Server load prediction using stream mining

Toumi, Hajer; Brahmi, Zaki; Benarfa, Zoulaykha; Gammoudi, Mohamed Mohsen

doi:10.1109/icoin.2017.7899568

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…Numerous studies have been carried out on the prediction in cloud computing according to various objectives of the research: Prediction of the servers' load [6], [7], [8], [9], [10], prediction of the VMs' load [11], [12], prediction of the VM use [13], [14], prediction of the host use [15].…”

Section: Related Workmentioning

confidence: 99%

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CPU-based Prediction with Self Organizing Map in Dynamic Cloud Data Centers

Djennane

Yacoub²,

Aoudjit

et al. 2021

SWCC

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Backgroud: The major objective of resource management systems in the cloud environments is to assist providers in making consistent and cost-effective decisions related to the dynamic resource allocation. However, because of the demand changes of the applications and the exponential evolution of the cloud, the resource management systems are constantly called into question with regard to their ability to guarantee an effective resource provisioning. Objective: To tackle these challenges, the future demand prediction is a practical solution that has been adopted in the literature. The prediction has widely relied on the CPU utilization since it is considered as a leading cause of the Quality of Service dropping. Method: The successful application of artificial intelligence techniques in forecasting problems motivated us to use the Kohonen Self Organizing Maps that tries to capture the gathered empirical CPU load time series in regular behaviors to perform an accurate forecast. The proposed solution is a two-step approach that first classifies the collected data and then predicts the future CPU load. Results and conclusion: The experimental results show that our proposed system outperforms other models reported in the literature. In addition, we proved that Self Organizing Maps known for its strength in classification is also effective for prediction.

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Section: Related Workmentioning

confidence: 99%

“…Cloud data centers contain hundreds of thousands of servers, which host millions of VMs of different sizes, types and applications. Hence, since server resources are strongly influenced by the VMs they host, it makes more sense to focus on VMs resource management rather than server management as in [6] [7] [8].…”

Section: Related Workmentioning

confidence: 99%

CPU-based Prediction with Self Organizing Map in Dynamic Cloud Data Centers

Djennane

Yacoub²,

Aoudjit

et al. 2021

SWCC

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show abstract

“…From the perspective of research objectives, some researchers have studied server load prediction [ [6][7][8][9][10], VM load prediction [11,12], VM utilization prediction [13,14], host utilization prediction [15], web application workload prediction [16], cloud service workload prediction [17][18][19], workflow workload prediction [20], service quality prediction [21], and workload characterization [22][23][24]. Toumi et al [6] described a server load according to the submitted task types and the submission rate and applied a stream mining technique to predict server loads. Jheng et al [11] proposed a VM workload prediction method based on the gray forecasting model, which determines the migrated VMs according to power savings and workload balance.…”

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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“…Interactive information retrieval has the following three characteristics: (1) No unlabeled sample set exists in the beginning of retrieval, and data on key words or behavioral habits are few. (2) During the interaction, "high-value samples" are selected to reduce the number of interaction, establish a high-quality training set, and form high-accuracy classifiers.…”

Section: General Frameworkmentioning

confidence: 99%

“…With the acceleration of mobile network access, the netizen population with access to internet network through mobile terminals is rapidly increasing and is accompanied with computational burden transfer to cloud end [1]. Some services such as online translation, voice cloud, and information push service are based on information retrieval in large corpus.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2017

JESTR

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Comprehending user demands through several human-computer interactions can effectively increase informationretrieval accuracy. Mainstream active learning algorithms use uncertainty sampling strategy. However, such algorithms cannot produce satisfactory results under few interactions. To improve interactive information-retrieval efficiency and accuracy, an sampling strategy based on the error-correcting capacity of samples was proposed for active learning. This strategy evaluated the expected value of unlabeled samples by calculating their potential error-correcting capacity associated with the classifier. Based on this sampling strategy, a fast interactive information-retrieval scheme adopting reinforcement learning and low-complexity classifier was designed in this study. The effects of three sampling strategies (random sampling, uncertainty sampling, and the proposed sampling strategy based on error-correcting capacity) on information-retrieval accuracy were examined using an experiment through a text set of Reuters-21578. Experimental results demonstrated that the proposed sampling strategy achieved higher retrieval accuracy and stability than random and uncertainty samplings. The retrieval accuracy of the proposed scheme was approximately 1.6% higher than that of the sampling algorithm based on uncertainty strategy. The proposed scheme can be used for real-time information retrieval because of its low computational complexity. The production of this study can improve the accuracy and latency of interactive information-retrieval services.

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Server load prediction using stream mining

Cited by 7 publications

References 25 publications

CPU-based Prediction with Self Organizing Map in Dynamic Cloud Data Centers

CPU-based Prediction with Self Organizing Map in Dynamic Cloud Data Centers

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

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