Future networks will be different from what an engineer would think today. I’m an engineer and I’ve experienced ways to design, to optimize and to manage network resources in order to guarantee QoS. But imagine plenty of tiny smart nodes, devices and objects interconnected by network of networks thus weaving themselves into the fabric of everyday life to provide any sort of services. Imagine plenty of viral networks at the edge where collective behaviors emerge from the interaction of large numbers of such nodes adopting very simple local rules.
Edge networks will become robust by definition, but potentially subject to state transitions and the traditional end-to-end QoS issues will assume other perspectives: the network arena will be transformed into dynamic games of many (also new) Players, not only Telcos. Networks dynamics will be governed by the mathematics of “Chaos”: different cooperation-competition strategies can be take place as interactions of states attractors, or even better, basins of attractions in a network phase space. By the way, this an image that can be applied also to the neuron networks in a brain: huge amout of entities, embedding communication, storage and processing capabilities, interacting with each other with simple rules. Then, it is like in Nature, at least metaphorically: no central control, but evolution, is managing complex networks. But evolution normally takes a long time. On the other hand, we have technologies which allows us mimiking a dramatic acceleration of the time variable, and markets will make the natural selection of the best viral solutions.
At the end, it will be possible by tuning very simple autonomic rules of plenty of nodes to guide the network dinamics according to predefined goals and strategies. The degree of “autonomicity” of such viral nodes will provide the basis for the concept of controlled self-organization.
This set of simple rules (mimiking their nervous system) of tiny smart nodes, devices and objects represents a sort of “link” between (generalised) sensors and actuators. Each node, through sensors, perceives its environment, detects the existence of other goals and, through actuators, put in place the required actions. Another similar way to see it is a reflexive coevolution of behavior and structure, which is typical of what they call adaptive networks (e.g. the Web).