Managing energy and server resources in hosting centers
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Machine Learning
Self-Organization in Biological Systems
Self-Organization in Biological Systems
Towards an Autonomic Computing Environment
DEXA '03 Proceedings of the 14th International Workshop on Database and Expert Systems Applications
Biologically-Inspired Design of Autonomous and Adaptive Grid Services
ICAS '06 Proceedings of the International Conference on Autonomic and Autonomous Systems
Self-organizing network services with evolutionary adaptation
IEEE Transactions on Neural Networks
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The BEYOND architecture applies biological principles and mechanisms to design evolvable network applications that autonomously adapt to dynamic environmental changes in the network. This paper describes two key components in BEYOND: (1) an evolutionary adaptation engine, called iNet, for network applications and (2) an application development environment, called BEYONDwork, for the adaptation engine. iNet is designed after the mechanisms behind how the immune system works. It models a set of environment conditions (e.g., network traffic) as an antigen and a behavior of network applications (e.g., migration and reproduction) as an antibody. iNet allows each network application to autonomously sense its surrounding environment conditions (i.e., antigens) and adaptively invoke a behavior (i.e., antibody) suitable for the conditions. The configuration of antibodies evolves via genetic operations such as mutation and crossover. BEYONDwork provides visual and textual languages to configure antigens and antibodies in iNet. The languages increase the ease of specifying and modifying iNet configurations. Simulation results show that iNet allows network applications designed with BEYONDwork to evolve themselves to adapt to changing network environments.