Distributed systems face challenges with time ordering and synchronization due to a lack of a global clock. Logical clocks provide a way to determine event ordering without precise time information. Lamport's algorithm uses logical clocks and the "happens before" relation to ensure proper synchronization. Physical clocks also require synchronization methods like Cristian's algorithm to limit clock drift between distributed nodes. Mutual exclusion in distributed systems can be achieved through centralized coordination of access to critical sections.

1. DISTRIBUTED OPERATING
SYSTEMS
By:Little Goku

2. CANONICAL PROBLEMS IN DISTRIBUTED SYSTEMS
 Time ordering and clock synchronization
 Election
 Mutual exclusion
 Distributed transactions

3. THE IMPORTANCE OF SYNCHRONIZATION
 Because various components of a distributed
system must cooperate and exchange information,
synchronization is a necessity.
 Constraints, both implicit and explicit, are therefore
enforced to ensure synchronization of components.

4. CLOCK SYNCHRONIZATION
 As in non-distributed systems, the knowledge
of “when events occur” is necessary.
 However, clock synchronization is often more
difficult in distributed systems because there
is no ideal time source.
 Distributed algorithms must overcome:
 Scattering of information
 Local, rather than global, decision-making.

5. Conti....
 Time is unambiguous in centralized systems
 Distributed systems: each node has own
system clock.
 Crystal-based clocks are less accurate (1 part in
million)
 Problem: An event that occurred after another
may be assigned an earlier time

6. LACK OF GLOBAL TIME IN DS (Clock syn.)
 It is impossible to guarantee that
physical clocks run at the same
frequency
 Lack of global time, can cause problem.
 Example: UNIX make
 Edit output.c at a client
 output.o is at a server (compile at server)
 Client machine clock can be lagging behind
the server machine clock

7. LACK OF GLOBAL TIME – EXAMPLE
When each machine has its own clock, an
event that occurred after another event may
nevertheless be assigned an earlier time.

8. LOGICAL CLOCKS
 Often, it is not necessary for a computer to know the exact
time, only relative time. This is known as “logical time”.
 Logical time is not based on timing but on the ordering of
events.
 Logical clocks can only advance forward, not in reverse.
 Non-interacting processes cannot share a logical clock.
 Computers generally obtain logical time using interrupts to
update a software clock. The more interrupts (the more
frequently time is updated), the higher the overhead.

9. LAMPORT’S LOGICAL CLOCK
SYNCHRONIZATION ALGORITHM
 The most common logical clock synchronization algorithm
for distributed systems is Lamport‟s Algorithm. It is used in
situations where ordering is important but global time is not
required.
 Based on the “happens-before” relation:
 Event A “happens-before” Event B (A→B) when all
processes involved in a distributed system agree that
event A occurred first, and B subsequently occurred.
 This DOES NOT mean that Event A actually occurred
before Event B in absolute clock time.

10. LAMPORT’S LOGICAL CLOCK SYNCHRONIZATION
ALGORITHM
 A distributed system can use the “happens-before”
relation when:
 Events A and B are observed by the same
process, or by multiple processes with the same
global clock
 Event A acknowledges sending a message and
Event B acknowledges receiving it, since a
message cannot be received before it is sent
 If two events do not communicate via messages,
they are considered concurrent – because order
cannot be determined and it does not matter.
Concurrent events can be ignored.

11. LAMPORT’S LOGICAL CLOCK SYNCHRONIZATION
ALGORITHM (CONT.)
 In the previous examples, Clock C(a) < C(b)
 If they are concurrent, C(a) = C(b)
 Concurrent events can only occur on the same system,
because every message transfer between two systems
takes at least one clock tick.
 In Lamport‟s Algorithm, logical clock values for events may
be changed, but always by moving the clock forward. Time
values can never be decreased.

12. LAMPORT’S LOGICAL CLOCK
SYNCHRONIZATION ALGORITHM (CONT.)
 Lamport‟s Algorithm can thus be used in distributed
systems to ensure synchronization:
 A logical clock is implemented in each node in
the system.
 Each node can determine the order in which
events have occurred in that system’s own point
of view.
 The logical clock of one node does not need to
have any relation to real time or to any other
node in the system.

13. PROCESS EACH WITH ITS OWN CLOCK
•At time 6 , Process 0 sends message A to Process 1
•It arrives to Process 1 at 16( It took 10 ticks to make journey
•Message B from 1 to 2 takes 16 ticks
•Message C from 2 to 1 leaves at
60 and arrives at 56 -Not Possible
•Message D from 1 to 0 leaves at
64 and arrives at 54 -Not Possible

14. LAMPORT’S ALGORITHM CORRECTS THE CLOCKS
 Use „happened-before‟
relation
 Each message carries the
sending time (as per sender‟s
clock)
 When arrives, receiver fast
forwards its clock to be one
more than the sending time.
(between every two events,
the clock must tick at least
once)

15. PHYSICAL CLOCKS
 The time difference between two computers is known as
drift. Clock drift over time is known as skew. Computer
clock manufacturers specify a maximum skew rate in their
products.
 Computer clocks are among the least accurate modern
timepieces.
 Inside every computer is a chip surrounding a quartz
crystal oscillator to record time. These crystals cost 25
seconds to produce.
 Average loss of accuracy: 0.86 seconds per day
 This skew is unacceptable for distributed systems. Several
methods are now in use to attempt the synchronization of
physical clocks in distributed systems:

16. PHYSICAL CLOCKS
 17th Century: time has been measured
astronomically
 Solar Day: interval between two consecutive
transit of sun
 Solar Second: 1/86400th of solar day

17. PHYSICAL CLOCKS
 1948: Atomic Clocks are invented
 Accurate clocks are atomic oscillators (one part in 1013)
 BIH decide TAI(International Atomic Time)
 TAI seconds is now about 3 msec less than solar day
 BIH solves the problem by introducing leap seconds
Whenever discrepancy between TAI and solar time grow to
800 msec
 This time is called Universal Coordinated Time(UTC)
 When BIH announces leap second, the power companies
raise their frequency to 61 & 51 Hz for 60 & 50 sec, to
advance all the clocks in their distribution area.

18. PHYSICAL CLOCKS - UTC
Coordinated Universal Time
(UTC) is the international
time standard.
UTC is the current term for
what was commonly
referred to as Greenwich
Mean Time (GMT).
Zero hours UTC is midnight in
Greenwich, England, which
lies on the zero longitudinal
meridian.
UTC is based on a 24-hour
clock.

19. CLOCK SYNCHRONIZATION
 Each clock has a maximum drift rate
 1- dC/dt <= 1+
 Two clocks may drift by 2 t in time t
 To limit drift to  resynchronize after every
2 seconds

20. CHRISTIAN’S ALGORITHM
 Assuming there is one time server with UTC(Coordinated universal
time):
 Each node in the distributed system periodically polls the time server.
 Time( treply) is estimated as t + (Treply + Treq)/2
 This process is repeated several times and an average is provided.
 Machine Treply then attempts to adjust its time.
 Disadvantages:
 Must attempt to take delay between server Treply andtime
server into account
 Single point of failure if time server fails

21. CRISTIAN’S ALGORITHM
Synchronize machines to a
time server with a UTC
receiver
Machine P requests time from
server every seconds
 Receives time t from
server, P sets clock to
t+treply where treply is the
time to send reply to P
 Use (treq+treply)/2 as an
estimate of treply
 Improve accuracy by
making a series of
measurements

22. PROBLEM WITH CRISTIAN’S ALGORITHM
 Time must never run
backward
 If sender‟s clock is
fast, CUTC will be
smaller than the
sender‟s current value
of C
Major Problem Minor Problem
 It takes nonzero time
for the time server‟s
reply
 This delay may be large
and vary with network
load

23. SOLUTION
Major Problem
 Control the clock
 Suppose that the timer set
to generate 100 intrpt/sec
 Normally each interrupt
add 10 msec to the time
 To slow down add only 9
msec
 To advance add 11 msec to
the time
Minor Problem
 Measure it
 Make a series of
measurements for accuracy
 Discard the measurements
that exceeds the threshold
value
 The message that came
back fastest can be taken to
be the most accurate.

24. BERKELEY ALGORITHM
 Used in systems without UTC receiver
 Keep clocks synchronized with one another
 One computer is master, other are slaves
 Master periodically polls slaves for their times
 Average times and return differences to slaves
 Communication delays compensated as in Cristian‟s
algo
 Failure of master => election of a new master

25. BERKELEY ALGORITHM
a)
b)
c)
The time daemon asks all the other machines for their clock values
The machines answer
The time daemon tells everyone how to adjust their clock

26. 30
DECENTRALIZED AVERAGING ALGORITHM
 Each machine on the distributed system has a daemon
without UTC.
 Periodically, at an agreed-upon fixed time, each machine
broadcasts its local time.
 Each machine calculates the correct time by averaging
all results.

27. TERMINATION DETECTION
 Detecting the end of a distributed computation
 Notation: let sender be predecessor, receiver be successor
 Two types of markers: Done and Continue
 After finishing its part of the snapshot, process Q sends a
Done or a Continue to its predecessor
 Send a Done only when
 All of Q‟s successors send a Done
 Q has not received any message since it check-pointed its local state
and received a marker on all incoming channels
 Else send a Continue
 Computation has terminated if the initiator receives Done
messages from everyone

28. DISTRIBUTED SYNCHRONIZATION
 Distributed system with multiple processes may
need to share data or access shared data
structures
 Use critical sections with mutual exclusion
 Single process with multiple threads
 Semaphores, locks, monitors
 How do you do this for multiple processes in a
distributed system?
 Processes may be running on different machines
 Solution: lock mechanism for a distributed
environment
 Can be centralized or distributed

29. CENTRALIZED MUTUAL EXCLUSION
 Assume processes are numbered
 One process is elected coordinator (highest ID
process)
 Every process needs to check with coordinator
before entering the critical section
 To obtain exclusive access: send request, await
reply
 To release: send release message
 Coordinator:
 Receive request: if available and queue empty, send
grant; if not, queue request
 Receive release: remove next request from queue and
send grant

30. MUTUAL EXCLUSION:
A CENTRALIZED ALGORITHM
a) Process 1 asks the coordinator for permission to enter a
critical region. Permission is granted
b) Process 2 then asks permission to enter the same critical
region. The coordinator does not reply.
c) When process 1 exits the critical region, it tells the
coordinator, when then replies to 2

31. PROPERTIES
 Simulates centralized lock using blocking calls
 Fair: requests are granted the lock in the order they were
received
 Simple: three messages per use of a critical section
(request, grant, release)
 Shortcomings:
 Single point of failure
 How do you detect a dead coordinator?
 A process can not distinguish between “lock in use” from a dead
coordinator
 No response from coordinator in either case
 Performance bottleneck in large distributed systems

32. DISTRIBUTED ALGORITHM
 [Ricart and Agrawala]: needs 2(n-1) messages
 Based on event ordering and time stamps
 Process k enters critical section as follows
 Generate new time stamp TSk = TSk+1
 Send request(k,TSk) all other n-1 processes
 Wait until reply(j) received from all other processes
 Enter critical section
 Upon receiving a request message, process j
 Sends reply if no contention
 If already in critical section, does not reply, queue request
 If wants to enter, compare TSj with TSk and send reply if TSk<TSj,
else queue

33. A DISTRIBUTED ALGORITHM
a)
b)
c)
Two processes want to enter the same critical region at the same
moment.
Process 0 has the lowest timestamp, so it wins.
When process 0 is done, it sends an OK also, so 2 can now enter
the critical region.

34. PROPERTIES
 Fully decentralized
 N points of failure!
 All processes are involved in all decisions
 Any overloaded process can become a
bottleneck

35. ELECTION ALGORITHMS
 Many distributed algorithms need one process
to act as coordinator
 Doesn‟t matter which process does the job, just
need to pick one
 Election algorithms: technique to pick a unique
coordinator (aka leader election)
 Examples: take over the role of a failed
process, pick a master in Berkeley clock
synchronization algorithm
 Types of election algorithms: Bully and Ring
algorithms

36. BULLY ALGORITHM
 Each process has a unique numerical ID
 Processes know the Ids and address of every other
process
 Communication is assumed reliable
 Key Idea: select process with highest ID
 Process initiates election if it just recovered from
failure or if coordinator failed
 3 message types: election, OK, I won
 Several processes can initiate an election
simultaneously
 Need consistent result
 O(n2) messages required with n processes

37. BULLY ALGORITHM DETAILS
 Any process P can initiate an election
 P sends Election messages to all process with
higher Ids and awaits OK messages
 If no OK messages, P becomes coordinator and
sends I won messages to all process with lower Ids
 If it receives an OK, it drops out and waits for an I
won
 If a process receives an Election msg, it returns an
OK and starts an election
 If a process receives a I won, it treats sender an
coordinator

38. BULLY ALGORITHM EXAMPLE




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

39. BULLY ALGORITHM EXAMPLE
d)
e)
Process 6 tells 5 to stop
Process 6 wins and tells everyone

40. RING-BASED ELECTION
 Processes have unique Ids and arranged in a logical ring
 Each process knows its neighbors
 Select process with highest ID
 Begin election if just recovered or coordinator has failed
 Send Election to closest downstream node that is alive
 Sequentially poll each successor until a live node is found
 Each process tags its ID on the message
 Initiator picks node with highest ID and sends a coordinator
message
 Multiple elections can be in progress
 Wastes network bandwidth but does no harm

41. A RING ALGORITHM
 Election algorithm using a ring.

42. A TOKEN RING ALGORITHM
a)
b)
An unordered group of processes on a network.
A logical ring constructed in software.
• Use a token to arbitrate access to critical section
• Must wait for token before entering CS
• Pass the token to neighbor once done or if not interested
• Detecting token loss in non-trivial

43. COMPARISON
 A comparison of three mutual exclusion
algorithms.
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

44. TRANSACTIONS
Transactions provide higher
level mechanism for atomicity
of processing in distributed
systems
 Have their origins in databases
Banking example: Three
accounts A:$100, B:$200,
C:$300
 Client 1: transfer $4 fromA to
B
 Client 2: transfer $3 from C to
B
Result can be inconsistent
unless certain properties are
Client 1 Client 2
Read A:$100
Write A: $96
Read C: $300
Write C:$297
Read B: $200
Read B: $200
Write B:$203
Write B:$204

45. Trans..ACID PROPERTIES
Atomic: all or nothing
Consistent: transaction takes
system from one consistent
state to another
Isolated: Immediate effects
are not visible to other
(serializable)
Durable: Changes are
permanent once transaction
completes (commits)
Client 1 Client 2
Read A:$100
Write A: $96
Read B: $200
Write B:$204
Read C: $300
Write C:$297
Read B: $204
Write B:$207