CRUPA: A container resource utilization prediction algorithm for auto-scaling based on time series analysis

Meng, Yunxiao; Rao, Ruonan; Zhang, Xin; Hong, Pei

doi:10.1109/pic.2016.7949546

Cited by 22 publications

(8 citation statements)

References 8 publications

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“…The optimization of the objectives and metrics of ML-based approaches for container orchestration has been investigated and multiple methods have been proposed over the years. To show the evolution and development of In 2016, the ARIMA [68] and nearest neighbor (NN) [18] algorithms were already leveraged for resource utilization prediction of containerized applications. ARIMA is a dynamic stochastic process proposed in the 1970s, which has been used for forecasting time series showing non-stationarity by identifying the seasonal diferences.…”

Section: Evolution Of Machine Learning-based Container Orchestration ...mentioning

confidence: 99%

Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions

Zhong

Rodríguez

et al. 2022

ACM Comput. Surv.

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Containerization is a lightweight application virtualization technology, providing high environmental consistency, operating system distribution portability, and resource isolation. Existing mainstream cloud service providers have prevalently adopted container technologies in their distributed system infrastructures for automated application management. To handle the automation of deployment, maintenance, autoscaling, and networking of containerized applications, container orchestration is proposed as an essential research problem. However, the highly dynamic and diverse feature of cloud workloads and environments considerably raises the complexity of orchestration mechanisms. Machine learning algorithms are accordingly employed by container orchestration systems for behavior modelling and prediction of multi-dimensional performance metrics. Such insights could further improve the quality of resource provisioning decisions in response to the changing workloads under complex environments. In this paper, we present a comprehensive literature review of existing machine learning-based container orchestration approaches. Detailed taxonomies are proposed to classify the current researches by their common features. Moreover, the evolution of machine learning-based container orchestration technologies from the year 2016 to 2021 has been designed based on objectives and metrics. A comparative analysis of the reviewed techniques is conducted according to the proposed taxonomies, with emphasis on their key characteristics. Finally, various open research challenges and potential future directions are highlighted.

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Section: Evolution Of Machine Learning-based Container Orchestration ...mentioning

confidence: 99%

Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions

Zhong

Rodríguez

et al. 2022

ACM Comput. Surv.

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“…In 2016, Yang et al presented a predictive autoscaling algorithm named CRUPA for container resource utilization based on time series analysis [15]. Auto Regressive Integrated Moving Average models are used for predicting CPU usage of containers, and based on predicted values, the same provision algorithm as HPA (1) is used.…”

Section: Proactive Autoscalersmentioning

confidence: 99%

Proactive Autoscaling for Edge Computing Systems with Kubernetes

Ju,

Singh,

Toor

2021

Preprint

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With the emergence of the Internet of Things and 5G technologies, the edge computing paradigm is playing increasingly important roles with better availability, latency-control and performance. However, existing autoscaling tools for edge computing applications do not utilize heterogeneous resources of edge systems efficiently, leaving scope for performance improvement. In this work, we propose a Proactive Pod Autoscaler (PPA) for edge computing applications on Kubernetes. The proposed PPA is able to forecast workloads in advance with multiple user-defined/customized metrics and to scale edge computing applications up and down correspondingly. The PPA is optimized and evaluated on an example CPU-intensive edge computing application further. It can be concluded that the proposed PPA outperforms the default pod autoscaler of Kubernetes on both efficiency of resource utilization and application performance. The article also highlights future possible improvements on the proposed PPA.

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“…Current prediction methods can be categorized into three groups: linear, non-linear, and hybrid methods [13]. The general linear prediction methods include Moving Average (MA) [14], Exponential Smoothing (ES) [15], Autoregressive (AR) [16], Autoregressive Moving Average (ARMA) [17], and ARIMA [18], [19]. These methods are usually applied to prediction in the case of a linear stationary or non-stationary time series.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Short-Term Prediction Algorithm for Resource Demands in Cloud Computing

Chen

Wang

2020

IEEE Access

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Cloud computing has been widely applied in various fields with the development of big data and artificial intelligence. The associated resource demands exhibit characteristics such as diversity, large scale, burst and uncertainty. This paper analyzes these characteristics of cloud resource demands based on Alibaba cluster data, and proposes an adaptive short-term prediction algorithm for those demands. The proposed algorithm uses a principal component analysis method to extract the primary types of container demands from a time series of resource demands, and executes outlier detection and replacement to obtain a more stationary sequence. An adaptive short-term prediction strategy is proposed to adaptively select a higher-accuracy short-term prediction method to implement the prediction. Further, an error adjustment factor is proposed to reduce the prediction error. Thus, the short-term prediction accuracy of cloud resource demands is improved via outlier detection and replacement, an adaptive selection strategy and an error adjustment. We evaluated the effectiveness of these improvements, and compared our algorithm with existing algorithms in terms of effectiveness and time cost. The experimental results demonstrate that the proposed algorithm improves short-term prediction accuracy effectively.

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CRUPA: A container resource utilization prediction algorithm for auto-scaling based on time series analysis

Cited by 22 publications

References 8 publications

Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions

Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions

Proactive Autoscaling for Edge Computing Systems with Kubernetes

An Adaptive Short-Term Prediction Algorithm for Resource Demands in Cloud Computing

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