Learning based utility maximization for multi-resource management = 학습 기반 네트워크 만족도 최대화를 통한 멀티 리소스 관리

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The future mobile infrastructure is expected to become a multi-resource environment where various re- sources participate in user's services. Moreover, as users' needs become diversi ed, network manager requires algorithms to allocate resources eciently and fairly. However managing multi-resource envi- ronment is challenge due to time varying nature and complex correlation among service requirement and existing algorithm have shown poor performance in metric like queuing delay. In this paper, we aim to overcome these limitations through learning based algorithm. We modeled the multi-resource manage- ment problem in multi-resource environment with computing/networking resources as utility maximiza- tion problem, and reformulate the problem to appropriate form to apply the reinforcement learning. As a result, the algorithm we proposed can achieve utility optimal without any trade-o parameter and does not cause high queuing delay unlike existing algorithms.
Advisors
Song, Chongresearcher정송researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2018.2,[iii, 22 p. :]

Keywords

Network function visualization▼anetwork resource management▼amulti-resource infrastructures▼areinforcement learning; 네트워크 기능 가상화▼a네트워크 자원 관리▼a다중 자원 인프라▼a강화 학습

URI
http://hdl.handle.net/10203/266920
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=734039&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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