Layering as Optimization Decomposition: A Mathematical Theory of Network Architectures
Network protocols in layered architectures have historically been obtained on an ad hoc basis, and many of the recent cross-layer designs are also conducted through piecemeal approaches. Network protocol stacks may instead be holistically analyzed and systematically designed as distributed solutions to some global optimization problems. This paper presents a survey of the recent efforts towards a systematic understanding of layering as optimization decomposition, where the overall communication network is modeled by a generalized network utility maximization problem, each layer corresponds to a decomposed subproblem, and the interfaces among layers are quantified as functions of the optimization variables coordinating the subproblems. There can be many alternative decompositions, leading to a choice of different layering architectures. This paper surveys the current status of horizontal decomposition into distributed computation, and vertical decomposition into functional modules such as congestion control, routing, scheduling, random access, power control, and channel coding. Key messages and methods arising from many recent works are summarized, and open issues discussed. Through case studies, it is illustrated how layering as Optimization Decomposition provides a common language to think about modularization in the face of complex, networked interactions, a unifying, top-down approach to design protocol stacks, and a mathematical theory of network architectures.
© 2007 IEEE. Manuscript received July 24, 2005; revised September 6, 2006. The works at Princeton University and Caltech that are summarized in this paper were supported by the National Science Foundation (NSF) Grants ANI-0230967, EIA-0303620, CNS-0417607, CNS-0435520, CCF-0440443, CCF-0448012, CNS-0417607, CNS-0427677, CNS-0430487, CCF-0635034, and CNS-0519880, by the Air Force Office of Scientific Research (AFOSR) Grants F49620-03-1-0119 and FA9550-06-1-0297, by the ARO Grant DAAD19-02-1-0283, by the Defense Advanced Research Projects Agency (DARPA) Grant HR0011-06-1-0008 and CBMANET program, and by the Cisco Grant GH072605.
Published - 04118456.pdf