1. Physical & Logical Clocks
CS4262 Distributed Systems
Dilum Bandara
Dilum.Bandara@uom.lk
Slides adapted from U Uthaiyashankar (WSO2) and Rajkumar Buyya’s (IU)

2. Outline
 Introduction to synchronization
 Time & clocks
 Synchronizing physical clocks
 Logical time & logical clocks
 Synchronizing logical clocks
 Lamport’s time stamp
 Vector clocks – Not covered
2

3. Interaction Model
 Computation occurs within processes
 Processes interact by passing messages,
resulting in
 Communication (information flow)
 Coordination (synchronization & ordering of activities)
 2 significant factors affecting interacting
processes in a distributed system
 Communication performance is often a limiting
characteristic
 Impossible to maintain a single global notion of time
3

4. Interaction Model (Cont.)
 Processes running on different nodes can
associate timestamps with their events
 This timestamp doesn’t make sense globally
 Clock drift
 Differing drift rates
 Complexity & cost of time synchronization
 GPS not accessible inside a building
4

5. 2 Variants of Interaction Model
 Hard to set limits on time taken for
 Process execution
 Message delivery
 Clock drift
 2 simple models
 Synchronous model – based on a strong assumption
of time
 Asynchronous model – makes no assumption about
time
5

6. Synchronous DS Model
 Defined by following bounds
 Time taken to execute a step of a process has known
lower & upper bounds
 Each message transmitted over a channel is received
within a known bounded time
 Each process has a local clock whose drift rate from
real time has a known bound
6

7. Asynchronous DS Model
 Defined by assuming no bounds on
 Process execution speeds
 Message transmission delays
 Clock drift rates
 Models Internet very closely
 Any solution that is valid for an asynchronous
DS is also valid for a synchronous DS
 There are many design problems that can’t be solved
for an asynchronous DS, but can be solved when
some aspects of time are used
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8. Event Ordering
 Whether an event occurred before, after, or
concurrently with another event at another
process?
 Execution of a system can be described in terms of
events & their ordering
 No need of accurate clocks
 Example
 Consider a mailing list with users X, Y, Z, & A
 User X sends a message with subject “meeting”
 Users Y & Z reply by sending messages with the
subject “Re: meeting”
8

9. Example – Real-Time Ordering of
Events
9Source: Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007

10. Example – Inbox of User A
 Due to independent delivery of messages, messages
may be delivered in different order
 If messages m1, m2, m3 carry their time t1, t2, t3, then they
can be displayed accordingly to their time ordering
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11. Time in Distributed Systems
 Scenario
 Running UNIX make program which recompiles only source files
whose corresponding object files carry an earlier time stamp
 Differences in time on editing machine & compiler machine & can
result in a file output.o to have a timestamp greater than recently
modified output.c program
 Result
 Modified source program, output.c will not be recompiled
 Programmer???
11

12. Time & Clocks
 Atomic Time
 Derived from atomic oscillators
 Very accurate
 Astronomical Time
 Based on Earth’s rotation about its axis & around sun
 Period of Earth’s rotation about its axis is gradually
getting longer
 Coordinated Universal Time (UTC)
 Broadcast from land-based radio stations & GPS
12

13. Clock Drift
 Different rates of counting time
 Physical variations of underlying oscillators
 Variance caused by temperature
 Extremely small differences that accumulate
over a large no of oscillations  Observable
difference in counters
 Drift rate
 Difference in reading between a clock & a nominal
“perfect clock” per unit of time
 10-6 seconds/sec for quartz crystals
 10-7 to 10-8 seconds/sec for high precision quartz
crystals 13

14. Clock Skew
 When a system has n computers, their n crystals
will oscillate at different rates
 This causes software clocks to gradually get out-
of synch & give different values
 This instantaneous difference in time values is
called clock skew
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15. Clock Synchronization
 External synchronization
 If 1 machine has a WWW or GPS receiver, use it to
synchronize others
 Internal synchronization
 If no machines have receivers, each machine keeps
track of its own time
 Then keep all the machines together as well as
possible
 Many clock synchronization algorithms have
been proposed to achieve these goals
 Christian’s Algorithm, Berkeley Algorithm, Averaging
Algorithms, Network Time Protocol, etc. 15

16. Synchronization in Asynchronous
Systems
 There is no upper bound on message
transmission delays
 e.g., there is no maximum transmission delay for
Internet
 Ttransmit = min + x, where x > 0
 Ttransmit – time taken to transmit m
 Min – minimum time to transmit
 x may not be known in a particular case, but it is
possible to measure a distribution of values for a
particular installation
16

17. Network Time Protocol (NTP)
 Christian’s & Berkeley algorithms for intranets
 NTP designed for Internet
 Large & variable message delays are handled through
statistical techniques for filtering timing data
 Discriminates between quality of timing data from different
servers
 Redundant servers & redundant paths between servers
 Scalable – handles a large no of clients & servers
 Authentication to ensure trusted time sources
 Synchronization accuracy
 ~10s of milliseconds over Internet paths
 ~1 millisecond on LANs
17

18. NTP (Cont.)
 Hierarchical network of servers
 Primary servers connected directly to a UTC time source
 Secondary servers synchronized with primary servers
 Servers connected in a logical hierarchy called a synchronization
subnet whose levels are called strata
 Lowest level (leaf) servers execute in users’ workstation
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19. Logical Time & Logical Clocks
 Single process
 Events are ordered uniquely by local clock time
 Lamport (1978) pointed out that,
 “since we can’t synchronize clocks perfectly across a
distributed system, we can’t use physical time to find
out order of any arbitrary pair of events within a
distributed system”
 In general, we can use a scheme that is similar
to physical causality, to order some events that
occur in different processes
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20. Physical Causality Based Ordering
 If events occur on same process P, then they
occur in the order in which P observes them
 If a & b are events within a single process Pi, and a
occurs before b, then a i b is true
 Whenever a message is sent between
processes, event of sending occurs before event
of receiving
 a = event of a message m being sent by process Pi
 b = event of message m being received
 then, a i b is true
20

21. Lamport’s Notion of Event Ordering
 Lamport generalized 2 (intuitive) physical-
causality-based event ordering points to obtain
happened-before partial ordering
 A.k.a relation of causal ordering or potential causal
ordering
 Define “happened-before relation” (denoted by
) as follows
 e i e/ for process Pi ⇒ e  e/
 For any message m, send(m)  receive(m)
 e  e/ and e/  e// ⇒ e  e//
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22. Happened-Before Relation
22
Source: Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007

23. Happened-Before Relation
23
 Events a & e that are not ordered by  are concurrent
 Denoted by a || e
Source: Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007

24. Logical Clocks
 Invented by Leslie Lamport to capture
happened-before ordering numerically
 Lamport Logical Clock
 Monotonically increasing software counter
 Value need not have a relationship to any physical
clock
 Each process Pi keeps its own logical clock Li,
which it uses to apply Lamport timestamps to
events
 Li(e) = timestamp of event e at Pi
 L(e) = timestamp of event e at process it occurred
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25. Lamport Timestamps
 Processes update their logical clocks & transmit
their logical clock values in messages as follows
 LC1
 Li = Li + 1, before each event is recorded at Pi
 LC2
 When Pi sends a message m, logical clock value t = Li, is piggy-
backed with message
 On receiving (m, t), a process Pj computes
 Lj = max (Lj, t)
 Then computes Lj = Lj + 1 before logically timestamping event
receive(m)
 Thus, e  e/ ⇒ L(e) < L(e/)
 However, L(e) < L(e/) ⇏ e  e/
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26. Lamport Timestamps – Example
 Each process has its logical clock initialized to 0
 L(b) > L(e) but b || e
26
Source: Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007

27. Lamport Timestamps – Example
 3 processes, each with its own clock
 Clocks run at different rates
 Lamport’s algorithm corrects clocks 27

28. Totally Ordered Logical Clocks
 Some pairs of distinct events generated by different
processes have identical Lamport timestamps
 A total order, where all pairs of distinct events are ordered,
can be created by noting IDs of processes
 Suppose,
 (Ti, i) = global logical timestamp for event e at process Pi with local
logical timestamp Ti
 Similarly (Tj, j)
 Then (Ti, i) < (Tj, j) ⇔ Ti < Tj or (Ti = Tj and i < j )
 No general physical significance since process IDs are
arbitrary
 e.g., 2 events in P1 & P2, both with timestamp 40 can be
globally timestamped as 40.1 & 40.2 28

29. Application of Lamport’s Timestamps
 Consider a database that has been replicated
across several sites
 e.g., a bank account database may be replicated in
Colombo & Kandy
 A query is always forwarded to the nearest copy
(for fast response)
 Update costs are higher since each update must
be carried out at each replica in same order
 Why?
29

30. Application (Cont.)
 Mr. Perera (lives in Kandy) has Rs. 1,000 in his account
 Mr. Perera wants to deposit Rs. 100 to his account
 At the same time, banking application (in Colombo)
initiates an update to increase account balance with 1%
interest
 Both updates must be carried out at both copies of the
database
 Due to communication delays, we may have following
situation:
 In Kandy DB, Mr. Perera’s deposit is performed before interest
update  (Rs. 1000 + Rs. 100) * 1.01 = Rs. 1,111
 In Colombo DB, interest update is performed before Mr. Perera’s
deposit  (Rs. 1000 * 1.01) + Rs. 100 = Rs. 1,110
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31. Application (Cont.)
 Problem
 Both updates were not performed in same order at
each DB
 Solution
 Totally ordered multicast
 Multicast operation where all messages are delivered in the
same order to each receiver 31

32. Totally Ordered Multicast
 If process Pi sends messages x, y to processes
Pj, Pk ,.. then all processes Pj, Pk … receive
messages in the same order (x, y or y, x)
 This doesn’t imply causal or even FIFO ordering
 Solutions
 Multicast through a central coordinator
 Lamport’s solution
32

33. Lamport’s Totally Ordered Multicast
 Each multicast has a Lamport time stamp
 All processors store received multicast in a queue
 Order based on time stamp
 Everyone has the same queue
 Assume all messages sent by a sender are received in the
order they were sent & no messages are lost
 A time stamped ACK is sent back
 Timestamp of multicast message < timestamp of ACK
 A process can deliver (act on) the message at the
head of queue only when it has received an ACK
from all other processes
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34. Lamport’s Totally Ordered Multicast
34
Source: Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007

35. Summary
 Synchronization is important
 Clock drift & clock skew
 Physical clock synchronization
 Logical time & logical clocks
 Lamport Timestamps
 Vector Clocks
 Totally Ordered Multicast
35