1. Terapaths Monitoring DWMI: Datagrid Wide area Monitoring Infrastructure
Les Cottrell & Yee-Ting Li, SLAC
US-LHC End-To-End Networking Meeting, FNAL October 25, 2006
Forecasting Network Performance
Predicting how long a file transfer will take, requires forecasting network and application performance. However, such forecasting is beset with problems. These include seasonal (e.g. diurnal) variations in the measurements, the increasing difficulty of making accurate active low network intrusiveness measurements especially on high speed (>1 Gbits/s) networks and with Network Interface Card (NIC) offloading, the intrusivenss of making more realistic active measurements on the network, the differences in network and large file transfer performance, and the difficulty of getting sufficient relevant passive measurements to enable forecasting. We will discuss each of these problems, compare and contrast the effectiveness of various solutions, look at how some of the methods may be combined, and identify practical ways to move forward.
Partially funded by DOE/MICS for Internet End-to-end Performance Monitoring (IEPM), and by Internet2

2. Active E2E Monitoring
Layer 3 or 4. Layers 1 and 2 are less well exploited/understood/related to apps. Also lots of instances: FC, FICON, 10GE, SONET, OC192, SDH …
Check vendor specs. E.g. Cisco, Juniper etc.
SONET monitoring (from Endace):
The PHYMON occupies 1U (OC3/12) or 2U (OC48/192) of vertical rack space and is equipped with two 10/100/1000 copper Ethernet interfaces for control and reporting via LAN.
Key Features
Monitors up to two OC3/OC12/OC48/OC192 network links Detects link-layer failures: LOS-S, LOF-S, AIS-L, REI-L, RDI-L, AIS-P, LOP_P, UNEQ-P, REI-P, RDI-P Derive errors: CV, ES, ESA, ESB, SES and UAS according to Bellcore GR-253, Issue 2 Rev 2 standard Sends SNMP traps for all failures and error thresholds according to user configuration Reports current status in real time via telnet, ssh or serial connection Reports accumulated status for 15m, 1h, 8h, 24h, 7d intervals Retains historical data for 35 days Supplies all the underlying data for SNMP SONET MIB (RFC2558)
Techniques:
Loop back, test patterns (BERT), e.g. ones & zeroes, various ITU-T specs, Loss of Signal, out of Frame, loss of frame, errored seconds, code violations, unavailable seconds, alarms, near & far end, how often, history

3. Active IEPM-BW measurements
Focus on high performance for a few hosts needing to send data to a small number of collaborator sites, e.g. HEP tiered model
Makes regular measurements with probe tools
ping (RTT, connectivity), owamp (1 way delay) traceroute (routes)
pathchirp, pathload (available bandwidth)
iperf (one & multi-stream), thrulay, (achievable throughput)
supports bbftp, bbcp (file transfer applications, not network)
Looking at GridFTP but complex requiring renewing certificates
Choice of probes depends on importance of path, e.g.
For major paths (tier 0, 1 & some 2) use full suite
For tier 3 use just ping and traceroute
Running at major HEP sites: CERN, SLAC, FNAL, BNL, Caltech, Taiwan, SNV to about 40 remote sites

4. IEPM-BW Measurement Topology
40 target hosts in 13 countries
Bottlenecks vary from 0.5Mbits/s to 1Gbits/s
Traverse ~ 50 AS’, 15 major Internet providers
5 targets at PoPs, rest at end sites
Added Sunnyvale for UltraLight
Covers all USATLAS tier 0, 1, 2 sites
Adding FZK Karlsruhe

5. Top page

6. Visualization & Forecasting in Real World

7. Examples of real data Caltech: thrulay Misconfigured windows New path
Very noisy
800
Mbps
Nov05
Mar06
UToronto: miperf
Seasonal effects
Daily & weekly
250
Mbps
Jan06
Nov05
Pathchirp
UTDallas
Some are seasonal
Others are not
Events may affect
multiple-metrics
120
thrulay
Mbps
Mar-10-06
iperf
Mar-20-06
Events can be caused by host or site congestion
Few route changes result in bandwidth changes (~20%)
Many significant events are not associated with route changes (~50%)

8. Scattter plots & histograms
Scatter plots: quickly identify correlations between metrics
Thrulay
Pathchirp
Iperf
Thrulay (Mbps)
RTT (ms)
Pathchirp & iperf (Mbps)
Throughput (Mbits/s)
Pathchirp
Thrulay
Histograms: quickly identify variability or multimodality

9. Esnet-LosNettos segment in the path
Changes in network topology (BGP) can result
in dramatic changes in performance
Hour
Samples of traceroute trees generated from the table
Los-Nettos (100Mbps)
Remote host
Snapshot of traceroute summary table
Notes:
1. Caltech misrouted via Los-Nettos 100Mbps commercial net 14:00-17:00
2. ESnet/GEANT working on routes from 2:00 to 14:00
3. A previous occurrence went un-noticed for 2 months
4. Next step is to auto detect and notify
Drop in performance
(From original path: SLAC-CENIC-Caltech
to SLAC-Esnet-LosNettos (100Mbps) -Caltech )
Back to original path
Dynamic BW capacity (DBC)
Changes detected by
IEPM-Iperf and AbWE
Mbits/s
Available BW = (DBC-XT)
Cross-traffic (XT)
Esnet-LosNettos segment in the path
(100 Mbits/s)
ABwE measurement one/minute for 24 hours Thurs Oct 9 9:00am to Fri Oct 10 9:01am

10. However… Elegant graphics are great to understand problems BUT:
Can be thousands of graphs to look at (many site pairs, many devices, many metrics)
Need automated problem recognition AND diagnosis
So developing tools to reliably detect significant, persistent changes in performance
Initially using simple plateau algorithm to detect step changes
Holt-Winters for forecasting if seasonal effects

11. Seasonal Effects on events
Change in bandwidth (drops) between 19:00 & 22:00 Pacific Time (7:00-10:00am PK time)
Causes more anomalous events around this time

12. Forecasting Over-provisioned paths should have pretty flat time series
Short/local term smoothing
Long term linear trends
Seasonal smoothing
But seasonal trends (diurnal, weekly need to be accounted for) on about 10% of our paths
Use Holt-Winters triple exponential weighted moving averages
Predicting how long a file transfer will take, requires forecasting network and application performance. However, such forecasting is beset with problems. These include seasonal (e.g. diurnal) variations in the measurements, the increasing difficulty of making accurate active low network intrusiveness measurements especially on high speed (>1 Gbits/s) networks and with Network Interface Card (NIC) offloading, the intrusivenss of making more realistic active measurements on the network, the differences in network and large file transfer performance, and the difficulty of getting sufficient relevant passive measurements to enable forecasting. We will discuss each of these problems, compare and contrast the effectiveness of various solutions, look at how some of the methods may be combined, and identify practical ways to move forward.

13. Experimental Alerting
Have false positives down to reasonable level (few per week), so sending alerts to developers
Saved in database
Links to traceroutes, event analysis, time-series

14. Passive Active monitoring What about Passive?
Pro: regularly spaced data on known paths, can make on-demand
Con: adds data to network, can interfere with real data and measurements
What about Passive?
Need replacement for active packet pair and throughput measurements. Evaluating whether we can use Netflow for this.

15. Netflow et. al. Switch identifies flow by sce/dst ports, protocol
Cuts record for each flow:
src, dst, ports, protocol, QoS, start, end time
Collect records and analyze
Can be a lot of data to collect each day, needs lot cpu
Hundreds of MBytes to GBytes
No intrusive traffic, real: traffic, collaborators, applications
No accounts/pwds/certs/keys
No reservations etc
Characterize traffic: top talkers, applications, flow lengths etc.
NetraMet, SCAMPI are a couple of non-commercial flow projects. IPFIX is an IETF standardization effort for Netflow type passive monitoring.

16. Application to LHCnet
LHC-OPN requires edge routers to provide Netflow data
SLAC developing Netflow visualization at BNL
Allows selection of destinations, services
Displays time series, tables, pie charts, spider plots
Will port to other LHCOPN sites
Choose aggregation
Choose services

17. Netflow limitations Use of dynamic ports makes harder to detect app.
GridFTP, bbcp, bbftp can use fixed ports (but may not)
P2P often uses dynamic ports
Discriminate type of flow based on headers (not relying on ports)
Types: bulk data, interactive …
Discriminators: inter-arrival time, length of flow, packet length, volume of flow
Use machine learning/neural nets to cluster flows
E.g.
Aggregation of parallel flows (needs care, but not difficult)
Can use for giving performance forecast
Unclear if can use for detecting steps in performance
For FTP port 21 only used as a control channel.
SCAMPI/FFPF/MAPI allows more flexible flow definition than Netflow
See

18. perfSONAR (pS) See Joe Metzger’s talk later today
SLAC/IEPM formally joined pS
De-centralised management of pS allows us to concentrate more on analysis rather than deployment/maintenance, provide rich source of data to analyze, leverages IEPM group skills
PerfSONAR allows transparent data access
Bring closer US HEP influence to pS
Make iepm-bw data available via pS infrastructure
Porting of our analysis tools to work with pS
Test perfSONAR api’s
Provide useful features such as analysis, visualization, event detection, alerting and diagnosis.
pS enables the unification of both end-to-end and router metric representation
Worry about finding correlations for diagnosis rather than determine ‘how’ to gather the data.

19. Questions, More information
Comparisons of Active Infrastructures:
Some active public measurement infrastructures:
www-iepm.slac.stanford.edu/
www-iepm.slac.stanford.edu/pinger/
e2epi.internet2.edu/owamp/
amp.nlanr.net/ # No longer funded
Monitoring tools
Google for iperf, thrulay, bwctl, pathload, pathchirp, pathneck
Event detection
Netflow:
Internet 2 backbone
SLAC:
BNL (SLAC developed, work in progress)