Dynamic Lightpath Scheduling in Next-Generation WDM Optical Networks

Lightpath scheduling is an important capability in next-generation wavelength-division multiplexing (WDM) optical networks to reserve resources in advance for a specified time period while provisioning end-to-end lightpaths. In this study, we propose an approach to support dynamic lightpath scheduling in such networks. To minimize blocking probability in a network that accommodates dynamic scheduled lightpath demands (DSLDs), resource allocation should be optimized in a dynamic manner. However, for the network users who desire deterministic services, resources must be reserved in advance and guaranteed for future use. These two objectives may be mutually incompatible. Therefore, we propose a two-phase dynamic lightpath scheduling approach to tackle this issue. The first phase is the deterministic lightpath scheduling phase. When a lightpath request arrives, the network control plane schedules a path with guaranteed resources so that the user can get a quick response with the deterministic lightpath schedule. The second phase is the lightpath re-optimization phase, in which the network control plane re-provisions some already scheduled lightpaths. Experimental results show that our proposed two-phase dynamic lightpath scheduling approach can greatly reduce WDM network blocking.

A Two-phase Approach for Dynamic Lightpath Scheduling in WDM Optical Networks

Lightpath scheduling is an important capability in next-generation wavelength-division multiplexing (WDM) optical networks to reserve resources in advance for a specified time period while provisioning end-to-end lightpaths. In a dynamic environment, the end user requests for dynamic scheduled lightpath demands (D-SLDs) need to be serviced without the knowledge of future requests. Even though the starting time of the request may be hours or days from the current time, the end-user however expects a quick response as to whether the request could be satisfied. We propose a two-phase approach to dynamically schedule and provision D-SLDs. In the first phase, termed the deterministic lightpath scheduling phase, upon arrival of a lightpath request, the network control plane schedules a path with guaranteed resources so that the user can get a quick response with a deterministic lightpath schedule. In the second phase, termed the lightpath re-optimization phase, we re-provision some already scheduled lightpaths to re-optimize for improving network performance. We study two reoptimization scenarios to reallocate network resources while maintaining the existing lightpath schedules. Experimental results show that our proposed two-phase dynamic lightpath scheduling approach can greatly reduce network blocking.

Crop, Native Vegetation, and Biofuels: Response of White- Tailed Deer to Changing Management Priorities

The expansion of the cellulosic biofuels industry throughout the United States has broad-scale implications for wildlife management on public and private lands. Knowledge is limited on the effects of reverting agriculture to native grass, and vice versa, on size of home range and habitat use of white-tailed deer (Odocoileus virginianus). We followed 68 radio-collared female deer from 1991 through 2004 that were residents of DeSoto National Wildlife Refuge (DNWR) in eastern Nebraska, USA. The refuge was undergoing conversion of vegetation out of row-crop agriculture and into native grass, forest, and emergent aquatic vegetation. Habitat in DNWR consisted of 30% crop in 1991 but removing crops to establish native grass and wetland habitat at DNWR resulted in a 44% reduction in crops by 2004. A decrease in the amount of crops on DNWR contributed to a decline in mean size of annual home range from 400 ha in 1991 to 200 ha in 2005 but percentage of crops in home ranges increased from 21% to 29%. Mean overlap for individuals was 77% between consecutive annual home ranges across 8 years, regardless of crop availability. Conversion of crop to native habitat will not likely result in home range abandonment but may impact disease transmission by increasing rates of contact between deer social groups that occupy adjacent areas. Future research on condition indices or changes in population parameters (e.g., recruitment) could be incorporated into the study design to assess impacts of habitat conversion for biofuel production.