Future networks will be able to share a same physical infrastructure: this will guarantee multiple networks simultaneously running without interfering with one another’s operation. Actually, allowing multiple networks to share the same physical infrastructure requires virtualizing and managing network resources (i.e., processing, storage, routing/forwarding nodes and links).
So let’s suppose we wish designing a virtual network capable of self-management operations: one can imagine a set of atomic functions to be performed on links (e.g. Label Switched Path – LSP) and virtual resources (slices of processing, storage and routing/forwarding). For example, some typical operations on a link may include: set-up/tear-down, modification of bandwidth, forwarding, monitoring, etc. In other words, we wish a virtual network able to function and learn over time without (or with a limited) supervision by either a human or some other external centralised system. As in humans, emotions seem to play the role of an evaluation instrument for learning, we wish including “emotions” in our network architectures to serve the self-management purposes.
Let’s start by referring to two well-known cognition theories (the Global Workspace and the Pandemonium Theory).
Global Workspace Theory
There is considerable empirical evidence from fMRI brain imaging experiments to show that individual human consciousness involves a single global workspace. Global Workspace Theory (GWT) (Baars, 1988, 1997, 2003) argues that human cognition is implemented by a multitude of relatively small, special purpose processes. Direct communication between them is rare and over a narrow bandwidth, whilst coalitions of such processes find their way into a global workspace.
Workspace serves to broadcast the message of the coalition to all the unconscious processors, in order to recruit other processors to join. All this takes place in a certain contexts which is itself a coalition of processes. According to GWT, brain can be viewed as a collection of distributed specialized networks (processors). Consciousness, associated with a global workspace in the brain, is a shared memory capacity whose contents are broadcasted to many unconscious specialized networks. A global workspace can also serve to integrate many competing and cooperating input networks.
Pandemonium Theory (Selfridge’s, 1958) is a connectionist architecture originally used for pattern recognition. Multiple independent processes called demons work simultaneously recognizing specific conditions (or a set of them). Demons have links that allows them to “call” other demons. J. Jackson (1987) extended the original Pandemonium theory by creating the stadium metaphor, organizing demons in two different locations, the equivalent of stands and arena of a stadium. A system consists of a crowd of usually dormant demons located at the stands, from where a few demons could go down to the arena and start exciting the crowd. Some demons in the crowd get more excited and start to yell louder. If a demon yells loudly enough, it gets to go down to the playing field and become active. Whenever a demon enters the playing field, the arena creates associations (if they do not already exist) between the incoming demon and any demons already on the field. These connections between demons are created or strengthened according to the time they are together on the arena, following a Hebbian learning scheme. Processes of the global workspace theory where correspond to demons of the Pandemonium theory. Global workspace is the arena.
Arena can perform the actual input and output functions of our network. It can also calculate a “gain”, i.e., a variable to measure how well the network is performing. Gain is up (goals achieved) if things are going well, down when doing poorly. The higher the gain, the more links are a strengthened by time together on the field. Gain determines how to strengthen or weaken associations between components: so network steers towards goals.
Lightweight components can be associated to each network entities (i.e., processing, storage, routing/forwarding virtual nodes and links). This component is in charge to build-up and tear down perceptual structures (of the associated virtual resource) and to direct the self-management operations.
If gain is modelled as a vector of four numbers each of which can be thought of as analogous to the four basic emotions, anger, sadness, happiness, and fear, then network’s emotional state at any one time is, therefore, considered to be the combination of the four emotions. Emotion components are then also introduced. When an emotion component’s preconditions are met it fires, modifying the value of a global variable representing the portion of the emotion vector associated with the component’s preconditions. The value of an individual element (emotion) in the gain vector can be modified when an emotion component fires.
Now last step: let’s imagine a function workspace (like an arena) populated with a myriad of these resource and emotion components behaving according the models of the GWT or/and the Pandemonium Theory. An interesting consequence is that the emergent network representation and self-management will be driven by a myriad of micro-behaviours rather than in traditional systems, where knowledge representation is driving management and behaviour.
Network will look like having associative memory capability based on a sparse distributed memory mechanisms. New perception associates with past experiences including actions and emotions. Remembered emotions activate emotion components that, in turn, influence actions selection.