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Synchronization Chapter 5 Synchronization Synchronization in distributed systems is harder than in centralized systems because the need for distributed algorithms. – PowerPoint PPT presentation

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Title: Synchronization

  • Chapter 5

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

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
  • Each crystal-based clock runs at slightly
    different rates. This difference is called clock
  • Problem An event that occurred after another may
    be assigned an earlier time.

Physical Clocks
  • Computation of the mean solar day.

Physical Clocks A Primer
  • Accurate clocks are atomic oscillators
  • Most clocks are less accurate (e.g., mechanical
  • 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
  • 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.

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

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

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

Cristian's Algorithm
  • Synchronize machines to a time server with a UTC
  • Machine P requests time from server every d/2r
  • Receives time t 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

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

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
  • Failure of master gt election of a new master

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

Distributed Approaches
  • Both approaches studied thus far are centralized
  • Decentralized algorithms use resynchronization
  • 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
  • Network Time Protocol (NTP) Uses advanced clock
    synchronization to achieve accuracy in 1-50 ms

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

Event Ordering
  • Problem define a total ordering of all events
    that occur in a system
  • Events in a single processor machine are totally
  • In a distributed system
  • No global clock, local clocks may be
  • 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)

Happenes-Before Relation
  • The expression A ? B is read A happens before
  • 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

Event Ordering Using HB
  • Goal define the notion of time of an event such
  • If A? B then C(A) lt C(B)
  • If A and B are concurrent, then C(A) lt, , or gt
  • Solution
  • Each processor maintains a logical clock LCi
  • Whenever an event occurs locally at I, LCi
  • 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

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

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

  • Lamports logical clocks
  • If A ? B then C(A) lt C(B)
  • Reverse is not true!!
  • Nothing can be said about events by comparing
  • 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
  • Need a time-stamping mechanism such that
  • If T(A) lt T(B) then A should have causally
    preceded B

Vector Clocks
  • Causality can be captured by means of vector
  • 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

Global State
  • The global state of a distributed system consists
  • Local state of each process
  • Messages sent but not received (state of the
  • Many applications need to know the state of the
  • 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.

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

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

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)

Global State (Snapshot Algorithm)
  1. Organization of a process and channels for a
    distributed snapshot

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

Termination Detection
  • Detecting the end of a distributed computation
  • Notation let sender be predecessor, receiver be
  • 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

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
  • Types of election algorithms Bully and Ring

Bully Algorithm
  • Each process has a unique numerical ID
  • Processes know the Ids and address of every other
  • 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
  • Need consistent result
  • O(n2) messages required with n processes

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
  • 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

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

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

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
  • 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

A Ring Algorithm
  • Election algorithm using a ring.

  • 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

Distributed Synchronization
  • Distributed system with multiple processes may
    need to share data or access shared data
  • 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
  • Can be centralized or distributed

Centralized Mutual Exclusion
  • Assume processes are numbered
  • One process is elected coordinator (highest ID
  • 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

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

  • 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

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
  • 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

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.

  • 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
  • Must wait for token before entering CS
  • Pass the token to neighbor once done or if not
  • Detecting token loss in not-trivial

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

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.

  • Transactions provide higher level mechanism for
    atomicity of processing in distributed systems
  • Have their origins in databases
  • Banking example Three accounts A100, B200,
  • 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
Read A 100
Write A 96
Read C 300
Write C297
Read B 200
Read B 200
Write B203
Write B204
ACID Properties
  • Atomic all or nothing (indivisible)
  • Consistent transaction takes system from one
    consistent state to another (hold certain
  • 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 B204
Read C 300
Write C297
Read B 204
Write B207
The Transaction Model
  • Updating a master tape is fault tolerant.

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
READ Read data from a file, a table, or otherwise
WRITE Write data to a file, a table, or otherwise
  • Examples of primitives for transactions.

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

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
  • A nest transaction is constructed from a number
    of subtransactions.
  • A distributed transaction is logically a flat,
    indivisible transaction that operates on
    distributed ata.

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

Implementation of transactions
  • Two methods can be used to implement
  • 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

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

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
  • Force logs on commit
  • If abort, read log records and undo changes
  • Log can be used to rerun transaction after
  • Both workspaces and logs work for distributed
  • Commit needs to be atomic will return to this
    issue in Ch. 7

Writeahead Log
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

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

Concurrency Control
  • Concurrency control can implemented in a layered
  • 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

Concurrency Control
  • General organization of managers for handling

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

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

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
  • a) c) Three transactions T1, T2, and T3
  • d) Possible schedules

  • 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.

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
  • Never release a lock until data manager finishes
    operation on x
  • One a lock is released, no further locks can be

Two-Phase Locking
  • Two-phase locking.

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

Two-Phase Locking
  • Strict two-phase locking.

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
  • 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.

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

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)

Pessimistic Timestamp Ordering
  • Concurrency control using timestamps.

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
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