Failure Models and Consensus Algorithms in Distributed Systems - Prof. Sayan Mitra, Study notes of Electrical and Electronics Engineering

Download Failure Models and Consensus Algorithms in Distributed Systems - Prof. Sayan Mitra and more Study notes Electrical and Electronics Engineering in PDF only on Docsity! Lecture 8 consensus 1: distributed systems CS425 / ECE 428 / CSE 424 sayan mitra failure models • crash failure: ceases to execute – permanent – dead for finite period of time then resumes – cause: e.g. power loss • omission failure: sender sends a sequence of messages but receiver does not receive some subset of messages – cause: e.g. interference in medium • transient failure: process jumps to arbitrary state and resumes normal execution – cause: e.g. gamma rays • Byzantine failure: arbitrary messages and transitions – cause: e.g. software bugs, malicious attacks interactive consistency (IC) • n processes ,0,1,2,…,n-1} try to agree on vector of values • pi begins in undecided state and proposes a value vi  D • pi sets its decision value di and enters decided state • consensus requirements – termination: all correct processes decide – agreement: the decision vector for all correct processes is the same – integrity: if pi is correct then for any correct process pj, dj[i] = vi C to BG to IC • how to solve IC from an algorithm for solving BG ? • how to solve C from an algorithm for IC ? • how to solve BG using an algorithm for C ? • how to solve solving consensus with reliable totally ordered-multicast • all processes form a group • pi performs RTO-multicast(vi,g) • pi sets di = mi where mi is the first msg delivered by RTO-multicast – termination guaranteed by reliable multicast – agreement and validity by definition of majority – could use max, min instead of majority if D is ordered • solving consensus using basic multicast in the case where upto f processes crash BG in synchronous system impossibility with N < 3f algorithm for N = 3f + 1