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Physical and Logical Clocks


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Time in distributed systems. Lack of global time and logical clocks as alternatives. Lamport’s time stamp and Lamport’s Totally Ordered Multicast

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Physical and Logical Clocks

  1. 1. Physical & Logical Clocks CS4262 Distributed Systems Dilum Bandara Slides adapted from U Uthaiyashankar (WSO2) and Rajkumar Buyya’s (IU)
  2. 2. Outline  Introduction to synchronization  Time & clocks  Synchronizing physical clocks  Logical time & logical clocks  Synchronizing logical clocks  Lamport’s time stamp  Vector clocks – Not covered 2
  3. 3. Interaction Model  Computation occurs within processes  Processes interact by passing messages, resulting in  Communication (information flow)  Coordination (synchronization & ordering of activities)  2 significant factors affecting interacting processes in a distributed system  Communication performance is often a limiting characteristic  Impossible to maintain a single global notion of time 3
  4. 4. Interaction Model (Cont.)  Processes running on different nodes can associate timestamps with their events  This timestamp doesn’t make sense globally  Clock drift  Differing drift rates  Complexity & cost of time synchronization  GPS not accessible inside a building 4
  5. 5. 2 Variants of Interaction Model  Hard to set limits on time taken for  Process execution  Message delivery  Clock drift  2 simple models  Synchronous model – based on a strong assumption of time  Asynchronous model – makes no assumption about time 5
  6. 6. Synchronous DS Model  Defined by following bounds  Time taken to execute a step of a process has known lower & upper bounds  Each message transmitted over a channel is received within a known bounded time  Each process has a local clock whose drift rate from real time has a known bound 6
  7. 7. Asynchronous DS Model  Defined by assuming no bounds on  Process execution speeds  Message transmission delays  Clock drift rates  Models Internet very closely  Any solution that is valid for an asynchronous DS is also valid for a synchronous DS  There are many design problems that can’t be solved for an asynchronous DS, but can be solved when some aspects of time are used 7
  8. 8. Event Ordering  Whether an event occurred before, after, or concurrently with another event at another process?  Execution of a system can be described in terms of events & their ordering  No need of accurate clocks  Example  Consider a mailing list with users X, Y, Z, & A  User X sends a message with subject “meeting”  Users Y & Z reply by sending messages with the subject “Re: meeting” 8
  9. 9. Example – Real-Time Ordering of Events 9Source: Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007
  10. 10. Example – Inbox of User A  Due to independent delivery of messages, messages may be delivered in different order  If messages m1, m2, m3 carry their time t1, t2, t3, then they can be displayed accordingly to their time ordering 10
  11. 11. Time in Distributed Systems  Scenario  Running UNIX make program which recompiles only source files whose corresponding object files carry an earlier time stamp  Differences in time on editing machine & compiler machine & can result in a file output.o to have a timestamp greater than recently modified output.c program  Result  Modified source program, output.c will not be recompiled  Programmer??? 11
  12. 12. Time & Clocks  Atomic Time  Derived from atomic oscillators  Very accurate  Astronomical Time  Based on Earth’s rotation about its axis & around sun  Period of Earth’s rotation about its axis is gradually getting longer  Coordinated Universal Time (UTC)  Broadcast from land-based radio stations & GPS 12
  13. 13. Clock Drift  Different rates of counting time  Physical variations of underlying oscillators  Variance caused by temperature  Extremely small differences that accumulate over a large no of oscillations  Observable difference in counters  Drift rate  Difference in reading between a clock & a nominal “perfect clock” per unit of time  10-6 seconds/sec for quartz crystals  10-7 to 10-8 seconds/sec for high precision quartz crystals 13
  14. 14. Clock Skew  When a system has n computers, their n crystals will oscillate at different rates  This causes software clocks to gradually get out- of synch & give different values  This instantaneous difference in time values is called clock skew 14
  15. 15. Clock Synchronization  External synchronization  If 1 machine has a WWW or GPS receiver, use it to synchronize others  Internal synchronization  If no machines have receivers, each machine keeps track of its own time  Then keep all the machines together as well as possible  Many clock synchronization algorithms have been proposed to achieve these goals  Christian’s Algorithm, Berkeley Algorithm, Averaging Algorithms, Network Time Protocol, etc. 15
  16. 16. Synchronization in Asynchronous Systems  There is no upper bound on message transmission delays  e.g., there is no maximum transmission delay for Internet  Ttransmit = min + x, where x > 0  Ttransmit – time taken to transmit m  Min – minimum time to transmit  x may not be known in a particular case, but it is possible to measure a distribution of values for a particular installation 16
  17. 17. Network Time Protocol (NTP)  Christian’s & Berkeley algorithms for intranets  NTP designed for Internet  Large & variable message delays are handled through statistical techniques for filtering timing data  Discriminates between quality of timing data from different servers  Redundant servers & redundant paths between servers  Scalable – handles a large no of clients & servers  Authentication to ensure trusted time sources  Synchronization accuracy  ~10s of milliseconds over Internet paths  ~1 millisecond on LANs 17
  18. 18. NTP (Cont.)  Hierarchical network of servers  Primary servers connected directly to a UTC time source  Secondary servers synchronized with primary servers  Servers connected in a logical hierarchy called a synchronization subnet whose levels are called strata  Lowest level (leaf) servers execute in users’ workstation 18
  19. 19. Logical Time & Logical Clocks  Single process  Events are ordered uniquely by local clock time  Lamport (1978) pointed out that,  “since we can’t synchronize clocks perfectly across a distributed system, we can’t use physical time to find out order of any arbitrary pair of events within a distributed system”  In general, we can use a scheme that is similar to physical causality, to order some events that occur in different processes 19
  20. 20. Physical Causality Based Ordering  If events occur on same process P, then they occur in the order in which P observes them  If a & b are events within a single process Pi, and a occurs before b, then a i b is true  Whenever a message is sent between processes, event of sending occurs before event of receiving  a = event of a message m being sent by process Pi  b = event of message m being received  then, a i b is true 20
  21. 21. Lamport’s Notion of Event Ordering  Lamport generalized 2 (intuitive) physical- causality-based event ordering points to obtain happened-before partial ordering  A.k.a relation of causal ordering or potential causal ordering  Define “happened-before relation” (denoted by ) as follows  e i e/ for process Pi ⇒ e  e/  For any message m, send(m)  receive(m)  e  e/ and e/  e// ⇒ e  e// 21
  22. 22. Happened-Before Relation 22 Source: Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007
  23. 23. Happened-Before Relation 23  Events a & e that are not ordered by  are concurrent  Denoted by a || e Source: Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007
  24. 24. Logical Clocks  Invented by Leslie Lamport to capture happened-before ordering numerically  Lamport Logical Clock  Monotonically increasing software counter  Value need not have a relationship to any physical clock  Each process Pi keeps its own logical clock Li, which it uses to apply Lamport timestamps to events  Li(e) = timestamp of event e at Pi  L(e) = timestamp of event e at process it occurred 24
  25. 25. Lamport Timestamps  Processes update their logical clocks & transmit their logical clock values in messages as follows  LC1  Li = Li + 1, before each event is recorded at Pi  LC2  When Pi sends a message m, logical clock value t = Li, is piggy- backed with message  On receiving (m, t), a process Pj computes  Lj = max (Lj, t)  Then computes Lj = Lj + 1 before logically timestamping event receive(m)  Thus, e  e/ ⇒ L(e) < L(e/)  However, L(e) < L(e/) ⇏ e  e/ 25
  26. 26. Lamport Timestamps – Example  Each process has its logical clock initialized to 0  L(b) > L(e) but b || e 26 Source: Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007
  27. 27. Lamport Timestamps – Example  3 processes, each with its own clock  Clocks run at different rates  Lamport’s algorithm corrects clocks 27
  28. 28. Totally Ordered Logical Clocks  Some pairs of distinct events generated by different processes have identical Lamport timestamps  A total order, where all pairs of distinct events are ordered, can be created by noting IDs of processes  Suppose,  (Ti, i) = global logical timestamp for event e at process Pi with local logical timestamp Ti  Similarly (Tj, j)  Then (Ti, i) < (Tj, j) ⇔ Ti < Tj or (Ti = Tj and i < j )  No general physical significance since process IDs are arbitrary  e.g., 2 events in P1 & P2, both with timestamp 40 can be globally timestamped as 40.1 & 40.2 28
  29. 29. Application of Lamport’s Timestamps  Consider a database that has been replicated across several sites  e.g., a bank account database may be replicated in Colombo & Kandy  A query is always forwarded to the nearest copy (for fast response)  Update costs are higher since each update must be carried out at each replica in same order  Why? 29
  30. 30. Application (Cont.)  Mr. Perera (lives in Kandy) has Rs. 1,000 in his account  Mr. Perera wants to deposit Rs. 100 to his account  At the same time, banking application (in Colombo) initiates an update to increase account balance with 1% interest  Both updates must be carried out at both copies of the database  Due to communication delays, we may have following situation:  In Kandy DB, Mr. Perera’s deposit is performed before interest update  (Rs. 1000 + Rs. 100) * 1.01 = Rs. 1,111  In Colombo DB, interest update is performed before Mr. Perera’s deposit  (Rs. 1000 * 1.01) + Rs. 100 = Rs. 1,110 30
  31. 31. Application (Cont.)  Problem  Both updates were not performed in same order at each DB  Solution  Totally ordered multicast  Multicast operation where all messages are delivered in the same order to each receiver 31
  32. 32. Totally Ordered Multicast  If process Pi sends messages x, y to processes Pj, Pk ,.. then all processes Pj, Pk … receive messages in the same order (x, y or y, x)  This doesn’t imply causal or even FIFO ordering  Solutions  Multicast through a central coordinator  Lamport’s solution 32
  33. 33. Lamport’s Totally Ordered Multicast  Each multicast has a Lamport time stamp  All processors store received multicast in a queue  Order based on time stamp  Everyone has the same queue  Assume all messages sent by a sender are received in the order they were sent & no messages are lost  A time stamped ACK is sent back  Timestamp of multicast message < timestamp of ACK  A process can deliver (act on) the message at the head of queue only when it has received an ACK from all other processes 33
  34. 34. Lamport’s Totally Ordered Multicast 34 Source: Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007
  35. 35. Summary  Synchronization is important  Clock drift & clock skew  Physical clock synchronization  Logical time & logical clocks  Lamport Timestamps  Vector Clocks  Totally Ordered Multicast 35