Fault Detection, Isolation, and Diagnosis In Multihop Wireless Networks

1. Fault Detection, Isolation, and Diagnosis In Multihop Wireless Networks Lili Qiu, Paramvir Bahl, Ananth Rao, and Lidong Zhou Microsoft Research Presented by -Maitreya Natu

2. Network Management Faults directory … Root cause Healthy network Corrective measure Faulty network

3. Tasks involved in Network Management • Continuously monitoring the functioning • Collecting information about the nodes and the links • Removing inconsistencies and noise from the reported information • Analyzing the information • Taking appropriate actions to improve network reliability and performance

4. Challenges in wireless networks • Dynamic and unpredictable topology • link errors due to fluctuating environment conditions • Node mobility • Limited capacity • Scarcity of resources • Link attacks

5. Proposed framework • Reproduce inside a simulator, the real-world events that took place • Use online trace driven simulation to detect faults and analyze the root causes

6. Network Management Network model Types of faults Healthy network Faults directory … Creating a network model

7. Network Management Network model Fault diagnosis Types of faults Faulty network Faults directory … Detected faults

8. Network Management Network model what-if analysis Types of faults Faults directory Corrective measures … Detected faults

9. Key issues • How to Accurately reproduce what happened in the network inside a simulator • How to build fault diagnosis on top of a simulator to perform root cause analysis

10. Accurate modeling • Use real traces from the diagnosed network • Removes dependency on generic theoretical models • Captures nuances of the hardware, software and environment of the particular network • Collect good quality data • By developing a technique to effectively rule out erroneous data

11. Fault diagnosis • Performance data emitted by trace driven simulation is used as baseline • Any significant deviation indicates a potential fault • Simulator selectively injects a set of suspected faults and searches a set that most produces the expected performance • An efficient algorithm is designed to determine root causes

12. System Overview 6. Search for set of faults that result in best explanation Link/Node failure Faults Directory 7. Report the cause of failure simulator Link RSS Interference Injection Link Load Error Traffic Simulator +/- Expected loss rate Throughput noise Topology changes Routing update 5. Discrepancy Found Loss rate Throughput noise 4. Compare Expected & Average Performance 1. Receive Cleaned Data 2. Drive Simulation 3. Compute Expected Performance

13. Why Simulation Based Diagnosis? • Much better insights into the network behavior than any heuristic or theoretical technique • Highly customizable and applies to a large class of networks • Ability to perform what-if analysis • Helps to foresee the consequences of a corrective action • Recent advances in simulators have made possible their use for real-time analysis

14. Accurate modeling Network model Types of faults Healthy network Faults directory …

15. Current network models • Bayesian networks to map symptom-fault dependencies • Context Free Grammars • Correlation Matrix

16. Can on-line simulations be used as core tool?

17. Building confidence in simulator accuracy • Problem • Hard to accurately model the physical layer and the RF propagation • Traffic demands on the router are hard to predict

18. Building confidence in simulator accuracy • Problem • Hard to accurately model the physical layer and the RF propagation • Traffic demands on the router are hard to predict • Solution • “after the fact” simulation • Agents periodically report information about the link conditions and traffic patterns to the link simulators

19. Simulations when the RF condition of the link is good Modeling the contention from flows within the interference and communication ranges. Modeling the overheads of the protocol stack such as parity bits, MAC-layer back-off, IEEE 802.11 inter-frame spacing and ACK, and headers.

20. Simulations with varying received signal strength Simulator estimate deviates from real, when signal strength is poor Throughput matches closely with the simulator’s estimate, when signal quality is good

21. Why simulation results deviate in case of poor signal strength? • Lack of accurate packet loss as a function of packet size, RSS and ambient noise. • Depends on signal processing hardware and the RF antenna within the wireless cards • Lack of accurate auto-rate control • Adjustment of sending rate done by WLAN cards based on the transmission conditions

22. How to model auto-rate control done by WLAN cards? • Use Trace driven simulation • When auto-rate is in use • Collect the rate at which the wireless card is operating and provide the reported rate to the simulator • Otherwise • Data rate is known to the simulator

23. How to model accurate packet loss as a function of packet-size, RSS and ambient noise? • Use offline analysis • Calibrate the wireless cards and create a database associating environmental factors with expected performance • E.g., mapping from signal strength and noise to loss rate

24. Experiment to model the loss rates due to poor signal strength • Collect another set of traces • Slowly send out packets • Place packet sniffers near both the sender and the receiver, and derive loss rate from the packet level trace • Seed the wireless link in the simulator with a Bernoulli loss rate that matches loss rate with the real traces

25. Even though the match is not perfect, its not expected to be a problem, because many routing protocols try to avoid the use of poor quality links Poor quality links are used only when certain parts of mesh network have poor connectivity to the rest of the network In a well-engineered network, not many nodes depend on such bad link for routing Estimated and measured throughput when compensating for the loss rate due to poor signal strength Loss rate and the measured throughput do not monotonically decrease with the signal strength due to the effect of auto-rate

26. Stability of channel conditions • How rapidly do channel conditions change and how often a trace should be collected?

27. Temporal fluctuation in RSS • Fluctuation magnitude is not significant • Relative quality of signals across different number of walls remain stable

28. Stability of channel conditions • How rapidly do channel conditions change and how often a trace should be collected? • When the environment is generally static, nodes may report only the average and standard deviation of the RSS to the manager every few minutes

29. Dealing with imperfect data • By neighborhood monitoring • Each node reports performance and traffic statistics for its incoming and outgoing links • And for other links in its communication range • Possible when node is in promiscuous mode • Thus multiple reports are sent for each link • Redundant reports can be used to detect inconsistency • Find the minimum set of nodes that can explain the inconsistency in the reports

30. Summary • How to accurately model the real behavior? • Solution: Use trace-based simulation • Problem: Simulation results are good for strong signals but deviate for bad RF conditions • Need to model the autorate control • Use trace-driven data • Need to model the loss rate due to poor signal strength • Use offline analysis • How often a trace should be collected? • Very little data (average and standard deviation of RSS), at fairly low time granularity, as channels are relatively stable • How to deal with imperfect data • By neighborhood monitoring

31. Fault diagnosis Network model Types of faults Faulty network Faults directory … Detected faults

32. Current fault diagnosis approaches • AI techniques • Rule based systems • Neural networks • Model traversing techniques • Dependency graphs • Causality graphs • Bayesian networks

33. Fault Isolation and Diagnosis • Establish the expected performance in the simulation • Find difference between expected and observed performance • Search over the fault space to detect which set of faults can re-produce performance similar to what has been observed

34. Collecting data from traces • Trace data collection • Network topology • Each node reports its neighbor and routing tables • Traffic statistics • Each node maintains counters of traffic sent and received from immediate neighbors • Physical medium • Each node reports signal strength of wireless links to neighbors • Network performance • Includes both the link and end-to-end performance, which can be measured through loss rate, delay, throughputs • Focus is on link level performance

35. Simulating the network performance • Traffic load simulation • Link based traffic simulation • Adjust application sending rate to match the observed link-level traffic counts • Route simulation • Use actual routes taken by packets as input to the simulator • Wireless signal • Use real measurement of signal strength • Fault injection • Random packet dropping • External noise sources • MAC misbehavior

36. Fault diagnosis algorithm • General approach Simulator Expected performance Network settings Simulator Observed performance Network settings Faults set How to find ?

37. How to search the faults efficiently? • Different types of faults often change one or few metrics • E.g., random dropping only affects link loss rate • Thus use metrics in which observed and expected performance is significantly different, to guide the search

38. Consider large deviation from expected performance as anomaly Use decision tree to determine the type of fault Fault type determines the metric to quantify performance difference Locate faults by finding the set of nodes and links with large difference between expected and observed performance Scenario where faults do not have strong interactions

39. Scenario where faults have strong interactions • Get the initial diagnosis set from the decision tree algorithm • Iteratively refine the fault set • Adjust the magnitudes of faults in the fault set • Translate difference in performance into change in faults’ magnitude • It maps the impact of a fault into its magnitude • Remove fault whose magnitude is too small • Add new faults that can explain large differences between the expected and observed performances • Iterate till the change in fault set is negligible

40. Example scenario 2 3 1 4 5

41. Example scenario • Observed performance • Increased loss rate at 1-4 and 1-2 • No increase in the sending rate of 1-4, 1-2 • No increase in noise experienced by • neighbors 2 3 Inference 1 Increased Sending Rate 4 5 Y N Too low CW Increased Noise Y N Increased Loss Noise Y N Packet Drop Normal

42. Example scenario • Observed performance • Increased loss rate at 1-4 and 1-2 • No increase in the sending rate of 1-4, 1-2 • No increase in noise experienced by • neighbors 2 3 Inference 1 Increased Sending Rate 4 5 Y N Too low CW Increased Noise Y N Increased Loss Noise Y N Packet dropping at node 1 Packet Drop Normal

43. Accuracy of fault diagnosis • Correctness of the model • Complete information • Consistent information • Timely information • Correctness of the reported symptoms • Right size of the threshold to report a symptom • Difference in the behavior of faults • Timely reporting of symptoms

44. System implementation • Windows XP • Agents run on every wireless node and reports information collected on demand • Managers collect and analyze information • Collected information is cast into performance counters supported by Windows • Manager is connected to a backend simulator. Collected information is converted to script to drive the simulation • Testbed: • Multihop wireless testbed built using IEEE 802.11a cards • Commercially available network sniffer called Airopeek is used for data collection • Native 802.11 NICs provide rich set of networking information

45. Evaluation: Data collection overhead Data collection traffic has little effect Overhead < 800 bits/s/node Management traffic overhead Performance of FTP flow with and without data collection No data cleaning: Each link is reported only once With data cleaning: Each link is reported by all observers for consistency check

46. Data cleaning effectiveness Coverage greater than 80% in all cases Higher accuracy with grid topology Higher coverage when using history Higher accuracy with denser networks Higher accuracy with client-server traffic

47. Evaluation: Fault diagnosis Detecting external noise Detecting random dropping • Symptom: Significant difference in noise level in nodes • Noise sources are correctly identified with • at most one or two false positives • Inference error in magnitudes of noises is • within 4% • Symptom: Significant difference in loss rates in links • Less than 20% of fault links are left • undetected • No-effect faults are faulty links sending • less that threshold (250) packets of data

48. Evaluation: Fault diagnosis Detecting combinations of all Detecting MAC misbehavior • Symptom: Significant discrepancy in throughput on links • Coverage is mostly around 80% or higher • False positives within 2

49. what-if analysis Network model Types of faults Faults directory Corrective measures … Detected faults

50. What-if analysis Topology Diagnosis Corrective measures