Improved Q Network Auto-Scaling in Microservice Architecture

Applied Sciences

Jaehyung Park¹  Yeonggwang Kim¹  Jinsul Kim¹  Junchurl Yoon² 

[1] Department of ICT Convergence System Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju 500757, Korea;Team of Energy Platform, Digital Transformation Department, Korea Electric Power Corporation (KEPCO), 55, Jeollyeok-ro, Naju 58322, Korea;

关键词: microservice;    Kubernetes;    auto-scaling;    artificial intelligence;   

DOI  :  10.3390/app12031206

来源: DOAJ

【 摘 要 】

Microservice architecture has emerged as a powerful paradigm for cloud computing due to its high efficiency in infrastructure management as well as its capability of largescale user service. A cloud provider requires flexible resource management to meet the continually changing demands, such as auto-scaling and provisioning. A common approach used in both commercial and open-source computing platforms is workload-based automatic scaling, which expands instances by increasing the number of incoming requests. Concurrency is a request-based policy that has recently been proposed in the evolving microservice framework; in this policy, the algorithm can expand its resources to the maximum number of configured requests to be processed in parallel per instance. However, it has proven difficult to identify the concurrency configuration that provides the best possible service quality, as various factors can affect the throughput and latency based on the workloads and complexity of the infrastructure characteristics. Therefore, this study aimed to investigate the applicability of an artificial intelligence approach to request-based auto-scaling in the microservice framework. Our results showed that the proposed model could learn an effective expansion policy within a limited number of pods, thereby showing an improved performance over the underlying auto expansion configuration.

【 授权许可】

Unknown