Matches in SemOpenAlex for { <https://semopenalex.org/work/W4300658574> ?p ?o ?g. }
- W4300658574 endingPage "829" @default.
- W4300658574 startingPage "815" @default.
- W4300658574 abstract "Traffic optimization and smart buffering are fundamental to achieve both great application performance and resource efficiency in data centers with heterogeneous workloads, including incast and elephant traffics. However, general performance models providing insights on how various factors affect traffic performance metrics needed by these management functions are missing. For the special case of incast, the existing models are analytical ones, either tightly coupled with a particular protocol version or specific to certain empirical data. Motivated by this observation, this paper proposes an SDN-enabled machine-learning-based performance modeling approach in data center networks that leverages random forest predictions. Evaluations based on datasets constructed through intensive NS-3 simulations show that we can achieve accurate predictions of incast and elephant performance metrics based on various features. With this performance modeling capability, smart buffering schemes or traffic optimization algorithms could anticipate and efficiently optimize system parameters adjustment to achieve optimal performance continuously in data centers." @default.
- W4300658574 created "2022-10-03" @default.
- W4300658574 creator A5019583021 @default.
- W4300658574 creator A5049604013 @default.
- W4300658574 creator A5052629636 @default.
- W4300658574 creator A5065018438 @default.
- W4300658574 creator A5091191810 @default.
- W4300658574 date "2023-03-01" @default.
- W4300658574 modified "2023-09-30" @default.
- W4300658574 title "ML-Based Performance Modeling in SDN-Enabled Data Center Networks" @default.
- W4300658574 cites W1534924226 @default.
- W4300658574 cites W2002303652 @default.
- W4300658574 cites W2027857686 @default.
- W4300658574 cites W2065940985 @default.
- W4300658574 cites W2077107182 @default.
- W4300658574 cites W2100092760 @default.
- W4300658574 cites W2110722699 @default.
- W4300658574 cites W2132353061 @default.
- W4300658574 cites W2147118406 @default.
- W4300658574 cites W2163310590 @default.
- W4300658574 cites W2163404313 @default.
- W4300658574 cites W2164878629 @default.
- W4300658574 cites W2192203593 @default.
- W4300658574 cites W2291150993 @default.
- W4300658574 cites W2293097037 @default.
- W4300658574 cites W2367397349 @default.
- W4300658574 cites W2381381716 @default.
- W4300658574 cites W2472192521 @default.
- W4300658574 cites W2516809705 @default.
- W4300658574 cites W2539125154 @default.
- W4300658574 cites W2736475007 @default.
- W4300658574 cites W2742215856 @default.
- W4300658574 cites W2744365997 @default.
- W4300658574 cites W2744387122 @default.
- W4300658574 cites W2751589611 @default.
- W4300658574 cites W2759910885 @default.
- W4300658574 cites W2789339130 @default.
- W4300658574 cites W2883910734 @default.
- W4300658574 cites W2913856657 @default.
- W4300658574 cites W2915905517 @default.
- W4300658574 cites W2917789045 @default.
- W4300658574 cites W2945295328 @default.
- W4300658574 cites W2945976633 @default.
- W4300658574 cites W2962772482 @default.
- W4300658574 cites W2962790223 @default.
- W4300658574 cites W2962858109 @default.
- W4300658574 cites W2963779067 @default.
- W4300658574 cites W2987549193 @default.
- W4300658574 cites W3005086430 @default.
- W4300658574 cites W3009333951 @default.
- W4300658574 cites W3011611247 @default.
- W4300658574 cites W3016712945 @default.
- W4300658574 cites W3044790590 @default.
- W4300658574 cites W3046330538 @default.
- W4300658574 cites W3046470751 @default.
- W4300658574 cites W3049627495 @default.
- W4300658574 cites W3088665058 @default.
- W4300658574 cites W3096663727 @default.
- W4300658574 cites W3106379731 @default.
- W4300658574 cites W3138819813 @default.
- W4300658574 cites W3147701132 @default.
- W4300658574 cites W3197818567 @default.
- W4300658574 cites W4232284301 @default.
- W4300658574 cites W4235670058 @default.
- W4300658574 cites W4239666938 @default.
- W4300658574 cites W4297957988 @default.
- W4300658574 doi "https://doi.org/10.1109/tnsm.2022.3197789" @default.
- W4300658574 hasPublicationYear "2023" @default.
- W4300658574 type Work @default.
- W4300658574 citedByCount "1" @default.
- W4300658574 countsByYear W43006585742023 @default.
- W4300658574 crossrefType "journal-article" @default.
- W4300658574 hasAuthorship W4300658574A5019583021 @default.
- W4300658574 hasAuthorship W4300658574A5049604013 @default.
- W4300658574 hasAuthorship W4300658574A5052629636 @default.
- W4300658574 hasAuthorship W4300658574A5065018438 @default.
- W4300658574 hasAuthorship W4300658574A5091191810 @default.
- W4300658574 hasConcept C120314980 @default.
- W4300658574 hasConcept C142724271 @default.
- W4300658574 hasConcept C153740404 @default.
- W4300658574 hasConcept C162324750 @default.
- W4300658574 hasConcept C203274722 @default.
- W4300658574 hasConcept C204787440 @default.
- W4300658574 hasConcept C21547014 @default.
- W4300658574 hasConcept C2778915421 @default.
- W4300658574 hasConcept C2780385302 @default.
- W4300658574 hasConcept C2780609101 @default.
- W4300658574 hasConcept C31258907 @default.
- W4300658574 hasConcept C41008148 @default.
- W4300658574 hasConcept C71924100 @default.
- W4300658574 hasConceptScore W4300658574C120314980 @default.
- W4300658574 hasConceptScore W4300658574C142724271 @default.
- W4300658574 hasConceptScore W4300658574C153740404 @default.
- W4300658574 hasConceptScore W4300658574C162324750 @default.
- W4300658574 hasConceptScore W4300658574C203274722 @default.
- W4300658574 hasConceptScore W4300658574C204787440 @default.
- W4300658574 hasConceptScore W4300658574C21547014 @default.
- W4300658574 hasConceptScore W4300658574C2778915421 @default.