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An immunologically-inspired autonomic framework for self-organizing and evolvable network applications

An immunologically-inspired autonomic framework for self-organizing and evolvable network... Network applications are increasingly required to be autonomous, scalable, adaptive to dynamic changes in the network, and survivable against partial system failures. Based on the observation that various biological systems have already satisfied these requirements, this article proposes and evaluates a biologically-inspired framework that makes network applications to be autonomous, scalable, adaptive, and survivable. With the proposed framework, called iNet, each network application is designed as a decentralized group of 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 reproduction, migration, energy exchange, and death. iNet is designed after the mechanisms behind how the immune system detects antigens (e.g., viruses) and produces specific antibodies to eliminate them. It models a set of environment conditions (e.g., network traffic and resource availability) as an antigen and an agent behavior (e.g., migration) as an antibody. iNet allows each agent to autonomously sense its surrounding environment conditions (an antigen) to evaluate whether it adapts well to the sensed environment, and if it does not, adaptively perform a behavior (an antibody) suitable for the environment conditions. In iNet, a configuration of antibodies is encoded as a set of genes, and antibodies evolve via genetic operations such as crossover and mutation. Empirical measurement results show that iNet is lightweight enough. Simulation results show that agents adapt to dynamic and heterogeneous network environments by evolving their antibodies across generations. The results also show that iNet allows agents to scale to workload volume and network size and to survive partial link failures in the network. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Autonomous and Adaptive Systems (TAAS) Association for Computing Machinery

An immunologically-inspired autonomic framework for self-organizing and evolvable network applications

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Publisher
Association for Computing Machinery
Copyright
The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc.
ISSN
1556-4665
DOI
10.1145/1636665.1636668
Publisher site
See Article on Publisher Site

Abstract

Network applications are increasingly required to be autonomous, scalable, adaptive to dynamic changes in the network, and survivable against partial system failures. Based on the observation that various biological systems have already satisfied these requirements, this article proposes and evaluates a biologically-inspired framework that makes network applications to be autonomous, scalable, adaptive, and survivable. With the proposed framework, called iNet, each network application is designed as a decentralized group of 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 reproduction, migration, energy exchange, and death. iNet is designed after the mechanisms behind how the immune system detects antigens (e.g., viruses) and produces specific antibodies to eliminate them. It models a set of environment conditions (e.g., network traffic and resource availability) as an antigen and an agent behavior (e.g., migration) as an antibody. iNet allows each agent to autonomously sense its surrounding environment conditions (an antigen) to evaluate whether it adapts well to the sensed environment, and if it does not, adaptively perform a behavior (an antibody) suitable for the environment conditions. In iNet, a configuration of antibodies is encoded as a set of genes, and antibodies evolve via genetic operations such as crossover and mutation. Empirical measurement results show that iNet is lightweight enough. Simulation results show that agents adapt to dynamic and heterogeneous network environments by evolving their antibodies across generations. The results also show that iNet allows agents to scale to workload volume and network size and to survive partial link failures in the network.

Journal

ACM Transactions on Autonomous and Adaptive Systems (TAAS)Association for Computing Machinery

Published: Nov 1, 2009

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