Lecture 14 Synchronization (cont)

1
Lecture 14Synchronization (cont)
2
Logistics

• Project
• P01 deadline on Wednesday November 3rd.
• Non-blocking IO lecture Nov 4th.
• P02 deadline on Wednesday November 15th.
• Next quiz
• Tuesday 16th.

3
Roadmap

• Clocks can not be perfectly synchronized.
• What can I do in these conditions?
• Figure out how large is the drift
• Example GPS systems
• Design the system to take drift into account
• Example server design to provide at-most-once
  semantics
• Do not use physical clocks!
• Consider only event order
• (1) Logical clocks (Lamport)
• But this does not account for causality!
• (2) Vector clocks!
• Mutual exclusion leader election

4
Last time Happens-before relation

• The happened-before relation on the set of events
  in a distributed system
• if a and b in the same process, and a occurs
  before b,
• then a ? b
• if a is an event of sending a message by a
  process, and b
• receiving same message by another
  process then a ? b
• Two events are concurrent if nothing can be said
  about the order in which they happened (partial
  order)

5
Lamports logical clocks

• Each process Pi maintains a local counter Ci and
  adjusts this counter according to the following
  rules
• For any two successive events that take place
  within process Pi, the counter Ci is incremented
  by 1.
• Each time a message m is sent by process Pi the
  message receives a timestamp ts(m) Ci
• Whenever a message m is received by a process Pj,
  Pj adjusts its local counter Cj to maxCj,
  ts(m) then executes step 1 before passing m to
  the application.

6
Updating Lamports logical timestamps

Physical Time
1
2
p 1
8
0
7
1
8
3
p 2
0
2
2
3
6
p 3
4
0
10
9
3
5
4
7
p 4
0
5
6
7
Clock Value
n
timestamp
Message
7
Problem with Lamport logical clocks

• Notation timestamp(a) is the Lamport logical
  clock associated with event a
• By definition if a ? b gt timestamp(a) lt
  timestamp(b)
• (if a happens before b, then Lamport_timestamp(a)
  lt Lamport_timestamp(b))
• Q is the converse true?
• That is if timestamp(a) lt timestamp(b) gt
  a ? b
• (If Lamport_timestamp(a) lt Lamport_timestamp(b),
  it does NOT imply that a happens before b

8
Example

Physical Time
1
2
p 1
8
0
7
1
8
3
p 2
0
2
2
3
6
p 3
4
0
10
9
3
5
4
7
p 4
0
5
6
7
Clock Value
n
timestamp
Message
Note Lamport Timestamps 3 lt 7, but event with
timestamp 3 is concurrent to event with timestamp
7, i.e., events are not in happen-before
relation.
9
Causality

• Timestamps dont capture causality
• Example news postings have multiple
• independent threads of messages
• To model causality use Lamports vector
  timestamps
• Intuition each item in vector logical clock for
  one causality thread.

10
Vector Timestamps
11
Vector clocks

• Each process Pi has an array VCi 1..n of clocks
  (all initialized at 0)
• VCi j denotes the number of events that process
  Pi knows have taken place at process Pj.
• Pi increments VCi i when an event occurs or
  when sending
• Vector value is the timestamp of the event
• When sending
• Messages sent by VCi include a vector timestamp
  vt(m).
• Result upon arrival, recipient knows Pis
  timestamp.
• When Pj receives a message sent by Pi with vector
  timestamp ts(m)
• for k ? j updates each VCj k to maxVCj k,
  ts(m)k
• for k j VCj k VCj k 1
• Note vector timestamps require a static notion
  of system membership
• Question What does VCij k mean in terms of
  messages sent and received?

12
Example Vector Logical Time

Physical Time
p 1
0,0,0,0
p 2
0,0,0,0
p 3
0,0,0,0
p 4
0,0,0,0
Vector logical clock
n,m,p,q
(vector timestamp)
Message
13
Comparing vector timestamps

• VT1 VT2, (identical)
• iff VT1i VT2i, for all i 1, , n
• VT1 VT2,
• iff VT1i VT2i, for all i 1, , n
• VT1 lt VT2, (happens before relationship)
• iff VT1 VT2 and
• ? j (1 j n) such that VT1j lt VT2
  j
• VT1 is concurrent with VT2
• iff (not VT1 VT2 AND not VT2 VT1)

14
Quiz like problem

• Show
• a ? b if and only if vectorTS(a) lt
  vectorTS(b)

15
Message delivery for group communication

• ASSUMPTIONS
• messages are multicast to named process groups
• reliable and fifo channels (from a given source
  to a given destination)
• processes dont crash (failure and restart not
  considered)
• processes behave as specified e.g., send the same
  values to all processes (i.e., we are not
  considering Byzantine behaviour)

application process
may specify delivery order to message
service e.g. total order, FIFO order, causal
order (last time total order)
Messaging middleware
may reorder delivery to application by buffering
messages
assume FIFO from each source (done at lower
levels)
OS comms. interface
16
Last time Totally Ordered Multicast

• Process Pi sends timestamped message msgi to all
  others. The message itself is put in a local
  queue queuei.
• Any incoming message at Pk is queued in queuek,
  according to its timestamp, and acknowledged to
  every other process.
• Pk passes a message msgi to its application if
• msgi is at the head of queuek
• for each process Px, there is a message msgx in
  queuek with a larger timestamp.
• Note We are assuming that communication is
  reliable and FIFO ordered.
• Guarantee all multicasted messages in the same
  order at all destination.
• Nothing is guaranteed about the actual order!

17
FIFO multicast

• Fifo or sender ordered multicast Messages are
  delivered in the order they were sent (by any
  single sender)

a
e
P1 P2 P3 P4
18
FIFO multicast

• Fifo or sender ordered multicast Messages are
  delivered in the order they were sent (by any
  single sender)

a
e
P1 P2 P3 P4
b
c
d
delivery of c to P1 is delayed until after b is
delivered
19
Implementing FIFO multicast

• Basic reliable multicast algorithm has this
  property
• Without failures all we need is to run it on FIFO
  channels (like TCP)
• Later dealing with node failures

20
Causal multicast

• Causal or happens-before ordering
• If send(a) ? send(b) then deliver(a) occurs
  before deliver(b) at common destinations

a
P1 P2 P3 P4
b
21
Ordering properties Causal

• Causal or happens-before ordering
• If send(a) ? send(b) then deliver(a) occurs
  before deliver(b) at common destinations

a
P1 P2 P3 P4
b
c
delivery of c to P1 is delayed until after b is
delivered
22
Ordering properties Causal

• Causal or happens-before ordering
• If send(a) ? send(b) then deliver(a) occurs
  before deliver(b) at common destinations

a
e
P1 P2 P3 P4
b
c
d
e is sent (causally) after b and c
e is sent concurrently with d
23
Ordering properties Causal

• Causal or happens-before ordering
• If send(a) ? send(b) then deliver(a) occurs
  before deliver(b) at common destinations

a
e
P1 P2 P3 P4
b
c
d
delivery of c to P1 is delayed until after b is
delivered
delivery of e to P3 is delayed until after bc
are delivered
delivery of e and d to P2 and P3 in any relative
order (concurrent)
24
Causally ordered multicast
VC0(2,2,0)
VC1(1,2,0)
VC1(1,1,0)
VC2(1,2,2)
VC2(1,0,1)
25
Implementing causal order

• Start with a FIFO multicast
• We can strengthen this into a causal multicast by
  adding vector time
• No additional messages needed!
• Advantage FIFO and causal multicast are
  asynchronous
• Sender doesnt get blocked and can deliver a copy
  to itself without stopping to learn a safe
  delivery order

26
So far

• Physical clocks
• Two applications
• Provide at-most-once semantics
• Global Positioning Systems
• Logical clocks
• Where only ordering of events matters
• Other coordination primitives
• Mutual exclusion
• Leader election

27
Mutual exclusion algorithms

• Problem A number of processes in a distributed
  system want exclusive access to some resource.
• Basic solutions
• Via a centralized server.
• Completely decentralized
• Completely distributed, with no roles imposed.
• Completely distributed along a (logical) ring.
• Additional objective Fairness

28
Mutual Exclusion A Centralized Algorithm

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, when then replies to 2

29
Decentralized Mutual Exclusion

• Principle Assume the resource is replicated n
  times, with each replica having its own
  coordinator
• Access requires a majority vote from m gt n/2
  coordinators.
• A coordinator always responds immediately to a
  request.
• Assumption When a coordinator crashes, it will
  recover quickly, but will have forgotten about
  permissions it had granted.
• Correctness probabilistic!
• Issue How robust is this system?

30
Decentralized Mutual Exclusion (cont)

• Principle Assume every resource is replicated n
  times, with each replica having its own
  coordinator
• Access requires a majority vote from m gt n/2
  coordinators.
• A coordinator always responds immediately to a
  request.
• Issue How robust is this system?
• p the probability that a coordinator resets
  (crashes and recovers) in an interval ?t
• p ?t /T, where T is the an average peer
  lifetime
• Quizlike question whats the probability to
  violate mutual exclusion?

31
Decentralized Mutual Exclusion (cont)

• Principle Assume every resource is replicated n
  times, with each replica having its own
  coordinator
• Access requires a majority vote from m gt n/2
  coordinators.
• A coordinator always responds immediately to a
  request.
• Issue How robust is this system?
• p the probability that a coordinator resets
  (crashes and recovers) in an interval ?t
• p ?t /T, where T is the an average peer
  lifetime
• The probability that k out m coordinators reset
  during ?t PkC(k,m)pk(1-p)m-k
• Violation when at least 2m-n coordinators reset

32
(No Transcript)
33
Performance issue starvation
34
Mutual Exclusion A Distributed Algorithm
(Ricart Agrawala)

• Idea Similar to Lamport ordered group
  communication except that acknowledgments arent
  sent.
• Instead, replies (i.e. grants) are sent only
  when
• The receiving process has no interest in the
  shared resource or
• The receiving process is waiting for the
  resource, but has lower priority (known through
  comparison of timestamps).
• In all other cases, reply is deferred
• (results in some more local administration)

35
Mutual Exclusion A Distributed Algorithm (II)

1. Two processes (0 and 2) want to enter the same
   critical region at the same moment.
2. Process 0 has the lowest timestamp, so it wins.
3. When process 0 is done, it sends an OK also, so 2
   can now enter the critical region.

Question Is a fully distributed solution, i.e.
one without a coordinator, always more robust
than any centralized coordinated solution?
36
Mutual Exclusion A Token Ring Algorithm

• Principle Organize processes in a logical ring,
  and let a token be passed between them. The one
  that holds the token is allowed to enter the
  critical region (if it wants to)

37
Logistics

• Project
• P01 deadline tomorrow.
• Project/Non-blocking IO lecture Thursday
• P02 deadline on Wednesday November 15th.
• Next quiz
• Tuesday 16th.

38
So far

• Physical clocks
• Two applications
• Provide at-most-once semantics
• Global Positioning Systems
• Logical clocks
• Where only ordering of events matters
• Lamport clocks
• Vector clocks
• Other coordination primitives
• Mutual exclusion
• Leader election How do I choose a coordinator?

39
Last time Mutual exclusion algorithms

• Problem A number of processes in a distributed
  system want exclusive access to some resource.
• Basic solutions
• Via a centralized server.
• Completely decentralized (voting based)
• Completely distributed, with no roles imposed.
• Completely distributed along a (logical) ring.
• Additional objectives Fairness no starvation

40
Mutual Exclusion Algorithm Comparison
Algorithm Messages per entry/exit Delay before entry (in message times) Problems
Centralized Coordinator crash
Decentralized Starvation, low efficiency
Distributed Crash of any process
Token ring Lost token, process crash
41
Mutual Exclusion Algorithm Comparison
Algorithm Messages per entry/exit Delay before entry (in message times) Problems
Centralized 3 Coordinator crash
Decentralized 3mk (knumber of attempts) Starvation, low efficiency
Distributed 2(n-1) Crash of any process
Token ring 1..8 Lost token, process crash
42
Mutual Exclusion Algorithm Comparison
Algorithm Messages per entry/exit Delay before entry (in message times) Problems
Centralized 3 2 Coordinator crash
Decentralized 3mk (knumber of attempts) 2m Starvation, low efficiency
Distributed 2(n-1) 2(n-1) Crash of any process
Token ring 1..8 0 to n-1 Lost token, process crash
43
So far

• Physical clocks
• Two applications
• Provide at-most-once semantics
• Global Positioning Systems
• Logical clocks
• Where only ordering of events matters
• Other coordination primitives
• Mutual exclusion
• Leader election How do I choose a coordinator?

44
Leader election algorithms

• Context An algorithm requires that some process
  acts as a coordinator.
• Question how to select this special process
  dynamically.
• Note In many systems the coordinator is chosen
  by hand (e.g. file servers). This leads to
  centralized solutions single point of failure.

45
Leader election algorithms

• Context Each process has an associated priority
  (weight). The process with the highest priority
  needs to be elected as the coordinator.
• Issue How do we find the heaviest process?
• Two important assumptions
• Processes are uniquely identifiable
• All processes know the identity of all
  participating processes
• Traditional algorithm examples
• The bully algorithm
• Ring based algorithm

46
Election by Bullying

• Any process can just start an election by sending
  an election message to all other (heavier)
  processes
• If a process Pheavy receives an election message
  from a lighter process Plight, it sends a
  take-over message to Plight. Plight is out of the
  race.
• If a process doesnt get a take-over message
  back, it wins, and sends a victory message to all
  other processes.

47
The Bully Algorithm

• Process 4 detects 7 has failed and holds an
  election
• Process 5 and 6 respond, telling 4 to stop
• Now 5 and 6 each hold an election (also send
  message to 7 as they have not detected 7 failure)

48
The Bully Algorithm (2)

1. Process 6 tells 5 to stop
2. Process 6 wins and announces itself everyone

49
Election in a Ring

• Principle Organize processes into a (logical)
  ring. Process with the highest priority should be
  elected as coordinator.
• Any process can start an election by sending an
  election message to its successor. If a successor
  is down, the message is passed on to the next
  successor.
• If a message is passed on, the sender adds itself
  to the list.
• The initiator sends a coordinator message around
  the ring containing a list of all living
  processes. The one with the highest priority is
  elected as coordinator.

50
The Ring Algorithm

• Question What happens if two processes initiate
  an election at the same time? Does it matter?
• Question What happens if a process crashes
  during the election?

51
Summary so far

• A distributed system is
• a collection of independent computers that
  appears to its users as a single coherent system
• Components need to
• Communicate
• Point to point sockets, RPC/RMI
• Point to multipoint multicast, epidemic
• Cooperate
• Naming to enable some resource sharing
• Naming systems for flat (unstructured)
  namespaces consistent hashing, DHTs
• Naming systems for structured namespaces EECE456
  for DNS
• Synchronization physical clocks, logical clocks,
  mutual exclusion, leader election