Synchronization

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Synchronization
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Clock SynchronizationIn a centralized system
time is unambiguous.In a distributed system
agreement on time is not obvious.

• When each machine has its own clock, an event
  that occurred after another event may be assigned
  an earlier time.

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Physical clocks

• Computer Clock Timer
• Oscillating crystal (under tension)
• 2 register counter and holding register
• Interrupt ( clock tick) when counter
  gets to zero
• N clocks n rates (clock skew)
• UTC (Universal Coordinated Time) is the current
  universal time
• standard
• Radio pulse to synchronize (10 msec)
• Satellite ( 0.5 msec)

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Clock Synchronization Algorithms
If 1 r lt dC/dt lt
1 r r maximum drift rate If 2 clock
drift from UTC in the opposite directions, after
?t, e 2r Dt Resynchronization interval
d/2r d maximum time difference

• The relation between clock time and UTC when
  clocks tick at different rates.

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Cristian's Algorithmsynchronization with a time
serverPeriodically (T lt d/2r s) synchronization
request to time server

• Time must never run backward gradual
  changes
• Non zero message propagation time (estimate
  value (T1-T0-I) /2 )

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The Berkeley Algorithmsynchronization without
time server

• The time daemon asks all the other machines for
  their clock value discrepancies
• The machines answer
• The time daemon tells everyone how to adjust
  their clock to the average
• no Universal Coordinated Time available

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• Centralized algorithms have disadvantages.
• Decentralized algorithms can use averaging
  methods.
• NTP (Network Time Protocol) provides an accuracy
  of 1-50 msec using advanced algorithms.
• For many purposes it is sufficient that all
  machines agree on the same time.
• Logical clocks
• Often processes need to agree on the order in
  which events occur.

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Lamport Timestamps
happens before
If a b C(a) lt
C(b) If a snd., b rcv. C(a) lt
C(b) C(a) ? C(b) If C(b) lt C(a)
C(b) C(a)1 We obtain a total
ordering of all events in the system but Lamport
timestamps dont capture causality Vector
timestamps each process Pi has a vector Vi so
that Vii is the number of events occurred so
far at Pi If Vijk Pi knows k events
occurred at Pj
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Global StateIt is the local state of each
process, together with the messages currently in
transit in a distributed systemA distributed
snapshot reflects a consistent global state
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Global State
Knowledge of the state in which a distributed
system currently is, is useful Any process P
may initiate the algorithm recording its local
state.
P

• Organization of a process and channels for a
  distributed snapshot
• A process P sends a marker along each of its
  outgoing channels

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Global State

• Process Q receives a marker for the first time,
  records its local state and send a marker along
  each outgoing channel
• Q records all incoming message
• Q receives a marker for its incoming channel and
  finishes recording the state of the incoming
  channel
• A process has finished its part of the algorithm
  when it has received a marker
• along each of its incoming channels, and
  processed each one. Then its state is collected
• Many snapshots may be in progress at the same
  time.

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Election AlgorithmsThe Bully Algorithm (1/2)
Selecting a coordinator

• The bully election algorithm
• Process 4 holds an election
• Process 5 and 6 respond, telling 4 to stop
• Now 5 and 6 each hold an election

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The Bully Algorithm (2/2)

• Process 6 tells 5 to stop
• Process 6 wins and tells everyone

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A Ring Algorithm
Each process knows who its successor is.

• Election algorithm using a ring (without token).

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Mutual Exclusion critical regions in
distributed systems A Centralized Algorithm(to
simulate a single processor system, needs a
coordinator)

1. Process 1 asks the coordinator for permission to
   enter a critical region. Permission is granted
2. Process 2 then asks permission to enter the same
   critical region. The coordinator does not reply.
3. When process 1 exits the critical region, it
   tells the coordinator, which then replies to 2

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A Distributed Algorithmrequires a total ordering
of all events in the systemA message contains
the critical region name, the process number and
the current time

• Two processes (0,2) want to enter the same
  critical region at the same moment.
• Process 0 has the lowest timestamp, so it wins
  and enters the critical region.
• When process 0 is done, it sends an OK also, so 2
  can now enter the critical region.
• This algorithm is worse than the centralized
  one (n points of failure, scaling, multiple
  messages)

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A Token Ring Algorithmwhen the process acquires
the token, it accesses the critical region (if
needed)
start

• An unordered group of processes on a network.
• A logical, ordered, ring constructed in software.
  Each process knows who
• is the next in line

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Mutual Exclusion
Algorithm Messages per entry/exit Delay before entry (in message times) Problems
Centralized 3 2 Coordinator crash
Distributed 2 ( n 1 ) 2 ( n 1 ) Crash of any process
Token ring 1 to ? 0 to n 1 Lost token, process crash

• A comparison of three mutual exclusion algorithms.

With dependence on the mechanism
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The Transaction Model A transaction permits that
a whole set of related instructionswould be
successfully completed or none would be
completed.Special primitives are requested (
supplied by system or language)
Primitive Description
BEGIN_TRANSACTION Make the start of a transaction
END_TRANSACTION Terminate the transaction and try to commit
ABORT_TRANSACTION Kill the transaction and restore the old values
READ Read data from a file, a table, or otherwise
WRITE Write data to a file, a table, or otherwise

• Examples of primitives for transactions.

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The Transaction Model

• Transactions are ACID
• Atomic to outside, they happen indivisibly
• Consistent they dont violate system
  invariants
• Isolated concurrent transactions dont
  interfere
• Durable the changes are permanent
• These flat transactions have some limitations...
• No partial result is permitted
• In a large system they can take a lot of time

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Nested and Distributed Transactions

1. A nested transaction it follows a logical
   (hierarchical) division of the work
2. A distributed transaction it is logically flat
   and operates on distributed data

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How to implement transactions?Private Workspace

• Copying everything in a private workspace is
  prohibitive
• Example dealing with a file system
• The file index and disk blocks for a three-block
  file
• The situation after a transaction has modified
  block 0 and appended block 3
• After committing

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Write-ahead Log
x 0 y 0 BEGIN_TRANSACTION x x 1 y y 2 x y y END_TRANSACTION (a) Log x 0 / 1 (b) Log x 0 / 1 y 0/2 (c) Log x 0 / 1 y 0/2 x 1/4 (d)
rollback

• Files are actually modified in place, but a
  record is written to log all the changes
• a) A transaction
• b) d) The log before each statement is executed
• In distributed systems each machine keeps its
  own log of changes to its local file system

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Concurrency Control allows several transaction
to be executed simultaneously, leaving data in a
consistent way. Transactions access data in a
specific order

• General (layered) organization of managers for
  handling transactions.

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Concurrency Control

• General organization of managers for handling
  distributed transactions.

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Serializability
BEGIN_TRANSACTION x 0 x x 1END_TRANSACTION (a) BEGIN_TRANSACTION x 0 x x 2END_TRANSACTION (b) BEGIN_TRANSACTION x 0 x x 3END_TRANSACTION (c)
Schedule 1 x 0 x x 1 x 0 x x 2 x 0 x x 3 Legal
Schedule 2 x 0 x 0 x x 1 x x 2 x 0 x x 3 Legal
Schedule 3 x 0 x 0 x x 1 x 0 x x 2 x x 3 Illegal
(d)

• a) c) Three transactions T1, T2, and T3
• Legal serialized result x 1,2,3 depending upon
  which one runs last
• d) Possible schedules
• Synchronization can be achieved or with mutex or
  with explicit timestamp and following pessimistic
  or optimistic approach.