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Design, implementation, and evaluation of learning algorithms for dynamic real‐time network monitoring
Author(s) -
Mijumbi Rashid,
Asthana Abhaya,
Koivunen Markku,
Haiyong Fu,
Zhu Qinjun
Publication year - 2020
Publication title -
international journal of network management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.373
H-Index - 28
eISSN - 1099-1190
pISSN - 1055-7148
DOI - 10.1002/nem.2108
Subject(s) - computer science , baseline (sea) , metric (unit) , network monitoring , reliability (semiconductor) , adaptation (eye) , context (archaeology) , set (abstract data type) , data mining , software deployment , real time computing , machine learning , computer network , software engineering , paleontology , power (physics) , operations management , oceanography , physics , quantum mechanics , optics , economics , biology , programming language , geology
Network monitoring is necessary so as to ensure high reliability and availability in telecom networks. One of the main challenges posed by state‐of‐the‐art monitoring tools is the creation of network baselines. Such baselines include thresholds that can be used to determine whether monitored values (with a given context, e.g., time) represent normal network operation or not. The size and complexity of current (and future) networks make it infeasible to manually determine and set baselines for each network operator and metric, let alone adapting the thresholds to changes in network conditions. This leads to the use of default baselines and/or setting baselines only once and never changing them throughout the lifetime of network elements. This does not only cause inefficient operation but could have implications for network reliability and availability. In this paper, we present the design, implementation, and evaluation of DARN: a collection of analytics and machine learning‐based algorithms aimed at ensuring that network baselines are automatically adapted to different metric evolution. DARN has been comprehensively evaluated on a deployment with real traffic to confirm accuracy of generated baselines, a 22% improvement in accuracy due to baseline adaptation and a 72% reduction in false alarms.