Discovering High-Impact Routing Events Using Traceroutes

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1 ISCC 2015 IEEE Symposium on Computers and Communications Discovering High-Impact Routing Events Using Traceroutes Dept. of Engineering, Roma Tre University, Italy July 7th, 2015 Larnaca, Cyprus Joint work with: M. Di Bartolomeo, M. Pizzonia, C. Squarcella, M. Rimondini

2 Background We consider "TRACEROUTE" A computer network diagnostic tool for displaying the route (path) and measuring transit delays of packets across an Internet Protocol (IP) network Source Destination ISCC

3 Background (II) "TRACEROUTE" is installed on all major measurement infrastructures such as RIPE Atlas CAIDA Ark SamKnows. [RIPE Atlas] ISCC 2015

4 Background (III) There are various types of TRACEROUTEs But not on the probes! We assume to work with plain TRACEROUTEs No Paris traceroute [Augustin et al., IMC '06] Each sample is just an ordered list of IPs from a probe (source) to a target (destination) Source IP 1 IP 2 IP Destination ISCC

5 The problem Input: a large set of unsynchronized traceroutes coming from probing systems S 1 D 1 S 2 D 2 S D Output: a set of high-impact routing events ISCC

6 The problem Input: a large set of unsynchronized traceroutes coming from probing systems S 1 D 1 S 2 D 2 S D Output: a set of high-impact routing events ISCC

7 State of the Art (I) ISCC

8 State of the Art (I) Binary tomography [Duffield, IEEE Trans. Inf. Theory, '06] -> Trees [Dhamdhere et al., CoNEXT, '07] -> General topologies [Kompella et al., INFOCOM, '07] -> General topologies ISCC

9 State of the Art (I) Binary tomography [Duffield, IEEE Trans. Inf. Theory, '06] -> Trees [Dhamdhere et al., CoNEXT, '07] -> General topologies [Kompella et al., INFOCOM, '07] -> General topologies Applicability problems discussed in [Huang et al., SIGCOMM '08] [Cunha et al., IMC '09] [Ma et al., IMC '14] ISCC

10 State of the Art (I) Binary tomography [Duffield, IEEE Trans. Inf. Theory, '06] -> Trees [Dhamdhere et al., CoNEXT, '07] -> General topologies [Kompella et al., INFOCOM, '07] -> General topologies Applicability problems discussed in [Huang et al., SIGCOMM '08] [Cunha et al., IMC '09] [Ma et al., IMC '14] Control plane/data plane [Katz-Bassett et al., NSDI '08] [Katz-Bassett et al, SIGCOMM '12] ISCC

11 S.o.A. vs our approach S.o.A. Rely on many sources of information Assume at least a partial knowledge on the network topology Impose restrictions on the schedule of traceroutes Our approach Rely on traceroutes only Does not assume any knowledge on the network topology Asynchronous measurements are allowed ISCC

12 Traceroute transition SD-PAIR (1,6) t ISCC

13 Traceroute transition SD-PAIR (1,6) t ISCC

14 Traceroute transition SD-PAIR (1,6) t t ISCC

15 Traceroute transition SD-PAIR (1,6) t t Transition active between t and t ISCC

16 Traceroute transition SD-PAIR (1,6) Δ = { pre, 4 post, 5 post } t t Transition active between t and t ISCC

17 Intuition of the algorithm Sources 1 Destinations 5 t 1 ISCC

18 Intuition of the algorithm Sources 1 Destinations t 1 t 2 ISCC

19 Intuition of the algorithm Sources 1 Destinations t 1 t 2 ISCC

20 Intuition of the algorithm Sources Destinations t 1 t t 2 ISCC

21 Intuition of the algorithm Sources Destinations t 1 t t 2 t 4 ISCC

22 Intuition of the algorithm Sources Destinations Δ={ pre, 7 post, 8 post } Δ={ pre, 2 post, 4 post } t 1 t t 2 t 4 ISCC

23 Intuition of the algorithm Sources Destinations Data reduction: event with impact 2 Δ={ pre, 7 post, 8 post } Δ={ pre, 2 post, 4 post } { pre } t 1 t t 2 t 4 ISCC

24 SD-Pairs empathy Source 2 4 Destination Source 2 4 Destination SD-pairs are pre-empathic iff i.e. a node/link disappears in both paths e.g. (1,5) and (6,9) share node before the event SD-pairs are post-empathic iff i.e. a node/link appears in both paths ISCC

25 Example

26 Identification of transitions Input samples are scanned and all transitions, with the corresponding changed sets Δ are identified ISCC

27 Identification of transitions 4 Input samples are scanned and all transitions, with the corresponding changed sets Δ are identified 7 t 1 ISCC

28 Identification of transitions Input samples are scanned and all transitions, with the corresponding changed sets Δ are identified 7 t 1 t 2 ISCC

29 Identification of transitions Input samples are scanned and all transitions, with the corresponding changed sets Δ are identified 7 t 1 t 2 t ISCC

30 Identification of transitions Input samples are scanned and all transitions, with the corresponding changed sets Δ are identified 7 t 1 t 2 t ISCC

31 Identification of transitions Input samples are scanned and all transitions, with the corresponding changed sets Δ are identified 9 7 t t 1 t 2 t 4 ISCC

32 Identification of transitions Input samples are scanned and all transitions, with the corresponding changed sets Δ are identified 9 7 t t 1 t 2 t 4 t 5 ISCC

33 Identification of transitions Input samples are scanned and all transitions, with the corresponding changed sets Δ are identified 9 7 t 1 t 2 t t 4 t 5 t 6 ISCC

34 Identification of transitions Input samples are scanned and all transitions, with the corresponding changed sets Δ are identified 9 7 Δ = {4 pre, 8 post } Δ = {4 pre, 8 post, 9 post } Δ = {4 pre, 9 post } t 1 t 2 t t 4 t 5 t 6 ISCC

35 Build events We track the evolution of empathic relations between SD-pairs involved in transitions Δ = {4 pre, 8 post } Δ = {4 pre, 8 post, 9 post } Δ = {4 pre, 9 post } observed in (1,5), (2,6) and (,7) {4 pre } {8 post } {9 post } t 1 t 2 t t 4 t 5 t 6 ISCC

36 Build events We track the evolution of empathic relations between SD-pairs involved in transitions Δ = {4 pre, 8 post } Δ = {4 pre, 8 post, 9 post } Δ = {4 pre, 9 post } observed in (1,5), (2,6) and (,7) observed in (1,5) and (2,6) {9 post } {4 pre } {8 post } t 1 t 2 t t 4 t 5 t 6 ISCC

37 Build events We track the evolution of empathic relations between SD-pairs involved in transitions Δ = {4 pre, 8 post } Δ = {4 pre, 8 post, 9 post } Δ = {4 pre, 9 post } observed in (1,5), (2,6) and (,7) observed in (1,5) and (2,6) observed in (2,6) and (,7) {9 post } {4 pre } {8 post } t 1 t 2 t t 4 t 5 t 6 ISCC

38 Build events Keep events with highest impact Δ = {4 pre, 8 post } Δ = {4 pre, 8 post, 9 post } Δ = {4 pre, 9 post } observed in (1,5), (2,6) and (,7) observed in (1,5),(2,6) observed in (2,6) and (,7) {9 post } {4 pre } {8 post } t 1 t 2 t t 4 t 5 t 6 ISCC

39 Build events Keep events with highest impact Δ = {4 pre, 8 post } Δ = {4 pre, 8 post, 9 post } Δ = {4 pre, 9 post } observed in (1,5), (2,6) and (,7) observed in (1,5),(2,6) observed in (2,6) and (,7) {9 post } {4 pre } {8 post } t 1 t 2 t t 4 t 5 t 6 ISCC

40 Summarizing Each inferred event has the following attributes: A interval of uncertainty A set of labeled IP addresses A set of affected sources and destinations Cardinality -> impact of the event A type: down, up, unknown based on the IPs labels ISCC

41 Application to real networks Real networks properties Aliasing No instantaneous routing propagation Simultaneous events Load balancing Effect Multiple small events rather than a single larger one in the worst case Similar effect Interference between events. The sets of SD-pairs can be identified only with a limited precision Many small ficticious events ISCC

42 Load balancing Pre-processing of the data A simple inference heuristic Unstable next hops are replaced by a single arbitrarily chosen representative IP address ISCC

43 Load balancing Pre-processing of the data A simple inference heuristic Unstable next hops are replaced by a single arbitrarily chosen representative IP address p t ISCC

44 Load balancing Pre-processing of the data A simple inference heuristic Unstable next hops are replaced by a single arbitrarily chosen representative IP address p t p' t' ISCC

45 Load balancing Pre-processing of the data A simple inference heuristic Unstable next hops are replaced by a single arbitrarily chosen representative IP address p p' p'' t t' t'' ISCC

46 Load balancing Pre-processing of the data A simple inference heuristic Unstable next hops are replaced by a single arbitrarily chosen representative IP address Red hop is unstable -> p = p' = p'' p p' p'' t t' t'' ISCC

47 Experiment I: Six induced events Support from a small Italian ISP 89 Italian RIPE Atlas probes ISCC

48 Avg. downspeed Experiment II: Big real fault Support from a big ISP Several thousands of probes ISCC

49 Conclusions and future work Conclusions We introduced the notion of empathic traceroute measurement Based on this concept we built a methodology for the identification and analysis of network events Evaluation through real-world examples Future work Validate our approach with other measurement platforms and other types of events Online version of the algorithm ISCC

50 Questions? ISCC

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