Real-Time Prediction of Docker Container Resource Load Based on a Hybrid Model of ARIMA and Triple Exponential Smoothing

Xie, Yulai; Jin, Minpeng; Zou, Zhuping; Xu, Guoying; Feng, Dan; Liu, Wenmao; Long, Darrell

doi:10.1109/tcc.2020.2989631

“…However, it is constrained to limited workload scenarios. To enhance the ARIMA model in the analysis of the nonlinear relationship and trend turning points within data sequences, Xie et al [79] apply ARIMA with triple exponential smoothing (ARIMA-TES) in the prediction of container workload resource usage with multi-dimension, which can achieve high robustness and accuracy but bring higher time overhead compared with ARIMA.…”

Section: Workload Characterizationmentioning

confidence: 99%

“…As the workload scenarios of these applications are relatively simple, their core drive of workload/behavior modelling is to predict their resource demands under certain QoS requirements. Assisted with such prediction results, heuristic and RL methods (both model-free and model-based) are adopted for optimization of the resource allocation process regarding cost, energy, and resource eiciency [8,16,28,44,63,79,80].…”

Section: Application Architecturementioning

confidence: 99%

“…As the internal structures of containerized applications like microservices are becoming rather complex and dynamic with continuously growing demands of higher modelling/prediction accuracy and lower response time, this requires ML models to be more robust and eicient with even lower error rates, computation costs, and data training time. Therefore, most studies have been investigating hybrid ML models composed of a mixture of multiple ML algorithms to solve one or more orchestration problems from 2019 [31,65,72,79,80,82,94].…”

Section: Infrastructurementioning

confidence: 99%

See 1 more Smart Citation

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

Zhong

¹

,

Xu

²

,

Rodríguez

³

et al. 2022

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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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“…Zhang et al [15] introduce the Time Series Nearest Neighbor Regression (TSNNR) algorithm for prediction of future workload resource requirements by matching the recent time series data trend to similar historical data. To enhance the ARIMA model in analysis of nonlinear relationship and trend turning points within data sequences, Xie et al [75] apply ARIMA with triple exponential smoothing (ARIMA-TES) in the prediction of container workload resource usage.…”

Section: Workload Characterizationmentioning

confidence: 99%

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

Zhong¹,

Xu²,

Rodríguez³

et al. 2021

Preprint

1

0

View full text Add to dashboard Cite

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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“…The performance of the artificial intelligence algorithm is overwhelming compared with other algorithms. Due to the high performance of artificial intelligence algorithms, convolutional neural networks and recurrent neural networks are applied to other problem tasks, such as computer vision and time–series data processing [ 1 , 2 , 3 , 4 ]. Recently, the sophisticated results of artificial intelligence algorithms such as generative pretrained transformer (GPT), DeepFake, and Deep Voice have had a high social impact to the extent that problems of ethics arise [ 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

ASimOV: A Framework for Simulation and Optimization of an Embedded AI Accelerator

Hwang

¹

,

Oh

²

,

Lee

³

2021

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Artificial intelligence algorithms need an external computing device such as a graphics processing unit (GPU) due to computational complexity. For running artificial intelligence algorithms in an embedded device, many studies proposed light-weighted artificial intelligence algorithms and artificial intelligence accelerators. In this paper, we propose the ASimOV framework, which optimizes artificial intelligence algorithms and generates Verilog hardware description language (HDL) code for executing intelligence algorithms in field programmable gate array (FPGA). To verify ASimOV, we explore the performance space of k-NN algorithms and generate Verilog HDL code to demonstrate the k-NN accelerator in FPGA. Our contribution is to provide the artificial intelligence algorithm as an end-to-end pipeline and ensure that it is optimized to a specific dataset through simulation, and an artificial intelligence accelerator is generated in the end.

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Real-Time Prediction of Docker Container Resource Load Based on a Hybrid Model of ARIMA and Triple Exponential Smoothing

Cited by 51 publications

References 20 publications

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

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

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

ASimOV: A Framework for Simulation and Optimization of an Embedded AI Accelerator

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