Ajoy Kumar Datta

Self-Stabilizing Local Mutual Exclusion and Daemon Refinement

Refining self-stabilizing algorithms which use tighter scheduling constraints (weaker daemon) into corresponding algorithms for weaker or no scheduling constraints (stronger daemon), while preserving the stabilization property, is useful and challenging. Designing transformation techniques for these refinements has been the subject of serious investigations in recent years. This paper proposes a transformation technique to achieve the above task. The heart of the transformer is a self-stabilizing local mutual exclusion algorithm. The local mutual exclusion problem is to grant a process the privilege to enter the critical section if and only if none of the neighbors of the process has the privilege. The contribution of this paper is twolold. First, we present a bounded-memory self-stabilizing local mutual exclusion algorithm for arbitrary network, assuming any arbitrary daemon. After stabilization, this algorithm maintains a bound on the service time (the delay between two successive executions of the critical section by a particular process). This bound is n×(n-1)/2 where n is the network size. Second, we use the local mutual exclusion algorithm to design two scheduler transformers which convert the algorithms working under a weaker daemon to ones which work under the distributed, arbitrary (or unfair) daemon, both transformers preserving the self-stabilizing property. The first transformer refines algorithms written under the central daemon, while the second transformer refines algorithms designed for the k-fair (k ? (n - 1)) daemon.

A Semantic Overlay for Self- Peer-to-Peer Publish/Subscribe

Publish/Subscribe systems provide a useful platform for delivering data (events) from publishers to subscribers in an anonymous fashion in distributed networks. In this paper, we promote a novel design principle for self-. dynamic and reliable content-based publish/subscribe systems and perform a comparative analysis of its probabilistic and deterministic implementations. More specifically, we present a generic content-based publish/subscribe system, called DPS (Dynamic Publish/Subscribe). DPS combines classical content-based filtering with self-. (self-organizing, selfconfiguring, and self-healing) subscription-driven clustering of subscribers. DPS gracefully adapts to failures and changes in the system while achieving scalable events delivery. DPS includes a variety of fault-tolerant deterministic and probabilistic content-based publication/subscription schemes. These schemes are targeted toward scalability, and aim at reducing and distributing the number of messages exchanged. Reliability and scalability of our system are shown through analytical and experimental evaluation.

Snap-stabilization and PIF in tree networks

The contribution of this paper is threefold. First, we present the paradigm of snap-stabilization. A snap- stabilizing protocol guarantees that, starting from an arbitrary system configuration, the protocol always behaves according to its specification. So, a snap-stabilizing protocol is a time optimal self-stabilizing protocol (because it stabilizes in 0 rounds). Second, we propose a new Propagation of Information with Feedback (PIF) cycle, called Propagation of Information with Feedback and Cleaning (

Anonymous publish/subscribe in P2P networks

\mathcal{PFC}

Self-stabilization with global rooted synchronizers

). We show three different implementations of this new PIF. The first one is a basic

Two-state self-stabilizing algorithms for token rings

\mathcal{PFC}