1. Group Communication Robbert van Renesse CS614 – Tuesday Feb 20, 2001

2. Distr. programming is hard True concurrency No shared memory No locks Host failures & recoveries Network failures Too many scenarios to wrap your brain around Coordination hard to achieve

3. Kinds of Distributed Apps Replicated Services Parallel Computing Factory Floor Control Management Services Cluster Services Distributed Games …

4. Commonality Each requires coordination between distributed, possibly flaky components over a possibly flaky network Each involves a dynamic group of processes communicating with one another

5. Basic operations JoinGroup(“group”, event-handler) –Events: Point-to-Point and multicast messages Member Join and Leave (Crash) events SendP2P(“group”, member-id, message) Multicast(“group”, message) LeaveGroup(“group”)

6. Programming Example Main(){ char buf [ BUF_SIZE ]; group = JoinGroup(“chat”, EventHandler); while (read(buf) != EOF) { Multicast(group, buf); }

7. Programming Example, cont’d EventHandler(event){ switch (event.type) { case VIEW: printf(“New view: %v”, event.view); break; case DATA: printf(“%s: %s”, event.source, event.data); }

8. What can go wrong? Message M gets delivered to X but not to Y –Lack of agreement on delivery M1 gets delivered before M2 at X, but the other way around at Y –Lack of order on delivery X thinks Z is up, while Y thinks Z is down –Lack of agreement on membership  lack of coordination  programmer has to consider many scenarios

9. Example 1: Replicated Service Updates are multicast Lack of agreement on delivery of messages can cause an update to be applied to only some of the replicas Lack of order on delivery of messages can cause updates to be applied in different orders at different replicas

10. Example 2: Parallel Computation Membership  Partitioning of task Lack of agreement on membership  Different processes partition the task differently  Too little or too much work is done

11. Making life easier Reduce the number of possible scenarios by supporting network protocols that guarantee –Agreement of message delivery –Order of message delivery –Agreement of membership updates –Order of membership updates Results in fewer things to think about

12. Some Terminology Group: a set of processes Member: a process in the group View: a uniquely identified set of members as seen by one or more group members –View should approach reachable set of members –Members install new views over time –Each installed view of a group member always includes itself –A member never installs the same view twice

13. Events A group member observes the following events: –Join() –Leave() –Crash() –View-Change(view) –Send-Multicast(msg) –Receive-Multicast(msg, sender) (We ignore multiple groups and point-to-point traffic for the rest of today)

14. Graphical representation Messag e Loss Crash Join View Change TIME Member

15. Traces and Properties Trace: history of events –E.g.: X sends msg4, Y receives msg3, X gets view3, Y receives msg4, X gets view3, … Property: predicate on potential traces –E.g.: Messages are delivered in the order they were sent –E.g.: Messages sent are eventually delivered to all correct processes

16. Protocols Properties are implemented by protocols Each protocol is a layer of software Syntax the same for each layer: –Join(), Send-Multicast(), … –Snap together like Lego blocks Semantics different: –Unreliable  Reliable –Unordered  Ordered –…

17. Protocols may be layered Seqno layer FIFO total unreliable Token layer For example: Seqno Token STACK

18. For example: Reliability Property: A message that is sent is eventually delivered to all correct processes Protocol: ack/timeout/retransmission

19. For example: Total Order Property: if two processes deliver the same two messages, they deliver them in the same order Protocol: centralized sequencer, or rotating token, or …

20. Other examples PropertyProtocol FIFO orderSequence number Bound on resource useFlow Control ConfidentialityEncryption IntegrityChecksum Consensus on MembershipMembership Failures are detectedHeartbeat ……

21. Dependencies Total ordering protocols typically depend on reliable delivery:  layer ordering protocol on top of reliability protocol

22. Toolkits here at Cornell Horus and Ensemble are both protocol stack toolkits, each supplying dozens of protocol layers for group communication Plug’n’play allows applications to choose just those protocols that they require, rather one-size-fits-all Good performance and flexibility

23. Typical protocol stack TOTAL ORDERING MEMBERSHIP FLOW CONTROL RETRANSMISSION CHECKSUM UDP/IP

24. Each layer adds header

25. Extreme example app: Replicated State Machine Model of replicated service: –Each replica is a state machine –Initial state is the same –They receive the same update messages –They receive them in the same order  keeps replicas in the same state

26. What we want State Transfer

27. What we don’t want No order Messag e Loss Inconsistent View

28. What we need: Virtual Synchrony Introduced by Ken Birman / Isis project Agreement and Ordering of messages State Transfer Failure Detection Discovery of New Members Consensus on views

29. Example: replicated integer int X; Main(){ Join(EventHandler); for (;;) { client := receive(ClientRequest); switch (ClientRequest.type) { case ReadInt: reply(client, X); case WriteInt:multicast(ClientRequest.value); }

30. Example cont’d EventHandler(event){ switch (event.type) { case View: /* nothing */break; case Update: X := event.value;break; case GetState:return X; case PutState: X := event.state;break; }

31. What is a correct process? Many properties talk about correct processes, but what is one? Process X calls process Y correct if –Y is currently in X’s view –Y will be in X’s next view  correctness relative to views!

32. Reliability revisited A message sent by X in view V is delivered to all correct members (from X’s perspective), and possibly to some incorrect members in V as well… If you don’t want the latter part, there’s something called “uniform” or “safe” delivery, which is a much more expensive property.

33. View Consensus A message can only be delivered to members in one and the same view.  requires members to agree on views. Also, a member cannot receive messages sent in a different view than its current view.

34. Failure Detection A crashed or unavailable member is eventually removed from views.  guarantees some form of progress (without this, every member would be correct, resulting in infinite blocking while the reliability protocol tries to deliver messages to faulty members.

35. Message Orderings Several orderings are optional: –Unordered –FIFO: messages from the same sender delivered in the order they were sent –Causal: message delivery respects Lamport’s causality relation –Total: as before

36. View Ordering Total: –If two processes both deliver V1 and V2, they do so in the same order Without it, the definition of correctness would not make much sense…

37. Why is all this good? The number of possible scenarios has been reduced significantly Between two views, you can pretend there is no message loss, and members do not crash State Transfer is usually a very easy way to deal with Joins/Recoveries

38. Useful for lots of stuff Replication Leader election: who’s going to be responsible for some external event –Primary back-up Partitioning work: consensus on views allows parallel computations to split up the work Distributed Games: consensus on participants’ views on the virtual world