1. Automatic Synthesis of Fault-Tolerance Ali Ebnenasir Software Engineering and Network Systems Laboratory Computer Science and Engineering Department Michigan State University Advisor: Dr. Sandeep Kulkarni

2. 2 Problem Given a program p and a class of faults f, Question: How do we add desired fault-tolerance properties to p in order to create a new program p’ such that: Requirements: 1.In the absence of f, the resulting fault-tolerant program p’ behaves similar to p 2.In the presence of f, the resulting fault-tolerant program p’ satisfies the desired fault-tolerance property.

3. 3 Solution Strategies Two possible approaches 1.Redesign p’ and verify its correctness w.r.t problem requirements Expensive approach 2.Automatically synthesize p’ from p Correct by construction

4. 4 Previous Work on Automated Synthesis

5. 5 Synthesis: Specification-Based Specification of p (Temporal Logic Expressions/ Automata) Synthesis Algorithm (prove the satisfiability of the specification) Faults Fault-tolerance requirements (Temporal Logic Expressions) Fault-tolerant program p’ Program synthesis:Fault-Tolerance synthesis: [EmersonClarke 1982][AroraAttieEmerson 1998] [AttieEmerson 2001] [KupfermannVardi 2001]

6. 6 Synthesis: Calculational [KulkarniArora 2000] [KulkarniAroraChippada 2001] Fault-intolerant program p (Transitions) Synthesis Algorithm (Calculate the set of transitions) Fault-tolerant program p’ (Transitions) Fault-tolerance requirements Faults (Transitions)

7. 7 The Complexity of Calculational Synthesis High atomicity model: processes can atomically read/write all program variables –Polynomial in the state space of the fault-intolerant program p [KA00] Low atomicity model (distributed programs): processes have read/write restrictions with respect to program variables –Exponential in the state space of the fault-intolerant program p for synthesizing masking fault-tolerance [KA00] [KA00] S.S. Kulkarni and A. Arora, Automating the addition of fault-tolerance, FTRTFT 2000. Propose techniques for the synthesis of fault-tolerant distributed programs

8. 8 Outline Preliminary concepts Synthesis problem Current results –Theoretical issues Step-wise automation Polynomial-time boundary Heuristics Pre-synthesized fault-tolerance components –Practical issues A framework for the synthesis of fault-tolerant programs Contributions Open problems

9. 9 Preliminary Concepts: Programs and Faults Program –Finite number of variables with finite domains –Finite number of processes State: a valuation of program variables Finite state space S p State predicate X X  S p Program p, Fault f  { (s 0, s 1 ) | (s 0, s 1 )  S p  S p } Closure: X is closed in p X SpSp p

10. 10 Preliminary Concepts: Specifications and Fault-Tolerance Safety specification: something bad never happens –Representation  { (s 0, s 1 ) | (s 0, s 1 )  S p  S p } E.g., transitions that change the value of a counter from non-zero values to zero Liveness specification: something good will eventually happen –In the absence of faults, fault-tolerant program p’ satisfies the liveness specification of the fault-intolerant program p Invariant S, fault-span T  S p Fault-tolerance: Failsafe, Nonmasking, Masking S T p/f p f SpSp Program Fault

11. 11 Preliminary Concepts: Distribution Model Read/Write restrictions (low atomicity model) –Assumption: a process cannot write a variable that it cannot read. Example: program p Two processes j, k Two Boolean variables a and b Process j cannot read b, but can read and write a Write restrictions –Can we include the following transition in the set of transitions of process j ? Write restrictions identify the set of transitions of each process. a=0,b=0 a=1,b=1 jk ab

12. 12 Preliminary Concepts: Distribution Model – Continued Read restrictions –Can we include the following transition in the set of transitions of process j ? Groups of transitions (instead of individual transitions) must be chosen. a=0,b=0 a=1,b=0 a=0,b=1 a=1,b=1 Only if we include the transition

13. 13 Outline Preliminary concepts Synthesis problem Current results –Theoretical issues Step-wise automation Polynomial-time boundary Heuristics Pre-synthesized fault-tolerance components –Practical issues A framework for the synthesis of fault-tolerant programs Contributions Open problems

14. 14 Synthesis Problem Synthesis Algorithm Fault-intolerant program p Specification Spec Invariant S (Masking/Nonmasking/Failsafe) Fault-tolerant program p' Invariant S' Faults f Distribution restrictions Requirements 1.No new behaviors are added in the absence of faults. 2.In the presence of faults, p’ provides desired level of fault-tolerance. Desired level of Fault-intolerance (Masking/Nonmasking/Failsafe)

15. 15 Outline Preliminary concepts Synthesis problem Current results –Theoretical issues step-wise automation Polynomial-time boundary Heuristics Pre-synthesized fault-tolerance components –Practical issues A framework for the synthesis of fault-tolerant programs Contributions Open problems

16. 16 Theoretical Issues: Step-Wise Automation Intolerant Program Nonmasking fault-tolerant Masking fault-tolerant Failsafe fault-tolerant Failsafe [KA00]

17. 17 Outline Preliminary concepts Synthesis problem Current results –Theoretical issues step-wise automation Polynomial-time boundary Heuristics Pre-synthesized fault-tolerance components –Practical issues A framework for the synthesis of fault-tolerant programs Contributions Open problems

18. 18 Theoretical Issues: Polynomial-Time Boundary Complexity: reduction from 3-SAT to the problem of synthesizing failsafe fault-tolerant distributed programs In general, the problem of synthesizing failsafe fault-tolerant distributed programs from their fault-intolerant version is NP-complete. Intuitively, the exponential complexity is due to the inability of a process to safely estimate unreadable variables even in the absence of faults (grouping issue). What are the necessary and sufficient conditions for polynomial synthesis of failsafe fault-tolerant distributed programs? Restrictions on –The transitions of the fault-intolerant programs –Specifications

19. 19 Theoretical Issues: Monotonicity of Specifications Definition: A specification spec is positive monotonic with respect to a Boolean variable x iff: For every (s 0, s 1 ) and (s’ 0, s’ 1 ) grouped together due to inability of reading x s1s1 s0s0 x = false If Does not violate safety s’0s’0 s’1s’1 x = true Does not violate safety Then

20. 20 Theoretical Issues: Monotonicity of Programs Definition: Program p with invariant S is positive monotonic with respect to a Boolean variable x iff: For every (s 0, s 1 ) and (s’ 0, s’ 1 ) grouped together due to inability of reading x s1s1 s0s0 Invariant S x = false s’0s’0 s’1s’1 x = true Monotonicity requirements capture the notion that safe assumptions can be made about variables that cannot be read

21. 21 Theoretical Issues: Monotonicity Theorem Sufficiency: if Program is negative monotonic, and Spec is positive monotonic –Or Program is positive monotonic, and Spec is negative monotonic Then Synthesis of failsafe fault-tolerance can be done in polynomial time Necessity: If only one of these conditions is satisfied then synthesizing failsafe fault-tolerance remains NP-complete. For many problems, these requirements are easily met (e.g., Byzantine agreement, consensus, atomic commit)

22. 22 Theoretical Issues: An Example for Monotonicity Theorem Dijkstra’s guarded commands (actions) –Guard  Statement –{ (s 0, s 1 ) | Guard holds at s 0 and atomic execution of Statement yields s 1 } Example: Byzantine agreement –Safety Specification of Byzantine agreement: Agreement: No two non-Byzantine non-generals can finalize with different decisions Validity: If g is not Byzantine then no non-Byzantine process can finalize with a different decision with respect to g –Processes: General, g, and three non-generals j, k, and l –d.g : {0, 1} –d.j, d.k, d.l : {0, 1, ┴ } –b.g, b.j, b.k, b.l : {0, 1} –f.j, f.k, f.l : {0, 1} g lkj

23. 23 Theoretical Issues: An Example for Monotonicity Theorem Program actions for process j d.j = ┴  f.j = 0  d.j := d.g d.j ≠ ┴  f.j = 0  f.j := 1 Fault transitions for process j ¬b.g  ¬b.j  ¬b.k  ¬b.l  b.j := 1 b.j  d.j :=0|1 Read/Write restrictions: –Readable variables for process j: b.j, d.j, f.j, d.g, d.k, d.l –Process j can write d.j, f.j

24. 24 Theoretical Issues: An Example for Monotonicity Theorem – Continued Observation 1: Negative monotonicity of specification with respect to f.j Observation 2: Positive monotonicity of program, consisting of the transitions of j, with respect to f.k Observation 3: Positive monotonicity of specification with respect to b.j –The specification does not stipulate anything about the Byzantine processes Observation 4: Negative monotonicity of program, consisting of the transitions of j, with respect to b.k Synthesis of agreement program that is failsafe to Byzantine faults can be done in polynomial time.

25. 25 Outline Preliminary concepts Synthesis problem Current results –Theoretical issues step-wise automation Polynomial-time boundary Heuristics Pre-synthesized fault-tolerance components –Practical issues A framework for the synthesis of fault-tolerant programs Contributions Open problems

26. 26 Theoretical Issues: Heuristics Heuristic: A strategy for making deterministic decisions to reduce the complexity of synthesis –Example: Reuse the structure of nonmasking programs in the synthesis of their masking versions Intolerant Program Nonmasking fault-tolerant Masking fault-tolerant Fault-Tolerance Enhancement

27. 27 Outline Preliminary concepts Synthesis problem Current results –Theoretical issues step-wise automation Polynomial-time boundary Heuristics Pre-synthesized fault-tolerance components –Practical issues A framework for the synthesis of fault-tolerant programs Contributions Open problems

28. 28 Theoretical Issues: Pre-Synthesized Fault-Tolerance Components What if existing heuristics fail? How can we reuse the techniques used in the synthesis of a program, in the synthesis of another program? Can we encapsulate commonly encountered synthesis patterns in terms of pre-synthesized fault-tolerance components? Detectors and correctors are necessary and sufficient in the design of fault-tolerance [AK98] –Detectors and correctors have the potential to provide a rich library of pre- synthesized fault-tolerance components [AK98] A. Arora and S.S. Kulkarni, Detectors and Correctors: A Theory of Fault-Tolerance, IEEE ICDCS 1998.

29. 29 Theoretical Issues: Using Pre-Synthesized Components If available heuristics fail to add recovery from a deadlock state s d Automatically specify the required component Extract the component from the component library Verify the interference-freedom of the composition Add extracted component to the fault-intolerant program

30. 30 Theoretical Issues: Pre-Synthesized Components - Achievements Reducing the chance of failure in the synthesis Providing a mechanism for the reuse of synthesis techniques Extending the scope of synthesis problem where the state space is expanded during the synthesis Controlling the way new variables are introduced

31. 31 Outline Preliminary concepts Synthesis problem Current results –Theoretical issues step-wise automation Polynomial-time boundary Heuristics Pre-synthesized fault-tolerance components –Practical issues A framework for the synthesis of fault-tolerant programs Contributions Open problems

32. 32 Practical Issues: Framework Goals Goals of the framework design –Ability to synthesize fault-tolerant programs from their fault- intolerant versions –Ability to integrate new heuristics into the framework –Ability to change implementation

33. 33 Practical Issues: Synthesis Framework Synthesis algorithm Interactive user interface The user (Fault-tolerance developer) Query Results p, S, f, spec p’, S’ Guarded commands, State predicates Guarded commands/ Promela, State predicates Library of pre-synthesized fault-tolerance components Pre-synthesized detectors/correctors Component specification

34. 34 Practical Issues: Framework Internals –Synthesis Algorithm Remove bad transitions Remove bad states Expand the reachability graph Initialization Ensure safety Ensure deadlock freedom Calculate a valid fault-span Preserve Invariant Ensure deadlock freedom Calculate a valid invariant Modify Invariant Resolve non-progress cycles Reachability graph of the fault-tolerant program Interaction points p, S, f, spec p’, S’ Fault-tolerant program Fault-intolerant program

35. 35 Practical Issues: Current Status of the Framework Example synthesized programs: –Token ring with 7 processes –Byzantine agreement with 4 non-general processes and one general process –An agreement program that is subject to both Byzantine and fail-stop faults (1.3 million states) Currently, the framework can –handle different types of faults (e.g., process restart, Byzantine, fail- stop) –synthesize programs that are simultaneously subject to multiple types of faults

36. 36 Outline Preliminary concepts Synthesis problem Current results –Theoretical issues step-wise automation Polynomial-time boundary Heuristics Pre-synthesized fault-tolerance components –Practical issues A framework for the synthesis of fault-tolerant programs Contributions Open problems

37. 37 Contributions Showing the NP-completeness of synthesizing failsafe fault-tolerance Identifying the necessary and sufficient conditions for polynomial-time synthesis of failsafe fault-tolerance Reusing the computational structure of fault-intolerant programs to reduce the complexity of synthesis (enhancement) Identifying synthesis patterns as pre-synthesized fault-tolerance components Developing a framework for the synthesis of fault-tolerant programs

38. 38 Outline Preliminary concepts Synthesis problem Current results –Theoretical issues step-wise automation Polynomial-time boundary Heuristics Pre-synthesized fault-tolerance components –Practical issues A framework for the synthesis of fault-tolerant programs Contributions Open problems

39. 39 Open Problems Theoretical issues –Non-monotonic programs/specifications to monotonic ones Extending the scope of the programs that can reap the benefit of efficient automation –Necessary and sufficient conditions for simultaneous addition of multiple pre-synthesized fault-tolerance components –Necessary and sufficient conditions for polynomial-time synthesis of nonmasking fault-tolerant programs –Automated synthesis of multitolerance

40. 40 Open Problems - Continued Practical issues –Distributed synthesis algorithm –Symbolic synthesis of fault-tolerance Distributed Synthesis Algorithm SAT solver Verify safetyY/NClosureY/NCycle detection Y/N... SAT solver

41. 41 Open Problems - Continued Using model checkers for acquiring behavioral information during synthesis Distributed Synthesis Algorithm... SPIN

42. 42 Publications Published papers –Sandeep S. Kulkarni and Ali Ebnenasir. "Enhancing The Fault- Tolerance of Nonmasking Programs". IEEE ICDCS 2003. –Ali Ebnenasir. "Algorithmic Synthesis of Fault-Tolerant Distributed Programs". Doctoral Symposium of ICDCS 2003. –Sandeep S. Kulkarni and Ali Ebnenasir. "The Complexity of Adding Failsafe Fault-Tolerance". IEEE ICDCS 2002. Submitted papers –Sandeep S. Kulkarni and Ali Ebnenasir. "Adding Fault-Tolerance Using Pre-Synthesized Components". Submitted to CBSE7, ICSE 2004. –Ali Ebnenasir and Sandeep S. Kulkarni. "A Framework for Automatic Synthesis of Fault-Tolerance". Submitted to DSN 2004. –Sandeep S. Kulkarni and Ali Ebnenasir. "Automated Synthesis of Multitolerance". Submitted to DSN 2004.

43. 43 Thank you! Questions and comments?