Abstract Dispatching systems, where jobs are routed to servers immediately upon arrival, appear frequently in parallel computing systems of different scales. With a dynamic dispatching policy, the system is generally analytically intractable and performance evaluation is carried out by means of Monte Carlo simulations. A typical performance metric is the mean response time which is often easy to estimate. In contrast, we consider systems where events generating costs are extremely rare. In our reference system, jobs have deadlines for waiting time. When deadlines are loose when compared to the system’s load, novel rare event simulation techniques must be applied. We consider both conditioning and importance sampling to this end. In numerical examples, the proposed techniques decrease the simulation time by several orders of magnitude. We also discover interesting performance relationships among the classical dispatching policies; Random split (RND), Round-robin (RR), Join-the-shortest-queue (JSQ) and Least-work-left (LWL).

中文翻译：

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关键任务调度系统仿真与性能评估

摘要 作业在到达后立即路由到服务器的调度系统经常出现在不同规模的并行计算系统中。对于动态调度策略，系统通常在分析上难以处理，性能评估是通过蒙特卡罗模拟进行的。典型的性能指标是平均响应时间，通常很容易估计。相比之下，我们考虑事件产生成本极其罕见的系统。在我们的参考系统中，工作有等待时间的截止日期。与系统负载相比，当最后期限较宽松时，必须应用新颖的稀有事件模拟技术。为此，我们考虑条件采样和重要性采样。在数值例子中，所提出的技术将模拟时间减少了几个数量级。我们还发现了经典调度策略之间有趣的性能关系；随机拆分 (RND)、循环 (RR)、加入最短队列 (JSQ) 和最少工作量 (LWL)。