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Characterizing VLAN-Induced Sharing in a Campus Network

Characterizing VLAN-Induced Sharing in a Campus Network. Mukarram Bin Tariq, Ahmed Mansy Nick Feamster, Mostafa Ammar {mtariq, amansy, feamster, ammar}@cc.gatech.edu. Virtual LANs (VLANs). Multiple LANs on top of a single physical network Typically map to IP subnets

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Characterizing VLAN-Induced Sharing in a Campus Network

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  1. Characterizing VLAN-Induced Sharing in a Campus Network Mukarram Bin Tariq, Ahmed Mansy Nick Feamster, Mostafa Ammar {mtariq, amansy, feamster, ammar}@cc.gatech.edu

  2. Virtual LANs (VLANs) • Multiple LANs on top of a single physical network • Typically map to IP subnets • Flexible design of IP subnets • Administrative ease • Sharing infrastructure among separate networks, e.g., for departments, experiments • Sharing: IP networks may depend on same Ethernet infrastructure VLAN3 VLAN Core Ethernet VLAN2 VLAN1

  3. Problems: Informal Operator’s Survey “I wish for insight. Better visibility into operational details” Lack of cross-layer visibility “[users] can end up on ports configured for the wrong VLAN …. difficult for end users to determine why their network isn't working ("but I have a link light!”)” Need for diagnostic tools for VLANs “deploy tomography tool [for the campus to isolate faulty switches]” • Shared failure modes among networks “Using only the information the switch can give [is difficult to determine] to which VLAN or VLANs are the busy ones”

  4. Key Questions and Contributions How to obtain visibility in sharing of Ethernet among IP networks? • EtherTrace: A tool for discovery of Ethernet devices on IP path • Passive discovery using bridge tables • Does not require CDP or LLDP How much sharing is there in a typical network? • Analysis of VLAN in Georgia Tech network • 1358 Switches, 1542 VLANs • Find significant sharing How much does Ethernet visibility help? • Network tomography • 2x improvement in binary tomography using Ethernet visibility

  5. EtherTrace: Maps IP to Ethernet Paths Frames arrive on same port for off-path switches C B D E F A • Due to spanning tree, frames from H1 and H2 are received on separate ports of same VLAN for switches that are on the path • EtherTrace automates discovery of Ethernet path by analyzing bridge and ARP tables, and iterating for each IP hop in IP traceroute H2 Frames arrive on separate ports for on-path switches • Works well for stable networks D C F A E B • Available at: http://www.gtnoise.net/ethertrace H1

  6. Georgia Tech Campus Network Dataset Data sources Dataset Bridge tables obtained every 4 hours ARP tables obtained every hour IP traceroutes among monitoring nodes every 5 minutes One-day snapshot on March 25, 2008 • 1358 Switches • 31 Routers • 79 monitoring nodes Analysis • Obtain Ethernet devices for IP traceroutes using EtherTrace • Quantify the sharing of Ethernet devices among IP hops and paths

  7. Ethernet Hops Shared among IP Hops Maximum IP hops on an Ethernet interface: 34. 17 considering disjoint only 57% of Ethernet Hops are shared by more than 2 disjoint IP Hops On average, an Ethernet Hop affects ~30 IP hops ~4 considering disjoint IP hops only

  8. Application: Improving Accuracy with Cross-layer Sharing Visibility • Experiment • Simulate failure of a random Ethernet hop • Determine IP paths that are affected by the failure • Use binary tomography to determine the hop that has fault

  9. Summary • Surprising amount of sharing • On average, an Ethernet hop affects ~30 IP hops • 57% of Ethernet hops affect two or more disjoint IP hops • Failure of an Ethernet device affects (on average) as many IP paths as failure of an IP device • Two orders of magnitude more Ethernet devices • Cross-layer visibility improves diagnosis • 2x improvement in accuracy and specificity • EtherTrace: www.gtnoise.net/ethertrace

  10. Comparison of Dependency of IP Paths on Ethernet and IP devices On average, a switch or switch interface is critical to similar number of IP paths as a router or IP interface, although there are two orders of magnitude more layer-2 devices

  11. Application: Improving Accuracy with Cross-layer Sharing Insight • We can improve fault-localization accuracy by using layer-2 topology information • Experiment • Simulate failure of a random layer-2 edge • Determine IP paths that are broken by the failure • Use Binary tomography to determine the network segment that has fault • Conventional Approach: Use Layer-3 path elements as dependencies • Cross-layer Approach: Use layer-2 elements determined with EtherTrace as dependencies • Metrics • Accuracy: diagnosed segment contains the failed network element • Specificity: ratio of actual number of elements that failed to the number of layer-2 elements in diagnosed segment

  12. EtherTrace • Collect Bridge tables from switches using SNMP • Table has entries of form <MAC, port, vlan-id> • Collect ARP tables from Routers • Given IP traceroute between two hosts find layer-2 path elements as: • De-alias router IP addresses • Obtain MAC addresses IP addresses on each IP hop • Obtain Layer-2 switches and ports for each IP hop

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