Awareness-Driven Phase Transitions in Very Large Scale Distributed Systems

Recent research in the field of complex networks has shown that - beyond microscopic structural qualities - global statistical parameters are sufficient to describe a surprising number of their macroscopic properties. This article argues that such statistical parameters can be monitored by nodes in a decentralized and efficient way. The so achieved awareness of a network\´s global parameters can be used by nodes for actively influencing them to optimize relevant characteristics of the overall network. For such an adaptation, the network-analogy of "phase transitions" in physical systems can be used. In this article the general concept of such an awareness-driven statistical adaptation is presented using power law networks as an example. For this important class of networks practical algorithms are introduced. Based on recent advances in reliable power law fitting, a gossip scheme has been developed which is suitable to make individual nodes aware of a power law network\´s critical exponent. In order to influence this parameter, decentralized reconnection rules are presented. The combination of both strategies facilitates a feedback control of large scale systems\´ emergent power law properties.