CPU load prediction for cloud environment based on a dynamic ensemble model

Cao, Jian; Fu, Jiwen; Li, Minglu; Chen, Jinjun

doi:10.1002/spe.2231

Cited by 47 publications

(31 citation statements)

References 32 publications

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“…Liang et al [10] propose a kNN-based approach to predict long-term CPU workloads. Cao et al [29] use ensemble learning to combine the result of several algorithms and dynamically adjust the parameters with the prediction residual. Singh et al [30] combine ARIMA and support vector regression model (SVR) to adapt to different workload features.…”

Section: Machine Learning Method-based Workload Predictionsmentioning

confidence: 99%

A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment

Zhu

Zhang

Chen

et al. 2019

J Wireless Com Network

108

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Server workload in the form of cloud-end clusters is a key factor in server maintenance and task scheduling. How to balance and optimize hardware resources and computation resources should thus receive more attention. However, we have observed that the disordered execution of running application and batching seriously cuts down the efficiency of the server. To improve the workload prediction accuracy, this paper proposes an approach using the long short-term memory (LSTM) encoder-decoder network with attention mechanism. First, the approach extracts the sequential and contextual features of the historical workload data through the encoder network. Second, the model integrates the attention mechanism into the decoder network, through which the prediction for batch workloads can be carried out. Third, experiments carried out on Alibaba and Dinda workload traces dataset demonstrate that our method achieves state-of-the-art performance in mixed workload prediction in cloud computing environment. Furthermore, we also propose a scroll prediction method, which splits a long prediction sequence into several small sequences to monitor and control prediction accuracy. This work helps to dynamically guide the configuration for workload balancing.

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Section: Machine Learning Method-based Workload Predictionsmentioning

confidence: 99%

A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment

Zhu

Zhang

Chen

et al. 2019

J Wireless Com Network

108

View full text Add to dashboard Cite

show abstract

“…They examined their strategy with four different ML algorithms with a large load trace to reduce the prediction errors, which are between 22% and 86% less than those incurred by four previous methods. Cao et al applied a novel ensemble model for online CPU load predictions in the cloud environment . The main focus of their model was the multiple predictor set, whose predictor members can be dynamically adjusted.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers

et al. 2018

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One of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime-ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center.

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“…Unlike [25], the main workhorse in the case of the technique in [53] is extreme learning machines, which are feedforward neural networks. An ensemble model [46] is presented in [9] targeted at predicting cpu usage in cloud environments. It relies on multiple traditional models, and the final prediction is obtained by combing these models via a scoring algorithm.…”

Section: Previous Workmentioning

confidence: 99%

“…In addition, note that the predictions delivered by the techniques proposed in [9,25,53] are coarse. They treat virtual-machine requests or computational resources as a fluid and predict the level that this fluid will attain at the next moment in time.…”

Section: Previous Workmentioning

confidence: 99%

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System-Level Analysis and Design under Uncertainty

Ukhov¹

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One major problem for the designer of electronic systems is the presence of uncertainty, which is due to phenomena such as process and workload variation. Very often, uncertainty is inherent and inevitable. If ignored, it can lead to degradation of the quality of service in the best case and to severe faults or burnt silicon in the worst case. Thus, it is crucial to analyze uncertainty and to mitigate its damaging consequences by designing electronic systems in such a way that uncertainty is effectively and efficiently taken into account.We begin by considering techniques for deterministic system-level analysis and design of certain aspects of electronic systems. These techniques do not take uncertainty into account, but they serve as a solid foundation for those that do. Our attention revolves primarily around power and temperature, as they are of central importance for attaining robustness and energy efficiency. We develop a novel approach to dynamic steady-state temperature analysis of electronic systems and apply it in the context of reliability optimization.We then proceed to develop techniques that address uncertainty. The first technique is designed to quantify the variability in process parameters, which is induced by process variation, across silicon wafers based on indirect and potentially incomplete and noisy measurements. The second technique is designed to study diverse system-level characteristics with respect to the variability originating from process variation. In particular, it allows for analyzing transient temperature profiles as well as dynamic steady-state temperature profiles of electronic systems. This is illustrated by considering a problem of design-space exploration with probabilistic constraints related to reliability. The third technique that we develop is designed to efficiently tackle the case of sources of uncertainty that are less regular than process variation, such as workload variation. This technique is exemplified by analyzing the effect that workload units with uncertain processing times have on the timing-, power-, and temperature-related characteristics of the system under consideration.We also address the issue of runtime management of electronic systems that are subject to uncertainty. In this context, we perform an early investigation into the utility of advanced prediction techniques for the purpose of finegrained long-range forecasting of resource usage in large computer systems.All the proposed techniques are assessed by extensive experimental evaluations, which demonstrate the superior performance of our approaches to analysis and design of electronic systems compared to existing techniques. The research presented in this thesis has been partially funded by the National Computer Science Graduate School (cugs) in Sweden.v Sammanfattning Ett stort problem för designern inom elektroniska system är förekomsten av osäkerhet, som beror på sådana fenomen som variationer relaterade till tillverkning och arbetsbelastning. Osäkerhet är i många fall naturlig och oundv...

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CPU load prediction for cloud environment based on a dynamic ensemble model

Cited by 47 publications

References 32 publications

A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment

A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment

A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers

System-Level Analysis and Design under Uncertainty

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