I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
A Deep Reinforcement Learning Approach for Deploying SDN Sw..:
, In:
2022 IEEE 23rd International Conference on High Performance Switching and Routing (HPSR)
,
Guo, Yingya
;
Chen, Jianshan
;
Huang, Kai
. - p. 195-200 , 2022
Link:
https://doi.org/10.1109/HPSR54439.2022.9831203
RT T1
2022 IEEE 23rd International Conference on High Performance Switching and Routing (HPSR)
: T1
A Deep Reinforcement Learning Approach for Deploying SDN Switches in ISP Networks from the Perspective of Traffic Engineering
UL https://suche.suub.uni-bremen.de/peid=ieee-9831203&Exemplar=1&LAN=DE A1 Guo, Yingya A1 Chen, Jianshan A1 Huang, Kai A1 Wu, Jianping YR 2022 SN 2325-5609 K1 Adaptive systems K1 Heuristic algorithms K1 Software algorithms K1 Clustering algorithms K1 Reinforcement learning K1 Switches K1 Routing K1 Hybrid Software Defined Networks K1 SDN Switches Deployment Strategy K1 Deep Reinforcement Learning K1 Traffic Engineering SP 195 OP 200 LK http://dx.doi.org/https://doi.org/10.1109/HPSR54439.2022.9831203 DO https://doi.org/10.1109/HPSR54439.2022.9831203 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)