PPT-Clustering in OpenDaylight

Author : myesha-ticknor | Published Date : 2016-12-18

Colin Dixon Technical Steering Committee Chair OpenDaylight Distinguished Engineer Brocade Borrowed ideas and content from Jan Medved Moiz Raja and Tom Pantelis

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Clustering in OpenDaylight: Transcript

Colin Dixon Technical Steering Committee Chair OpenDaylight Distinguished Engineer Brocade Borrowed ideas and content from Jan Medved Moiz Raja and Tom Pantelis MultiProtocol SDN Controller Architecture. Colin Dixon. Technical Steering Committee Chair, . OpenDaylight. Senior Principal . Engineer, . Brocade. Some content from: David Meyer, . Neela. . Jaques. , and Kevin . Woods. Outline. State of networking and SDN. Colin Dixon. Technical Steering Committee Chair, . OpenDaylight. Principal . Engineer, . Brocade. Some content from: David Meyer, . Neela. . Jaques. , . Kevin Woods, and others. Outline. What I know a lot about. Unveil Lithium Upcoming OpenDaylight Release June 3, 2015 Masashi Kudo OpenDaylight Ambassador www.opendaylight.org  Masashi Kudo  CTO of SDN Group, Chief Advanced Technologist (Sof OpenDaylight Beryllium. Colin Dixon. TSC Chair, OpenDaylight. Distinguished Engineer, Brocade. 2016 ODL User Survey: Demographics. ODL User Survey, February 2016. 2016 ODL User Survey: Deployment Insights. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . of OpenDaylight. Colin Dixon. TSC Chair. , . OpenDaylight. Distinguished Engineer. , . Brocade. Luis Gomez. Principal Engineer, Brocade. Model-Driven Service . Abstraction Layer (MD-SAL). Core Architecture. Colin Dixon. Technical Steering Committee Chair, . OpenDaylight. Senior Principal . Engineer, . Brocade. Some content from: David Meyer, . Neela. . Jaques. , and Kevin . Woods. Outline. Introduction…. issue in . computing a representative simplicial complex. . Mapper does . not place any conditions on the clustering . algorithm. Thus . any domain-specific clustering algorithm can . be used.. We . What is clustering?. Why would we want to cluster?. How would you determine clusters?. How can you do this efficiently?. K-means Clustering. Strengths. Simple iterative method. User provides “K”. Unsupervised . learning. Seeks to organize data . into . “reasonable” . groups. Often based . on some similarity (or distance) measure defined over data . elements. Quantitative characterization may include. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . Exploring OpenDaylight Matt Younkins younkinsm @ou.edu SDN, NFV and OpenDaylight 2 Open, Programmable APIs Virtualization and Abstraction Layer New Revenue Lower Cost SDN NFV Orchestration, Automation and MANO Log. 2. transformation. Row centering and normalization. Filtering. Log. 2. Transformation. Log. 2. -transformation makes sure that the noise is independent of the mean and similar differences have the same meaning along the dynamic range of the values.. Randomization tests. Cluster Validity . All clustering algorithms provided with a set of points output a clustering. How . to evaluate the “goodness” of the resulting clusters?. Tricky because .

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