Proceedings Article | 19 November 2008

Proc. SPIE. 7137, Network Architectures, Management, and Applications VI

KEYWORDS: Mathematical modeling, Natural disasters, Matrices, Networks, Computer programming, Optical networks, Optimization (mathematics), Failure analysis, Stochastic processes, Network architectures

One of the main focuses for network planning is on the optimization of network resources required to build a network
under certain traffic demand projection. Traditionally, the inputs to this type of network planning problems are treated as
deterministic. In reality, the varying traffic requirements and fluctuations in network resources can cause uncertainties in
the decision models. The failure to include the uncertainties in the network design process can severely affect the
feasibility and economics of the network. Therefore, it is essential to find a solution that can be insensitive to the
uncertain conditions during the network planning process. As early as in the 1960's, a network planning problem with
varying traffic requirements over time had been studied. Up to now, this kind of network planning problems is still being
active researched, especially for the VPN network design.
Another kind of network planning problems under uncertainties that has been studied actively in the past decade
addresses the fluctuations in network resources. One such hotly pursued research topic is survivable network planning. It
considers the design of a network under uncertainties brought by the fluctuations in topology to meet the requirement
that the network remains intact up to a certain number of faults occurring anywhere in the network. Recently, the authors
proposed a new planning methodology called Generalized Survivable Network that tackles the network design problem
under both varying traffic requirements and fluctuations of topology. Although all the above network planning problems
handle various kinds of uncertainties, it is hard to find a generic framework under more general uncertainty conditions
that allows a more systematic way to solve the problems. With a unified framework, the seemingly diverse models and
algorithms can be intimately related and possibly more insights and improvements can be brought out for solving the
problem. This motivates us to seek a generic framework for solving the network planning problem under uncertainties.
In addition to reviewing the various network planning problems involving uncertainties, we also propose that a unified
framework based on robust optimization can be used to solve a rather large segment of network planning problem under
uncertainties. Robust optimization is first introduced in the operations research literature and is a framework that
incorporates information about the uncertainty sets for the parameters in the optimization model. Even though robust
optimization is originated from tackling the uncertainty in the optimization process, it can serve as a comprehensive and
suitable framework for tackling generic network planning problems under uncertainties.
In this paper, we begin by explaining the main ideas behind the robust optimization approach. Then we demonstrate the
capabilities of the proposed framework by giving out some examples of how the robust optimization framework can be
applied to the current common network planning problems under uncertain environments. Next, we list some practical
considerations for solving the network planning problem under uncertainties with the proposed framework. Finally, we
conclude this article with some thoughts on the future directions for applying this framework to solve other network
planning problems.