Active Virtual Network Management Prediction: Complexity as a Framework for Prediction, Optimization, and Assurance

TL;DR

This paper introduces a complexity-based framework for active virtual network management prediction, leveraging Kolmogorov Complexity to enhance network performance and provide a holistic approach to vulnerability analysis.

Contribution

It presents a novel complexity framework using Kolmogorov Complexity for network prediction and assurance, integrating computation and communication in active networking.

Findings

Kolmogorov Complexity effectively models prediction accuracy.

Experimental validation supports the complexity-performance relationship.

Framework offers a holistic view for vulnerability analysis.

Abstract

Research into active networking has provided the incentive to re-visit what has traditionally been classified as distinct properties and characteristics of information transfer such as protocol versus service; at a more fundamental level this paper considers the blending of computation and communication by means of complexity. The specific service examined in this paper is network self-prediction enabled by Active Virtual Network Management Prediction. Computation/communication is analyzed via Kolmogorov Complexity. The result is a mechanism to understand and improve the performance of active networking and Active Virtual Network Management Prediction in particular. The Active Virtual Network Management Prediction mechanism allows information, in various states of algorithmic and static form, to be transported in the service of prediction for network management. The results are generally applicable to algorithmic transmission of information. Kolmogorov Complexity is used and experimentally validated as a theory describing the relationship among algorithmic compression, complexity, and prediction accuracy within an active network. Finally, the paper concludes with a complexity-based framework for Information Assurance that attempts to take a holistic view of vulnerability analysis.