Managing energy and server resources in hosting centers
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Self-Organization in Biological Systems
Self-Organization in Biological Systems
Coevolution of Form and Function in the Design of Micro Air Vehicles
EH '02 Proceedings of the 2002 NASA/DoD Conference on Evolvable Hardware (EH'02)
Towards an Autonomic Computing Environment
DEXA '03 Proceedings of the 14th International Workshop on Database and Expert Systems Applications
Anomaly Detection Using Real-Valued Negative Selection
Genetic Programming and Evolvable Machines
Evolution of behaviors in autonomous robot using artificial neural network and genetic algorithm
Information Sciences: an International Journal
Internet, GRID, Self-Adaptability and Beyond: Are We Ready?
DEXA '04 Proceedings of the Database and Expert Systems Applications, 15th International Workshop
Self-Sizing of Clustered Databases
WOWMOM '06 Proceedings of the 2006 International Symposium on on World of Wireless, Mobile and Multimedia Networks
Biologically-Inspired Design of Autonomous and Adaptive Grid Services
ICAS '06 Proceedings of the International Conference on Autonomic and Autonomous Systems
Autonomic Communication
The organic grid: self-organizing computation on a peer-to-peer network
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Self-organizing network services with evolutionary adaptation
IEEE Transactions on Neural Networks
Architectures & infrastructure
Service research challenges and solutions for the future internet
Bio-inspired service management framework: green data-centres case study
International Journal of Grid and Utility Computing
| Hi-index | 0.00 |
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.