A structure-preserving method of sampling self-similar traffic

This paper presents a new structure-preserving method of sampling self-similar traffic with direct applications to network monitoring and resource provisioning. Predicting the bandwidth required by upcoming traffic plays a key role for providing an efficient and intelligent resource provisioning, especially in the context of IP over WDM. To achieve this, we are proposing a periodic sampling method (called maximum-based sampling) that picks one measurement during a sampling interval of size T. Mathematical analysis and simulation results demonstrate that the proposed maximum-based sampling method preserves the self-similarity property of the original traffic over many time scales. The LMMSE (linear minimum mean square error) prediction method is used for traffic forecast. The numerical results show that the accuracy of traffic prediction performed on the proposed sampled process remains stable for different sampling interval size T.