workload [36]- [39]. Table 1 concludes the prediction approaches of resource utilization based on five characteristics: 1) prediction method, 2) workload datasets, 3) performance metrics, 4) preprocessing strategies, and 5) prediction windows.…”

Section: Related Workmentioning

confidence: 99%

Predicting Multi-Attribute Host Resource Utilization Using Support Vector Regression Technique

Abdullah

Al-Jamali

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Studies on the resource workload demand in cloud computing environment aim at reducing resource wastage by optimizing the resource utilization in a cloud data center. Based on this goal, most of the existing approaches rely on resource management mechanisms such as resource allocation and Virtual Machine (VM) consolidation to reach an ideal solution for reducing resource wastage. Because of instability and high variability of the cloud resource usage and workloads, there is a demand for cloud providers to apply the prediction methods for forecasting the future cloud resource utilization. This paper employs a supervised statistical learning method, i.e., Support Vector Regression Technique (SVRT), to forecast the future usage of multi-attribute host resource. The method is particularly suitable to handle a non-linear cloud resource workload. To improve the prediction accuracy of SVRT, we decide Radial Basis Function as the kernel function of SVRT and apply Sequential Minimal Optimization Algorithm (SMOA) for the training and regression estimation of the prediction method. Besides, compared with the existing work, we consider the multi-attribute cloud resources other than the single resource. The method is employed under eight sets of real-world workloads, which are collected from BitBrain (BB), PlanetLab (PL) and Google Cluster Workload Traces (GCWT). Series of experiments conducted on the workload dataset show the effectiveness of our approach. Based on evaluation metrics, the final results show that the accuracy was enhanced by approximately 4%-16% and the error percentage was reduced by approximately 8%-60% compared with the state-of-the-art methods.

show abstract

“…Li and Zhang [29] proposed an optimal combination prediction method for resource demands, which combines the induced ordered weighted geometry averaging operator and the generalized dice coefficient with the improved Elman neural network and gray model to enhance the prediction accuracy. Minarolli and Freisleben [30] presented a cross-correlation prediction approach based on support vector machine (SVM), which considers the cross relation of VMs running the same application to improve prediction accuracy. Zhang et al [31] proposed a deep belief network-(DBN-) based prediction approach of cloud resource requests in which orthogonal experimental design and analysis of variance are used to enhance the prediction accuracy.…”

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

Cross-Correlation Prediction of Resource Demand for Virtual Machine Resource Allocation in Clouds

Cited by 16 publications

References 14 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

Predicting Multi-Attribute Host Resource Utilization Using Support Vector Regression Technique

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

Contact Info

Product

Resources

About