Forecasting Cloud Application Workloads With<i>CloudInsight</i>for Predictive Resource Management

Kim, In Kee; Wang, Wei; Qi, Yanjun; Humphrey, Marty

doi:10.1109/tcc.2020.2998017

Cited by 52 publications

(14 citation statements)

References 75 publications

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“…The experiments with traces from cloud data centers and traditional data centers have validated the efectiveness of the proposed approach. Kim et al [32] introduced a cloud prediction framework named CloudInsight that combines multiple predictors based on traditional machine learning techniques to enable accurate predictions for real cloud workloads. The ensemble supports dynamic and periodical optimization to handle the variations of workloads.…”

Section: Learning-based Approaches For Cloud Workload Predictionmentioning

confidence: 99%

esDNN: Deep Neural Network Based Multivariate Workload Prediction in Cloud Computing Environments

Xu¹,

Song²,

Wu³

et al. 2022

ACM Trans. Internet Technol.

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Cloud computing has been regarded as a successful paradigm for IT industry by providing benefits for both service providers and customers. In spite of the advantages, cloud computing also suffers from distinct challenges, and one of them is the inefficient resource provisioning for dynamic workloads. Accurate workload predictions for cloud computing can support efficient resource provisioning and avoid resource wastage. However, due to the high-dimensional and high-variable features of cloud workloads, it is difficult to predict the workloads effectively and accurately. The current dominant work for cloud workload prediction is based on regression approaches or recurrent neural networks, which fail to capture the long-term variance of workloads. To address the challenges and overcome the limitations of existing works, we proposed an e fficient s upervised learning-based D eep N eural N etwork ( esDNN ) approach for cloud workload prediction. Firstly, we utilize a sliding window to convert the multivariate data into supervised learning time series that allow deep learning for processing. Then we apply a revised Gated Recurrent Unit (GRU) to achieve accurate prediction. To show the effectiveness of esDNN, we also conduct comprehensive experiments based on realistic traces derived from Alibaba and Google cloud data centers. The experimental results demonstrate that esDNN can accurately and efficiently predict cloud workloads. Compared with the state-of-the-art baselines, esDNN can reduce the mean square errors significantly, e.g. 15% than the approach using GRU only. We also apply esDNN for machines auto-scaling, which illustrates that esDNN can reduce the number of active hosts efficiently, thus the costs of service providers can be optimized.

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Section: Learning-based Approaches For Cloud Workload Predictionmentioning

confidence: 99%

esDNN: Deep Neural Network Based Multivariate Workload Prediction in Cloud Computing Environments

Xu¹,

Song²,

Wu³

et al. 2022

ACM Trans. Internet Technol.

View full text Add to dashboard Cite

show abstract

“…The experiments with traces from cloud data centers and traditional data centers have validated the effectiveness of the proposed approach. Kim et al [32] introduced a cloud prediction framework named CloudInsight that combines multiple predictors based on traditional machine learning techniques to enable accurate predictions for real cloud workloads. The ensemble supports dynamic and periodical optimization to handle the variations of workloads.…”

Section: Learning-based Approaches For Cloud Workload Predictionmentioning

confidence: 99%

EsDNN: Deep Neural Network based Multivariate Workload Prediction Approach in Cloud Environment

Xu,

Song,

et al. 2022

Preprint

View full text Add to dashboard Cite

Cloud computing has been regarded as a successful paradigm for IT industry by providing benefits for both service providers and customers. In spite of the advantages, cloud computing also suffers from distinct challenges, and one of them is the inefficient resource provisioning for dynamic workloads. Accurate workload predictions for cloud computing can support efficient resource provisioning and avoid resource wastage. However, due to the high-dimensional and high-variable features of cloud workloads, it is difficult to predict the workloads effectively and accurately. The current dominant work for cloud workload prediction is based on regression approaches or recurrent neural networks, which fail to capture the long-term variance of workloads. To address the challenges and overcome the limitations of existing works, we proposed an efficient supervised learning-based Deep N eural N etwork (esDNN ) approach for cloud workload prediction. Firstly, we utilize a sliding window to convert the multivariate data into supervised learning time series that allow deep learning for processing. Then we apply a revised Gated Recurrent Unit (GRU) to achieve accurate prediction. To show the effectiveness of esDNN, we also conduct comprehensive experiments based on realistic traces derived from Alibaba and Google cloud data centers. The experimental results demonstrate that esDNN can accurately and efficiently predict cloud workloads. Compared with the state-of-the-art baselines, esDNN can reduce the mean square errors significantly, e.g. 15% than the approach using GRU only. We also apply esDNN for machines auto-scaling, which illustrates that esDNN can reduce the number of active hosts efficiently, thus the costs of service providers can be optimized. CCS Concepts: • General Literature; • Distributed Parallel and Cluster Computing; • System and Software; • Management of Cloud Computing Systems;

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“…The accurate knowledge of VM startup time is critical for designing effective predictive auto-scaling policies [28], [29], [40]- [43], [62]- [64]. In predictive auto-scaling, new VMs are provisioned in advance to handle the increased workloads before the increased workload arrives.…”

Section: B Use Casesmentioning

confidence: 99%

“…Moreover, implications and findings from this study will help various research in cloud resource and application management. In particular, autoscaling algorithms [28], [29], [39]- [43] with the accurate VM startup time can determine the exact scaling point for handling increased user demands. And, cloud simulators [44]- [48] can generate more reliable simulation results with this study.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Analysis of VM Startup Times in Public IaaS Clouds: An Extended Report

Hao,

Jiang,

Wang

et al. 2021

Preprint

Self Cite

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VM startup time is an essential factor in designing elastic cloud applications. For example, a cloud application with autoscaling can reduce under-and over-provisioning of VM instances with a precise estimation of VM startup time, and in turn, it is likely to guarantee the application's performance and improve the cost efficiency. However, VM startup time has been little studied, and available measurement results performed previously did not consider various configurations of VMs for modern cloud applications.In this work, we perform comprehensive measurements and analysis of VM startup time from two major cloud providers, namely Amazon Web Services (AWS) and Google Cloud Platform (GCP). With three months of measurements, we collected more than 300,000 data points from each provider by applying a set of configurations, including 11+ VM types, four different data center locations, four VM image sizes, two OS types, and two purchase models (e.g., spot/preemptible VMs vs. on-demand VMs). With extensive analysis, we found that VM startup time can vary significantly because of several important factors, such as VM image sizes, data center locations, VM types, and OS types. Moreover, by comparing with previous measurement results, we confirm that cloud providers (specifically AWS) made significant improvements for the VM startup times and currently have much quicker VM startup times than in the past.

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Forecasting Cloud Application Workloads WithCloudInsightfor Predictive Resource Management

Cited by 52 publications

References 75 publications

esDNN: Deep Neural Network Based Multivariate Workload Prediction in Cloud Computing Environments

esDNN: Deep Neural Network Based Multivariate Workload Prediction in Cloud Computing Environments

EsDNN: Deep Neural Network based Multivariate Workload Prediction Approach in Cloud Environment

An Empirical Analysis of VM Startup Times in Public IaaS Clouds: An Extended Report

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