Modeling Application Performance in Docker Containers Using Machine Learning Techniques

Ye, Kejiang; Kou, Yanmin; Lu, Chengzhi; Wang, Yang; Xu, Cheng‐Zhong

doi:10.1109/padsw.2018.8644581

Cited by 18 publications

(11 citation statements)

References 8 publications

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“…To be noted, some ML models can be utilized under multiple optimization scenarios. For example, artiicial neural network (ANN) models can be applied for time series analysis of resource utilization or regression of application performance metrics [28,44]. Container orchestration empowers cloud service providers to decide how containerized applications are conigured, deployed, and maintained under cloud computing environments [5].…”

Section: Machine Learningmentioning

confidence: 99%

“…Their training module had demonstrated signiicant accuracy improvement over ARIMA and LSTM models in terms of time series prediction. Ye et al [44] applied a series of traditional regression methods based on the statistical process for relationship estimation, including support vector regression (SVR), linear regression (LR), and modern ANN based on deep learning, to conduct performance analysis of relevant resource metrics. They attempted to evaluate the relationship between resource allocation and application performance.…”

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

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%

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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: Machine Learningmentioning

confidence: 99%

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

confidence: 99%

Section: Application Architecturementioning

confidence: 99%

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

Zhong

Rodríguez

et al. 2022

ACM Comput. Surv.

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“…In [12], autoregressive models for CPU resource demand prediction and allocation were suggested to virtualized servers operating in enterprise datacenters. In [14], linear regression, artificial neural network (ANN) and support vector regression models were used to control the CPU, memory and storage resources of Docker containers for hosting Spark applications. In [15], a random forest (RF) regression model was used to select the best VM instances based on the workload and user goals.…”

Section: Related Workmentioning

confidence: 99%

Intelligent and Agile Control of Edge Resources for Latency-Sensitive IoT Services

Kafle

Muktadir

2020

IEEE Access

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“…In [24], an engine based on a modified k-nearest neighbor algorithm was designed to recommend resource configuration automatically for Hadoop workloads running on container-driven clouds. Reference [25] adopted several machine learning techniques, such as linear regression (LR), support vector machine (SVM) and artificial neural network (ANN), to model the relationship between application performance and the resource parameter configuration for Docker containers, and assessed the accuracy of the established performance model. However, these approaches only aim at a specific application (Spark or Hadoop), and are not suitable for complex application environments.…”

Section: Related Workmentioning

confidence: 99%

Improving Resource Usages of Containers Through Auto-Tuning Container Resource Parameters

Cai

Wei

et al. 2019

IEEE Access

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Recently, the container-based virtualization has gained increasing attention and been widely used in cloud computing. In container products such as Docker, there are a number of parameters that can control container resource usages, to avoid the resource contention occurred when running too many containers concurrently. However, it is difficult to set parameter values accurately only based on experience while tuning the parameters manually is too time-consuming to be impractical. Therefore, it becomes a challenge to set appropriate resource parameter values automatically and quickly to optimize the resource usages of container. In this paper, we present an adaptive tuning framework, conTuner, to optimize the resource configuration of container online for a new application. conTuner contains two components: an optimized configuration pool that offers candidate resource configurations, as well as a configuration optimizer that gets the appropriate optimized configuration from the pool. We have deployed conTuner in a Docker cluster. The experimental results demonstrated that, for a new application, compared to the pre-set upper limit of container resource usages, the container performance is equal or better when using conTuner, and the set resource usage constraint is more accurate. Besides, conTuner can also forecast whether resource contention among multiple containers occurs before running them concurrently. The evaluation results indicate that the prediction accuracy is 87%.

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Modeling Application Performance in Docker Containers Using Machine Learning Techniques

Cited by 18 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

Intelligent and Agile Control of Edge Resources for Latency-Sensitive IoT Services

Improving Resource Usages of Containers Through Auto-Tuning Container Resource Parameters

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