A Resource Demand Prediction Method Based on EEMD in Cloud Computing

Chen, Jing; Wang, Yinglong

doi:10.1016/j.procs.2018.04.193

Cited by 27 publications

(12 citation statements)

References 11 publications

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“…This method decomposes the non-stationary users" resource demands into a plurality of intrinsic mode function components (IMFs) and residual component (RES) through EEMD method to improve the prediction accuracy. The experimental results show that our method has a higher prediction accuracy compared with the existing ARIMA prediction model in the short term prediction of cloud resource demands [6].…”

Section: Related Workmentioning

confidence: 94%

Proposing Priority based Dynamic Resource Allocation [PDRA] Model in Cloud Computing

Chaturvedi¹,

Sengar²,

Sharma³

2018

IJCA

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Scaling of resources in cloud computing is essential for the better utilization of resources. Dynamic allocation of the resources / VMs in the multi-tenant environment is the need of the cloud computing. Virtualization technologies evolved to help IT organizations and to improve the efficiency of their hardware resources by partitioning hardware to provide simultaneous support to multiple applications and their corresponding software stacks. If the resource utilization is not properly allocated to applications, it will lead to the faulty services to the customers. The Cloud is the hub of resources, and can be used by any client on rental bases and on no demand resources can left with no usage. Clients/ Brokers may request for the multiple VMs/ other resources like, applications, database, operating system etc, but the resources are limited. So, there is the need of such a system to handle this allocation and deallocation of resources or VMs. By this PDRA model, authors have presented an idea to handle the resources/ VMs allocation and deallocation system.

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Section: Related Workmentioning

confidence: 94%

Proposing Priority based Dynamic Resource Allocation [PDRA] Model in Cloud Computing

Chaturvedi¹,

Sengar²,

Sharma³

2018

IJCA

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“…Ensemble Empirical Mode Decomposition (EEMD) is an effective method for handling a nonlinear and non-stationary time series [9], [10]. The EEMD-Autoregressive Integrated Moving Average (ARIMA) and EEMD-runs test (RT)-ARIMA methods reported in our previous studies [11], [12] demonstrate the effectiveness of non-stationary time series prediction. This paper further proposes an adaptive short-term prediction algorithm based on our previous EEMD-ARIMA and EEMD-RT-ARIMA methods.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Wang

2020

IEEE Access

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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.

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“…But when the signal contains pulse signal, intermittent signal, and noisy signal, the mode mixing of EMD would arise and cause wrong judgment [12,13]. Ensemble empirical mode decomposition (EEMD) can restrain modal aliasing of EMD at a certain level; however, the calculation amount grows, and the completeness loses due to residual noise [14,15]. Complementary ensemble empirical mode decomposition (CEEMD) can decrease the reconstruction error by adding white noise to the target signal and using the ensemble mean to extract intrinsic mode functions [16,17].…”

Section: Introductionmentioning

confidence: 99%

Faulty Line Selection Based on Modified CEEMDAN Optimal Denoising Smooth Model and Duffing Oscillator for Un-Effectively Grounded System

Hou

Guo

2020

Mathematical Problems in Engineering

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As the un-effectively grounded system fails, the zero-sequence current contains strong noise and nonstationary features. This paper proposes a novel faulty line selection method based on modified complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) and Duffing oscillator. Here, based on multiscale permutation entropy, fuzzy c-means clustering, and general regression neural network for abnormal signal detection, the MCEEMDAN is proposed. The endpoint mirror method is used to suppress the endpoint effect problem in the decomposition stage. The proposed algorithm is able to decompose the original signal into a series of intrinsic mode functions, which can complete the first filtering. The research shows that it can efficiently suppress the mode confusing phenomenon of empirical mode decomposition (EMD) and is also more complete and orthogonal than ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD). The optimal denoising smooth model is established for choosing optimal intrinsic mode functions to complete the second filtering. It can ensure that the reconstructed filtered signal has better smoothness and similarity. The optimal denoising smooth model of MCEEMDAN can not only keep useful details of the original signal but also reduce the noise and smooth signal. The bifurcation characteristic of the chaotic oscillator is applied in weak signal detection. The zero-sequence current’s denoising result is extracted as the input signal of the Duffing system. The faulty line could be selected by observing the phase diagram of the system. The research results verify the usability and effectiveness of the proposed method.

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A Resource Demand Prediction Method Based on EEMD in Cloud Computing

Cited by 27 publications

References 11 publications

Proposing Priority based Dynamic Resource Allocation [PDRA] Model in Cloud Computing

Proposing Priority based Dynamic Resource Allocation [PDRA] Model in Cloud Computing

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

Faulty Line Selection Based on Modified CEEMDAN Optimal Denoising Smooth Model and Duffing Oscillator for Un-Effectively Grounded System

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