Network traffic prediction (NTP) represents an essential component in planning large-scale networks which are general unpredictable and must adapt to unforeseen circumstances. In small medium-size networks, the administrator can anticipate fluctuations without need of using forecasting tools, but scenario where hundreds new users be added a matter weeks, more efficient tools required avoid cong...

In today’s day and age, a mobile phone has become basic requirement needed for anyone to thrive. With the cellular traffic demand increasing so dramatically, it is now necessary accurately predict user in networks, improve performance terms of resource allocation utilization. Since learning prediction classical appealing field, which still yields many meaningful results, there been an interest ...

Managing the bandwidth allocated to a Label Switched Path in MPLS networks plays a major role for provisioning of Quality of Service and efficient use of resources. In doing so, twomain contrasting factors have to be considered: not only the bandwidth should be adapted to the traffic profile but also the effort for bandwidth renegotiation associated with a variation of the allocated bandwidth s...

Seasonal ARIMA model is a good traffic model capable of capturing the behavior of a network traffic stream. In this paper, we give a general expression of seasonal ARIMA models with two periodicities and provide procedures to model and to predict traffic using seasonal ARIMA models. The experiments conducted in our feasibility study showed that seasonal ARIMA models can be used to model and pre...

Modern Internet routers require powerful forwarding facilities to cope with extremely high rate Forwarding Information Base (FIB) lookups. In general, the FIB is constrained to a small highly efficient but expensive memory. Unfortunately, the BGP route table (RIB) keeps increasing, and this subsequently results in severe FIB inflation at BGP routers. What if we only load a small portion of the ...

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction for unmanned ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach that incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize estimation-theoretic short-term predictions...