A novel approach to quality of service control in an active service network (application layer active network) is described. The approach makes use of a distributed genetic algorithm based on the unique methods that bacteria use to transfer and share genetic material. We have used this algorithm in the design of a robust adaptive control system for the active nodes in an active service network. The system has been simulated and results show that it can offer clear differentiation of active services. The algorithm places the right software, at the right place, in the right proportions; allows different time dependencies to be satisfied and simple payment related increases in performance.
The new distributed learning algorithm described and applied in this paper was the starting point for the autonomic self-management ideas that form the core of the subsequent SECOAS and Prosen projects, and are also the basis of 3 patents with BT (WO/2002/005495, WO/2001/0059991, CA2489901). RAE_import_type : Journal article RAE_uoa_type : Computer Science and Informatics