A New Intelligent Biologically-Inspired Model for Fault Tolerance in Distributed Embedded Systems

Abstract

The objective of this work is to present a new heuristic for solving the problem of fault tolerance in real time distributed embedded systems. The proposed idea is to model the distributed embedded architecture inspiring from the rennin-angiotensin aldosterone (RAAS) biological system which plays a major role in the pathophysiology of the cardiovascular system, from the point of view of pressure regulation and vascular, cardiac and nephrological remodeling. The proposed heuristic deals with uncertain information on a set of periodic tasks that run on multiple processors and satisfies certain temporal and energetic constraints from which the scheduling and the distribution of these tasks on the different processors are performed. In order to respect the energy constraints, this article proposes the introduction of energy consumption at the dynamic task scheduling level by using the dynamic voltage scaling (DVS) technique. The authors have seen that the introduction of a detection/prevention mechanism against potential errors in the proposed algorithm is a must for good results.

Performance Optimization Technology of Fault Tolerance Mechanism in Distributed System Based on Neural Network

“…These redundant data can directly or indirectly generate the original data and solve the problem of data transmission or storage. Loss problem, in order to ensure the stable operation of the system [1][2].…”