Experiences with PerfSONAR and a Control Plane
for Software Defined Measurement
Yan Luo
Department of Electrical and Computer Engineering
University of Massachusetts Lowell
Slide 1
Slide 1
FLowell Project at a Glance
•
Legend
Campus
Backbone Network
Core
Router/
Switch
Science Network (10G)
Science DMZ
Non Science Network
SDN
OpenFlow
Switch
OpenFlow
Controller
Internet
ESnet
PerfSONAR
Academic Buildings
OpenFlow
OpenFlow
Switch
Switch
research
research
labs
labs
Border
Router
(*.97)
UMassNet
Internet2
Firewall
Firewall
Border
Router
(*.219)
UMASS LOWELL CAMPUS
Science DMZ
Core
Router/
Switch
SDN
OpenFlow
Switch
FLowell
Rack
Other
collocated
racks
MGHPCC
Slide 2
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)
Slide 3
Performance Tests
• DTN
– CPU: Intel Core 2 @2.33GHz, 2 cores
– Hard Disk: Read/Write Speed: 125MB/s
– 1Gbps edge link (production net)
• PerfSONAR
Inside firewall
File Size
Speed
Ave(MB/s)
10M
3.62
50M
11.92
100M
23.84
1G
42.47
10G
37.48
50G
36.51
Test server: anl-diskpt1.es.net
Outside firewall
Slide 4
PerfSONAR Today
• Over 1400 public perfSONAR nodes
Slide 5
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)
Slide 6
Motivation of PerfSONAR Control Plane
• Typical Workflow
• Finding the Longest Clean Path
Slide 7
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
Slide 8
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)
Slide 9
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
Slide 10
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
Slide 11
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
Slide 12
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/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
Slide 13