Self-Configuration of Network Services with Biologically Inspired Learning and Adaptation
Journal of Network and Systems Management
Digitally Evolving Models for Dynamically Adaptive Systems
SEAMS '07 Proceedings of the 2007 International Workshop on Software Engineering for Adaptive and Self-Managing Systems
Biologically inspired self-governance and self-organisation for autonomic networks
Proceedings of the 1st international conference on Bio inspired models of network, information and computing systems
Bio-Inspired Computing and Communication
Biologically-inspired distributed middleware management for stream processing systems
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Policy-constrained bio-inspired processes for autonomic route management
Computer Networks: The International Journal of Computer and Telecommunications Networking
Adaptive real-time monitoring for large-scale networked systems
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
MMS: an autonomic network-layer foundation for network management
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
Automated assembly of Internet-scale software systems involving autonomous agents
Journal of Systems and Software
A game theoretic framework for peer-to-peer market economy
International Journal of Grid and Utility Computing
Biologically inspired future service environment
Computer Networks: The International Journal of Computer and Telecommunications Networking
Applying blood glucose homeostatic model towards self-management of IP qos provisioned networks
IPOM'06 Proceedings of the 6th IEEE international conference on IP Operations and Management
Frontiers of Computer Science in China
A biological approach to autonomic communication systems
Transactions on Computational Systems Biology IV
Cellular differentiation-based service adaptation
ICSOC'11 Proceedings of the 9th international conference on Service-Oriented Computing
Self-Optimization and Self-Stabilization in Autonomic Clouds
Concurrency and Computation: Practice & Experience
Multi-robot navigation based QoS routing in self-organizing networks
Engineering Applications of Artificial Intelligence
Bio-inspired service management framework: green data-centres case study
International Journal of Grid and Utility Computing
Self-Adaptive Resource Allocation in Cloud Applications
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
Federation Lifecycle Management Incorporating Coordination of Bio-inspired Self-management Processes
Journal of Network and Systems Management
Hi-index | 0.07 |
This work describes and empirically evaluates the middleware platform of a new network architecture called the Bio-Networking Architecture. The Bio-Networking Architecture is inspired by the observation that the biological systems (e.g., bee colonies) have already developed mechanisms necessary to achieve future network requirements such as autonomy, scalability, adaptability, and simplicity. In the Bio-Networking Architecture, a network application is implemented as a group of distributed, autonomous and diverse objects called cyber-entities (CEs) (analogous to a bee colony consisting of multiple bees). Each CE implements a functional service related to the application and follows simple behaviors similar to biological entities (e.g., reproduction and migration). In the Bio-Networking Architecture, beneficial application characteristics (e.g., autonomy, scalability, adaptability, and simplicity) arise from the autonomous interaction of CEs. The middleware platform in the Bio-Networking Architecture, the bionet platform, provides reusable software components for developing, deploying, and executing CEs. The components abstract low-level operating and networking details, and implement high-level runtime services that CEs use to perform their services and behaviors. The components in the bionet platform are designed based on several biological concepts (e.g., energy exchange and pheromone emission). This work describes key designs of the bionet platform and empirically demonstrates that the bionet platform is efficient, scalable, reusable, and significantly simplifies development of network applications.