The design philosophy of the DARPA internet protocols
SIGCOMM '88 Symposium proceedings on Communications architectures and protocols
On the stability of projected dynamical systems
Journal of Optimization Theory and Applications
Cluster-based scalable network services
Proceedings of the sixteenth ACM symposium on Operating systems principles
On the economics of Internet peering
Netnomics
Internet Economics: When Constituencies Collide in Cyberspace
IEEE Internet Computing
Interactions, competition and innovation in a service-oriented internet: an economic model
INFOCOM'10 Proceedings of the 29th conference on Information communications
In-network services for customization in next-generation networks
IEEE Network: The Magazine of Global Internetworking
IEEE/ACM Transactions on Networking (TON)
Internet service classes under competition
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
Choice as a principle in network architecture
Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication
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This paper develops a new dynamic network economic model of Cournot-Nash competition for a service-oriented Internet in the case of service differentiation and quality competition. Each service provider seeks to maximize its own profit by determining its service volumes and service quality. We utilize variational inequality theory for the formulation of the governing Nash equilibrium as well as for the computational approach. We then construct the projected dynamical systems model, which provides a continuous-time evolution of the service providers service volumes and service quality levels, and whose set of stationary points coincides with the set of solutions to the variational inequality problem. We recall stability analysis results using a monotonicity approach and construct a discrete-time version of the continuous-time adjustment process, which yields an algorithm, with closed form expressions at each iteration. The algorithm is then utilized to compute the solutions to several numerical examples. A sensitivity analysis is also conducted.