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In this paper, we present a Failure Prediction System (FPS) using a novel algorithm that extracts frequent anomalous behaviors based on multi-scale trend analysis of multiple network parameters. The proposed Correlation Analysis Across Parameters algorithm (CAAP) utilizes multiple levels of timescale analysis to reveal the frequent anomalous behaviors. The CAAP philosophy is that failures usually...
In this paper, we present a novel algorithm that extracts frequent anomalous behaviors based on multi-scale trend analysis of individual network parameters. The proposed Frequent Anomalous Behavior Mining (FABM) algorithm utilizes multiple levels of time-scale analysis to reveal the frequent anomalous behaviors. This makes the proposed algorithm robust to unreliable, redundant, incomplete and contradictory...
The telecommunication network has a large scale and an intense complexity. Agents distributed over diverse network elements have collected an immense number of KPI data, the key indicators of network performance. These time series data can have mutual impact. This paper puts forward an improved algorithm named AFP-Growth to mine association rules of inter-transaction time series in the telecommunication...
In large telecommunication network management system, substantial data containing the information of network traffic, network element status, device running situation and all other messages are continuously sent from each special network management system to the integrated network management system. This kind of data is typically the stream data. Current network management system employs traditional...
Knowing the traffic matrix, i.e., packet/byte counts between pairs of nodes in a network, is important for network management. The main challenges for accurate traffic matrix estimation in a high speed network are the computation and memory limitations. In this paper, we propose a novel algorithm for traffic matrix estimation that can yield accurate estimates whereas uses small memory and per packet...
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