Predicting host CPU utilization in the cloud using evolutionary neural networks

Mason, Karl; Donoval, Martin; Barrett, Enda; Duggan, Jim; Howley, Enda

doi:10.1016/j.future.2018.03.040

Cited by 89 publications

(56 citation statements)

References 41 publications

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“…But k-NN is inefficient, which usually leads to huge computational overheads. Swarm and evolutionary optimization algorithms are applied to train the neural networks for predicting the host utilization [20], but this approach might suffer from the difficulty of selecting parameters (e.g., mutation and crossover rates). Kaur et al [21] developed an ensemble-based prediction method for CPU usage of scientific applications, which takes the average accuracy of eight regression-based prediction models into the consideration of final prediction results.…”

Section: Classic Methods For Workload Predictionmentioning

confidence: 99%

Towards Accurate Prediction for High-Dimensional and Highly-Variable Cloud Workloads with Deep Learning

Chen

Min

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

134

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Resource provisioning for cloud computing necessitates the adaptive and accurate prediction of cloud workloads. However, the existing methods cannot effectively predict the high-dimensional and highly-variable cloud workloads. This results in resource wasting and inability to satisfy service level agreements (SLAs). Since recurrent neural network (RNN) is naturally suitable for sequential data analysis, it has been recently used to tackle the problem of workload prediction. However, RNN often performs poorly on learning longterm memory dependencies, and thus cannot make the accurate prediction of workloads. To address these important challenges, we propose a deep Learning based Prediction Algorithm for cloud Workloads (L-PAW). First, a top-sparse auto-encoder (TSA) is designed to effectively extract the essential representations of workloads from the original high-dimensional workload data. Next, we integrate TSA and gated recurrent unit (GRU) block into RNN to achieve the adaptive and accurate prediction for highly-variable workloads. Using realworld workload traces from Google and Alibaba cloud data centers and the DUX-based cluster, extensive experiments are conducted to demonstrate the effectiveness and adaptability of the L-PAW for different types of workloads with various prediction lengths. Moreover, the performance results show that the L-PAW achieves superior prediction accuracy compared to the classic RNN-based and other workload prediction methods for high-dimensional and highly-variable real-world cloud workloads.

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Section: Classic Methods For Workload Predictionmentioning

confidence: 99%

Towards Accurate Prediction for High-Dimensional and Highly-Variable Cloud Workloads with Deep Learning

Chen

Min

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

134

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“…Many studies have been conducted on various predictions in cloud computing. 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.…”

Section: Related Workmentioning

confidence: 99%

“…Dabbagh et al [13] proposed a prediction approach that uses Wiener filters to predict the future resource utilization of VMs. Mason et al [15] predicted host CPU utilization for a short time using evolutionary neural networks, which showed a high prediction accuracy and a high degree of generality. In this paper, we focus on host utilization prediction using EEMD and ARIMA methods to not only improve prediction accuracy but also reduce prediction time as much as possible.…”

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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“…However, such methods often lend themselves to less efficient resource utilization and redundant decisions in the data center. Application workloads often exhibit time‐varying demand patterns; failing to consider future demand can quickly result in redundant migration decisions, having a negative impact on energy and performance . By estimating future resource demand, resources utilization can be more efficiently planned, allowing providers to determine when a host will become overutilized while also enabling data to be transferred at more optimal times to improve the overall QoS provided.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

“…Their approach improves short‐term prediction by reducing the MSE by 76% and 61% and long‐term predictions by 83% and 67% for both CPU and RAM. Mason et al implemented a number of state‐of‐the‐art swarm and evolutionary optimization algorithms of a neural network to predict CPU usage, such as particle swarm optimization, differential evolution, and covariance matrix adaptation evolutionary strategy. Their results show that the covariance matrix adaptation evolutionary strategy‐trained neural network out performs both differential evolution and particle swarm optimization, but most importantly, all swarm and evolutionary‐trained neural network outperform traditional approaches such as linear regression (LR) and MA.…”

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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Predicting host CPU utilization in the cloud using evolutionary neural networks

Cited by 89 publications

References 41 publications

Towards Accurate Prediction for High-Dimensional and Highly-Variable Cloud Workloads with Deep Learning

Towards Accurate Prediction for High-Dimensional and Highly-Variable Cloud Workloads with Deep Learning

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

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

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