Distributed Facility Location and Migration


How can we determine in a distributed and scalable manner the number and location of service facilities? We propose an innovative approach in which topology and demand information is limited to neighborhoods, or balls of small radius around selected facilities, whereas demand information is captured implicitly for the remaining (remote) clients outside these neighborhoods, by mapping them to clients on the edge of the neighborhood. Through an iterative local reoptimization of the location and the number of facilities within such balls, our distributed approach achieves performance that is comparable to that of optimal, centralized approaches requiring full topology and demand information. We demonstrate the efficiency and scalability of our framework under various synthetic and real Internet topologies.

Toward Dynamic Service Deployment
Where should the Service Facility migrate?

Main Results


Revised code will be released soon.

Group Members:
Georgios Smaragdakis (PhD BU'08, now with Deutsche Telekom Labs/Technical University of Berlin)
Nikolaos Laoutaris (Telefonica Research, Barcelona)
Azer Bestavros (Professor, Boston University)
Konstantinos Oikonomou (Assistant Professor, Ionian University)
Ioannis Stavrakakis (Professor, University of Athens)

For any further information or bug report please send e-mail to Georgios Smaragdakis


last update: October 13, 2007

Creative Commons License
All code on this page is licensed under a Creative Commons License.
Sponsors: The DFL project is supported partially by a number of National Science Foundation grants, including CISE/CSR Award #0720604, ENG/EFRI Award #0735974, CISE/CNS Award #0524477, CNS/CNS Award #0520166, CNS/ITR Award #0205294, and CISE/EIA RI Award #0202067.
Disclaimer: Any opinions, findings, conclusions, or recommendations expressed in materials available from this site are those of their author(s) and do not necessarily reflect the views of Boston University or of the National Science Foundation.