Polymorphic Self-* Agents for Stigmergic Fault Mitigation in Large-Scale Real-Time Embedded Systems

TL;DR

This paper introduces polymorphic self-* agents that adapt roles based on environmental cues, enhancing fault mitigation in large-scale, real-time distributed systems like BTeV, outperforming traditional approaches.

Contribution

The paper presents a novel design of polymorphic self-* agents that evolve roles dynamically, integrating game theory and stigmergy for fault mitigation in large-scale systems.

Findings

Agents demonstrate polymorphic behavior in simulations.

Design exceeds performance of centralized approaches.

Improves reliability in real-time distributed systems.

Abstract

Organization and coordination of agents within large-scale, complex, distributed environments is one of the primary challenges in the field of multi-agent systems. A lot of interest has surfaced recently around self-* (self-organizing, self-managing, self-optimizing, self-protecting) agents. This paper presents polymorphic self-* agents that evolve a core set of roles and behavior based on environmental cues. The agents adapt these roles based on the changing demands of the environment, and are directly implementable in computer systems applications. The design combines strategies from game theory, stigmergy, and other biologically inspired models to address fault mitigation in large-scale, real-time, distributed systems. The agents are embedded within the individual digital signal processors of BTeV, a High Energy Physics experiment consisting of 2500 such processors. Results obtained using a SWARM simulation of the BTeV environment demonstrate the polymorphic character of the agents, and show how this design exceeds performance and reliability metrics obtained from comparable centralized, and even traditional decentralized approaches.