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Seeing through black boxes: Tracking transactions through queues under monitoring resource constraints

机译:观察黑箱:在监视资源约束下通过队列跟踪事务

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摘要

The problem of optimal allocation of monitoring resources for tracking transactions progressing through a distributed system, modeled as a queueing network, is considered. Two forms of monitoring information are considered, viz., locally unique transaction identifiers, and arrival and departure timestamps of transactions at each processing queue. The times-tamps are assumed to be available at all the queues but in the absence of identifiers, only enable imprecise tracking since parallel processing can result in out-of-order departures. On the other hand, identifiers enable precise tracking but are not available without proper instrumentation. Given an instrumentation budget, only a subset of queues can be selected for the production of identifiers, while the remaining queues have to resort to imprecise tracking using timestamps. The goal is then to optimally allocate the instrumentation budget to maximize the overall tracking accuracy. The challenge is that the optimal allocation strategy depends on accuracies of timestamp-based tracking at different queues, which has complex dependencies on the arrival and service processes, and the queueing discipline. We propose two simple heuristics for allocation by predicting the order of timestamp-based tracking accuracies of different queues. We derive sufficient conditions for these heuristics to achieve optimality through the notion of the stochastic comparison of queues. Simulations show that our heuristics are close to optimality, even when the parameters deviate from these conditions.
机译:考虑了监视资源的最佳分配问题,该资源用于跟踪建模为排队网络的分布式系统中进行的交易。考虑了两种形式的监视信息,即本地唯一事务标识符以及每个处理队列中事务的到达和离开时间戳。假定时间戳在所有队列中都可用,但是在没有标识符的情况下,仅启用不精确的跟踪,因为并行处理可能会导致乱序偏离。另一方面,标识符可以进行精确的跟踪,但是如果没有适当的工具就无法使用。在给定仪器预算的情况下,只能选择队列的一个子集来生成标识符,而其余队列则不得不诉诸使用时间戳的不精确跟踪。然后的目标是优化分配仪器预算,以最大程度地提高整体跟踪精度。挑战在于,最佳分配策略取决于在不同队列中基于时间戳的跟踪的准确性,这对到达和服务过程以及排队规则有着复杂的依赖性。通过预测不同队列基于时间戳的跟踪精度的顺序,我们提出了两种简单的启发式分配方法。我们通过队列的随机比较的概念,为这些启发式算法获得了充分的条件,以实现最优性。仿真表明,即使参数偏离这些条件,我们的启发式算法也接近最优。

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