L_stabilization - Lecture15 selfstabilization:partI...

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Lecture 15 self-stabilization: part I distributed systems CS425 / ECE 428 / CSE  424
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acknowledgment these slides are based on ideas and material from the following sources: slides from professor S. Ghosh’s course at university of Iowa
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motivation as the number of computing elements increase in distributed  systems failures become more common fault tolerance should be automatic, without external  intervention two kinds of fault tolerance masking : application layer does not see faults, e.g.,  redundancy and replication non-masking : system deviates, deviation is detected and  then corrected: e.g., roll back and recovery self-stabilization  is a general technique for non-masking FT  distributed systems
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self-stabilization technique for  spontaneous  healing guarantees eventual safety  following failures f easibility demonstrated by  Dijkstra (CACM `74) E. Dijkstra
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self-stabilizing systems recover from  any initial configuration  to a  legitimate configuration in a bounded number of  steps,  as long as the codes are not corrupted assumption: failures affect the state (and data) but not the  the program
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self-stabilizing systems transient failures  perturb the global state. The ability to  spontaneously recover from any initial state implies that  no initialization is ever required . such systems can be deployed ad hoc, and are guaranteed  to function properly in bounded time guarantees fault tolerance when the mean time between  failures (MTBF) >> mean time to recovery (MTTR)
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self-stabilizing systems self-stabilizing systems  exhibits  non-masking  fault-tolerance they two criteria convergence closure Not L L convergence closure fault
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example 1:  stabilizing mutual exclusion in  unidirectional ring 0 1 6 2 4 7 5 3 N-1 consider a unidirectional ring of processes. Legal configuration = exactly one token in the ring desired “normal” behavior: single token circulates in the ring
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Dijkstra’s stabilizing mutual exclusion 0 p0 if x[0] = x[N-1] then x[0] := x[0] + 1 pj j > 0 if x[j] ≠ x[j -1] then x[j] := x[j-1] N processes: 0, 1, …, N-1 state of process j is x[j]  {0, 1, 2, K-1}, where K > N (TOKEN = if condition is true) Legal configuration: only one process has token start the system from an arbitrary initial configuration
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example execution 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 1 1 1 1 1 1 2 1 1 1 1 1 K-1 K-1 K-1 K-1 K-1 K-1
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example stabilizing execution 0 1 0 1 4 0 0 0 0 1 4 0 0 0 4 1 4 0 0 0 4 0 4 0 0 0 4 0 0 0 0 0 0 0 0 0
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why does it work ? 1. at any configuration, at least one process can  make a move (has token) suppose  p1,…,pN-1  cannot make a move then x[N-1] = x[N-2] = … x[0] then  p0  can make a move
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why does it work ?
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This note was uploaded on 02/08/2012 for the course ECE 428 taught by Professor Hu during the Spring '08 term at University of Illinois, Urbana Champaign.

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L_stabilization - Lecture15 selfstabilization:partI...

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