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E.Kalishenko, K.Krinkin, S.P.Shiva Prakash. Process Mining Approach for Traffic Analysis in Wireless Mesh Networks
 

E.Kalishenko, K.Krinkin, S.P.Shiva Prakash. Process Mining Approach for Traffic Analysis in Wireless Mesh Networks

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Abstract. Short-time traffic flow prediction in particular systems will expedite discovering of an optimal path for packet transmitting in dynamic wireless networks. The main goal is to predict ...

Abstract. Short-time traffic flow prediction in particular systems will expedite discovering of an optimal path for packet transmitting in dynamic wireless networks. The main goal is to predict traffic overload while changing a network topology. Machine learning techniques and process mining can help analyze traffic produced by several moving nodes. Several related approaches are observed. Research framework structure is presented. The idea of process mining approach is proposed.

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    E.Kalishenko, K.Krinkin, S.P.Shiva Prakash. Process Mining Approach for Traffic Analysis in Wireless Mesh Networks E.Kalishenko, K.Krinkin, S.P.Shiva Prakash. Process Mining Approach for Traffic Analysis in Wireless Mesh Networks Presentation Transcript

    • Process Mining Approach for TrafficAnalysis in Wireless Mesh Networks E.Kalishenko, K.Krinkin, S.P.Shiva Prakash NEW2AN 27-29.08.12
    • Research goalsLong-term Develop and implement effective traffic prediction methods and topology suggesting algorithm for WMN;Short-term Create a framework for traffic analysis in WMS based on process mining; Investigate and identify real-life traffic patterns; Implement topology suggesting scheme; Enable power awareness in routing. NEW2AN 27-29.08.12 2
    • Problem on the picture NEW2AN 27-29.08.12 3
    • Existing approaches • Wavelet neural networks • Clustering Approach • Graph Mining • Time series analysis NEW2AN 27-29.08.12 4
    • Time series analysis widely used for WMN analysis input sequences like – bytes per time period – drops per time period statistical processing and prediction NEW2AN 27-29.08.12 5
    • Wavelet-neural networks network as a three-layer structure – P inputs for P values of time series; – N neurons on hidden layer; – one output neuron which presents predisction for time series; Scheme allows predict next parameter value by fixed history (time series length). NEW2AN 27-29.08.12 6
    • Clustering Clustering is based on threshold value for cells in cellular networks; Euclids distance is used as a metric; Network topology depends on geographical positions leaders of clusters. NEW2AN 27-29.08.12 7
    • Limitations Wavelet neural networks: – not efficient if lack of representative traffic statistics; Clustering: – works only for fixed topologies, doesnt work for WMN; Graph mining: – Resource intensive, mainly dedicated for dynamic topologies; Time series: – takes into account only internal traffic, mostly with periodic nature NEW2AN 27-29.08.12 8
    • Process mining in two words NEW2AN 27-29.08.12 9
    • Process mining for WMN NEW2AN 27-29.08.12 10
    • ProM – process analysis tool Control-flow Discovery Organizational Mining Conformance Analysis / Process Model Evaluation Performance Analysis Simulation Process redesign Semantic Process Mining Analysis / Verification see: processmining.org NEW2AN 27-29.08.12 11
    • Basic steps Network modeling (NS-3) – real life or synthetic processes can be used; Trace transformation – converting NS-3 output to ProM input format; Process extraction (ProM) – significant processes identification and classification; Template search – checking in Db similar processes or patterns; Recommendations; NEW2AN 27-29.08.12 12
    • Implementation details MXML plugin MXML logs NS-3 event callbacks NS-3Simulation ProM analysis XML «pcap» Wireshark report traces analysis NEW2AN 27-29.08.12 13
    • Applications Planning Wireless Mesh Backbone; Adaptive topology reconstruction; Traffic optimization; Enabling power save awareness in routing protocols; NEW2AN 27-29.08.12 14
    • Current work status Framework for modeling; Data extractor (MXML) plugin; Network configurator (preliminary version): – topology; – node moving; – traffic generators; NEW2AN 27-29.08.12 15
    • Under development Real-life traffic extractor (based on wireshark); Network visualizer; Topology advising algorithm; NEW2AN 27-29.08.12 16
    • Long-term plans Connection with city maps: – roads; – access points; Power awareness implementation in routing; NEW2AN 27-29.08.12 17
    • Contacts and references NS3 homepage http://www.nsnam.org/ ProM homepage: http://www.processmining.org/ MXML sources: https://github.com/OSLL/promin Project tracker: http://osll.spb.ru/projects/promin email: promin@osll.spb.ru NEW2AN 27-29.08.12 18
    • Questions & Answers NEW2AN 27-29.08.12
    • ===BACKUP SLIDES=== NEW2AN 27-29.08.12
    • Simple test network NEW2AN 27-29.08.12 21
    • Petri Network and Events Network  Start send packet  Packet dropped  Packet transmitted...Moving Distance changed... NEW2AN 27-29.08.12 22
    • PossibilitiesAnalysis and Optimization – Conformance checking – Repairing models – Extending the model with frequencies and temporal information – Constructing predictive models – Operational support (prediction, recommendation, etc.) NEW2AN 27-29.08.12 23
    • Results & Further WorkCurrent Results Set of NS-3 simple dynamic mesh networks MXML plug-in for NS-3 as a library Network process is extracted by some algorithms in ProM framework Some algorithms are marked as improperFurther Work Elaborate an algorithm for routes optimization Implement routing metric in the mesh-network routing protocol in NS-3 Integrate metric with QoS service NEW2AN 27-29.08.12 24
    • Process mining in two words NEW2AN 12 27- 29.08.12 NEW2AN 27-29.08.12 25