Distributed Snapshots
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Distributed Snapshots

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My presentation on distributed snapshots for graduate OS course

My presentation on distributed snapshots for graduate OS course

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    Distributed Snapshots Distributed Snapshots Presentation Transcript

    • Distributed Snapshots: Determining Global States of Distributed Systems K. Mani Chandy Leslie Lamport
    • Overview
      • Paper shows the Snapshot Algorithm
      • Aims to discover a global state of the distributed system
    • Motivation
      • We want Global State Discovery
      • Communication latency and clock skew prevent us from doing this well
      • Applications of global state discovery
        • Checkpointing
        • Detection of Deadlock with Global Resources … why?
        • Consistent view of Distributed Bank Accounts
        • Phase Detection (e.g. Barriers)
    • What is a Global State?
      • Processes are finite state machines (FSM’s)
      • A global state of a system is a set of states {p 1 , … ,p n } such that p i represents the state of process i.
      • … is this sufficient?
    • NO! What about channels?
      • Insufficient characterization of the system!
      • Processes communicate using channels
      • Must account for messages currently in transit
    • Stable Properties
      • Algorithm targeted at specific problems
      • Check if a stable property holds
        • Once it is true, remains true for all later points
      • “ Are all lights currently green?” Is this an example of a stable property?
    • Quick Recap
      • We want Global State Detection
      • Stable Properties
      • Moving on …
      • System Model
      • Assumptions
      • Chandy-Lamport Algorithm
    • Eagle’s Eye View
    • Definitions
      • The state of a channel is the sequence of messages moving through it
      • An event is an atomic action that
        • May change the state of a process
        • May change at most one channel incident on the process
        • Defined as a 5-tuple <p,s,s’, M, c>
    • Assumptions (oh no!)
      • Channels
        • FIFO
        • Infinite Buffers
        • Error-free
        • Finite delivery time
      • No failures
      • States can be captured in finite time
      • Hidden assumption: steps in algorithm must be atomic in terms of process state (why?)
    • Snapshot (Chandy-Lamport) Algorithm
      • A process decides to take a snapshot “spontaneously” and sends itself a marker .
      • Upon receiving the marker over a channel c a process will …
        • If marker not previously seen, record state, state of c is empty, start recording other incoming channels, and send marker to neighbors
        • Else stop recording, state of c is the sequence of messages recorded since [1]
      • Will a marker ever be received on the same channel twice?
    • Algorithm in Action
    • Termination of Algorithm
      • When a marker received on every incoming channel
      • How could you distribute the actual snapshot?
      • How would we handle multiple concurrent snapshots?
    • Properties of Snapshot
      • Global state returned is reachable from start and before end of snapshot
      • System never necessarily in the state of a snapshot
      • Can obtain a consistent global state with it.
      • How can we guarantee state returned actually occurred?
    • Stability Detection
      • If the stable property is true, it is true by the end of the algorithm.
      • If it is false, it was false at the beginning of the snapshot.
      • Intuitive explanation?
    • Issues
      • Many assumptions necessary
        • Overhead becomes high with methods that work around assumptions
      • Cannot discover transient properties
      • Hard to see type of problems to solve with algorithm
      • How would you deal with failures? Termination?
      • At best a good guess. How would you do this?
    • Questions