Predicting resource demand in heterogeneous active networks

Virginie Galtier 1 Kevin Mills 2 Yannick Carlinet 2 Stephen Bush Amit Kulkarni
1 RESEDAS - Software Tools for Telecommunications and Distributed Systems
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Recent research, such as the Active Virtual Network Management Prediction (AVNMP) system, aims to use simulation models running ahead of real time to predict resource demand among network nodes. If accurate, such predictions can be used to allocate network capacity and to estimate quality of service. Future deployment of active-network technology promises to complicate prediction algorithms because each active message can convey its own processing logic, which introduces variable demand for processor (CPU) cycles. This paper describes a means to augment AVNMP, which predicts message load among active-network nodes, with adaptive models that can predict the CPU time required for each active message at any active-network node. Typical CPU models cannot adapt to heterogeneity among nodes. This paper shows improvement in AVNMP performance when adaptive CPU models replace more traditional non-adaptive CPU models. Incorporating adaptive CPU models can enable AVNMP to predict active-network resource usage farther into the future, and lowers prediction overhead.
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Communication dans un congrès
Military Communications Conference - MILCOM 2001, 2001, Vienna, Virginia, USA, 5 p, 2001
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https://hal.inria.fr/inria-00100483
Contributeur : Veronique Pretre <>
Soumis le : mardi 26 septembre 2006 - 14:46:08
Dernière modification le : jeudi 11 janvier 2018 - 06:20:00

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  • HAL Id : inria-00100483, version 1

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Virginie Galtier, Kevin Mills, Yannick Carlinet, Stephen Bush, Amit Kulkarni. Predicting resource demand in heterogeneous active networks. Military Communications Conference - MILCOM 2001, 2001, Vienna, Virginia, USA, 5 p, 2001. 〈inria-00100483〉

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