lect-10 - Computer Science 425 Distributed Systems Lecture...

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2006, 2007, M. T. Harandi Lecture 10- 1 Computer Science 425 Distributed Systems Lecture 10 Consensus
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2006, 2007, M. T. Harandi Lecture 10- 2 Consensus Consensus: N Processes agree on a value. e.g. synchronized action (go / abort) Consensus may have to be reached in the presence of failure. Process failure – process crash (fail-stop failure), arbitrary failure. Communication failure – lost or corrupted messages. In a consensus algorithm: All P i start in an “undecided” state. Each P i proposes a value v i from a set D and communicates it to some or all other processes. A consensus is reached if all non-failed processes agree on the same value, d . Each non-failed P i sets its decision variable to d and changes its state to “decided.”
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2006, 2007, M. T. Harandi Lecture 10- 3 Consensus Requirements Termination: Eventually each correct process sets its decision value. This may not be possible in the presence of process crashes in asynchronous systems Agreement: The decision value is the same for all correct processes, i.e., if p i and p j are correct and have entered the decided state, then d i =d j Arbitrary (Byzantine) failures may cause inconsistency and prevent agreement Integrity: If all correct processes P i ’s propose the same value, d , then any correct process in the decided state has decision value = d. Consensus may involve a proposal stage and an agreement stage.
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2006, 2007, M. T. Harandi Lecture 10- 4 For a system with at most f processes crashing, the algorithm proceeds in f+1 rounds (with timeout), using basic multicast. Values r i : the set of proposed values known to P i at the beginning of round r . Initially
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This note was uploaded on 07/01/2009 for the course CS CS421 taught by Professor Kirillmechitov during the Summer '09 term at University of Illinois, Urbana Champaign.

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lect-10 - Computer Science 425 Distributed Systems Lecture...

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