Towards a biologically-inspired architecture for self-regulatory and evolvable network applications

The BEYOND architecture applies biological principles and mechanisms to design network applications that autonomously adapt to dynamic environmental changes in the network. In BEYOND, each network application consists of distributed software agents, analogous to a bee colony (application) consisting of multiple bees (agents). Each agent provides a particular functionality of a network application, and implements biological behaviors such as energy exchange, migration, reproduction and replication. This paper describes two key components in BEYOND: (1) a self-regulatory and evolutionary adaptation mechanism for agents, called iNet, and (2) an agent development environment, called BEYONDwork. iNet is designed after the mechanisms behind how the immune system detects antigens (e.g., viruses) and produces antibodies to eliminate them. It models a set of environment conditions (e.g., network traffic) as an antigen and an agent behavior (e.g., migration) as an antibody. iNet allows each agent to autonomously sense its surrounding environment conditions (i.e., antigens) and adaptively invoke a behavior (i.e., antibody) suitable for the conditions. In iNet, a configuration of antibodies is encoded as a gene. Agents evolve their antibodies so that they can adapt to unexpected environmental changes. iNet also allows each agent to detect its own deficiencies to detect antigen invasions (i.e., environmental changes) and regulate its policy for antigen detection. Simulation results show that agents adapt to changing network environments by self-regulating their antigen detection and evolving their antibodies through generations. BEYONDwork provides visual and textual languages to design agents in an intuitive manner.