An Alternating Direction Method Approach to Cloud Traffic Management

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number7833029
Pages (from-to)2145-2158
Journal / PublicationIEEE Transactions on Parallel and Distributed Systems
Issue number8
Online published25 Jan 2017
Publication statusPublished - Aug 2017


In this paper, we introduce a unified framework for studying various cloud traffic management problems, ranging from geographical load balancing to backbone traffic engineering. We first abstract these real-world problems as a multi-facility resource allocation problem, and then present two distributed optimization algorithms by exploiting the special structure of the problem. Our algorithms are inspired by Alternating Direction Method of Multipliers (ADMM), enjoying a number of unique features. Compared to dual decomposition, they converge with non-strictly convex objective functions; compared to other ADMM-type algorithms, they not only achieve faster convergence under weaker assumptions, but also have lower computational complexity and lower message-passing
overhead. The simulation results not only confirm these desirable features of our algorithms, but also highlight several additional advantages, such as scalability and fault-tolerance.

Research Area(s)

  • ADMM, Cloud traffic management, datacenters, distributed optimization, load balancing, traffic engineering