Title :
Self-organizing network services with evolutionary adaptation
Author :
Nakano, Tadashi ; Suda, Tatsuya
Author_Institution :
Donald Bren Sch. of Inf. & Comput. Sci., Univ. of California, Irvine, CA, USA
Abstract :
This paper proposes a novel framework for developing adaptive and scalable network services. In the proposed framework, a network service is implemented as a group of autonomous agents that interact in the network environment. Agents in the proposed framework are autonomous and capable of simple behaviors (e.g., replication, migration, and death). In this paper, an evolutionary adaptation mechanism is designed using genetic algorithms (GAs) for agents to evolve their behaviors and improve their fitness values (e.g., response time to a service request) to the environment. The proposed framework is evaluated through simulations, and the simulation results demonstrate the ability of autonomous agents to adapt to the network environment. The proposed framework may be suitable for disseminating network services in dynamic and large-scale networks where a large number of data and services need to be replicated, moved, and deleted in a decentralized manner.
Keywords :
evolutionary computation; multi-agent systems; self-adjusting systems; telecommunication services; adaptive network services; autonomous agents; dynamic networks; evolutionary adaptation; evolutionary computation; genetic algorithms; large-scale networks; network environment; scalable network services; self-organizing network services; swarm intelligence; Adaptive systems; Algorithm design and analysis; Autonomous agents; Centralized control; Delay; Evolutionary computation; Large-scale systems; Particle swarm optimization; Peer to peer computing; Self-organizing networks; Adaptive and scalable network services; autonomous agents; evolutionary computation; self-organization; swarm intelligence; Algorithms; Artificial Intelligence; Computer Simulation; Information Storage and Retrieval; Internet; Models, Statistical; Pattern Recognition, Automated; Signal Processing, Computer-Assisted; Telecommunications;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2005.853421