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DISTRIBUTED SYSTEMS Principles and Paradigms Second Edition ANDREW S. TANENBAUM MAARTEN VAN STEEN Chapter 8 Fault Toler

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DISTRIBUTED SYSTEMS Principles and Paradigms Second Edition ANDREW S. TANENBAUM MAARTEN VAN STEEN Chapter 8 Fault Toler

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    1. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 DISTRIBUTED SYSTEMS Principles and Paradigms Second Edition ANDREW S. TANENBAUM MAARTEN VAN STEEN Chapter 8 Fault Tolerance

    2. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Fault Tolerance Basic Concepts Being fault tolerant is strongly related to what are called dependable systems Dependability implies the following: Availability Reliability Safety Maintainability Types of faults: Transient, Intermittent, Permanent

    3. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Failure Models Figure 8-1. Different types of failures.

    4. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Failure Masking by Redundancy

    5. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Failure Masking by Physical Redundancy Figure 8-2. Triple modular redundancy.

    6. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Process Resilience

    7. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Flat Groups versus Hierarchical Groups Figure 8-3. (a) Communication in a flat group. (b) Communication in a simple hierarchical group.

    8. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Failure Masking and Replication Primary-backup protocol. A primary coordinates all write operations. If it fails, then the others hold an election to replace the primary Replicated-write protocols. Active replication as well as quorum based protocols. Corresponds to a flat group A system is said to be k fault tolerant if it can survive faults in k components and still meet its specifications. For fail-silent components, k+1 are enough to be k fault tolerant For Byzantine failures, at least 2k+1 extra components are needed to achieve k fault tolerance Requires atomic multicasting: all requests arrive at all servers in same order

    9. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Agreement in Faulty Systems (1)? Possible cases: Synchronous versus asynchronous systems Communication delay is bounded or not Message delivery is ordered or not Message transmission is done through unicasting or multicasting

    10. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Agreement in Faulty Systems (2)? Figure 8-4. Circumstances under which distributed agreement can be reached.

    11. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Two Army Problem

    12. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Byzantine Generals problem

    13. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Conditions for a Solution All loyal generals decide upon the same plan of action A small number of traitors cannot cause the loyal generals to adopt a bad plan

    14. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Agreement in Faulty Systems (3)? Figure 8-5. The Byzantine agreement problem for three nonfaulty and one faulty process. (a) Each process sends their value to the others.

    15. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Byzantine Example

    16. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Byzantine Example (2)?

    17. Reliable Client-Server Communication

    18. RPC semantics in the presence of failures

    19. Lost Request Messages Server Crashes (1)? A server in client-server communication Normal case Crash after execution Crash before execution

    20. Server Crashes (1)

    21. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Server Crashes (2)? Three events that can happen at the server: Send the completion message (M) Print the text (P) Crash (C)

    22. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Server Crashes (3)? These events can occur in six different orderings: M ?P ?C: A crash occurs after sending the completion message and printing the text. M ?C (?P): A crash happens after sending the completion message, but before the text could be printed. P ?M ?C: A crash occurs after sending the completion message and printing the text. P?C(?M): The text printed, after which a crash occurs before the completion message could be sent. C (?P ?M): A crash happens before the server could do anything. C (?M ?P): A crash happens before the server could do anything.

    23. Server Crashes (4)? Different combinations of client and server strategies in the presence of server crashes.

    24. RPC Semantics continued...

    25. Idempotent Operations

    26. Reliable Group Communication

    27. Basic Reliable-Multicasting Schemes (1)

    28. Basic Reliable-Multicasting Schemes (2)

    29. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Basic Reliable-Multicasting Schemes (3) Figure 8-9. A simple solution to reliable multicasting when all receivers are known and are assumed not to fail. (a) Message transmission. (b) Reporting feedback.

    30. Scalability in Reliable Multicasting

    31. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Nonhierarchical Feedback Control Figure 8-10. Several receivers have scheduled a request for retransmission, but the first retransmission request leads to the suppression of others.

    32. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Hierarchical Feedback Control Figure 8-11. The essence of hierarchical reliable multicasting. Each local coordinator forwards the message to its children and later handles retransmission requests.

    33. Atomic Multicasting

    34. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Virtual Synchrony (1)? Figure 8-12. The logical organization of a distributed system to distinguish between message receipt and message delivery.

    35. Virtual Synchrony (2)

    36. Virtual Synchrony (3)

    37. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Virtual Synchrony (4)? Figure 8-13. The principle of virtual synchronous multicast.

    38. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Message Ordering (1)? Virtual synchrony allows us to think about multicasts as taking place in epochs. But we can have several possible orderings of the multicasts: Unordered multicasts FIFO-ordered multicasts Causally-ordered multicasts (requires vector timestamps) Totally-ordered multicasts

    39. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Message Ordering (2)? Figure 8-14. Three communicating processes in the same group. The ordering of events per process is shown along the vertical axis. This shows unordered multicasts.

    40. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Message Ordering (3)? Figure 8-15. Four processes in the same group with two different senders, and a possible delivery order of messages under FIFO-ordered multicasting

    41. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Comparison of Message Orderings Figure 8-16. Six different versions of virtually synchronous reliable multicasting.

    42. Implementing Virtual Synchrony (1)?

    43. Implementing Virtual Synchrony (2)? Process 4 notices that process 7 has crashed, sends a view change Process 6 sends out all its unstable messages, followed by a flush message Process 6 installs the new view when it has received a flush message from everyone else

    44. Distributed Commit The distributed commit problem involves having an operation being performed by each member of a group or none at all. Examples: Reliable multicasting is a specific example with the operation being the delivery of a message With distributed transactions, the operation may be the commit of a transaction at a single site that takes part in the transaction Solutions: One-phase commit, two-phase commit and three-phase commit

    45. One-Phase Commit The coordinator tells all processes whether or not to (locally) perform the operation in question If one of the participants cannot perform the operation, then there is no way to tell the coordinator

    46. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Two-Phase Commit (1)? Figure 8-18. The finite state machine for the coordinator in 2PC. The finite state machine for a participant.

    47. Two-Phase Commit (2) The protocol can fail if a process crashes for other processes may be waiting indefinitely for a message from the crashed process. We deal with this using timeouts Participant blocked in INIT: A participant is waiting for a VOTE_REQUEST message from the coordinator. On a timeout, it can locally abort the transaction and thus send a VOTE_ABORT message to the coordinator Coordinator blocked in WAIT: If it doesn’t get all the votes, it votes for an abort and sends a GLOBAL_ABORT to all participants Participant blocked in READY: Participant cannot simply decide to abort. It needs t know what message did the coordinator send. We can simply block until the coordinator recovers Or contact another participant Q to see if it can decide from Q’s state what to do. Four cases to deal with here that are summarized on next slide

    48. Two-Phase Commit (3)? Actions taken by a participant P when residing in state READY and having contacted another participant Q.

    49. Two-Phase Commit (4)? Outline of the steps taken by the coordinator in a two phase commit protocol

    50. Two-Phase Commit (5)? Steps taken by process in 2PC.

    51. Two-Phase Commit (6)? Steps taken for handling incoming decision requests.

    52. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Three-Phase Commit (1)? If the coordinator crashes in two-phase commit, the processes may not be able to reach a final decision and have to block until the coordinator recovers Three-phase commit protocol avoids blocking processes in presence of crashes. The states of the coordinator and each participant satisfy the following two conditions: There is no single state from which it is possible to make a transition directly to either a COMMIT or an ABORT state There is no state in which it is not possible to make a final decision, and from which a transition to a COMMIT state can be made

    53. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Three-Phase Commit (2)? Figure 8-22. (a) The finite state machine for the coordinator in 3PC. (b) The finite state machine for a participant.

    54. Recovery

    55. Stable Storage We need fault-tolerant disk storage for the checkpoints and message logs. Examples are various RAID (Redundant Array of Independent Disks) schemes (although they are used for both improved fault tolerance as well as improved performance). Some common schemes: RAID-0 (block-level striping) RAID-1 (mirroring) RAID-5 (block-level striping with distributed parity) RAID-6 (block-level striping with double distributed parity)

    56. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Recovery – Stable Storage Figure 8-23. (a) Stable storage. (b) Crash after drive 1 is updated. (c) Bad spot due to spontaneous decay can be dealt with.

    57. Checkpointing

    58. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Checkpointing Figure 8-24. A recovery line.

    59. Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5 Independent Checkpointing Figure 8-25. The domino effect.

    60. Coordinated Checkpointing

    61. Message Logging

    62. Message Logging Incorrect replay of messages after recovery, leading to an orphan process.

    63. Message Logging Schemes

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