Approximating the Traffic Grooming Problem with Respect to ADMs and OADMs

  • Authors:
  • Michele Flammini;Gianpiero Monaco;Luca Moscardelli;Mordechai Shalom;Shmuel Zaks

  • Affiliations:
  • Dipartmento di Informatica, Università degli Studi dell'Aquila, L'Aquila, Italy;Dipartmento di Informatica, Università degli Studi dell'Aquila, L'Aquila, Italy;Dipartmento di Informatica, Università degli Studi dell'Aquila, L'Aquila, Italy and Dipartimento di Informatica ed Applicazioni "R. M. Capocelli", Università degli Studi di Salerno, Ital ...;Tel Hai Academic College, , Upper Galilee, Israel;Department of Computer Science, , Technion, Israel

  • Venue:
  • Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
  • Year:
  • 2008

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Abstract

We consider the problem of switching cost in optical networks, where messages are sent along lightpaths. Given lightpaths, we have to assign them colors, so that at most glightpaths of the same color can share any edge (gis the grooming factor). The switching of the lightpaths is performed by electronic ADMs (Add-Drop-Multiplexers) at their endpoints and optical ADMs (OADMs) at their intermediate nodes. The saving in the switching components becomes possible when lightpaths of the same color can use the same switches. Whereas previous studies concentrated on the number of ADMs, we consider the cost function - incurred also by the number of OADMs - of f(茂戮驴) = 茂戮驴|OADMs| + (1 茂戮驴 茂戮驴)|ADMs|, where 0 ≤ 茂戮驴≤ 1. We concentrate on chain networks, but our technique can be directly extended to ring networks. We show that finding a coloring which will minimize this cost function is NP-complete, even when the network is a chain and the grooming factor is g= 2, for any value of 茂戮驴. We then present a general technique that, given an r-approximation algorithm working on particular instances of our problem, i.e. instances in which all requests share a common edge of the chain, builds a new algorithm for general instances having approximation ratio r茂戮驴logn茂戮驴. This technique is used in order to obtain two polynomial time approximation algorithms for our problem: the first one minimizes the number of OADMs (the case of 茂戮驴= 1), and its approximation ratio is 2 茂戮驴logn茂戮驴; the second one minimizes the combined cost f(茂戮驴) for 0 ≤ 茂戮驴