Synchronization

1
Synchronization

• Chapter 5

2
Synchronization

• Synchronization in distributed systems is harder
  than in centralized systems because of 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 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 same as Greenwich
  Mean Time
• Add leap seconds to be consistent with
  astronomical time
• UTC broadcast on radio (satellite and earth)
• Receivers accurate to 0.1 10 ms
• The goal is to synchronize machines with a master
  (UTC receiver machine) or with one another.

5
Physical Clocks

• Computation of the mean solar day (transit of the
  sun noon)

6
Physical Clocks

• TAI (Temps Atomique International) seconds are of
  constant length, unlike solar seconds. Leap
  seconds are introduced when necessary to keep in
  phase with the sun.

7
Clock Synchronization Algorithms

• The relation between clock time and UTC when
  clocks tick at different rates.

8
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 (2r Dt lt d, Dt d/2r)

9
Cristian's Algorithm

• Synchronize machines to a time server that has a
  UTC receiver.
• Machine P requests time from server every d/2r
  seconds
• Receives time t (Cutc) from server, P sets clock
  to ttreply where treply is the time to send
  reply to P
• Use (treqtreply)/2 as an estimate of treply
• 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 ? election of a new master

12
The Berkeley Algorithm

• 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

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 needs 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
• This algorithm meets the above goals

18
Lamport Timestamps

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

19
Example Totally-Ordered Multicasting

• Updating a replicated database and leaving it in
  an inconsistent state without a totally-ordered
  logic clock.

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 )
• Receiver is told about how many events the sender
  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

• A consistent cut receipts corresponds a send
  event
• 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)

• Organization of a process and channels for a
  distributed snapshot

27
Global State (Snapshot Algorithm)

• Process Q receives a marker for the first time
  and records its local state
• Q records all incoming message
• 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
  receives Done messages from everyone

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
  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

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

• Process 6 tells 5 to stop
• 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
  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

39
Mutual Exclusion A Centralized Algorithm

• Process 1 asks the coordinator for permission to
  enter a critical region. Permission is granted
• Process 2 then asks permission to enter the same
  critical region. The coordinator does not reply.
• 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
  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

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
• 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 TSkltTSj, else queue

42
A Distributed Algorithm

• 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.

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
• 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 not-trivial

44
A Toke Ring Algorithm

• An unordered group of processes on a network.
• A logical ring constructed in software.

45
Comparison

• 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

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)

48
The Transaction Model

• Updating a master tape is fault tolerant.

49
The Transaction Model

• Examples of primitives for transactions.

50
The Transaction Model

• Transaction to reserve three flights commits
  (White Plains ? New York ? Nairobi ? Malindi)
• 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 data.

52
Distributed Transactions

• A nested transaction (transaction is decomposed
  into subtransactions)
• A distributed transaction (subtransaction on
  different data)

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
  called shadow blocks.)
• Commit requires making local workspace global

54
Private Workspace

• The file index and disk blocks for a three-block
  file
• The situation after a transaction has modified
  block 0 and appended block 3
• After committing

55
Implementation Write-ahead Logs

• 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
• Commit needs to be atomic will return to this
  issue in Ch. 7

56
Writeahead Log

• 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 is possible if at least one operation is
  write
• Read-write conflict
• Write-write conflict

(d)

• a) c) Three transactions T1, T2, and T3
• d) Possible schedules (Schedule 2 is legal
  because it results in a valid x value.)

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
• Once 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.
• Advantages
• 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.
• Problem deadlock possible
• 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)
• When a transaction aborts, it must restart with a
  new (larger) time stamp
• Two values for each data item x
• Max-rts(x) max time stamp of a transaction that
  read x
• Max-wts(x) max time stamp of a transaction that
  wrote x

69
Reads and Writes using Timestamps

• Readi(x)
• 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
• Advantage
• Deadlock free
• Maximum parallelism
• Disadvantage
• Rerun transaction if aborts
• Probability of conflict rises substantially at
  high loads
• Not used widely