Automated Detection of Load Changes in Large-Scale Networks

This paper presents a new online algorithm for automated detection of load changes, which provides statistical evidence of stationary changes in traffic load. To this end, we perform continuous measurements of the link load, then look for clusters in the dataset and finally apply the Behrens-Fisher hypothesis testing methodology. The algorithm serves to identify which links deviate from the typical load behavior. The rest of the links are considered normal and no intervention of the network manager is required. Due to the automated selection of abnormal links, the Operations Expenditure (OPEX) is reduced. The algorithm has been applied to a set of links in the Spanish National Research and Education Network (RedIRIS) showing good results.

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