Active virtual network management prediction: complexity as a framework for prediction, optimization, and assurance

The paper considers the blending of computation and communication by means of complexity. The specific service examined 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.