1. Issues with Clocks

2. Context The tree correction protocol was based on the idea of local detection and correction. Protocols of this type are complex to build We will study some of the classic protocols that help solve different problems

3. Models We reviewed the idea of different models –Higher level models make it easier to write the programs –Lower level models are easy to implement

4. Strongest vs Weakest Model What is strongest model? –No private memory –Can update all variables at once –Essentially a centralized program What is the weakest model –Process speed arbitrary –No bounds on delays –No global time Which should we study first?

5. Asynchronous Model Fairly weak model –No global clock –No shared memory –Arbitrary process speed –Arbitrary message delays –We will assume messages are eventually delivered but after arbitrary delay

6. Problems in Asynchronous Systems We will study problems that we solve easily in centralized systems –Examples: Clocks Ordering among events Checkpoints.. We will study solving these problems in asynchronous systems

7. Use of Clocks Clocks allow us to order events In asynchronous systems, there is no notion of time. What is the purpose of time –To say: something happened at 5pm We cannot do this in asynchronous systems –To say: x1 happened first then y1 We can do this sometimes in asynchronous systems –E.g., the class began before it ended

8. Problem Lack of global time –Need to compare different events in a distributed system In asynchronous systems, time cannot be used to order events –We need to develop an alternative to order events in such a system

9. Problem (Continued) Consider the problem of detecting insider trading in a stock exchange –Assume that no communication occurs outside the computer systems –Each entity is represented by a computer and they communicate among themselves –Whenever a process sends a message, it includes ALL the information it has learnt so far Lets not worry about the cost of implementing this –Each entity is required to follow the protocol you choose

10. Problem (Continued) Consider two events –An inside event, say e, that affects the company A –An event f, where an officer, X, of company A sells stock Question –Is it possible that X is guilty of insider trading?

11. Situation 1 Assume global time –Event e occurred at 8am –Event f occurred at 10am (same day) –Answer: –Event e occurred at 10am –Event f occurred at 8am (same day) –Answer:

12. Approach What we need is a way to define causality –Can event e affect event f? –This is defined as the happened before relation –Please do not confuse it with the English meaning of this

13. A Realistic Variation Consider a experiment that you are performing that includes –Events e and event f –Event f is a safety violation –Can changing what happens in event e affect what happens in f? Important in debugging, checkpointing, etc.

14. Another Problem from Debugging Consider the requirement: –Valve must be open if pressure exceeds 10 –Event e : at time 8:00:00:valve = open –Event f : at time 8:00:01, pressure = 11 –Looks ok. –But is it possible that due to some race conditions (e.g., processor @e being slow) delays e and violates this constraint.

15. happened before Let a, b, c be events –An event can be a local event, send event or a receive event Definition : a  b (read as a happened before b) iff either one of the following condition is true –a and b are events on the same process and a occurred before b –a is a send event and b is the corresponding receive event –there exists event c such that a  c and c  b

16. Revisiting the Previous Problems Consider two events –An inside event, say e, that affects the company A –An event f, where an officer, X, of company A sells stock If e  f then X is guilty of insider trading

17. Revisiting the Previous Problems Parallel experiment If e  f then changing what happens in e can affect what happens in f

18. Concurrent Events a || b iff (NOT (a  b)) & (NOT (b  a)) –Question? (a || b) & (b || c) => (a || c) ?

19. Using Event Diagrams to Understand Causality

20. Logical Clocks Goal of logical clock is –Assign each event a timestamp –If e  f then the timestamp of e should be less than that of f. –To solve this, each process maintains a logical clock cl cl.j : clock value of process j cl.m : clock value of message m cl.a : clock value of event a

21. Program for Logical Timestamps Let a be a new event –If a is a local event at j cl.j := cl.j + 1 cl.a := cl.j –If a is a send event at j cl.j := cl.j + 1 cl.m := cl.j cl.a := cl.j –If a is a receive of message m at j cl.j := max(cl.j, cl.m) + 1 cl.a := cl.j

22. Properties of Logical Clocks If a  b then cl.a < cl.b

23. Proving Properties of Logical Clocks Theorem: At any time, the following statement is true for all events (that have occurred in the system so far) –  x, y :: x  y  cl.x < cl.y, –where x and y range over all the events, processes (latest event on the process) and messages (corresponding send event) Proof by induction Base case –No events created. Hence, trivially true –Show that whenever a new event is created

24. Invariant The predicate –a  b  cl.a < cl.b –Is an invariant of the logical timestamp program.

25. Logical Timestamps The time associated with an event is a pair, the clock and the process where the event occurred. For event a at process j, the timestamp ts.a is –ts.a = Lexicographical comparison iff x1 < y1  ( (x1 = y1)  (x2 < y2) )

26. Observation about Logical Clocks For any two distinct events a and b, either ts.a < ts.b  ts.b < ts.a The event timestamps form a total order.

27. So What? Consider two events –An inside event, say e, that affects the company A –An event f, where an officer, X, of company A sells stock Question –Is it possible that X is guilty of insider trading? Case 1 –cl.e = 8, cl.f = 10 –Answer: Case 2 –cl.e = 10, cl.f = 8 –Answer:

28. Problem Logical timestamps provide a partial information about causality. Extending logical timestamps for complete knowledge of causality

29. Accurate Causality Information Desire –a  b  clock.a < clock.b Can clock.a be an integer?

30. Accurate Causality Information To obtain accurate causality information, clock must consist of several integers –It turns out that accurate causality information requires clocks to consist of n values

31. Vector Clocks Each process maintains a vector vc –vc.j is the clock maintained by j –vc.j is an array vc.j.k denotes the knowledge that j has about the clock of k => vc.j.j denotes the clock of j

32. Updating Vector Clocks Let a be a new event –If a is a send event at j vc.j.j++ vc.m := vc.j vc.a := vc.j –If a is a receive of message m at j vc.j := max(vc.j, vc.m) vc.j.j++ vc.a := vc.j

33. Causality Checking with Vector Clocks vc.a < vc.b iff –(Subject to requirement a  b iff vc.a < vc.b)

34. Causality Checking with Vector Clocks Suppose a is an event at process j and b is an event at process k –vc.a < vc.b iff

35. Causal Delivery Problem requirement –Given two messages m1 and m2 sent to the same process If send(m1)  send(m2) Then Delivery(m1)  Delivery(m2)

36. Solving Causal Broadcast Special case of causal delivery where all messages are broadcast in nature –Applications in checkpointing etc. Variables –num.j.k = knowledge that j has about the number of messages sent by k num.j.j = number of messages sent by j

37. Solving Causal Broadcast Algorithm –When j sends a message Num.j.j++ –When j receives a message. Buffer it until j is certain that causal delivery constraints wont be violated Constraint j should check before delivering a message from k

38. Suppose j has a message m from k in its buffer. J can deliver it iff the following condition is satisfied: Comparison between num.j.k & num.m.k –Check if num.j.k = num.m.k – 1 Comparison between num.j.l and num.m.l, where l is any process other than k –Check if num.j.l >= num.m.l If all these conditions are satisfied then deliver m How do I update num upon delivery? –Num.j.k++

39. Observations Regarding Vector Clocks Could we have used a `reasonably fine- grained’ physical clock to obtain vc.j.j?

40. Augmented Time (Brief Overview) What if clocks are synchronized within some epsilon and we are interested in ordering events in the system. For example, let epsilon = 10 –Can we say something about events e and f such that ph.e = 10, ph.f = 15 ph.e = 10, ph.f = 1000 –Note that this was not possible without clock synchronization

41. Augmented Time Create timestamp with clocks synchronized upto epsilon –Need to order these events ph.e = 10, ph.f = 15 –No need to order these events ph.e = 10, ph.f = 1000

42. Idea Each process maintains at.j.k –at.j.k = knowledge j has about clock of k How would this variable be updated? –Upon send? –Upon receive?

43. Space requirement for augmented time Worst case Average case? Can we optimize?

44. Properties of Augmented Time If two events are close If two events are far off

45. Augmented Logical Time

46. Observations (Continued) What if this physical clock was accurate within some time bound –Notion of communication via time –This could potentially bound the size of vector clocks

47. Observations (Continued) Does the clock have to be a vector of size n? –In general yes –But under certain topologies, it can be reduced

48. Observations (Continued) Causality captures whether event e can potentially affect event f –Illustration with simple code below: 1.ptr = NULL; 2.X = 0 3.ptr->next = … event2 happened before event3

49. Applications of Causality Checkpointing and Recovery Concurrent database updates …