Flexible scheduling of multicast sessions with different granularities for large data distribution over WDM networks

Authors:
Dragos Andrei;Massimo Tornatore;Charles U. Martel;Biswanath Mukherjee
Affiliations:
University of California, Davis, CA;University of California, Davis, CA;University of California, Davis, CA;University of California, Davis, CA
Venue:
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Year:
2009

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Abstract

Many networking applications require distribution of data from a central point to multiple destinations; this distribution can be efficiently achieved by the means of multicasting. Traditionally, multicasting has been considered for on-demand applications such as HDTV, Video-on-Demand (VoD), IPTV, which usually require to start data transmission immediately. However, in the case of emerging e-Science and high-performance applications (which frequently need to replicate large datasets to multiple locations), the data distribution does not necessarily need to take place instantaneously; instead, the multicast session can be accommodated considering a flexible start time for the large data transfer. We study the efficient provisioning of Multicast Data-Distribution Requests (MDDRs) with flexible scheduling over WDM networks. We consider the practical case of multicast sessions that may require less than the entire capacity of a wavelength; hence the multicast sessions need to be "trafficgroomed". Our first multicast provisioning approach (named Rand) generates randomized alternate multicast trees on which we try to provision the multicast session, and then attempts to assign wavelengths and schedule the session's start time. In our second approach (named AllSlots), for each available start time S, we dynamically generate trees depending on the network state at time S. In our next approach (named Break), for the cases when provisioning an entire multicast tree fails, we enable the possibility of "breaking" the tree into subtrees (with independent start times) serving subsets of destinations. Moreover, we study the impact of partitioning the datasets into pieces on our multicast provisioning approaches, and also compare our multicast algorithms with an unicast approach.