The local detection paradigm and its applications to self-stabilization 1

Abstract

A new paradigm for the design of self-stabilizing distributed algorithms, called local detection, is introduced. The essence of the paradigm is in defining a local condition based on the state of a processor and its immediate neighborhood such that the system is in a globally legal state if and only if the local condition is satisfied at all the nodes. In this work we also extend the model of self-stabilizing networks traditionally assuming memory failure to include the model of dynamic networks (assuming edge failures and recoveries). We apply the paradigm to the extended model which we call "dynamic self-stabilizing networks". Without loss of generality, we present the results in the least restrictive shared memory model of read/write atomicity, to which end we construct basic information transfer primitives. Using local detection, we develop deterministic and randomized self-stabilizing algorithms that maintain a rooted spanning tree in a general network whose topology changes dynamically. The deterministic algorithm assumes unique identities while the randomized assumes an anonymous network. The algorithms use a constant number of memory words per edge in each node; and both the size of memory words and of messages is the number of bits necessary to represent a node identity (typically O(log n) bits where n is the size of the network). These algorithms provide for the easy construction of self-stabilizing protocols for numerous tasks: reset, routing, topology-update and self-stabilization transformers that automatically self-stabilize existing protocols for which local detection conditions can be defined.