1. 1 The Case for Byzantine Fault Detection

2. 2 Challenge: Byzantine faults Distributed systems are subject to a variety of failures and attacks Hacker break-in Freeloading Censorship Data corruption Software/hardware failure Byzantine failure model: Faulty nodes may exhibit arbitrary behavior Dependable systems must be protected against Byzantine faults

3. 3 Existing approach: Fault tolerance Byzantine fault tolerance (BFT) can mask a limited number of Byzantine faults Example: Castro and Liskov [OSDI'99] Client Server replicas

4. 4 Alternative approach: Fault detection Nodes monitor each other for faulty behavior When a fault occurs, the correct nodes identify the faulty node(s) distribute evidence of the fault Nodes can isolate the faulty node + initiate recovery Byzantine Fault Detection

5. 5 Alternative approach: Fault detection Nodes monitor each other for faulty behavior When a fault occurs, the correct nodes identify the faulty node(s) distribute evidence of the fault Nodes can isolate the faulty node + initiate recovery D C B A E Set X=5 D C A E D C B A E OK X=? X=7 E: X=5  7! B

6. 6 Level3 Best approach depends on the application Best-effort service Goal: Find faulty components Wide-area delays, limited bandwidth, many nodes Air traffic control Inter-domain routing Failures may be fatal! Goal: Mask fault symptoms Delays negligible, bandwidth plentiful, few nodes Machine room AT&T Sprint Typical application for Fault DetectionTypical application for Fault Tolerance

7. 7 Detection can provide accountability In an accountable system: Actions are undeniable State is tamper-evident Correctness can be certified Good nodes can provide evidence that they are good Bad nodes cannot hide evidence of misbehavior Proven concept in society Banking, administration... Desirable for distributed systems [Yumerefendi05] Example: Building trust in federated systems

8. 8 What about performance? If up to f nodes can be faulty, we need f+1 replicas to guarantee detection (fault tolerance: 3f+1) More throughput using the same resources Works even when >33% of the nodes can become faulty Detection can defer overhead to periods of low load System can deliver high peak throughput Detection does not require consensus Potentially less expensive than BFT

9. 9 Outline Introduction BFD abstraction PeerReview algorithm Conclusion

10. 10 How is BFD used? Each correct node has state machine + detector Detector can inspect all messages at its local node When detector observes a fault on another node, it informs its local application, and it provides evidence of the fault to other detectors ? Application State machine Detector Network Node X is faulty! No assumptions about faulty nodes

11. 11 Only observable faults can be detected Two classes of observable faults: Detectable faultiness: Node breaks the protocol Detectable ignorance: Node refuses to respond As long as the faulty node continues to follow the protocol, BFD cannot detect this! Set X=5 OK Get X 5 ABC Correct Set X=5 OK Get X ABC Set X=5 OK Get X 7 ABC Detectably ignorant Detectably faulty

12. 12 BFD can give strong guarantees Three types of detector output Trusted, suspected, exposed Strong completeness "No false negatives" Strong accuracy "No false positives" Precise definitions are in the paper Trusted Suspected Exposed

13. 13 Outline Introduction BFD abstraction PeerReview algorithm Conclusion

14. 14 Assumptions 1. Protocol can be modeled as a deterministic state machine 2. Each node has a strong identity, as well as a public/private keypair for signing messages 3. The faulty nodes cannot  prevent two correct nodes from communicating  break the cryptographic keys

15. 15 Secure logging All messages are signed and acknowledged Each node keeps a log of all local inputs and outputs Nodes must commit to the contents of their log Log is tamper-evident [Maniatis02] Rcv(A, "Set X=5") Send(A, "Okay") Rcv(C, "Get X") Send(C, "5") Snd(B, "Set X=5") Rcv(B, "Okay") Snd(B, "Get X") Rcv(B, "5") B's log A B C

16. 16 Detecting ignorance If a node refuses to acknowledge a message Send message as evidence to other nodes Correct nodes will challenge the ignorant node to prove that its log contains a 'Rcv' entry for that message A correct node can always respond Rcv(A, "Set X=5") Send(A, "Okay") Recv(C, "Get X") A B C

17. 17 Detecting faultiness Nodes can audit each other's log at any time Auditors replay input in the log, compare output If a divergence is detected Send log as evidence to other nodes Other nodes can repeat the same procedure to check whether the node is really faulty (no he-said-she-said!) Rcv(A, "Set X=5") Send(A, "Okay") Rcv(B, "Get X") Send(B, "7") A B C B' Rcv(A, "Set X=5") Send(A, "Okay") Rcv(B, "Get X") Send(B, "5") State machine B is expected to run Rcv(A, "Set X=5") Send(A, "Okay") Rcv(B, "Get X") Send(B, "7") Snap- shots

18. 18 Summary New approach: Byzantine Fault Detection Alternative to fault tolerance Provides accountability Fault Detection can give strong guarantees Eventual strong accuracy and completeness Early results indicate Fault Detection is practical Example: PeerReview algorithm