Joint Optimization of Resources and Routes for Minimum Resistance: From Communication Networks to Power Grids
Ali Tizghadam (),
Alireza Bigdeli (),
Alberto Leon-Garcia () and
Hassan Naser ()
Additional contact information
Ali Tizghadam: University of Toronto (and Lakehead University)
Alireza Bigdeli: University of Toronto (and Lakehead University)
Alberto Leon-Garcia: University of Toronto (and Lakehead University)
Hassan Naser: University of Toronto (and Lakehead University)
Chapter Chapter 4 in Handbook of Optimization in Complex Networks, 2012, pp 97-142 from Springer
Abstract:
Abstract In this chapter, we are concerned with robustness design in complex communication networks and power grids. We define robustness as the ability of a network to adapt to environmental variations such as traffic fluctuations, topology modifications, and changes in the source (sink) of external traffic. We present a network theory approach to the joint optimization of resources and routes in a communication network to provide robust network operation. Our main metrics are the well-known point-to-point resistance distance and network criticality (average resistance distance) of a graph. We show that some of the key performance metrics in a communication network, such as average link betweenness sensitivity or average network utilization, can be expressed as a weighted combination of point-to-point resistance distances. A case of particular interest is when the external demand is specified by a traffic matrix. We extend the notion of network criticality to be a traffic-aware metric. Traffic-aware network criticality is then a weighted linear combination of point-to-point resistance distances of the graph. For this reason, in this chapter, we focus on a weighted linear sum of resistance distances (which is a convex function of link weights) as the main metric and we discuss a variety of optimization problems to jointly assign routes and flows in a network. We provide a complete mathematical analysis of the network planning problem (optimal weight assignment), where we assume that a routing algorithm is already in place and governs the distribution of network flows. Then, we extend the analysis to a more general case involving the simultaneous optimization of resources and flows (routes) in a network. Furthermore, we briefly discuss the problems of finding the best set of demands that can be matched to a given network topology (joint optimization of resources, flows, and demands) subject to the condition that the weighted linear sum of all point-to-point resistance distances of the network should remain below a certain threshold. We discuss applications of the proposed optimization methods to the design of virtual networks. Moreover, we show how our techniques can be used in the design of robust communication networks and robust sparse power grids.
Keywords: Power Grid; Betweenness Centrality; Virtual Network; Link Weight; Joint Optimization (search for similar items in EconPapers)
Date: 2012
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-1-4614-0857-4_4
Ordering information: This item can be ordered from
http://www.springer.com/9781461408574
DOI: 10.1007/978-1-4614-0857-4_4
Access Statistics for this chapter
More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().