1. Distributed Systems 31. Theoretical Foundations of Distributed Systems - Coordination Simon Razniewski Faculty of Computer Science Free University of Bozen-Bolzano A.Y. 2015/2016

2. 2 Co-ordination Algorithms are fundamental in distributed systems: to dynamically re-assign the role of master – choose primary server after crash – co-ordinate resource access for resource sharing: concurrent updates of – entries in a database (data locking) – files – a shared repository to agree on actions: whether to – commit/abort database transaction – agree on readings from a group of sensors

3. 3 Co-ordination Problems 1.Clock Synchronization – processes must agree on order of events – crucial e.g. for concurrent transactions in databases or change polling in distributed file systems 2.Leader election – after crash failure has occurred – after network reconfiguration 3.Mutual exclusion – distributed form of synchronized access problem – not covered here

4. 1. CLOCK SYNCHRONIZATION

5. Why to synchronize clocks? Important to end users – Ebay auctions – Enrolment for sport courses/student dorms – Order of chat/mail messages Important internally in distributed systems – Concurrent updates in distributed databases – Distributed file systems

6. End Users Synchronize with an authoritative source Network time protocol (NTP): UDP on port 123 “In operation since before 1985, NTP is one of the oldest Internet protocols in current use.” How?

7. Network Time Protocol (NTP) e.g. time.windows.com  Wireshark

8. System internal Exact time not necessarily important Important is a relative ordering between events Database updates File system changes

9. 9 Distributed Bank Database A bank keeps replicas of bank accounts in Milan and Rome Event 1: Customer pays 100 € into his account of 1000 € Event 2: The bank adds 1% interest Info is broadcast to Milan and Rome

10. 10 Message Ordering How can A know the order in which the messages were sent?

11. 11 Time Ordering of Events (Lamport) Observation: For some events E1, E2, it is “obvious” that E1 happened before E2 (written E1  E2) If E1 happens before E2 in process P, then E1  E2 If E1 = send(M) and E2 = receive(M), then E1  E2 (M is a message) If E1  E2 and E2  E3 then E1  E3 (transitivity)

12. 12 Logical Clocks Goal: Assign “timestamps” ti to events Ei such that E1  E2  t1 < t2 (partial order!) Approach: Processes incrementally number their events send numbers with messages update their “logical clock” to max(OwnTime, SendTime) +1 when they receive a message

13. 13 Logical Clocks in the Message Scenario 2 4 5 For a tie break, use process numbers as second component! Messages carry numbers 5 1 3

14. Combined with classical clocks Three processes, each with its own clock. The clocks run at different rates.

15. Combined with classical clocks (2) Lamport’s algorithm corrects the clocks.

16. Lamport’s Logical Clocks (4) Figure 6-10. The positioning of Lamport’s logical clocks in distributed systems.

17. Back to the distributed database… 1.On receive: Queue message based on their logical sending timestamp 2.Acknowledge receipt of messages to all other peers 3.Only process messages upon receipt of acknowledgment by all other peers  Unique processing order for all peers “Totally-ordered multicast”

18. 2. Leader Election

19. 19 Leader Election The problem: – N processes, may or may not have unique IDs (UIDs) – Old coordinator has crashed/disappeared due to network issues – must choose a new unique coordinator among themselves – one or more processes might start process simultaneously Qualitative properties: – Correctness: Every process has the same value in the variable elected – Liveness: All processes participate and eventually discover the identity of the leader (elected cannot stay undefined). Quantitative properties – Bandwidth : total number of messages sent around – Turnaround : number of steps needed to come to a result

20. 20 Election on a Ring (Chang/Roberts 1979) Assumptions: – each process has a UID, UIDs are linearly ordered ideas how to obtain them? – processes form a unidirectional logical ring, i.e., each process has channels to two other processes from one it receives messages, to the other it sends messages Goal: – process with highest UID becomes leader

21. 21 Election on a Ring (cntd) Processes send two kinds of messages: elect(UID), elected(UID) can be in two states: non-participant, participant Two phases Determine leader Announce winner Initially, each process is non-participant

22. 22 Algorithm: Determine Leader Some process with UID id0 initiates the election by – becoming participant – sending the message elect(id0) to its neighbour When a non-participant receives a message elect(id) – it forwards elect(idmax), where idmax is the maximum of its own and the received UID – becomes participant When a participant receives a message elect(id) – it forwards the message if id is greater than its own UID – it ignores the message if id is less than its own UID

23. 23 Algorithm: Announce Winner When a participant receives a message elect(id) where id is its own UID – it becomes the leader – it becomes non-participant – sends the message elected(id) to its neighbour When a participant receives a message elected(id) – it records id as the leader’s UID – Becomes non-participant – forwards the message elected(id) to its neighbour

24. 24 Election on a Ring: Example 24 15 9 4 3 28 17 24 1 participants non-participants

25. 25 Properties Correctness: – Liveness – clear, if only one election is running – what, if several elections are running at the same time?  participants do not forward smaller IDs Bandwidth: – at most 3n – 1 (if a single process starts the election, what if several processes start an election?) Turnaround: – at most 3n-1 (if …)

26. 26 Under Which Conditions can it Work? What if there is a failure (process or connection)? – the election gets stuck  assumption: no failures or timeouts during election (in token rings, nodes are connected to the network by a connector, which may pass on tokens, even if the node has failed) When is this applicable? – token ring/token bus/virtual ring (Chord)

27. 27 Bully Algorithm (Garcia-Molina) Idea: Process with highest ID imposes itself as the leader Assumption: – each process has a unique ID – each process knows the IDs of the other processes When is it applicable? – IDs don't change – Set of participants constant – Possibly much faster than ring algorithm Turing Award 2015!

28. 28 Bully Algorithm: Principles A process detects failure of the leader The process starts an election by notifying the potential candidates (i.e., processes with greater ID) – if no candidate replies, the process declares itself the winner of the election – if there is a reply, the process stops its election initiative When a process receives a notification – it replies to the sender – and starts an election if its ID is higher than the one of the sender

29. 29 Bully Algorithm: Messages Election message: – to “call elections” (sent to nodes with higher UID) Answer message: – to “vote” (… against the caller, sent to nodes with lower UID) Coordinator message: – to announce own acting as coordinator

30. 30 Bully Algorithm: Actions Initially: The process with highest UID sends coordinator message A process starting an election sends an election message – if no answer within time T = 2 T transmission + T process, then it sends a coordinator message If a process receives a coordinator message – it sets its coordinator variable If a process receives an election message – it answers and begins another election (if needed) If a new process starts to coordinate (highest UID), – it sends a coordinator message and “bullies” the current coordinator out

31. Example (1) (a)Process 4 holds an election. (b) Processes 5 and 6 respond, telling 4 to stop. (c) Now 5 and 6 each hold an election. (d) Process 6 tells 5 to stop. (e) Process 6 wins and tells everyone.

32. 32 Properties of the Bully Algorithm Liveness – guaranteed because of timeouts Correctness – clear if group of processes is stable (no new processes) – not guaranteed if new process declares itself as the leader during election (e.g., old leader is restarted) two processes may declare themselves as leaders at the same time but no guarantee can be given on the order of delivery of those messages

33. 33 Quantitative Properties Best case: – process with 2nd highest ID detects failure Worst case: – process with lowest ID detects failure Bandwidth: – N - 1 messages in best case – O(N 2 ) in worst case Turnaround: – 1 message in best case – 3 messages in worst case

34. Comparison

35. 35 Election without UIDs (Itai/Rodeh) Assumptions – N processes, unidirectional ring – processes do not have UIDs Election – each process selects ID at random from set {1,…,K} non-unique! but fast – processes pass all IDs around the ring – after one round, if there exists a unique ID then elect maximum unique ID – otherwise, repeat Liveness? Probabilistically!

36. 36 Election without UIDs (cntd) How many rounds does it take? – the larger the probability of a unique ID, the faster the algorithm – expected time: N=4, K=16, expected 1.01 rounds

37. Take-home Clock synchronization – Synchronization with reference server – Relative ordering: Lamport’s logical clocks Leader Election – Bully algorithm – Ring-based election

38. Appendix: Mutual Exclusion

39. Mutual Exclusion Motivation: Transactions Algorithms: Centralized algorithm – Server/Leader needed Distributed algorithm Token-based algorithm – Server/Leader needed for generating token

40. Mutual Exclusion A Centralized Algorithm (1) Process 1 asks the coordinator for permission to access a hared resource. Permission is granted.

41. Mutual Exclusion A Centralized Algorithm (2) Process 2 then asks permission to access the same resource. The coordinator does not reply.

42. Mutual Exclusion A Centralized Algorithm (3) When process 1 releases the resource, it tells the coordinator, which then replies to 2.

43. Advantages/Disadvantages? Correctness/Liveness Turnaround Bandwidth

44. A Distributed Algorithm (1) Ricart and Agrawala, 1981 Access requests send to everyone 1.If the receiver is not accessing the resource and does not want to access it, it sends back an OK message to the sender. 2.If the receiver already has access to the resource, it simply does not reply. Instead, it queues the request. 3.If the receiver wants to access the resource as well but has not yet done so, it compares the timestamp of the incoming message with the one contained in the message that it has sent everyone. The lowest one wins. – Logical timestamp sufficient

45. A Distributed Algorithm (2) Two processes want to access a shared resource at the same (global) moment.

46. A Distributed Algorithm (3) Process 0 has the lowest timestamp, so it wins.

47. A Distributed Algorithm (4) When process 0 is done, it sends an OK also, so 2 can now go ahead.

48. Advantages/Disadvantages? Correctness/Liveness Turnaround Bandwidth

49. A Token Ring Algorithm Token handed around continuously Token owner may access resource

50. A Comparison of the Three Algorithms