Workload Prediction for Cloud Cluster Using a Recurrent Neural Network

Zhang, Weishan; Li, Bo; Zhao, Dehai; Gong, Fengkui; Lu, Qinghua

doi:10.1109/iiki.2016.39

Cited by 33 publications

(16 citation statements)

References 12 publications

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“…However, at lower granularities past six time steps, we find that BPTT is more effective. Zhang et al demonstrated the usefulness of RNN to predict CPU and RAM . As shown through our results and highlighted by Zhang et al, the abilities of RNN to retain information and to create its own representation of the data enable the algorithm to achieve high accuracy of workload predictions on the Google cloud trace data set.…”

Section: Resultssupporting

confidence: 59%

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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Section: Resultssupporting

confidence: 59%

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

et al. 2018

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“…9(b) to (f), the TSA for workload compression is effective under proper settings of top hidden units, which is able to provide an effective feature representation that can greatly reduce the computational complexity of our proposed method for cloud workload prediction. Based on the Google datasets and the preprocessed results of workload compression by using the TSA, we evaluate the proposed L-PAW and other recent RNN-based methods for workload prediction, including recurrent neural network (RNN) [22], long short-term memory (LSTM) [27], gated recurrent unit (GRU) [30], and echo state networks (ESN) [28]. We compare both the prediction accuracy and learning efficiency among these methods, measured by MSE and the average training time, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Over the past few years, RNN has also been used to deal with the problem of workload prediction in cloud computing. Zhang et al [22] proposed an RNN-based model for improving the accuracy of workload prediction. Similarly, the classic RNN architecture was adopted in [23] and [24] to forecast the future workloads in cloud data centers.…”

Section: Rnn-based Approaches 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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“…Cloud resource management forecasts the future workload of each cloud service and allocates the resources to their cloud services based on the expected value. Currently, there are many techniques and methods applied to the prediction of the workload of computer systems, such as the e-learning ensemble approach [12], ARIMAR (Auto Regressive and Moving Average) models [13], Recurrent Neural Networks (RNN) [14], Long and Short Memory Networks (LSTM) [15]. However, deciding on the exact amount of resources with proactive approaches during the execution time of cloud services is a difficult and not insignificant task.…”

Section: Introductionmentioning

confidence: 99%

Fractional Rider Deep Long Short Term Memory Network for Workload Prediction-Based Distributed Resource Allocation Using Spark in Cloud Gaming

Désiré¹,

Francis²,

Kouassi³

et al. 2021

ENG

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The modern development in cloud technologies has turned the idea of cloud gaming into sensible behaviour. The cloud gaming provides an interactive gaming application, which remotely processed in a cloud system, and it streamed the scenes as video series to play through network. Therefore, cloud gaming is a capable approach, which quickly increases the cloud computing platform. Obtaining enhanced user experience in cloud gaming structure is not insignificant task because user anticipates less response delay and high quality videos. To achieve this, cloud providers need to be able to accurately predict irregular player workloads in order to schedule the necessary resources. In this paper, an effective technique, named as Fractional Rider Deep Long Short Term Memory (LSTM) network is developed for workload prediction in cloud gaming. The workload of each resource is computed based on developed Fractional Rider Deep LSTM network. Moreover, resource allocation is performed by fractional Rider-based Harmony Search Algorithm (Rider-based HSA). This Fractional Rider-based HSA is developed by combining Fractional calculus (FC), Rider optimization algorithm (ROA) and Harmony search algorithm (HSA). Moreover, the developed Fractional Rider Deep LSTM is developed by integrating FC and Rider Deep LSTM. In addition, the multi-objective parameters, namely gaming experience loss QE, Mean Opinion Score (MOS), Fairness, energy, network parameters, and predictive load are considered for efficient resource allocation and workload prediction. Additionally, the developed workload prediction model achieved better performance using various parameters, like fairness, MOS, QE, energy and delay.

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Workload Prediction for Cloud Cluster Using a Recurrent Neural Network

Cited by 33 publications

References 12 publications

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

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

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

Fractional Rider Deep Long Short Term Memory Network for Workload Prediction-Based Distributed Resource Allocation Using Spark in Cloud Gaming

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