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홈 홈 > 연구문헌 > 학술대회 프로시딩 > 대한전자공학회 학술대회 > 2021년 대한전자공학회 하계종합학술대회

2021년 대한전자공학회 하계종합학술대회

Current Result Document : 516 / 761 이전건 이전건   다음건 다음건

한글제목(Korean Title) An Auto-Scaling Architecture for Container Clusters Using Deep Learning
영문제목(English Title) An Auto-Scaling Architecture for Container Clusters Using Deep Learning
저자(Author) Isomiddin Abdunabiev   Choonhwa Lee   Muhammad Hanif  
원문수록처(Citation) VOL 44 NO. 01 PP. 1660 ~ 1663 (2021. 06)
한글내용
(Korean Abstract)
영문내용
(English Abstract)
In the past decade, cloud computing has become one of the essential techniques of many business areas, including social media, online shopping, music streaming, and many more. It is difficult for cloud providers to provision their systems in advance due to fluctuating changes in input workload and resultant resource demand. Therefore, there is a need for auto-scaling technology that can dynamically adjust resource allocation of cloud services based on incoming workload. In this paper, we present a predictive auto-scaler for Kubernetes environments to improve the quality of service. Being based on a proactive model, our proposed auto-scaling method serves as a foundation on which to build scalable and resource-efficient cloud systems.
키워드(Keyword) Micro-services   Container   Autoscaling   LSTM   RNN   Horizontal Pod Auto-Scaling (HPA)  
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