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An overlay network is a computer network which is built on top of another network. Nodes in the overlay can be thought of as being connected by virtual or logical links, each of which corresponds to a path, perhaps through many physical links, in the underlying network. For example, many peer-to-peer networks are overlay networks because they run on top of the Internet. Dial-up Internet is an overlay upon the telephone network.
Uses of overlay networks
Overlay networks can be constructed in order to permit routing messages to destinations not specified by an IP address. For example, Freenet and distributed hash tables can be used to route messages to a node storing a specified file, whose IP address is not known in advance.
Overlay networks have also been proposed as a way to improve Internet routing, such as through quality of service guarantees to achieve higher-quality streaming media. Previous proposals such as IntServ, DiffServ, and IP multicast have not seen wide acceptance largely because they require modification of all routers in the network. On the other hand, an overlay network can be incrementally deployed on end-hosts running the overlay protocol software, without cooperation from ISPs.
The overlay has no control over how packets are routed in the underlying network between two overlay nodes, but it can control, for example, the sequence of overlay nodes a message traverses before reaching its destination.
For example, Akamai is essentially an overlay network which provides reliable, efficient content delivery (a kind of multicast). Academic research includes End System Multicast and Overcast for multicast; RON (Resilient Overlay Network) for resilient routing; and OverQoS for quality of service guarantees, among others.
Swarm intelligence (SI) is an artificial intelligence technique based around the study of collective behaviour in decentralised, self-organised, systems. The expression "swarm intelligence" was introduced by Beni & Wang in 1989, in the context of cellular robotic systems (see also Cellular automata, Evolutionary computation).
SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. Although there is normally no centralised control structure dictating how individual agents should behave, local interactions between such agents often lead to the emergence of global behaviour. Examples of systems like this can be found in nature, including ant colonies, bird flocking, animal herding, bacteria molding and fish schooling.
Two of the most successful swarm intelligence techniques currently in existence are Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). ACO is a metaheuristic optimization algorithm that can be used to find approximate solutions to difficult combinatorial optimization problems. In ACO artificial ants build solutions by moving on the problem graph and they, mimicking real ants, deposit artificial pheromone on the graph in such a way that future artificial ants can build better solutions. ACO has been successfully applied to an impressive number of optimization problems. PSO is a global minimisation technique for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles. Particles then move through the solution space, and are evaluated according to some fitness criterion after each timestep. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimisation strategies such as simulated annealing is that the large number of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.