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あぁかがさざただなはばぱまやゃらわゎんいぃきぎしじちぢにひびぴみりうぅくぐすずつづっぬふぶぷむゆゅるえぇけげせぜてでねへべぺめれおぉこごそぞとどのほぼぽもよょろを

アァカサザタダナハバパマヤャラワヮンイィキギシジチヂニヒビピミリウゥクグスズツヅッヌフブプムユュルエェケゲセゼテデヘベペメレオォコゴソゾトドノホボポモヨョロヲ ―

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RISS 인기검색어

Asynchronous Deep Reinforcement Learning based QoS Supporting Scheduler for Kubernetes

한글로보기

https://www.riss.kr/link?id=A107995459

저자

Minh Ngoc Tran ; Young Han Kim
발행기관
한국통신학회
학술지명
한국통신학회 학술대회논문집
권호사항

Vol.2021 No.11 [2021]
발행연도
2021
작성언어
English
자료형태
학술저널
발행기관 URL
http://www.kics.or.kr
수록면

893-895(3쪽)
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다국어 초록 (Multilingual Abstract)

Due to the nature of task scheduling problem in cloud environment is a decision-making problem, Deep Reinforcement Learning has been utilized to achieve adaptive intelligence cloud scheduler. Most of Deep Reinforcement Learning proposed design for cloud scheduler focus on optimizing resources utilization between cloud clusters and nodes. However, in 5G networks, QoS guarantee for tasks is another important requirement where there are large amounts of critical-delay tasks. In this paper, we use apply Deep Reinforcement Learning to build a QoS-guarantee scheduler but with an asynchronous design to improve training time and accuracy of previous Deep Reinforcement Learning system.

번역하기

Due to the nature of task scheduling problem in cloud environment is a decision-making problem, Deep Reinforcement Learning has been utilized to achieve adaptive intelligence cloud scheduler. Most of Deep Reinforcement Learning proposed design for clo...

Due to the nature of task scheduling problem in cloud environment is a decision-making problem, Deep Reinforcement Learning has been utilized to achieve adaptive intelligence cloud scheduler. Most of Deep Reinforcement Learning proposed design for cloud scheduler focus on optimizing resources utilization between cloud clusters and nodes. However, in 5G networks, QoS guarantee for tasks is another important requirement where there are large amounts of critical-delay tasks. In this paper, we use apply Deep Reinforcement Learning to build a QoS-guarantee scheduler but with an asynchronous design to improve training time and accuracy of previous Deep Reinforcement Learning system.