The Motivation for Analyzing perfSONAR · 2020. 2. 11. · site3 → site4. Output: Number of hops...
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Transcript of The Motivation for Analyzing perfSONAR · 2020. 2. 11. · site3 → site4. Output: Number of hops...
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The Motivation for Analyzing perfSONAR
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Network Measurement Platform Overview
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Collector
Store (long-term) Store (short-term)
pS MonitoringpS Configuration
Tape
Experiments
MONIT-GRAFANA
pS Dashboard
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The NSF SAND Project
SAND: Service Analysis and Network Diagnosis1827116) focusing on combining,
visualizing, and analyzing disparate network monitoring and service logging data.
. (GOAL: capitalize on our rich network dataset!!)
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Website https://sand-ci.org/ (Project started in September 2018 and has 2 years funding)
PI: Brian Bockelman, Co-PIs: Shawn McKee, Rob Gardner
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Finding Relevant Information
https://toolkitinfo.opensciencegrid.org/toolkitinfo/
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Available Data Overview
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Machine Learning and a Network Database
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perfSONAR Data Details
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https://atlas-kibana.mwt2.org/s/networking/app/kibana#/discover?_g=()8
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Network Topology
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Updates to Traceroute Data
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Gathering ESnet Interface Data
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Replay of 2018, 2019 Network Data
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Students Working with SAND on Network Analytics
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Sushant Bansal’s Work
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Yuan Li’s Work
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5. Plot owd data of top 10 `dest_host` with a given `src_host` and a predefined time range. E.g.,
Code snippets: dest_hosts = getDestHosts('perfsonar01.datagrid.cea.fr')
plotTop10DestHosts(src_host='perfsonar01.datagrid.cea.fr', dest_hosts = dest_hosts, from_date='2019/10/05', to_date='2019/11/05')
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Manjari Trivedi’s Work
https://gitlab.com/963/sand/tree/master/common_hops )
Method: Find the list of hops from site1 → site2 and compare with the list of hops from site3 → site4.Output: Number of hops in common, followed by a list of hops in common between site1 → site2 and site3 → site4.Problems:
● There may be several paths between a source and destination.● For now, found the most common hash and mapped it to a sequence of hops. In the future, this
could be extended to the n most common hashes.● The most common hash from site1 → site2 may map to IPv4 hops, but the hash from site3→site4
maps to IPv6 hops (or vice versa), so they cannot be compared.● Added an argument to the function to specify whether we are looking at IPv4 or IPv6 (it may be
useful to do both). This is a prerequisite for the most common hash.16
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Petya Vasileva’s Work
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Site-wise in some cases it is clear there is a problem with a specific site for a long period.
Average packet loss for each month and for each site being a source and a destination
SourceDestination
Data analysis on the packet loss tests
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Analyzing data for a single day (12-13 Jan 2020) to see problematic points
Test entries > 6 MIn most cases packet loss is between 0 and 5%
Results aggregated by 1h Distribution of average packet loss in %
Results aggregated by src-dest Distribution of average packet loss in %
Petya Vasileva - work reportSummary of Packet Loss for a Day (Jan 12-13, 2020)
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Petya Vasileva - work reportAverage Packet Loss by Src-Dest Host 12-13 Jan 2020
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Petya Vasileva - work reportAverage Packet Loss by Src-Dest Site 12-13 Jan 2020
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Average packet loss by src-dest IPs
Petya Vasileva - work reportAverage Packet Loss by Src-Dest IP 12-13 Jan 2020
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Related Work In Canada
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Collaboration with MEPhI on Network Visualization
The SAND project is collaborating with MEPHI (Moscow Engineering Physics Institute) on network path
visualization
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https://perfsonar.uc.ssl-hep.org/graph/viewer
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Near-term Work and Beyond...
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Summary
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CHEP 2019, Adelaide
Acknowledgements
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Questions, Comments?
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References
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Issues with Traceroute and Network Paths
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https://www.cellstream.com/reference-reading/tipsandtricks/403-ecmp-linux-paristr
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Some Context: IRIS-HEP
The Institute for Research and Innovation in Software in High Energy Physics (IRIS-HEP) project has been funded by National Science Foundation in the US as grant OAC-1836650 as of 1 September, 2018.
The institute focuses on preparing for High Luminosity (HL) LHC and is funded at $5M / year for 5 years. There are three primary development areas:
● Innovative algorithms for data reconstruction and triggering;● Highly performant analysis systems that reduce `time-to-insight’ and maximize the HL-LHC
physics potential; ● Data organization, management and access systems for the community’s upcoming Exabyte era.
The institute also funds the LHC part of Open Science Grid, including the networking area and will create a new integration path (the Scalable Systems Laboratory) to deliver its R&D activities into the distributed and scientific production infrastructures.
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