The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Layering as Optimization Decomposition: A Mathematical Theory of Network Architectures
Abstract
Network protocols in layered architectures have historically been obtained on anad hocbasis, 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.
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Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
A tutorial on decomposition methods for network utility maximization
Palomar, D. P., & Chiang, M. (2006) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2006.879350 · Cited by 1,647
Self-citationWalking the Tightrope: The Balancing Acts of a Large e-Research Project
Lawrence, K. A. (2006) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-006-9025-0 · Cited by 121
Cited by
Distributed Subgradient Methods for Multi-Agent Optimization
Nedic, A., & Ozdaglar, A. (2009) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2008.2009515 · Cited by 3,719
Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014) · Journal of Complex Networks · doi:10.1093/comnet/cnu016 · Cited by 3,235
Fog and IoT: An Overview of Research Opportunities
Chiang, M., & Zhang, T. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2584538 · Cited by 2,362
Self-citationA tutorial on decomposition methods for network utility maximization
Palomar, D. P., & Chiang, M. (2006) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2006.879350 · Cited by 1,647
Self-citationSubgradient Methods for Saddle-Point Problems
Nedić, A., & Ozdaglar, A. (2009) · Journal of Optimization Theory and Applications · doi:10.1007/s10957-009-9522-7 · Cited by 438
Metadata
- Authors
- Chiang, M., Low, S. H., Calderbank, A. R., & Doyle, J. C.
- Venue
- Proceedings of the IEEE
- Published
- 2007-01
- Volume
- 95
- Issue
- 1
- Pages
- 255-312
- ISSN
- 0018-9219 (ISSN-L)
- OpenAlex
- W2158893758
- Cited by
- 1,352
- References
- 4
- FWCI
- 116.55
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chiang, M., Low, S. H., Calderbank, A. R., & Doyle, J. C. (2007). Layering as Optimization Decomposition: A Mathematical Theory of Network Architectures. Proceedings of the IEEE. https://doi.org/10.1109/jproc.2006.887322
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