DNA: An SDN framework for distributed network analytics

Clemm, Alexander; Chandramouli, Mouli; Krishnamurthy, Sailesh

doi:10.1109/inm.2015.7140271

Cited by 15 publications

(5 citation statements)

References 14 publications

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“…Besides the previously cited [94], [111], Apiletti et al in [4] developed SeLINA, a network analyzer that offers human-readable models of traffic data, combining unsupervised and supervised approaches for traffic inspection. A specific framework for distributed network analytics that operates using Netflow and IPFIX flows is presented in [27]. Here, SDN controllers are used for the processing to improve scalability and analytics quality by dynamically adjusting traffic record generation.…”

Section: E the Rare Cases Of Online Analysismentioning

confidence: 99%

A Survey on Big Data for Network Traffic Monitoring and Analysis

D’Alconzo

Drago

Morichetta

et al. 2019

IEEE Trans. Netw. Serv. Manage.

127

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Network Traffic Monitoring and Analysis (NTMA) represents a key component for network management, especially to guarantee the correct operation of large-scale networks such as the Internet. As the complexity of Internet services and the volume of traffic continue to increase, it becomes difficult to design scalable NTMA applications. Applications such as traffic classification and policing require real-time and scalable approaches. Anomaly detection and security mechanisms require to quickly identify and react to unpredictable events while processing millions of heterogeneous events. At last, the system has to collect, store, and process massive sets of historical data for post-mortem analysis. Those are precisely the challenges faced by general big data approaches: Volume, Velocity, Variety, and Veracity. This survey brings together NTMA and big data. We catalog previous work on NTMA that adopt big data approaches to understand to what extent the potential of big data is being explored in NTMA. This survey mainly focuses on approaches and technologies to manage the big NTMA data, additionally briefly discussing big data analytics (e.g., machine learning) for the sake of NTMA. Finally, we provide guidelines for future work, discussing lessons learned, and research directions.

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Section: E the Rare Cases Of Online Analysismentioning

confidence: 99%

A Survey on Big Data for Network Traffic Monitoring and Analysis

D’Alconzo

Drago

Morichetta

et al. 2019

IEEE Trans. Netw. Serv. Manage.

127

View full text Add to dashboard Cite

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“…Moreover, all these applications still require someone to set the network baselines and to manually adapt them. Clemm et al 15 propose enhancing monitoring approaches by distributing the analytics of collected network data such that the processing of the data is performed at the network elements using specialized agents. Furthermore, the authors propose the use of an SDN controller to orchestrate the analytics across the network to enable network‐wide interactions.…”

Section: Related Workmentioning

confidence: 99%

Design, implementation, and evaluation of learning algorithms for dynamic real‐time network monitoring

et al. 2020

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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.

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“…Because in SDN, managers can effectively and timely collect statistical data of traffic flows [12,13], some researchers put forward the concept of Knowledge-Defined Networking (KDN) [14] based on SDN and Network Analytics (NA) [15]. As shown in Fig.…”

Section: Introductionmentioning

confidence: 99%