Synchronization

1. Synchronization

2. Clock SynchronizationIn a centralized system time is unambiguous.In a distributed system agreement on time is not obvious. • When each machine has its own clock, an event that occurred after another event maybe assigned an earlier time.

3. Physical clocks • Computer Clock ≡ Timer • Oscillating crystal (under tension) • 2 register: counter and holding register • Interrupt ( clock tick) when counter gets to zero • Nclocks n rates (clock skew) • UTC (Universal Coordinated Time) is the current universal time • standard • Radio pulse to synchronize (±10 msec) • Satellite (± 0.5 msec)

4. Clock Synchronization Algorithms If 1 – r < dC/dt < 1 + rrmaximum drift rate If 2 clock drift from UTC in the opposite directions, after Δt, ε = 2r Dt Resynchronization interval d/2r dmaximum time difference • The relation between clock time and UTC when clocks tick at different rates.

5. Cristian's Algorithmsynchronization with a time serverPeriodically (T < d/2r s) synchronizationrequest to time server • Time must never runbackward gradual changes • Non zero message propagation time (estimate value = (T1-T0-I) /2 )

6. The Berkeley Algorithmsynchronization without time server • The time daemon asks all the other machines for their clock value discrepancies • The machines answer • The time daemon tells everyone how to adjust their clock to the average • no Universal Coordinated Time available

7. Centralized algorithms have disadvantages. • Decentralized algorithms can use averaging methods. • NTP (Network Time Protocol) provides an accuracy of 1-50 msec using advanced algorithms. • For many purposes it is sufficient that all machines agree on the same time. • Logical clocks • Often processes need to agree on the order in which events occur.

8. Lamport Timestamps happens before If a b C(a) < C(b) If a snd., b rcv. C(a) < C(b) C(a)  C(b) If C(b) < C(a) C(b) = C(a)+1 We obtain a total ordering of all events in the system but Lamport timestamps don’t capture causality Vector timestamps each process Pi has a vector Vi so that: Vi[i] is the number of events occurred so far at Pi If Vi[j]=k Pi knows k events occurred at Pj

9. Global StateIt is the local state of each process, together with the messages currently in transit in a distributed systemA distributed snapshot reflects a consistent global state

10. Global State Knowledge of the state in which a distributed system currently is, is useful Any process P may initiate the algorithm recording its local state. P • Organization of a process and channels for a distributed snapshot • A process P sends a marker along each of its outgoing channels

11. Global State • Process Q receives a marker for the first time, records its local state and send a marker along each outgoing channel • Q records all incoming message • Q receives a marker for its incoming channel and finishes recording the state of the incoming channel • A process has finished its part of the algorithm when it has received a marker • along each of its incoming channels, and processed each one. Then its state is collected • Many snapshots may be in progress at the same time.

12. Election AlgorithmsThe Bully Algorithm (1/2) Selecting a coordinator • 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

13. The Bully Algorithm (2/2) • Process 6 tells 5 to stop • Process 6 wins and tells everyone

14. A Ring Algorithm Each process knows who its successor is. • Election algorithm using a ring (without token).

15. Mutual Exclusion:critical regions in distributed systemsA Centralized Algorithm(to simulate a single processor system, needs a coordinator) • 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, which then replies to 2

16. A Distributed Algorithmrequires a total ordering of all events in the systemA message contains the critical region name, the process number and the current time • Two processes (0,2) want to enter the same critical region at the same moment. • Process 0 has the lowest timestamp, so it wins and enters the critical region. • When process 0 is done, it sends an OK also, so 2 can now enter the critical region. • This algorithm is worse than the centralized one (n points of failure, scaling, multiple messages…)

17. A Token Ring Algorithmwhen the process acquires the token, it accesses the critical region (if needed) start • An unordered group of processes on a network. • A logical, ordered, ring constructed in software. Each process knows who • is the next in line

18. Mutual Exclusion • A comparison of three mutual exclusion algorithms. *With dependence on the mechanism

19. The Transaction ModelA transaction permits that a whole set of related instructionswould be successfully completed or none would be completed.Special primitives are requested ( supplied by system or language) • Examples of primitives for transactions.

20. The Transaction Model • Transactions are ACID: • Atomic : to outside, they happen indivisibly • Consistent : they don’t violate system invariants • Isolated : concurrent transactions don’t interfere • Durable : the changes are permanent • These flat transactions have some limitations... • No partial result is permitted • In a large system they can take a lot of time

21. Nested and Distributed Transactions • A nested transaction: it follows a logical (hierarchical) division of the work • A distributed transaction: it is logically flat and operates on distributed data

22. How to implement transactions?Private Workspace • Copying everything in a private workspace is prohibitive… • Example: dealing with a file system: • 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

23. Write-ahead Log rollback • Files are actually modified in place, but a record is written to log all the changes • a) A transaction • b) – d) The log before each statement is executed • In distributed systems each machine keeps its own log of changes to its local file system

24. Concurrency Control allows several transaction to be executed simultaneously, leaving data in a consistent way. Transactions access data in a specific order • General (layered) organization of managers for handling transactions.

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

26. Serializability (d) • a) – c) Three transactions T1, T2, and T3 • Legal serialized result : x= 1,2,3 depending upon which one runs last • d) Possible schedules • Synchronization can be achieved or with mutex or with explicit timestamp and following pessimistic or optimistic approach.