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Synchronization
Part 1

REK’s adaptation of Claypool’s
adaptation of
Tanenbaum’s
Distributed Systems
Chapter 5
1

Outline

 Clock Synchronization
 Clock Synchronization Algorithms
 Logical Clocks
 Election Algorithms
 Mutual Exclusion
 Distributed Transactions
 Concurrency Control
Distributed
Computing Systems 2

Clock Synchronization
make example

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

• Same holds when using NFS mount
• Can all clocks in a distributed system be synchronized?

Distributed
Computing Systems 3

Physical Clocks

 It is impossible to guarantee that crystals in different
computers all run at exactly the same frequency. This
difference in time values is clock skew.
 “Exact” time was computed by astronomers
 The difference between two transits of the sun is termed a
solar day. Divide a solar day by 24*60*60 yields a solar
second.
 However, the earth is slowing! (35 days less in a year
over 300 million years)
 There are also short-term variations caused by
turbulence deep in the earth’s core.
 A large number of days (n) were used used to the average
day length, then dividing by 86,400 to determine the
mean solar second.
Distributed
Computing Systems 4

Physical Clocks

Computation of the mean solar day.
Distributed
Computing Systems 5

Physical Clocks

 Physicists take over from astronomers and count
the transitions of cesium 133 atom
 9,192,631,770 cesium transitions == 1 solar

second
 50 International labs have cesium 133 clocks.

 The Bureau Internationale de l’Heure (BIH)

averages reported clock ticks to produce the
International Atomic Time (TAI).
 The TAI is mean number of ticks of cesium 133

clocks since midnight on January 1, 1958 divided
by 9,192,631,770 .
Distributed
Computing Systems 6

Physical Clocks

 To adjust for lengthening of mean solar day,
leap seconds are used to translate TAI into
Universal Coordinated Time (UTC).
 UTC is broadcast by NIST from Fort Collins,
Colorado over shortwave radio station WWV.
WWV broadcasts a short pulses at the start
of each UTC second. [accuracy 10 msec.]
 GEOS (Geostationary Environment
Operational Satellite) also offer UTC service.
[accuracy 0.5 msec.]
Distributed
Computing Systems 7

Outline

 Logical Clocks
Distributed
Computing Systems 8

Clock Synchronization Algorithms

 Computer timers go off H times/sec, and increment the count of ticks
(interrupts) since an agreed upon time in the past.
 This clock value is C.
 Using UTC time, the value of clock on machine p is C p(t).
 For a perfect time, Cp(t) = t and dC/dt = 1.
 For an ideal timer, H =60, should generate 216,000 ticks per hour.
Distributed
Computing Systems 9

Clock Synchronization
Algorithms
 But typical errors, 10–5, so the range of ticks per
second will vary from 215,998 to 216,002.
 Manufacturer specs can give you the maximum drift
rate (ρ).
 Every ∆t seconds, the worst case drift between two
clocks will be at most 2ρ∆t.
 To guarantee two clocks never differ by more than
δ, the clocks must re-synchronize every δ/2ρ
seconds using one of the various clock
synchronization algorithms.

Distributed
Computing Systems 10

Clock Synchronization Algorithms

 Centralized Algorithms
 Cristian’s Algorithm (1989)
 Berkeley Algorithm (1989)
 Decentralized Algorithms
 Averaging Algorithms (e.g. NTP)
 Multiple External Time Sources

Distributed

Cristian's Algorithm
 Assume one machine (the time server) has
a WWV receiver and all other machines are
to stay synchronized with it.
 Every δ/2ρ seconds, each machine
sends a message to the time server
asking for the current time.
 Time server responds with message
containing current time, CUTC.
Distributed


Getting the current time from a time server
Distributed


 A major problem – the client clock is
fast  arriving value of CUTC will be
smaller than client’s current time, C.
 What to do?

One needs to gradually slow down client
clock by adding less time per tick.

Distributed

Cristian’s Algorithm
 Minor problem – the one-way delay from
the server to client is “significant” and
may vary considerably.
 What to do?
 Measure this delay and add it to CUTC.
 The best estimate of delay is (T1 – T0)/2.
 In cases when T1 – T0 is above a threshold,
then ignore the measurement. {outliers}
 Can subtract off I (the server interrupt
handling time).
 Can use average delay measurement or
relative latency (shortest recorded delay).
Distributed

The Berkeley Algorithm

a) The time daemon asks all the other machines for their clock values.
b) The machines answer and the time daemon computes the average.
c) The time daemon tells everyone how to adjust their clock.
Distributed

Averaging Algorithms

 Every R seconds, each machine
broadcasts its current time.
 The local machine collects all other
broadcast time samples during some
time interval, S.
 The simple algorithm:: the new local
time is set as the average of the value
received from all other machines.

Distributed

Averaging Algorithms
 A slightly more sophisticated algorithm :: Discard
the m highest and m lowest to reduce the effect of a
set of faulty clocks.
 Another improved algorithm :: Correct each
message by adding to the received time an estimate
of the propagation time from the ith source.
 extra probe messages are needed to use this

scheme.
 One of the most widely used algorithms in the
Internet is the Network Time Protocol (NTP).
 Achieves worldwide accuracy in the range of 1-50

msec.
Distributed

Outline

 Logical Clocks
Distributed

Logical Clocks

 For a certain class of algorithms, it is
the internal consistency of the clocks
that matters. The convention in these
algorithms is to speak of logical
clocks.
 Lamport showed clock synchronization
need not be absolute. What is
important is that all processes agree
Distributed
on the order Computing Systems occur.
in which events 20

Lamport Timestamps [1978]

 Lamport defined a relation ”happens
before”. a  b ‘a happens before b’.
 Happens before is observable in two
situations:
1. If a and b are events in the same process,
and a occurs before b, then a  b is true.
2. If a is the event of a message being sent
by one process, and b is the event of the
message being received by another
process, then a  b is also true.
Distributed

Lamport Timestamps

(impossible)

a) Each processes with own clock with different rates.
b) Lamport's algorithm corrects the clocks.
c) Can add machine ID to break ties
Distributed

Example: Totally-Ordered Multicasting

(+$100) (+1%)

(San Francisco) (New York)

 San Fran customer adds $100, NY bank adds 1% interest
 San Fran will have $1,111 and NY will have $1,110

 Updating a replicated database and leaving it in an
inconsistent state.
 Can use Lamport’s to totally order
Distributed

Totally-Ordered Multicast
 A multicast operation by which all
messages are delivered in the same order
to each receiver.
 Lamport Details:
 Each message is timestamped with the
current logical time of its sender.
 Multicast messages are conceptually sent to
the sender.
 Assume all messages sent by one sender are
received in the order they were sent and that
no messages are lost.
Distributed

Totally-Ordered Multicast
 Lamport Details (cont):
 Receiving process puts a message into a local
queue ordered according to timestamp.
 The receiver multicasts an ACK to all other
processes.
 Key Point from Lamport: the timestamp of the
received message is lower than the timestamp
of the ACK.
 All processes will eventually have the same
copy of the local queue  consistent global
ordering. Distributed

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