1. Slide 1 Experiences with PerfSONAR and a Control Plane for Software Defined Measurement Yan Luo Department of Electrical and Computer Engineering University of Massachusetts Lowell

2. Slide 2 FLowell Project at a Glance

3. Slide 3 FLowell Project Status UML Campus SDN Network –In-lab Testbed Completed: Six OpenFlow switches (Extreme Networks) + OpenDayLight Controller –Campus-wide Deployment In Progress (two buildings completed, other two buildings by Oct’15) Layer-2 10Gbps Link (UML MGHPCC) in progress (new Ciena optics pack in order) Layer-2 10Gbps Link (UML Internet2) in progress (new Cisco switch ordered) Science DMZ in progress (architectural design and verification, vendor selection) GENI Rack in progress (vendor selection)

4. Slide 4 Performance Tests DTN –CPU: Intel Core 2 @2.33GHz, 2 cores –Hard Disk: Read/Write Speed: 125MB/s –1Gbps edge link (production net) PerfSONAR Test server: anl-diskpt1.es.net Inside firewallOutside firewall

5. Slide 5 PerfSONAR Today Over 1400 public perfSONAR nodes

6. Slide 6 PerfSONAR’s Scaling Challenges Challenges –Control, coordination and execution of network measurements –Monitor healthiness of networks besides major networks –Network issues caused by multiple problematic links Our Research –Using measurement archives (MA) to build a traceroute graph –Propose a control plane on top of perfSONAR to support software defined measurement and troubleshooting –A joint work with ESnet (Brian Tierney) and AMPATH/FIU (Jeronimo Bezerra)

7. Slide 7 Motivation of PerfSONAR Control Plane Typical Workflow Finding the Longest Clean Path

8. Slide 8 Objectives of PerfSONAR Control Plane Measurement Archive Data Analysis –How were the measurement results? –What can we learn from them? Automatic perfSONAR Peer Selection –Quickly identify the best suitable PS node(s) on the routes in question Programmable Measurement and Troubleshooting –Define measurement task and conditions with software

9. Slide 9 The Design of PerfSONAR Control Plane PerfSONAR Node Discovery –Finding nearest perfSONAR node of a target router on the path Measurement Task Control –Initiating tests between (any) two chosen perfSONAR nodes –Monitoring the performance on the path –Locating the problematic link(s)

10. Slide 10 The Operation of PerfSONAR Control Plane Obtain traceroute information from MAs Build a traceroute graph based on the dataset Find a set of perfSONAR node pairs to start bandwidth tests and monitor the results Diagnostic analysis and troubleshooting network issues O f f l i n e O n l i n e

11. Slide 11 Evaluation of PerfSONAR Control Plane Traceroute Dataset –95 MA hosts in the central US and eastern US regions –1831 traceroute records Traceroute Graph –2377 perfSONAR hosts and routers in total

12. Slide 12 A Use Case of the New Control Plane pr20.uml.edu --- typhoon.pub.alcf.anl.gov (140.221.68.2) A python program with less than 300 lines of code “Troubleshooting” procedure took about 15 minutes

13. Slide 13 Conclusion and Future Work FLowell project at UMass Lowell in good progress Gained experiences with DTN and PerfSONAR A Control Plane for PerfSONAR show promising results –Open source at: https://github.com/ACANETS/pscphttps://github.com/ACANETS/pscp –Community feedback welcomed! Tasks in the Upcoming FY: –Science DMZ Deployment at UML –GENI Rack operational at MGHPCC –Campus wide outreach to researchers –PerfSONAR Control Plane Collaboration Contact info: Yan Luo, yan_luo@uml.edu