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 "traffic-groomed". 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.

Many large-scale scientific applications need to aggregate large amounts of data from multiple distributed sites to a centralized facility. We call such a request as a data-aggregation request (DAR). In this study, we investigate the novel problem of on-demand DAR provisioning over a wavelength-division multiplexing (WDM) backbone network. We provide a mathematical formulation for our problem as a mixed integer linear program (MILP). To solve large versions of our problem, we propose a DAR provisioning heuristic (called DARP). We use the MILP with various objectives as a benchmark for studying the performance of DARP.

We investigate dynamic bandwidth allocation with flexible transmission rates for deadline-driven requests (DDRs) in wavelength-division multiplexing (WDM) mesh networks. DDRs provide more scheduling flexibility to the network operator compared with typical requests with no deadline and requiring a fixed bandwidth. By choosing the bandwidth and adapting the transmission rate depending on network state, the network's performance can be improved. We investigate several bandwidth allocation policies and study their benefits on network performance. Our investigation shows that an adaptive policy generally performs the best. Further improvement can be achieved by using dynamic readjustment of the allocated bandwidth.