Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Principles of Reliable Distributed Systems Recitation 5: Reliable Broadcasts Spring 2005 Aran Bergman

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Last on Consistent Global State –FIFO Order –Happens before relation (Causal Order) Synchronous vs. Asynchronous models Failure Models (Processes and Links) Reliable Broadcast Services

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Process Failure Models (Reminder) The diagram is organized in terms of severity. The arrows present proper subsets, i.e. Crash failure model is a proper subset of Receive Omission model. –Receive Omission: A faulty process stops prematurely, or intermittently omits to receive messages sent to it, or both. Crash Receive OmissionSend Omission General Omission Timing Authenticated Byzantine Byzantine Benign Malicious

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Link Failure Models (Reminder) Reliable links: –every message sent is eventually delivered Failure types: –Crash –Loss (omission) –Timing –Byzantine

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Reliable Broadcast Specifications Validity: if a correct process broadcasts m then all correct processes eventually deliver m Agreement: if a correct process delivers m then all correct processes eventually deliver m –Uniform Agreement: if any process delivers m then all correct processes eventually deliver m Integrity: m is delivered by a correct process at most once, and only if it was previously broadcast

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Reliable Broadcast (cont’d) What happens if a process fails during the broadcast of a message?

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring FIFO Broadcast If a process broadcasts a message m before it broadcasts a message m’, then no correct process delivers m’ unless it has previously delivered m. Alternative definition? –“all messages broadcast by the same process are delivered to all processes in the order they are sent” Are these definitions equivalent?

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Example 1 Also, this alternative definition forces faulty processes to deliver messages. (impossible)

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Causal Broadcast If the broadcast of a message m causally precedes the broadcast of a message m’, then no correct process delivers m’ unless it has previously delivered m. Event e causally precedes event f (e→f) iff: –a process executes both e and f, in that order, or –e is the broadcast of some message m and f is the delivery of m, or –There is an event h, such that e→h and h→f.

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Causal Broadcast (cont’d) Alternative definition? –“if the broadcast of m causally precedes the broadcast of m’, then every correct process that delivers both messages must deliver m before m’.” Are these definitions equivalent?

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Example 2 In a system with failures – –A delivers a message that is only delivered by B. –B broadcasts a response to A. –C delivers a response to a message it never delivers.

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Atomic Broadcast and Uniformity Atomic Broadcast = Total Order Uniform – limit the behavior of faulty processes –Agreement, Integrity –FIFO Order, Causal Order, Total Order

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Benign Failures Suppose processes are only subject to crash failures. –They operate correctly up to the time they crash (by definition). Can we assume that the message deliveries that a process makes before crashing are always ‘correct’?

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Benign Failures (cont’d) Even if a faulty process behaves correctly until it crashes, it may still deliver messages out-of-order before it crashes! Coordinator-based Atomic Broadcast algorithm: –When a process intends to broadcast a message m, it first sends m to a coordinator. –The coordinator delivers messages in the order in which it receives them, and periodically informs the other processes of this message delivery order. –Other processes deliver messages according to this order.

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Benign Failures (cont’d) –If the coordinator crashes, another process takes over as coordinator.

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Broadcast Primitives

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Broadcast Algorithms Our model- –Asynchronous –Benign process failures –Link specifications: Validity: If p sends m to q, and both p and q and the link between them are correct, then q eventually receives m. Uniform Integrity: For any message m, q receives m at most once from p, and only if p previously sent m to q. Our algorithms – –Satisfy Uniform Integrity. –Not optimized.

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Notations Reliable broadcast: –broadcast (R,m), deliver (R,m) FIFO broadcast: –broadcast (F,m), deliver (F,m) Causal broadcast: –broadcast (C,m), deliver (C,m) Every message includes: –The sender’s ID, denoted: sender(m) –A sequence number, denoted: seq#(m)

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Reliable Broadcast

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Reliable Broadcast (cont’d) When does the algorithm provide Reliable Broadcast? If we assume that: –There are only receive-omission failures –Every process p (whether correct or faulty) is connected to every correct process via a path consisting entirely of correct processes and links (with the possible exception of p itself) Then the algorithm satisfies Uniform Agreement.

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring FIFO Broadcast We give a reduction of FIFO Broadcast to Reliable Broadcast. The only assumption is that we have Reliable Broadcast. We don’t need the other assumptions (apart for benign failures for Uniform Integrity).

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring FIFO Broadcast (cont’d)

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring FIFO Broadcast (cont’d) The given algorithm also satisfies Uniform FIFO Broadcast. If the Reliable Broadcast algorithm used satisfies Uniform Agreement, the algorithm also satisfies Uniform Agreement.

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Causal Broadcast Why not use LTS? –It gives us causal delivery order + total order! In the lecture notes you saw an implementation with Vector Clocks

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Causal Broadcast (cont’d)

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Causal Broadcast (cont’d) We give a reduction of Causal Broadcast to Uniform FIFO Broadcast. The algorithm satisfies Uniform Causal Order. If the FIFO Broadcast satisfies Uniform Agreement, the derived algorithm also satisfies Uniform Agreement.

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Examples

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Causal Broadcast (cont’d) The above algorithm is a “brute force” one (and very inefficient in message length) Instead of sending the messages in rcntDlvrs, we can maintain a msgList (like msgSet, but maintains order) of F-delivered messages and send only message IDs. Each process, when F-delivering a message, should check the msgList to see if it can deliver messages according to the order of received IDs.

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Causal Broadcast (cont’d) Since we have FIFO Broadcast, we don’t need to send all the IDs. Only the ID of the last message a process delivered from each process. Thus we get  Vector Clocks

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Causal Broadcast (Take II)

Aran Bergman, Principles of Reliable Distributed Systems, Technion EE, Spring Uniform Specifications Uniform Agreement: If a process (whether correct or faulty) delivers a message m, then all correct processes eventually deliver m. Uniform Integrity: For any message m, every process (whether correct or faulty) delivers m at most once, and only if some process broadcast m. Uniform FIFO Order: If a process broadcasts a message m before it broadcasts a message m’, then no process (whether correct of faulty) delivers m’ unless it has previously delivered m. Uniform Causal Order: If the broadcast of a message m causally precedes the broadcast of a message m’, then no process (whether correct or faulty) delivers m’ unless it has previously delivered m. Uniform Total Order: if any processes p and q (whether correct or faulty) both deliver messages m and m’, then p delivers m before m’ iff q delivers m before m’.