Modeling and Analyzing Distributed Systems Using I/O Automata

1. Modeling and Analyzing Distributed Systems Using I/O Automata Nancy Lynch, MIT Draper Laboratory, IR&D Mid-Year Meeting December 11, 2002

2. Project Description • Develop I/O-automata-based methods and tools for modeling and analyzing distributed systems, with emphasis on systems for military and space applications. • Methods and tools can be used for: • System documentation/specification • Design validation: • Simulation • Stating correctness and performance theorems • Proving theorems, manually or with interactive theorem-provers • Automatic code generation • Use the methods and tools to describe and analyze Draper’s ACME system. • Project participants: • MIT: Nancy Lynch, Stephen Garland, Vida Ha, Amittai Axelrod • Draper: Joe Kochocki, Alan Tanzman

3. I/O Automata • Infinite-state, nondeterministic, interacting state machines. • Support modular system description, using parallel composition and levels of abstraction. • Static description: • Actions a (input, output, internal) • States s, start states • Transitions (s, a, s') • Dynamic description: • Execution: s0 a1s1a2s2 … • Trace: Project on external actions. • A implements B: traces(A)  traces(B). • Operations for building automata: • Parallel composition, action hiding. • Reasoning methods: • Invariant assertions: Property holds in all reachable states. • Simulation relations: Imply one automaton implements another.

4. Reliable FIFO Channel Model send(m) receive(m) Channel(M) • Signature: • Inputs: • send(m), m in M • Outputs: • receive(m), m in M • States: • queue, a finite sequence of elements of M, initially empty • Transitions: • send(m) • Effect: Add m to end of queue • receive(m) • Precondition: m is first on queue • Effect: remove first element of queue

5. Example Applications • Basic distributed algorithms: • Resource allocation, consensus, atomic objects, concurrency control, group communication,… • Distributed systems: • Orca distributed shared memory system [Fekete, Kaashoek, Lynch] • Transis group communication system [Fekete, Lynch, Shvartsman] • Ensemble GCS [Hickey, Lynch, van Renesse] • Algorithms for dynamic networks: • Reconfigurable atomic memory [Lynch, Shvartsman 02] [Gilbert, Lynch, Shvartsman 02] [Musial, Shvartsman 02] • Dynamic atomic broadcast [Bar-Joseph, Keidar, Lynch 02]

6. IOA Language + Toolset I A O • Formally-defined programming/modeling language for describing and analyzing systems modelled as I/O automata. • Current tools: Simulator, connection to Larch theorem-prover. • In progress: Invariant detector, connection to Isabelle/HOL theorem-prover, automatic code generator. • Steve Garland will say more.

7. Additions to I/O Automaton Models • Timing behavior: TIOA • For describing timeout-based algorithms. • Local clocks, clock synchronization. • Timing/performance analysis. • Hybrid (continuous/discrete) behavior: HIOA • Systems with real world + computer components • Vehicle control: ground, air, space • Embedded systems

8. Timed I/O Automata (TIOA) • Add special time-passage actions, pass(t), to IOA model. • Example: Reliable FIFO channel that always delivers messages within time d. • send(m) • Effect: Add (m, now + d) to end of queue • receive(m) • Precondition: (m,u) is first on queue (for some u) • Effect: remove first element of queue • pass(t) • Precondition: for all (m,u) in queue, now + t  u • Effect: now := now + t • Can use standard automaton-based reasoning methods: • Invariant: for all (m,u) in queue, now  u  now + d. • Inductive proofs.

9. Example Applications • Distributed algorithms: • Resource allocation, consensus,… • Timeout-based communication protocols: • TCP, reliable multicast,… • Performance (latency) analysis: • Group communication systems: • Using GCS to build TO-Bcast [Fekete, Lynch, Shvartsman] • Scalable GCS [Khazan, Keidar 01] • RAMBO reconfiguration atomic memory • Hybrid (continuous/discrete) systems (toy examples): • RR crossing [Heitmeyer, Lynch, Archer] • Steam boiler controller

10. Hybrid I/O Automata (HIOA) • TIOA plus facilities for representing continuous behavior. • Static description: • States: input, output, internal variables; start states • Actions: input, output, internal • Discrete steps (s, a, s') • Trajectories , mapping time intervals to states • Dynamic description: • Execution 0a11a22… • Trace: Project on external variables, external actions. • A implements B if traces(A)  traces(B). • Operations: Composition, hiding • Reasoning methods: Invariants, simulation relations, compositional methods

11. Example Applications • Ground transportation: • People-mover (Raytheon) [Livadas, Lynch, Weinberg, Delisle]. • California PATH automated highway system: Analysis of platoon maneuvers [Dolginova, Lynch, Lygeros]. • Aircraft control: • TCAS (Lincoln Labs): Models, proofs [Livadas, Lygeros, Lynch]. • Quanser helicopter system (MIT Aero/Astro). Models, proofs [Mitra, Wang, Feron, Lynch 02]. • Spacecraft: • ACME [Ha, Axelrod, Lynch, Garland, Kochocki, Tanzman 03]

12. TCAS model Aircraft Aircraft Sensor Sensor Pilot Pilot Conflict detector Conflict detector Channel Conflict resolver Conflict resolver Channel

13. Quanser Model Helicopter System[Mitra, Wang, Feron, Lynch 02] • 3 DoF models manufatured by Quanser • User Controllers not safe • Supervisory pitch controller • Sensor inaccuracies • Actuator delay • Limited sampling frequency

14. HIOA model of the system • New language constructs for specifying trajectories • State models and Activities • Composition of activities

15. Discrete communication among components sample control command dequeue usrCtrl sensor sensor plant supervisor actuator 0 D D tact

16. Executions in the User and Supervisor modes Back to User mode Recovery Phase Cannot jump from U to outside of R in a single step Switch to supervisor : settling phase

17. Future Directions Contributions • Application of HIOA model to verification • Realistic dynamics, inaccuracies, delays • Design of safe Supervisory Controller • For arbitrary user controller • Language constructs for HIOA • Study systems with more complicated discrete behavior and dynamics. • Develop a set of ‘useful lemmas’ from control theory to be directly used in invariant proofs • Partially automate proofs using theorem provers