Alternative decompositions for distributed maximization of network utility: Framework and applications

Daniel P. Palomar, Mung Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Scopus citations


Network utility maximization (NUM) problems provide an important approach to conduct network resource management and to view layering as optimization decomposition. In the existing literature, distributed implementations are typically achieved by the means of the so-called dual decomposition technique. However, the span of decomposition possibilities includes many other elements which thus far have not been fully exploited, such as the use of the primal decomposition technique, the versatile introduction of auxiliary variables, and the potential of multilevel decompositions. This paper presents a systematic framework to exploit the potential of the alternative decomposition structures as a way to obtain different distributed algorithms, each with a different tradeoff among convergence speed, message passing amount and asymmetry, and distributed computation architecture. Many specific applications are considered to illustrate the proposed framework, including resourceconstrained and direct-control rate allocation, and rate allocation among QoS classes and with multipath routing. For each of these applications, the associated generalized NUM formulation is first presented, followed by the development of novel alternative decompositions and numerical experiments on the resulting new distributed algorithms.

Original languageEnglish (US)
Title of host publicationProceedings - INFOCOM 2006
Subtitle of host publication25th IEEE International Conference on Computer Communications
StatePublished - 2006
EventINFOCOM 2006: 25th IEEE International Conference on Computer Communications - Barcelona, Spain
Duration: Apr 23 2006Apr 29 2006

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


OtherINFOCOM 2006: 25th IEEE International Conference on Computer Communications

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Electrical and Electronic Engineering


  • Congestion control
  • Distributed algorithm
  • Mathematical programming/optimization
  • Network control by pricing
  • Network utility maximization
  • Rate control
  • Resource allocation


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