Learning, Indexing and Diagnosing Network Faults

1. Learning, Indexing and Diagnosing Network Faults Ting Wang†, Mudhakar Srivatsa‡, Dakshi Agrawal‡ and Ling Liu† Georgia Institute of Technology† IBM T.J. Watson Research Center‡

2. Complex Networks • Network as a graph • Vertices represent network entities • Edges represent pair-wise (local) interactions between network entities • Even simple interactions give rise to complex global network phenomena • Fault cascading in communication networks • Information spread (e.g., via emails) in social networks • Infection propagation in protein interaction networks • Key challenge is to detect and understand emerging global phenomena

3. Network Monitoring Data • Networks generate massive monitoring data (aka events) • Monitored data consists of local (in both space & time) observations on the network • Monitored data is incomplete and sometimes even erroneous (e.g., imprecise, out-of-order wrt to both time and causality, etc) • Examples • Ping failure, interface down, high CPU utilization, etc. in communication networks • Email threads (time stamp, tokenized subject, MIME type, etc.) between members in a organizational hierarchy • Pathological symptoms in biological networks – protein interaction networks (PINs) • Key observation: monitoring data gathered from network entities are correlated through the network topology

4. Network Patterns • Network patterns attempt to efficiently capture spatial (topological) and temporal correlations in monitored data • Key challenges • Understand the semantics of network patterns • Identify domain-specific network patterns (e.g., fault diagnosis & prediction in IT systems, information spread and access control on social networks, disease propagation in protein networks, etc) • How to learn and represent network patterns? • How to scalably match network patterns against an online stream of network events? e1 e3 e2 Simplified Examples

5. Network Patterns • Notation and Formalism • Event data: <nodeId, type, timestamp, monitorId> • Network Pattern: <event types, spatial pattern, temporal pattern> • INTERFACE DOWN  <LINK DOWN, NEIGHBOR, TIME WINDOW> • Temporal Pattern • E.g.: markov chains, frequent item sets • Spatial Pattern: Composition/Closures of one or more topological relationships • Communication networks: upstream, downstream, neighbor, tunnel • Social networks: manages, friends, team members, IM buddies • Biological network: catalyst, inhibitor, suppressor Temporal Pattern: Markov Chain Temporal Pattern: Frequent Item Sets Spatial Pattern: Downstream (transitive closure)

6. Fault Diagnosis and Prediction in Communication Networks Topology Topological Index • Challenges: improve scalability & expressiveness of fault-diagnosis • Limitation of current solutions: a complexity that growsas square of the network size • Correlation rules are pair-wise: expensive to support complex fault diagnosis (e.g., predicting soft failures, router failure from VRF tunnel events, etc) • Lacks predictive capability • Approach: • Fault signatures encode temporal patterns: frequent item sets, Markov chains; and topological patterns (spans the network): upstream, downstream, neighbors, VPN tunnels, etc • Topologically index streaming monitoring data to facilitate scalable single-pass event correlation and fault-diagnosis • Results in linear complexity – increased scalability Traditional RCA Engine vs. Proposed Approach Monitoring Data (Omnibus) Correlation Engine (ITNM RCA) Fault diagnosis Fault Signatures (Network Patterns) Pair-wise correlation rules Complexity: Monitoring data x Monitoring data x Rules Monitoring data x Network Diameter x Signatures Monitoring data ~ linear in network size Network diameter ~ logarithmic in network size for power-law networks

7. Step 1: Learning Network Faults • Learn fault signatures from historical network event data • Fault Synopsis: Fault Type  Network Pattern • Fault Signature: Network Pattern  <Fault Type, Spatial Pattern to Localize Faulty Node> • Fault Diagnosis: <Spatial Pattern to Localize Faulty Node, Network Topology>  Faulty Node • Fault Prediction: Use incrementally matchable network patterns • Use indexable network patterns • Topological relationships are invertible: neighbor-1 = neighbor, downstream-1 = upstream Fault Synopsis Fault Signature

8. Step 2: Online Matching • Fault localization using topological indices and hierarchical evidence aggregation • Topology indexing algorithms + space-time trade off in computing R(x) and R-1(x) • R Є {upstream, downstream, neighbor, tunnel, …} • Scalable hierarchical evidence aggregation for efficient fault diagnosis c2 c3 c1 Evidence Aggregation Scalable Hierarchical Evidence Aggregation

9. Details Set of topological relationships: SE, NE, DS, US, TN Principle of minimum explanation Interval Filter: segment event dataset into event bursts Support Filter: eliminate high frequency (regular n/w ops) and low frequency burst sets (noise) Periodicity Filter: eliminate burst sets with high periodicity (maintenance ops) Markov chains and maximum likelihood estimation Fault Signatures Extract temporal patterns Extract topological patterns Preparation of training data OFFLINE LEARNING Event Datasets Network Topology ONLINE MATCHING Scalable Evidence Aggregation Evidences: <f, v, Rv> Match temporal patterns Fault Diagnosis and Prediction Event Stream Fault Signatures Indexed network topology BIRCH data structure (hierarchical aggregation) Optimizations: filter-and-refine (Bloom filter) + slotted aggregation (BIGTABLE) Min-Heap + incremental pattern matching Inverted Index for constant time lookup Network Topology Space-Time tradeoffs

10. Fault Diagnosis & Prediction: Scalability • Result Summary: • SNMP Trap messages from a large enterprise (7 ASes, 32 IGP networks, 871 subnets, 1,268 VPN tunnels, 2,068 main nodes, 18,747 interfaces and 192,000 entities) over 14 days in 2007 • Topology dataset – European backbone network (2,383 main nodes, spans 7 countries, 11 ASes and over 100,000 entities) • Network fault simulator and monitoring data generation • Linear scalability; further optimizations: prune-and-search; slotted hierarchical aggregation • Ongoing activities • Integration with IBM Tivoli Network Management suite (ITNM) for live testing and fine-tuning • Network patterns for access control on information flows over : (i) ENRON email data & organization role topology; (ii) Smallblue data & social + information network topology

11. Summary • Network patterns encode spatial-temporal properties of various networks • Ability to scalably mine and match network patterns is key for understanding global network phenomena • Case study on fault diagnosis and prediction in communication networks • Complexity of solution has to be linear in network size • Topologically indexed databases was a key tool for addressing scalability • Explore more complex network patterns for information, social and biological networks which exhibit stronger coupling relationships • A failed router does not cause its neighboring router to fail • A corrupt information node can corrupt its neighbor (e.g., summary node) • A diseased enzyme can catalyze/inhibit its neighbors

12. Questions? Mudhakar Srivatsa msrivats@us.ibm.com