Joint Computing and Network Resource Scheduling in a Lambda Grid Network

Data-intensive Grid applications require huge data transfers between grid computing nodes. These computing nodes, where computing jobs are executed, are usually geographically separated. A grid network that employs optical wavelength division multiplexing (WDM) technology and optical switches to interconnect computing resources with dynamically provisioned multi-gigabit rate bandwidth lightpath is called a Lambda Grid network. A computing task may be executed on any one of several computing nodes which possesses the necessary resources. In order to reflect the reality in job scheduling, allocation of network resources for data transfer should be taken into consideration. However, few scheduling methods consider the communication contention on Lambda Grids. In this paper, we investigate the joint scheduling problem while considering both optical network and computing resources in a Lambda Grid network. The objective of our work is to maximize the total number of jobs that can be scheduled in a Lambda Grid network. An adaptive routing algorithm is proposed and implemented for accomplishing the communication tasks for every job submitted in the network. Four heuristics (FIFO, ESTF, LJF, RS) are implemented for job scheduling of the computational tasks. Simulation results prove the feasibility and efficiency of the proposed solution.

Joint Computing and Network Resource Scheduling in a Lambda Grid Network

Data-intensive Grid applications require huge data transfers between grid computing nodes. These computing nodes, where computing jobs are executed, are usually geographically separated. A grid network that employs optical wavelength division multiplexing (WDM) technology and optical switches to interconnect computing resources with dynamically provisioned multi-gigabit rate bandwidth lightpath is called a Lambda Grid network. A computing task may be executed on any one of several computing nodes which possesses the necessary resources. In order to reflect the reality in job scheduling, allocation of network resources for data transfer should be taken into consideration. However, few scheduling methods consider the communication contention on Lambda Grids. In this paper, we investigate the joint scheduling problem while considering both optical network and computing resources in a Lambda Grid network. The objective of our work is to maximize the total number of jobs that can be scheduled in a Lambda Grid network. An adaptive routing algorithm is proposed and implemented for accomplishing the communication tasks for every job submitted in the network. Four heuristics (FIFO, ESTF, LJF, RS) are implemented for job scheduling of the computational tasks. Simulation results prove the feasibility and efficiency of the proposed solution.

REDUCING NUISANCE CANADA GOOSE PROBLEMS THROUGH HABITAT MANIPULATION

Urban populations of Canada geese (Branta canadensis) cause considerable problems when large numbers congregate in parks, playing fields, and backyards. In most cases, geese are drawn to these sites to feed on the lawns. I tested whether geese have feeding preferences for different grass species. Captive Canada geese preferred Kentucky bluegrass (Poa pratensis) and disliked tall fescue (Festuca arundinaceae) over colonial bentgrass (Agrostis tenuis cv. Highland), perennial ryegrass (Lolium perenne), and red fescue (Festuca rubra). They refused to eat some other ground covers such as pachysandra (Pachysandra terminalis) and English ivy (Hedera helix). These results suggest that goose numbers at problem sites could be reduced by changing the ground cover. I also compared the characteristics of foraging sites used by geese to other foraging sites that geese avoided. Occupied sites were more open so that geese had clearer visibility and greater ease in taking off and landing. This suggests that goose numbers at problem sites also could be reduced by planting tall trees to make it harder for the geese to fly away, and planting bushes and hedges to obstruct a goose's visibility.