1. Diagnosing Spatio-Temporal Internet Congestion Properties Leiwen Deng Aleksandar Kuzmanovic EECS Department Northwestern University http://networks.cs.northwestern.edu

2. A. KuzmanovicDiagnosing Spatio-Temporal Internet Congestion Properties 2 Problem Detect congestion events on an end-to-end path and reveal their spatio-temporal properties: –Where they happen (edge, core, intra-AS, inter-AS)? –How long they last / frequently occur? SD

3. A. KuzmanovicDiagnosing Spatio-Temporal Internet Congestion Properties 3 Why Do We Care? Fault diagnosis Advanced congestion control Distributed monitoring systems Overlay design We want to know! SD

4. A. KuzmanovicDiagnosing Spatio-Temporal Internet Congestion Properties 4 Challenges Congestion events relatively infrequent –Measure queuing delay instead of Ploss No/low support from the network –Combine e2e with probes to intermediate nodes Path asymmetry –Measurements still possible via “measurable pairs”

5. A. KuzmanovicDiagnosing Spatio-Temporal Internet Congestion Properties 5 Outline Methodology Implementation (Pong) Validation Measurements Results

6. A. KuzmanovicDiagnosing Spatio-Temporal Internet Congestion Properties 6 Methodology Highlights Coordinated probing –Send 4, 3, or 2 packets from two endpoints Quality of Measurability (QoM) –Able to deterministically detect its own inaccuracy Self-adaptivity –Switch between different probing schemes based on QoM and path properties

7. A. KuzmanovicDiagnosing Spatio-Temporal Internet Congestion Properties 7 Coordinated Probing SD Probe f s d b 4-p probing: a symmetric path scenario f probeb probes probed probe,,,

8. A. KuzmanovicDiagnosing Spatio-Temporal Internet Congestion Properties 8 Coordinated Probing SD f s d b ΔfsΔfs ΔfdΔfd Half-path queuing delay Locating Congestion Points Tracing Congestion Status Probe ΔdΔd ΔbΔb ΔfΔf ΔsΔs

9. A. KuzmanovicDiagnosing Spatio-Temporal Internet Congestion Properties 9 Locating Congestion Points SD Probe ΔfsΔfs ΔfdΔfd ΔfsΔfs ΔfdΔfd ΔfsΔfs ΔfdΔfd ΔfsΔfs ΔfsΔfs ΔfdΔfd ΔfdΔfd 1. Probe Scheduling Sequentially probe (4-p) nodes on the path

10. A. KuzmanovicDiagnosing Spatio-Temporal Internet Congestion Properties 10 Locating Congestion Points Correlate probes to neighboring nodes SD Probe 2. Switch Point Approach Detect Switch Point Congestion

11. A. KuzmanovicDiagnosing Spatio-Temporal Internet Congestion Properties 11 Tracing Congestion Status SD Probe Link 1 (Located Congestion Point) Link 1 Congestion Status Time Congestion Reuse probes sent to un-congested routers

12. A. KuzmanovicDiagnosing Spatio-Temporal Internet Congestion Properties 12 Measurable Pairs S D f s b d Measurable Pair Complementary d probe Congestion 4-p probing scenario

13. A. KuzmanovicDiagnosing Spatio-Temporal Internet Congestion Properties 13 Quality of Measurability S D f s b d Measurable Pair Complementary d probe Congestion Condition: Δf +Δb ≈Δs +Δd max(Δf +Δb, Δs +Δd) QoM 4p = 1 − |(Δf +Δb) − (Δs +Δd)|

14. A. KuzmanovicDiagnosing Spatio-Temporal Internet Congestion Properties 14 Experiments 400 PlanetLab nodes Measure each pair for 1 hour 23,351 paths within 8 days

15. A. KuzmanovicDiagnosing Spatio-Temporal Internet Congestion Properties 15 Results Edge vs. core –Edge more frequently congested than the core: 14 times on average Intra-AS vs. Inter-AS –Edge: Intra-AS > Inter-AS –Core: Intra-AS < Inter-AS Time domain –Edges: congestion events clustered in time –Core: congestion events dispersed in time Links vs. Paths –Links: 12% congested, 3% considerably –Paths: 20% considerably congested

16. A. KuzmanovicDiagnosing Spatio-Temporal Internet Congestion Properties 16 Conclusions Spatio-temporal Internet congestion properties New methodology –Coordinated probing –Detect its own inaccuracy –Self adaptive to path properties –Handles path asymmetries Implemented, deployed, evaluated, measured –High accuracy in both spatial and temporal domains Future work: –Triggered monitoring system to learn more