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## Synchronization

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Title: Synchronization

1
Synchronization
• Chapter 5

2
Synchronization
• Synchronization in distributed systems is harder
than in centralized systems because the need for
distributed algorithms.
• Distributed algorithms have the following
properties
• No machine has complete information about the
system state.
• Machines make decisions based only on local
information.
• Failure of one machine does not ruin the
algorithm.
• There is no implicit assumption that a global
clock exists.
• Clocks are needed to synchronize in a distributed
system.

3
Clock Synchronization
• Time is unambiguous in centralized systems.
• System clock keeps time, all entities use this
for time.
• In distributed systems each node has own system
clock.
• Each crystal-based clock runs at slightly
different rates. This difference is called clock
skew.
• Problem An event that occurred after another may
be assigned an earlier time.

4
Physical Clocks
• Computation of the mean solar day.

5
Physical Clocks A Primer
• Accurate clocks are atomic oscillators
• Most clocks are less accurate (e.g., mechanical
watches)
• Computers use crystal-based blocks
• Results in clock drift
• How do you tell time?
• Use astronomical metrics (solar day)
• Coordinated universal time (UTC) international
standard based on atomic time
• Add leap seconds to be consistent with
astronomical time
• Receivers accurate to 0.1 10 ms
• The goal is to synchronize machines with a master
(UTC receiver machine) or with one another.

6
Physical Clocks
• TAI seconds are of constant length, unlike solar
seconds. Leap seconds are introduced when
necessary to keep in phase with the sun.

7
Clock Synchronization
• Each clock has a maximum drift rate r
• 1-r lt dC/dt lt 1r
• Two clocks may drift by 2r Dt in time Dt
• To limit drift to d gt resynchronize every d/2r
seconds

8
Clock Synchronization Algorithms
• The relation between clock time and UTC when
clocks tick at different rates.

9
Cristian's Algorithm
• Synchronize machines to a time server with a UTC
• Machine P requests time from server every d/2r
seconds
• Receives time t from server, P sets clock to
to P
• Improve accuracy by making a series of
measurements

10
Cristian's Algorithm
• Getting the current time from a time server.

11
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 Cristians
algorithm
• Failure of master gt election of a new master

12
The Berkeley Algorithm
1. The time daemon asks all the other machines for
their clock values
3. The time daemon tells everyone how to adjust
their clock

13
Distributed Approaches
• Both approaches studied thus far are centralized
• Decentralized algorithms use resynchronization
intervals
• Broadcast time at the start of the interval
• Collect all other broadcast that arrive in a
period S
• Use average value of all reported times
• Can throw away few highest and lowest values
• Approaches in use today
• rdate synchronizes a machine with a specified
machine
• Network Time Protocol (NTP) Uses advanced clock
synchronization to achieve accuracy in 1-50 ms

14
Logical Clocks
• For many problems, only internal consistency of
clocks matters.
• Absolute (real) time is less important
• Use logical clocks
• Key idea
• Clock synchronization need not be absolute
• If two machines do not interact, no need to
synchronize them
• More importantly, processes need to agree on the
order in which events occur rather than the time
at which they occurred

15
Event Ordering
• Problem define a total ordering of all events
that occur in a system
• Events in a single processor machine are totally
ordered
• In a distributed system
• No global clock, local clocks may be
unsynchronized
• Can not order events on different machines using
local times
• Key idea Lamport
• Processes exchange messages
• Message must be sent before received
• Send/receive used to order events (and
synchronize clocks)

16
Happenes-Before Relation
• The expression A ? B is read A happens before
B.
• If A and B are events in the same process and A
executed before B, then A ? B
• If A represents sending of a message and B is the
receipt of this message, then A ? B
• Relation is transitive
• A ? B and B ? C ? A ? C
• Relation is undefined across processes that do
not exchange messages
• Partial ordering on events

17
Event Ordering Using HB
• Goal define the notion of time of an event such
that
• If A? B then C(A) lt C(B)
• If A and B are concurrent, then C(A) lt, , or gt
C(B)
• Solution
• Each processor maintains a logical clock LCi
• Whenever an event occurs locally at I, LCi
LCi1
• When i sends message to j, piggyback LCi
• When j receives message from i
• If LCj lt LCi then LCj LCi 1 else do nothing
• Claim this algorithm meets the above goals

18
Lamport Timestamps
1. Three processes, each with its own clock. The
clocks run at different rates.
2. Lamport's algorithm corrects the clocks.

19
Example Totally-Ordered Multicasting
• Updating a replicated database and leaving it in
an inconsistent state.

20
Causality
• Lamports logical clocks
• If A ? B then C(A) lt C(B)
• Reverse is not true!!
• Nothing can be said about events by comparing
time-stamps!
• If C(A) lt C(B), then ??
• Need to maintain causality
• Causal deliveryIf send(m) ? send(n) ? deliver(m)
? deliver(n)
• Capture causal relationships between groups of
processes
• Need a time-stamping mechanism such that
• If T(A) lt T(B) then A should have causally
preceded B

21
Vector Clocks
• Causality can be captured by means of vector
timestamps.
• Each process i maintains a vector Vi
• Vii number of events that have occurred at i
• Vij number of events I knows have occurred at
process j
• Update vector clocks as follows
• Local event increment ViI
• Send a message piggyback entire vector V
• Receipt of a message Vjk max( Vjk,Vik )
knows occurred at another process k
• Also Vji Vji1

22
Global State
• The global state of a distributed system consists
of
• Local state of each process
• Messages sent but not received (state of the
queues)
• Many applications need to know the state of the
system
• Failure recovery, distributed deadlock detection
• Problem how can you figure out the state of a
distributed system?
• Each process is independent
• No global clock or synchronization
• A distributed snapshot reflects a consistent
global state.

23
Global State
1. A consistent cut receipts corresponds a send
event
2. An inconsistent cut sender cannot be identified

24
Distributed Snapshot Algorithm
• Assume each process communicates with another
process using unidirectional point-to-point
channels (e.g, TCP connections)
• Any process can initiate the algorithm
• Checkpoint local state
• Send marker on every outgoing channel
• On receiving a marker
• Checkpoint state if first marker and send marker
on outgoing channels, save messages on all other
channels until
• Subsequent marker on a channel stop saving state
for that channel

25
Distributed Snapshot
• A process finishes when
• It receives a marker on each incoming channel and
processes them all
• State local state plus state of all channels
• Send state to initiator
• Any process can initiate snapshot
• Multiple snapshots may be in progress
• Each is separate, and each is distinguished by
tagging the marker with the initiator ID (and
sequence number)

B
M
A
M
C
26
Global State (Snapshot Algorithm)
1. Organization of a process and channels for a
distributed snapshot

27
Global State (Snapshot Algorithm)
1. Process Q receives a marker for the first time
and records its local state
2. Q records all incoming message
3. Q receives a marker for its incoming channel and
finishes recording the state of the incoming
channel

28
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 Qs 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

29
Election Algorithms
• Many distributed algorithms need one process to
act as coordinator
• Doesnt matter which process does the job, just
need to pick one
• Election algorithms technique to pick a unique
• 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

30
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

31
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

32
The Bully Algorithm
• 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

33
Bully Algorithm
1. Process 6 tells 5 to stop
2. Process 6 wins and tells everyone

34
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

35
A Ring Algorithm
• Election algorithm using a ring.

36
Comparison
• Assume n processes and one election in progress
• Bully algorithm
• Worst case initiator is node with lowest ID
• Triggers n-2 elections at higher ranked nodes
O(n2) msgs
• Best case immediate election n-2 messages
• Ring
• 2 (n-1) messages always

37
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

38
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
• 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

39
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

40
Properties
• Simulates centralized lock using blocking calls
• Fair requests are granted the lock in the order
• 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
• No response from coordinator in either case
• Performance bottleneck in large distributed
systems

41
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 TSk1
• Send request(k,TSk) all other n-1 processes
processes
• Enter critical section
• Upon receiving a request message, process j
• Sends reply if no contention
queue request
• If wants to enter, compare TSj with TSk and send

42
A Distributed Algorithm
1. Two processes 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.

43
Properties
• Fully decentralized
• N points of failure!
• All processes are involved in all decisions
• Any overloaded process can become a bottleneck
• A Token Ring Algorithm
section
• Must wait for token before entering CS
• Pass the token to neighbor once done or if not
interested
• Detecting token loss in not-trivial

44
A Toke Ring Algorithm
1. An unordered group of processes on a network.
2. A logical ring constructed in software.

45
Comparison
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.

46
Transactions
• Transactions provide higher level mechanism for
atomicity of processing in distributed systems
• Have their origins in databases
• Banking example Three accounts A100, B200,
C300
• Client 1 transfer 4 from A to B
• Client 2 transfer 3 from C to B
• Result can be inconsistent unless certain
properties are imposed on the accesses

Client 1 Client 2
Write A 96
Write C297
Write B203
Write B204
47
ACID Properties
• Atomic all or nothing (indivisible)
• Consistent transaction takes system from one
consistent state to another (hold certain
invariants)
• Isolated Immediate effects are not visible to
other (serializable)
• Durable Changes are permanent once transaction
completes (commits)

Client 1 Client 2
Write A 96
Write B204
Write C297
Write B207
48
The Transaction Model
• Updating a master tape is fault tolerant.

49
The Transaction Model
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
WRITE Write data to a file, a table, or otherwise
• Examples of primitives for transactions.

50
The Transaction Model
BEGIN_TRANSACTION reserve WP -gt JFK reserve JFK -gt Nairobi reserve Nairobi -gt MalindiEND_TRANSACTION (a) BEGIN_TRANSACTION reserve WP -gt JFK reserve JFK -gt Nairobi reserve Nairobi -gt Malindi full gtABORT_TRANSACTION (b)
1. Transaction to reserve three flights commits
2. Transaction aborts when third flight is
unavailable

51
Classification of Transactions.
• A flat transaction is a series of operations that
satisfy the ACID properties.
• It does not allow partial results to be committed
or aborted. Example flight reservation, Web link
update.
• A nest transaction is constructed from a number
of subtransactions.
• A distributed transaction is logically a flat,
indivisible transaction that operates on
distributed ata.

52
Distributed Transactions
1. A nested transaction
2. A distributed transaction

53
Implementation of transactions
• Two methods can be used to implement
transactions
• Private workspace Until the transaction either
commits or aborts, all of its reads and writes go
to the private workspace.
• Writeahead log Use a log to record the change.
Only after the log has been written successfully
is the change made to the file.
• Private workspace
• Each transaction get copies of all files, objects
• It can optimize for reads by not making copies
• It can optimize for writes by copying only what
is required (An appended block and a copy of
modified block are created. These new blocks are
• Commit requires making local workspace global

54
Private Workspace
1. The file index and disk blocks for a three-block
file
2. The situation after a transaction has modified
block 0 and appended block 3
3. After committing

55
• In-place updates transaction makes changes
directly to all files/objects and keeps these
changes in a log.
• Write-ahead log prior to making change,
transaction writes to log on stable storage
• Transaction ID, block number, original value, new
value
• Force logs on commit
• If abort, read log records and undo changes
rollback
• Log can be used to rerun transaction after
failure
• Both workspaces and logs work for distributed
transactions
issue in Ch. 7

56
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)
• a) A transaction
• b) d) The log before each statement is executed

57
Concurrency Control
• Goal Allow several transactions to be executing
simultaneously such that
• Collection of manipulated data item is left in a
consistent state
• Achieve consistency by ensuring data items are
accessed in an specific order
• Final result should be same as if each
transaction ran sequentially

58
Concurrency Control
• Concurrency control can implemented in a layered
fashion
• Bottom layer - A data manager performs the actual
read and write operations on data.
• Middle layer - A scheduler carries the main
responsibility for properly controlling
concurrency. Scheduling can be based on the use
of locks or timestamps.
• Highest layer The transaction manager is
responsible for guaranteeing atomicity of
transactions.

59
Concurrency Control
• General organization of managers for handling
transactions.

60
Concurrency Control
• General organization of managers for handling
distributed transactions.

61
Serializability
• Key idea properly schedule conflicting
operations
• Conflict possible if at least one operation is
write
• Write-write conflict

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
• d) Possible schedules

62
Serializability
• Two approaches are used in concurrency control
• Pessimistic approaches operations are
synchronized before they are carried out.
• Optimistic approaches operations are carried out
and synchronization takes place at the end of
transaction. At the conflict point, one or more
transactions are aborted.

63
Two-phase Locking (2PL)
• Widely used concurrency control technique
• Scheduler acquires all necessary locks in growing
phase, releases locks in shrinking phase
• Check if operation on data item x conflicts with
existing locks
• If so, delay transaction. If not, grant a lock on
x
• Never release a lock until data manager finishes
operation on x
• One a lock is released, no further locks can be
granted

64
Two-Phase Locking
• Two-phase locking.

65
Two-phase Locking (2PL)
• In strict two-phase locking, the shrinking phase
does not take place until the transaction has
finished running.
• A transaction always reads a value written by a
committed transaction.
• All lock acquisitions and releases can be handled
by the system without the transaction being aware
of them.
• Example acquiring two locks in different order

66
Two-Phase Locking
• Strict two-phase locking.

67
Two-phase Locking (2PL)
• In centralized 2PL, a single site is responsible
for granting and releasing locks.
• In primary 2PL, each data item is assigned a
primary copy. The lock manager on that copys
machine is responsible for granting and releasing
locks.
• In distributed 2PL, the schedulers on each
machine not only take care that locks are granted
and released, but also that the operation is
forwarded to the (local) data manager.

68
Timestamp-based Concurrency Control
• Each transaction Ti is given timestamp ts(Ti)
• If Ti wants to do an operation that conflicts
with Tj
• Abort Ti if ts(Ti) lt ts(Tj)
new (larger) time stamp
• Two values for each data item x
• Max-rts(x) max time stamp of a transaction that
• Max-wts(x) max time stamp of a transaction that
wrote x

69
• If ts(Ti) lt max-wts(x) then Abort Ti
• Else
• Perform Ri(x)
• Max-rts(x) max(max-rts(x), ts(Ti))
• Writei(x)
• If ts(Ti)ltmax-rts(x) or ts(Ti)ltmax-wts(x) then
Abort Ti
• Else
• Perform Wi(x)
• Max-wts(x) ts(Ti)

70
Pessimistic Timestamp Ordering
• Concurrency control using timestamps.

71
Optimistic Concurrency Control
• Transaction does what it wants and validates
changes prior to commit
• Check if files/objects have been changed by
committed transactions since they were opened
• Insight conflicts are rare, so works well most
of the time
• Works well with private workspaces