Detecting Peering Infrastructure Outages
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- Erik Ford
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1 Detecting Peering Infrastructure Outages ENOG14, Minsk Vasileios Giotsas, Christoph Dietzel, Georgios Smaragdakis, Anja Feldmann, Arthur Berger, Emile Aben # TU Berlin CAIDA DE-CIX MIT Akamai # RIPE NCC
2 2 Peering Infrastructures are critical part of the interconnection ecosystem Internet Exchange Points (IXPs) provide a shared switching fabric for layer-2 bilateral and multilateral peering. Largest IXPs support > 100 K of peerings, > 5 Tbps peak traffic Typical SLA 99.99% (~52 min. downtime/year) 1 Carrier-neutral co-location facilities (CFs) provide infrastructure for physical co-location and cross-connect interconnections. Largest facilities support > 170 K of interconnections Typical SLA % (~5 min. downtime/year)
3 3 Outages in peering infrastructures can severely disrupt critical services and applications
4 4 Outages in peering infrastructures can severely disrupt critical services and applications Outage detection crucial to improve situational awareness, risk assessment and transparency.
5 5 Current practice: Is anyone else having issues? ASes try to crowd-source the detection and localization of outages. Inadequate transparency/responsiveness from infrastructure operators.
6 6 Our Research Goals 1. Outage detection: ly, at the finest granularity possible 2. Outage localization: Distinguish cascading effects from outage source 3. Outage tracking: Determine duration, shifts in routing paths, geographic spread
7 Passive outage detection: Initialization 7 For each vantage point (VP) collect all the stable BGP routes tagged with the communities of the target facility (Facility 2)
8 Passive outage detection: Initialization AS_PATH: 1 x COMM: 1:FAC2 AS_PATH: COMM: 2:FAC2 8 AS_PATH: 4 x COMM: 4:FAC2 For each vantage point (VP) collect all the stable BGP routes tagged with the communities of the target facility (Facility 2)
9 Passive outage detection: Monitoring 9 Track the BGP updates of the stable paths for changes in the communities values that indicate ingress point change.
10 Passive outage detection: Monitoring 10 AS_PATH: COMM: 2:FAC1 We don t care about AS-level path changes if the ingress-tagging communities remain the same.
11 Passive outage detection: Outage signal AS_PATH: 1 x COMM: 1:FAC1 11 AS_PATH: COMM: 2:FAC1 AS_PATH: 4 x COMM: 4:FAC4 4:IXP Concurrent changes of communities values for the same facility. Indication of outage but not final inference yet!
12 Passive outage detection: Outage signal 12 Partial outage AS_PATH: 1 x COMM: 1:FAC1 AS_PATH: COMM: 2:FAC1 AS_PATH: 4 x COMM: 4:FAC4 4:IXP Concurrent changes of communities values for the same facility. Indication of outage but not final inference yet!
13 Passive outage detection: Outage signal 13 Partial outage? De-peering of large ASes? Major routing policy change? AS_PATH: 1 x COMM: 1:FAC1 AS_PATH: COMM: 2:FAC1 AS_PATH: 4 x COMM: 4:FAC4 4:IXP Concurrent changes of communities values for the same facility. Indication of outage but not final inference yet!
14 Passive outage detection: Outage signal 14 Partial outage? De-peering of large ASes? Major routing policy change? AS_PATH: 1 x COMM: 1:FAC1 AS_PATH: COMM: 2:FAC1 AS_PATH: 4 x COMM: 4:FAC4 4:IXP Signal investigation: Targeted active measurements. How disjoint are the affected paths? How many ASes and links have been affected?
15 Passive outage detection: Outage tracking AS_PATH: 1 x COMM: 1:FAC2 15 AS_PATH: COMM: 2:FAC2 End of outage inferred when the majority of paths return to the original facility.
16 De-noising of BGP routing activity 16 Number of BGP messages (log) The aggregated activity of BGP messages (updates, withdrawals, states) provides no outage indication.
17 De-noising of BGP routing activity 17 Number of BGP messages (log) Fraction of infrastructure paths Number of BGP messages (log) The aggregated activity of BGP messages (updates, withdrawals, states) provides no outage indication. The BGP activity filtered using communities provides strong outage signal.
18 Outage localization is more complicated! 18 The location of community values that trigger outage signals may not be the outage source! Communities encode the ingress point closest to our VPs (near-end infrastructure) ASes may be interconnected over multiple intermediate infrastructures Failures in intermediate infrastructures may affect the near-end infrastructure paths
19 Outage localization is more complicated! 19
20 Outage localization is more complicated! 20
21 Outage localization is more complicated! 21 Outage in Facility 2 causes drop in the paths of Facility 4!
22 Outage localization is more complicated! 22
23 Outage localization is more complicated! 23 Outage in Facility 3 causes drop in the paths of Facility 4!
24 Outage source disambiguation and localization 24 Create high-resolution co-location maps: AS to Facilities, AS to IXPs, IXPs to Facilities Sources: PeeringDB, DataCenterMap, operator websites Decorrelate the behaviour of affected ASes based on their infrastructure colocation.
25 Outage source disambiguation and localization 25 Paths not investigated in aggregated manner, but at the granularity of separate (AS, Facility) co-locations. London Telecity HE8/9 outage London Telehouse North outage
26 Outage source disambiguation and localization 26 Paths not investigated in aggregated manner, but at the granularity of separate (AS, Facility) co-locations. London Telehouse North outage London Telecity HE8/9 outage London Telehouse North outage London Telecity HE8/9 outage
27 Tracking the progress of outages 27 Passive tracking: Monitor how location-tagging BGP Communities change during the outage. Active tracking: Execute targeted traceroutes based on the hints of the BGP signals.
28 28 Detecting peering infrastructure outages in the wild 159 outages in 5 years of BGP data 76% of the outages not reported in popular mailing lists/websites Validation through status reports, direct feedback, social media 90% accuracy, 93% precision (for trackable PoPs)
29 29 Effect of outages on Service Level Agreements ~70% of failed facilities below % uptime ~50% of failed IXPs below 99.99% uptime 5% of failed infrastructures below 99.9% uptime!
30 Measuring the impact of outages 30 Number of affected links (log) K 4K 6K 8K 10K 12K Distance from outage source (km) > 56 % of the affected links in different country, > 20% in different continent! CDF Fraction of paths RTT (ms) Median RTT rises by > 100 ms for rerouted paths during AMS-IX outage.
31 31 Conclusions ly and accurate infrastructure-level outage detection through passive BGP monitoring Majority of outages not (widely) reported Remote peering and infrastructure interdependencies amplify the impact of local incidents Hard evidence on outages can improve accountability, transparency and resilience strategies
32 Questions / Comments? 32
33 Challenges in detecting infrastructure outages Capturing the infrastructure-level hops between ASes 2. Correlating the paths from multiple vantage points 3. Continuous monitoring of the routing system Passive BGP monitoring VP Before outage During outage The initial hops changed VP During outage No hop changes
34 Challenges in detecting infrastructure outages Capturing the infrastructure-level hops between ASes 2. Correlating the paths from multiple vantage points 3. Continuous monitoring of the routing system BGP encodes AS paths Passive BGP monitoring VP Before outage During outage The initial hops changed VP During outage No hop changes
35 Deciphering location-metadata in BGP 35 BGP not entirely information-hiding! Communities BGP attribute: Optional, tags BGP routes with arbitrary metadata Often encodes the ingress location of prefixes
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