Topics: Network Function Virtualization; Techno-Economics
Authors: Mathis Obadia (Thales Communications & Security & Telecom Paristech, France); Jean-Louis Rougier (TELECOM ParisTech / LTCI, France); Luigi Iannone (Telecom ParisTech, France); Mathieu Bouet and Vania Conan (Thales Communications & Security, France)
Presenter bio: Mathis OBADIA is a PhD student currently in the last year of his thesis
with works focusing on SDN and NFV resilience. He is particularly
interested in applying game theory to this area and by how pricing can
affect a softwerized network. His is advised and financed in this work
both in an academic setting at Telecom Paristech and in the corporate
world at Thales communications and Security.
Abstract:
Network Function Virtualization (NFV) is an
emerging approach that has received attention from both academia and
industry as a way to improve flexibility, efficiency, and manageability
of networks.
NFV enables new ways to operate networks and to provide composite
network services, opening the path toward new business models.
As in cloud computing with the Infrastructure as a Service model,
clients will be offered the capability to provision and instantiate
Virtual Network Functions (VNF) on the NFV infrastructure of the network
operators.
In this paper, we consider the case where leftover VNF capacities are
offered for bid. This approach is particularly interesting for clients
to punctually provision resources to absorb peak or unpredictable
demands and for operators to increase their revenues.
We propose a game theoretic approach and make use of Multi-Unit
Combinatorial Auctions to select the winning clients and the price they
pay.
Such a formulation allows clients to express their VNF requests
according to their specific objectives.
We solve this problem with a greedy heuristic and prove that this
approximation of economic efficiency is the closest attainable in
polynomial time and provides a payment system that motivates bidders to
submit their true valuations.
Simulation results show that the proposed heuristic achieves a market
valuation close to the optimal (less than 10 % deviation) and guarantees
that an important part of this valuation is paid as revenue to the
operator.
