A virtual private network (VPN) service is likely to be used by customers as a replacement for networks constructed using private lines, and thus its functionality should include the performance guarantee provided to those customers. To provide guaranteed services, the network provider allocates appropriate capacities to multiple virtual backbone networks such that the underlying network can be shared among them. As VPN users are demanding reliable and dynamic allocation of capacities, recently the capacity resizing approach has been considered as a cost efficient way of providing virtual network services. We propose a new scheme for dynamic allocation of virtual link capacities. The allocated capacities are adjusted dynamically according to the users' requests such that their capacities are increased in a fair manner and the total reservation does not overwhelm the underlying network. Depending on the network's status and allocation policy, a virtual link may increase or decrease its capacity, for example, for a monetary incentive. VPN users send control packets whenever they want to resize their capacities, and the network handles them in an efficient and fair way. The simulation and analytic results in this paper show that our scheme is simple and robust such that the users and the network communicate using simple control packets and the link capacities are allocated efficiently.

The available bit rate (ABR) is an ATM service category that provides an economical support of connections having vague requirements. An ABR session may specify its peak cell rate (PCR) and minimum cell rate (MCR), and available bandwidth is allocated to competing sessions based on the max-min policy. In this paper, we investigate the ABR traffic control from a different point of view: Based on the decentralized bandwidth allocation model studied in [9], we prove that the max-min rate vector is the equilibrium of a certain system of noncooperative optimizations. This interpretation suggests a new framework for ABR traffic control that allows the max-min optimality to be achieved and maintained by end-systems, and not by network switches. Moreover, in the discussion, we consider the constrained version of max-min fairness and develop an efficient algorithm with theoretical justification to determine the optimal rate vector.