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On Specifying and Monitoring Epistemic Properties of Distributed Systems. Koushik Sen Abhay Vardhan Gul Agha Grigore Rosu. University of Illinois at Urbana-Champaign, USA. Software Reliability. Software Validation Rigorous and Complete Methods Model Checking Theorem Proving

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on specifying and monitoring epistemic properties of distributed systems

On Specifying and Monitoring Epistemic Properties of Distributed Systems

Koushik Sen

Abhay Vardhan

Gul Agha

Grigore Rosu

University of Illinois at Urbana-Champaign, USA

software reliability
Software Reliability
  • Software Validation
    • Rigorous and Complete Methods
      • Model Checking
      • Theorem Proving
    • Infeasible for large-scale open distributed systems (Actors)
      • Non-determinism and Asynchrony
    • Testing
      • Widely used
      • Ad-Hoc
      • Good Test Coverage Required
    • Runtime Monitoring
      • Adds rigor to Testing
centralized monitoring approach
Centralized Monitoring Approach
  • Monitoring – Use Formal Methods in Testing
    • Synthesize light-weight Monitors from Specification
      • Automata, Rewriting-based Monitors
    • Instrument code to insert monitors
    • Execute instrumented code
  • Distributed System Monitoring
    • Global state is distributed
    • For every state update send state to a central monitor
    • Central monitor assembles them to form consistent execution traces
      • Sequence of global states
    • Monitor execution traces
an example
An Example
  • Mobile node a requests certain value from node b
  • b computes the value and sends it to a
  • Property: no node receives a value from another node to which it had not sent a request
centralized monitoring example
Centralized Monitoring Example

“If a receives a value from b then b calculated the value after receiving request from a”

valRcv → (valComputed  valReq)

valReq

valReq

valComputed  valReq

valRcv → (valComputed  valReq)

(valComputed  valReq)

b

valComputed

a

valReq

valRcv

decentralized monitoring approach
Decentralized Monitoring Approach

“If a receives a value from b then b calculated the value after receiving request from a”

valRcv → @b((valComputed  @a(valReq)))

valComputed  @a(valReq)

@a(valReq)

(valComputed  @a(valReq))

b

valComputed

a

valReq

valRcv

valReq

valRcv → @b((valComputed  @a(valReq)))

past time distributed temporal logic pt dtl
Past time Distributed Temporal Logic (pt-DTL)
  • Based on epistemic logic
    • [Aumann76][Meenakshi et al. 00]
  • Properties with respect to a process, say p
leader election example
Leader Election Example

“If a leader is elected then if the current process is a leader then, at its knowledge, none of the other processes (b and c) is a leader”

elected → (state=leader →

(@b(state ≠ leader) Æ @c(state ≠ leader)))

leader election stronger property
Leader Election (Stronger Property)
  • Every process must know the name of the process that has been elected leader

elected → (let k=leaderName in

(@b(leaderName = k) Æ @c(leaderName = k)))

leader election open system
Leader Election (Open System)
  • There are arbitrary number of processes whose names are not known before-hand

elected → (let k=leaderName in

@8 {j | j  i}(leaderName = k))

extended distributed temporal logic xdtl
Extended Distributed Temporal Logic (xDTL)
  • Suitable for Open Distributed Systems (Actors)
    • Ids of all processes are not known before-hand
  • Quantification over processes
    • All processes satisfying a predicate
      • @8 {j | pred(j)}
    • Some process satisfying a predicate
      • @9 {j | pred(j)}
  • Value-binding (Increases Expressive Power)
    • let k = x in F
    • To refer to values in remote states
xdtl syntax
xDTL syntax
  • Fi ::= true | false | P(Ei) | : Fi | FiÆ Fi propositional

| ¯ Fi | ¡ Fi | Fi | Fi S Fi temporal

| @8 JFj | @9 JFj epistemic

| let k = Ei in Fibinding

  • Ei ::= c | vi2 Vi | f(Ei) | k functional

| @jEj epistemic

interpretation of @ 8 j e j at process i
Interpretation of @8 JEj at process i

p3

m4

m1

m2

p2

@ {1}(x=9)

m3

p1

x=7

x=9

monitoring algorithm
Monitoring Algorithm
  • Requirements
    • Should be fast so that online monitoring is possible
    • Little memory overhead
    • Additional messages sent should be minimal; ideally zero
  • Monitoring using KnowledgeVector
    • Maintain knowledge of global state at each process
    • Update knowledge with incoming messages
    • Attach knowledge with outgoing messages
    • At each process monitor local knowledge
conclusion
Conclusion
  • Decentralized Technique to effectively verify open distributed systems at runtime
  • No extra message over-head for monitoring
  • xDTL can express interesting and useful safety properties of distributed systems
  • How to instrument code running on all processes so that monitoring can be done?
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