Organic Design of Massively Distributed Systems: A Complex Networks Perspective

TL;DR

This paper explores how principles from complex networks and statistical physics can inform the design of organic, self-managing distributed systems with properties like self-organization and self-healing.

Contribution

It reviews the relationship between complex systems science and networked computing, proposing applications for engineering organic systems with predictable self-* behaviors.

Findings

Links between statistical physics and networked systems are established.

Applications for designing self-organizing, self-adaptive systems are discussed.

Frameworks for predictable and controllable organic systems are proposed.

Abstract

The vision of Organic Computing addresses challenges that arise in the design of future information systems that are comprised of numerous, heterogeneous, resource-constrained and error-prone components or devices. Here, the notion organic particularly highlights the idea that, in order to be manageable, such systems should exhibit self-organization, self-adaptation and self-healing characteristics similar to those of biological systems. In recent years, the principles underlying many of the interesting characteristics of natural systems have been investigated from the perspective of complex systems science, particularly using the conceptual framework of statistical physics and statistical mechanics. In this article, we review some of the interesting relations between statistical physics and networked systems and discuss applications in the engineering of organic networked computing systems with predictable, quantifiable and controllable self-* properties.