Decentralized Periodic Approach for Adaptive Fault Diagnosis in Distributed Systems

TL;DR

This paper introduces a decentralized, periodic fault diagnosis algorithm for distributed systems that detects faulty nodes without relying on a central leader, enabling robust fault detection and system reconfiguration.

Contribution

It proposes a novel decentralized, periodic approach that allows multiple nodes to collaboratively detect faults in arbitrary network topologies, improving fault tolerance.

Findings

Detects up to n-1 faulty nodes in an n-node system

Enables load redistribution after fault detection

Supports node repair and entry processes

Abstract

In this paper, Decentralized Periodic Approach for Adaptive Fault Diagnosis (DP-AFD) algorithm is proposed for fault diagnosis in distributed systems with arbitrary topology. Faulty nodes may be either unresponsive, may have either software or hardware faults. The proposed algorithm detects the faulty nodes situated in geographically distributed locations. This algorithm does not depend on a single node or leader to detect the faults in the system. However, it empowers more than one node to detect the fault-free and faulty nodes in the system. Thus, at the end of each test cycle, every fault-free node acts as a leader to diagnose faults in the system. This feature of the algorithm makes it applicable to any arbitrary network. After every test cycle of the algorithm, all the nodes have knowledge about faulty nodes and each node is tested only once. With this knowledge, there can be redistribution of load, which was earlier assigned to the faulty nodes. Also, the algorithm permits repaired node re-entry and new node entry. In a system of n nodes, the maximum number of faulty nodes can be (n-1) which is detected by DP-AFD algorithm. DP-AFD is periodic in nature which executes test cycles after regular intervals to detect the faulty nodes in the given distributed system.