Studying Network Optimization in the Context of Self-Stabilization

Self-stabilization is a theoretical framework of nonmasking fault-tolerance for distributed networks. A self-stabilizing system is capable of tolerating any unexpected transient fault without outside intervention and, regardless of the initial state, it can converge to a legitimate global state, a predefined vector of local states, in finite time. Self-stabilization has rendered a good problem solving paradigm of networks over the last decade. In this paper, we survey the selfstabilizing solutions for various network optimization problems such as network flow, load balancing, load and resource distribution, routing, file distribution, shortest paths etc. The paper also summarizes some recent works presenting how the convergence of a selfstabilizing distributed network can be modelled as a convex optimization problem with the exploitation of an analogy between self-stabilizing systems and stable feedback systems. The works pertaining to gradient adaptation of self-stabilizing system are also presented.