FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3000018437> ?p ?o ?g. }

• W3000018437 abstract "Application-layer transfer configurations play a crucial role in achieving desirable performance in high-speed networks. However, finding the optimal configuration for a given transfer task is a difficult problem as it depends on various factors including dataset characteristics, network settings, and background traffic. The state-of-the-art transfer tuning solutions rely on real-time sample transfers to evaluate various configurations and estimate the optimal one. However, existing approaches to run sample transfers incur high delay and measurement errors, thus significantly limit the efficiency of the transfer tuning algorithms. In this paper, we introduce adaptive feed forward deep neural network (DNN) to minimize the error rate of sample transfers without increasing their execution time. We ran 115K file transfers in four different high-speed networks and used their logs to train an adaptive DNN that can quickly and accurately predict the throughput of sample transfers by analyzing instantaneous throughput values. The results gathered in various networks with rich set of transfer configurations indicate that the proposed model reduces error rate by up to 50% compared to the state-of-the-art solutions while keeping the execution time low. We also show that one can further reduce delay or error rate by tuning hyperparameters of the model to meet specific needs of user or application. Finally, transfer learning analysis reveals that the model developed in one network would yield accurate results in other networks with similar transfer convergence characteristics, alleviating the needs to run an extensive data collection and model derivation efforts for each network." @default.
• W3000018437 created "2020-01-23" @default.
• W3000018437 creator A5019077099 @default.
• W3000018437 creator A5034710733 @default.
• W3000018437 creator A5065990691 @default.
• W3000018437 date "2019-11-01" @default.
• W3000018437 modified "2023-09-24" @default.
• W3000018437 title "Sample Transfer Optimization with Adaptive Deep Neural Network" @default.
• W3000018437 cites W1995653323 @default.
• W3000018437 cites W1997209582 @default.
• W3000018437 cites W2008480287 @default.
• W3000018437 cites W2013350532 @default.
• W3000018437 cites W2117162727 @default.
• W3000018437 cites W2142096121 @default.
• W3000018437 cites W2523251154 @default.
• W3000018437 cites W2783301772 @default.
• W3000018437 cites W2783595768 @default.
• W3000018437 cites W2806429069 @default.
• W3000018437 cites W2807821571 @default.
• W3000018437 cites W2949126449 @default.
• W3000018437 cites W2954999090 @default.
• W3000018437 doi "https://doi.org/10.1109/indis49552.2019.00013" @default.
• W3000018437 hasPublicationYear "2019" @default.
• W3000018437 type Work @default.
• W3000018437 sameAs 3000018437 @default.
• W3000018437 citedByCount "1" @default.
• W3000018437 countsByYear W30000184372022 @default.
• W3000018437 crossrefType "proceedings-article" @default.
• W3000018437 hasAuthorship W3000018437A5019077099 @default.
• W3000018437 hasAuthorship W3000018437A5034710733 @default.
• W3000018437 hasAuthorship W3000018437A5065990691 @default.
• W3000018437 hasConcept C154945302 @default.
• W3000018437 hasConcept C173608175 @default.
• W3000018437 hasConcept C185592680 @default.
• W3000018437 hasConcept C198531522 @default.
• W3000018437 hasConcept C2776175482 @default.
• W3000018437 hasConcept C41008148 @default.
• W3000018437 hasConcept C43617362 @default.
• W3000018437 hasConcept C50644808 @default.
• W3000018437 hasConceptScore W3000018437C154945302 @default.
• W3000018437 hasConceptScore W3000018437C173608175 @default.
• W3000018437 hasConceptScore W3000018437C185592680 @default.
• W3000018437 hasConceptScore W3000018437C198531522 @default.
• W3000018437 hasConceptScore W3000018437C2776175482 @default.
• W3000018437 hasConceptScore W3000018437C41008148 @default.
• W3000018437 hasConceptScore W3000018437C43617362 @default.
• W3000018437 hasConceptScore W3000018437C50644808 @default.
• W3000018437 hasLocation W30000184371 @default.
• W3000018437 hasOpenAccess W3000018437 @default.
• W3000018437 hasPrimaryLocation W30000184371 @default.
• W3000018437 hasRelatedWork W2010874444 @default.
• W3000018437 hasRelatedWork W2159443810 @default.
• W3000018437 hasRelatedWork W2383503060 @default.
• W3000018437 hasRelatedWork W2386387936 @default.
• W3000018437 hasRelatedWork W2387284662 @default.
• W3000018437 hasRelatedWork W2793851716 @default.
• W3000018437 hasRelatedWork W3001020386 @default.
• W3000018437 hasRelatedWork W3107474891 @default.
• W3000018437 hasRelatedWork W644753246 @default.
• W3000018437 hasRelatedWork W1629725936 @default.
• W3000018437 isParatext "false" @default.
• W3000018437 isRetracted "false" @default.
• W3000018437 magId "3000018437" @default.
• W3000018437 workType "article" @default.