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CS525 – In Byzantium

CS525 – In Byzantium. The Byzantine Generals Problem Leslie Lamport, Robert Shostak, and Marshall Pease ACM TOPLAS 1982. Presented by Keun Soo Yim March 19, 2009. Dr. Lamport Byzantine Clock Sync. Dist. Snapshot. Byzantine Generals Problem (BGP). A.C. 330. Goals

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CS525 – In Byzantium

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  1. CS525 – In Byzantium The Byzantine Generals ProblemLeslie Lamport, Robert Shostak, and Marshall PeaseACM TOPLAS 1982 Presented by Keun Soo Yim March 19, 2009 • Dr. Lamport • Byzantine • Clock Sync. • Dist. Snapshot

  2. Byzantine Generals Problem (BGP) A.C. 330 • Goals • Consensus (same plan) btw. loyal generals • A small number of traitors cannot cause the loyals to adopt a bad plan • Do not have to identify the traitors 100K • N Generals • Some are traitors • Message passing 50K 30K 10K 20K (commander) 40K

  3. BGP in Distributed Systems A thousand years later… • Goals • All correct nodes share the same global info. • Ensure that N corrupted nodes can not change the shared global info., and maximize N • Identification of corrupted nodes would be needed • What’s difference btw. BGP and consensus algo.? • Fail-stop vs. fail-silent violation. Design goal. • N Computers • Some misbehave • HW Fault, SW bug, Security attack, misconfiguration • Message passing

  4. Naïve Sol. & 3-General Impossibility • Naïve solution • Each general sends its value, v(i), to all others • Majority vote using v(1), v(2), …, v(n) • Is it true that no solutions with fewer than 3m+1 generals can cope with m traitors? If so, why?

  5. 3m-General Impossibility • If there is a solution for 3m generals with m traitors, it can be reduced to a solution of 3-General problem “3m+1<=n” “3m+1>n”

  6. Solution I – Oral Messages • Formal definition of OM(M) • Command broadcasts its value to all lieutenants • Each lieutenant acts as commander of OM(m-1) • n = 4, m = 1 • L1 and L2 both receive v,v,x. (Consensus) • L1 and L2 obey C • All lieutenants receive x,y,z • Lieutenant can identify commander is a traitor • What is communication complexity of this algorithm?

  7. Communication Complexity • O(nm) OM(m) triggers n-1 OM(m-1) OM(m-1) triggers n-2 OM(m-2) … OM(m-k) will be called by (n-1)…(n-k) times … OM(0)

  8. Solution II – Signed Messages • Can we cope with any number of traitors? If so, how? • Prevent traitors lie about the commander’s order • Message are signed by commander • The sign can be verified by all loyal lieutenants • When lieutenant receives no new messages,and select majority as the desired action • All loyals receive the same set of cmds eventually • If the commander is loyal, it works • What if the commanderis not loyal?

  9. Discussion Point • Are the assumptions realistic? • Reliable communication channel • Absence of a message can be detected. (e.g., Timeouts or synchronized clocks ) • Failure of communication line cannot be distinguished from failure of nodes. • This is acceptable since we tolerate failures of m nodes. • Can we determine the origin of message?Anyone can verify authenticity of signature? • Unforgeable signatures using asymmetric cryptograph.

  10. PeerReview: Practical accountability for distributed systems Andreas Haeberlen, Petr Kuznetsov, and Peter DruschelSOSP 2007 (Acknowledgement: Some of this presentation slide are borrowed from the original author’s one)

  11. Practical Use Case of BGP • Distributed file systems • Many small, latency-sensitive requests (tampering with files, lost updates) • Overlay multicast • Transfers large volume of data (tampering with content, freeloading) • P2P email • Complex, large, decentralized (Denial of service by misrouting) •  Not only consensus but also identifying faulty nodes is important!

  12. PeerReview • Providing accountability for distributed systems • Stores all I/O events as a log • Selected nodes are responsible for auditing the log • Assumptions: • System is modeled as deterministic state machines • State machines have reference implementations • Eventual communication • Signe d message 12

  13. Fault Detection • How to recognize faults in a log? • Assumption • Node can be modeled as a deterministic state machine • To audit a node • Start from a snapshot in the log • Replay inputs to a trusted copy of the state machine • Check outputs against the log Module A State machine Module B Network Log Module A Module B Input if ≠ =? Output

  14. Communication Algorithrm • All nodes keep a log of their inputs & outputs • Including all messages • Each node has a set of witnesses, who audit its log periodically • If the witnesses detect misbehavior, they • generate evidence • make the evidence avai-lable to other nodes • Other nodes check evi-dence, report fault A's witnesses C D E M M A M B A's log B's log

  15. B's log H4 Recv(M) H3 Hash chain Send(Z) H2 Recv(Y) H1 H0 Send(X) Tamper-Proofing Message • What if a node modifies its log entries ? • Log entries form a hash chain • Inspired by secure histories [Maniatis02] • Signed hash is included with every message •  mi = (si, ti, ci) • hi = H(hi-1||si||ti||H(ci)) • Commitement protocol •  Sender and receviercommit to its current state Hash(log) B A ACK Hash(log)

  16. Provable Guarantees • Completeness: Faults will be detected • Accuracy: Good nodes cannot be accused If node commits a fault and has a correct witness, then witness obtains • a proof of misbehavior (PoM), or • a challenge that the faulty node cannot answer If node is correct • there can never be a PoM, and • it can answer any challenge

  17. Communication Overhead 100 80 60 Checking logs Avg traffic (Kbps/node) 40 Signatures and ACKs 20 Baseline traffic 0 Baseline 2 1 5 3 4 Number of witnesses

  18. Discussion Point • How would you determine the number of witnesses in a practical system? How to select them? • PeerReview is the first, practically applicable, faulty node detection technique. Then how can we make a consensus between correct nodes in a scalable manner?

  19. Zyzzyva: Speculative Byzantine Fault Tolerance Ramakrishna Kotla, Lorenzo Alvisi, Mike Dahlin, Allen Clement and Edmund Wong University of Texas at Austin SOSP 2007 Presented by Hui Xue, UIUC

  20. MotivationByzantine Fault Tolerance • Why we need BFT systems? • Software systems : Valuable + Not reliable enough • Amazon S3 crashed for hours in 2008 Reason: One corrupted bit • Akami central nodes • Hardware : Cheaper now • Idea • Use more hardwareMake software systems more reliable

  21. Motivation for Zyzzyva

  22. Assumptions (System Model) • (Almost) asynchronous system • Multicast; unordered • Independent failures • Replica: at most f any kind of faults • Network: unreliable – can delay, duplicate, corrupt or drop messages • Sufficiently strong cryptographic techniques • All public keys known by everyone • Need bounded msg delay in rare cases (liveness)

  23. Background:Practical Byzantine Fault Tolerance PBFT: establish order before execution Pre-Prepare Prepare Commit Reply Client Primary Replica Replica FaultyReplica OK, Req, # n! Req, # n Req, # n? What is the problem? Before execution 4 network delays Many messages

  24. Zyzzyva: Just Do It Speculative execution: Just do it! Pre-Prepare Spec-Exe Reply Client Primary Replica Replica Replica Just do it ! Req, # n GREAT! Just do it ! Who is making the difference? Just do it !

  25. Client Can Correct Order CASE 1 Client’s Power OrderCorrect Pre-Prepare Spec-exe Reply Client Primary Replica Replica FaultyReplica OrderCorrectNow! Just do it ! Just do it ! To This state!

  26. Restart Req! Client Can Correct Order CASE 2 Client’s Power Pre-Prepare Spec-exe Reply Client Primary Replica Replica FaultyReplica Just do it ! Just do it !

  27. Client Can Correct Order CASE 3 Client’s Power Pre-Prepare Spec-exe Reply Client Primary Replica Replica Replica Just do it ! Change Primary! Just do it !

  28. Design of Protocol • Other Sub protocols: • Fill hole Sequence # received: N+4 Sequence # expected: N+1 < N+4 (hole in between) Send <FILL-HOLE> to 1. Primary 2. Slow primary, then all replicas

  29. Optimizations • Separating agreement from execution • Batching requests • Caching out of order requests • Read only operations: 2f+1 consistent is enough • Single full response

  30. Performance: Throughput

  31. Performance: Latency

  32. Conclusion • Clever Observation: • We can execute before the order is established, hoping we are right. • Pros • Practical, High throughput + low latency • Cons • BFT suffer from deterministic bugs • Malicious behaviors may affect performance

  33. Questions • Why Zyzzyva is fast? • What is the main difference between Zyzzyva and previous BFT papers? • What does “zyzzyva” mean? • Do you buy the idea of BFT at all? • Name some examples of BFT in real applications.

  34. Thank you! • This is the end of Zyzzyva • Questions?

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