论文标题
通过资源协作和多任务处理对并行处理网络的最佳控制
On the Optimal Control of Parallel Processing Networks with Resource Collaboration and Multitasking
论文作者
论文摘要
我们研究了对并行处理网络的调度控制，其中一些资源需要同时协作以执行某些活动和一些资源多任务。资源协作和多任务处理在资源不可分解时，即当资源无法分配时，会产生资源调度的同步约束。同步约束严重影响系统性能。例如，由于这些限制，系统容量可能严格小于瓶颈资源的容量。此外，在这些限制下，资源调度决策并非微不足道。例如，并非所有静态优先级策略都保留最大系统容量，并且保留最大系统容量的系统不一定会最大程度地减少延迟（或一般而言的持有成本）。我们研究了一类平行网络的最佳调度控制，并提出了动态优先级策略，该策略保留了最大的系统容量，并且就预期的折扣总持有成本目标而言，在扩散量表和常规的重型交通状态下在扩散量表和常规的重型交通状态上是最佳的。
We study scheduling control of parallel processing networks in which some resources need to simultaneously collaborate to perform some activities and some resources multitask. Resource collaboration and multitasking give rise to synchronization constraints in resource scheduling when the resources are not divisible, that is, when the resources cannot be split. The synchronization constraints affect the system performance significantly. For example, because of those constraints, the system capacity can be strictly less than the capacity of the bottleneck resource. Furthermore, the resource scheduling decisions are not trivial under those constraints. For example, not all static prioritization policies retain the maximum system capacity and the ones that retain the maximum system capacity do not necessarily minimize the delay (or in general the holding cost). We study optimal scheduling control of a class of parallel networks and propose a dynamic prioritization policy that retains the maximum system capacity and is asymptotically optimal in diffusion scale and conventional heavy-traffic regime with respect to the expected discounted total holding cost objective.
