No abstract

In hard-real-time environment, scheduling periodic tasks upon multiprocessors is one of the most popular problems where uniform multiprocessor scheduling is a well-known one. In this platform, execution time of each task in one processor is proportional to the computing capacity of this processor. From previous works, we know there are only approximate feasible solutions for on-line scheduling on uniform multiprocessors. In this paper, with task migration, we first present a novel model called T-L er plane to describe the behavior of tasks and processors, and two optimal on-line algorithms based on T-L er plane to schedule real-time tasks with dynamic-priority assignment on uniform multiprocessors. To make it practical and to reduce context switches, we also present a polynomial-time algorithm to bound the times of rescheduling in a T-L er plane. Since task migration is easier in SOC multicore processors, our result might be applicable and adapted to many asymmetric multicore platforms.

No abstract

No abstract

Early data from the 2009 H1N1 pandemic (H1N1pdm) suggest that previous studies over-estimated the within-country rate of spatial spread of pandemic influenza. As large spatially-resolved data sets are constructed, the need for efficient simulation code with which to investigate the spatial patterns of the pandemic becomes clear. Here, we describe a significant improvement in the efficiency of an individual-based stochastic disease simulation framework that has been used for multiple previous studies. We quantify the efficiency of the revised algorithm and present an alternative parameterization of the model in terms of the basic reproductive number. We apply the model to the population of Taiwan and demonstrate how the location of the initial seed can influence spatial incidence profiles and the overall spread of the epidemic. Differences in incidence are driven by the relative connectivity of alternate seed locations.