distributed snapshot. one-dollar bank let a $1 coin circulate in a network of a million banks. how...
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Distributed Snapshot
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One-dollar bank
01
2
(0,1)
(1,2)(2,0)
Let a $1 coin circulate in a network of a million banks.
How can someone count the total $ in circulation? If
not counted “properly,” then one may think the total $
in circulation to be one million.
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Importance of snapshots
Major uses in
- deadlock detection
- termination detection
- rollback recovery
- global predicate computation
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Example 1
• Suppose you want to take a picture of a scenic view– Your camera cannot fit the entire scene in
one picture
– Take several pictures– Combine them to get overall picture
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Example 2
• Suppose you want to take a picture of basketball game– Your camera cannot fit the entire scene in
one picture
– Take several pictures– Combine them to get overall picture
• Care needs to be taken to ensure that the several pictures you took are consistent
– E.g., the same player cannot be in two places
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Example: Distributed Systems
• You want to take a picture (global snapshot) of the distributed system– You can take a picture (local snapshot) of
one process at a time– Need to combine these local snapshots– Need for consistency
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Example: Distributed Systems
• Local snapshot– Can be viewed in terms of the last event on
the process• When we combine such snapshots, we call it a
global snapshot
– Can be viewed in terms of the last event and all preceding events on a process
• When we combine such snapshots, we call it a (global) cut
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Consistent cut
(a consistent cut C) (b happened before a) b C
a b c d g
m e f
k i h jCut 1 Cut 2
A cut is a set of events.
(Not consistent)(Consistent)
P1
P2
P3
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Consistent snapshot
The set of states immediately following a
consistent cut forms a consistent snapshot
of a distributed system.
• A snapshot that is of practical interest is the
most recent one. Let C1 and C2 be two
consistent cuts and C1 C2. Then C2 is more
recent than C1.
• Assumption: The cut lines do not go through
any event
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Consistent snapshot
How to record a consistent snapshot? Note that
1. The recording must be non-invasive
2. Recording must be done on-the-fly.
You cannot stop the system.
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Chandy-Lamport Algorithm
Works on a
(1) strongly connected graph
(2) each channel is FIFO.
An initiator initiates the
algorithm by sending out
a marker ( )
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White and red processes
Initially every process is white. When a
process receives a marker, it turns
red if it has not already done so.
Every action by a process, and every
message sent by a process gets the
color of that process.
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Two steps
Step 1. In one atomic action, the initiator (a) Turns red (b) Records its own state (c) sends a marker along all outgoing channels
Step 2. Every other process, upon receiving a marker for the first time (and before doing anything else) (a) Turns red (b) Records its own state (c) sends markers along all outgoing channels
The algorithm terminates when (1) every process turns red, and (2) Every process has received a marker through each incoming channel.
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Why does it work?
Lemma 1. No red message is received in a white action.
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Why does it work?
Theorem. The global state recorded by Chandy-Lamport algorithm is equivalent to the ideal snapshot state SSS.
Hint. A pair of actions (a, b) can be scheduled in any order, if there is no causal order between them, so (a; b) is equivalent to (b; a)
SSSEasy conceptualization of the snapshot state
All white All red
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Why does it work?
Let an observer observe the following actions:
w[i] w[k] r[k] w[j] r[i] w[l] r[j] r[l] … w[i] w[k] w[j] r[k] r[i] w[l] r[j] r[l] … [Lemma 1]w[i] w[k] w[j] r[k] w[l] r[i] r[j] r[l] … [Lemma 1]w[i] w[k] w[j] w[l] r[k] r[i] r[j] r[l] … [done!]
Recorded state
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Understanding snapshot
The observed state is a feasible state that is reachable
from the initial configuration. It may not actually be visited
during a specific execution.
The final state of the original computation is always
reachable from the observed state.
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Discussions
What good is a snapshot if that state has never been visited by the system?
- It is relevant for the detection of stable predicates.
- Useful for checkpointing.
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Discussions
What if the channels are not FIFO?
Study how Lai-Yang algorithm works. It does not use any marker
LY1. The initiator records its own state. When it needs to send a
message m to another process, it sends a message (m, red).
LY2. When a process receives a message (m, red), it records its state
if it has not already done so, and then accepts the message m.
Question 1. Why will it work?
Question 1 Are there any limitations of this approach?
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Another related problem
Distributed snapshot = distributed read.
Distributed reset = distributed write
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Global state collection
Some applications
- computing network topology
- termination detection
- deadlock detection
Chandy Lamport algorithm does a partial job. Each process collects a
fragment of the global state, but these pieces have to be stitched together to
form a global state.
All to all broadcast can be achieved via computation similar to
diffusing computation
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Recall: Global State
• The global state of a system consists of – One local state for each process
• Contains all the messages sent and received upto a point in computation
• A local state could be specified by the `last’ event on the respective process
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Consistency in Global State
• Consistent iff– If reception of any message is recorded in
the global state then the corresponding send is also recorded
• If global snapshot is consistent then what is the causal relation between the `last’ events of respective processes?
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Revisit Dijkstra Safra Termination Detection Algorithm• Note that the token is collecting a global
snapshot of the system– Can we see if it is consistent?
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2 Phase Termination Detection
• Maintain c.j similar to Dijkstra-Safra Termination Detection– But no color variable maintained
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Application of Global State Detection
• Termination detection
• Checkpointing and recovery