A Case Study on Five Maturity Levels of A Kubernetes Operator

被引:4
作者
Duan, Ruxiao [1 ]
Zhang, Fan [2 ]
Khan, Samee U. [3 ]
机构
[1] Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China
[2] IBM Data & AI, Littleton, MA 01460 USA
[3] Mississippi State Univ, Dept Elect & Comp Engn, Mississippi State, MS 39762 USA
来源
2021 IEEE CLOUD SUMMIT (CLOUD SUMMIT 2021) | 2021年
关键词
Kubernetes; Operator; maturity; auto-scaling;
D O I
10.1109/IEEECloudSummit52029.2021.00008
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Deploying distributed applications using their Operators in a containerized platform on the state-of-art cloud orchestration tooling, such as Kubernetes, has truly become widely accepted. However, the quality of an Operator has a significant impact on a few core metrics of the application, such as its availability, consistency, and quality of service. This paper introduces the Kubernetes Operator maturity model and its five maturity levels, and then gives a demonstration on how a demo Kubernetes Operator is capable of reaching all the five levels respectively by using an example Operator named New Visitors Site Operator. Finally, an experiment illustrating the capability of the example Operator's auto-scaling functions to improve the application performance is presented. This example Operator will enable developers and researchers to design containerized applications with more enhanced features. The code is available at https://github.com/ringdrx/visitors-operator.
引用
收藏
页码:1 / 6
页数:6
相关论文
共 11 条
  • [1] [Anonymous], 2021, HELM CHARTS PROM MYS
  • [2] [Anonymous], 2021, GITHUB BITP OP BULL
  • [3] [Anonymous], 2021, PROM MON SYST TIM SE
  • [4] [Anonymous], 2021, WELC OP FRAM
  • [5] Adaptive scaling of Kubernetes pods
    Balla, David
    Simon, Csaba
    Maliosz, Markosz
    [J]. NOMS 2020 - PROCEEDINGS OF THE 2020 IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM 2020: MANAGEMENT IN THE AGE OF SOFTWARIZATION AND ARTIFICIAL INTELLIGENCE, 2020,
  • [6] Dobies J., 2020, Kubernetes operators: Automating the container orchestration platform
  • [7] Helm, 2021, HELM
  • [8] Intelligent operator: Machine learning based decision support and explainer for human operators and service providers in the fog, cloud and edge networks
    Laskawiec, Sebastian
    Choras, Michal
    Kozik, Rafal
    Varadarajan, Vijayakumar
    [J]. JOURNAL OF INFORMATION SECURITY AND APPLICATIONS, 2021, 56
  • [9] Horizontal Pod Autoscaling in Kubernetes for Elastic Container Orchestration
    Nguyen, Thanh-Tung
    Yeom, Yu-Jin
    Kim, Taehong
    Park, Dae-Heon
    Kim, Sehan
    [J]. SENSORS, 2020, 20 (16) : 1 - 18
  • [10] RedSwitches, 2019, DIFF HOR VS VERT SCA