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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