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Increasing Aggregation Convergecast Data Collection Frequency through Pipelining

机译：通过流水线提高聚合会聚数据收集频率

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摘要

We consider the problem of increasing the data collection frequency of aggregation convergecast. Previous studies attempt to increase the data collection frequency by shortening the completion of a single data collection cycle. We aim at increasing the frequency at which data collection updates are collected by the use of pipelining and, consequently, increasing the overall data collection frequency and throughput. To achieve this, we overlap the propagation schedule of multiple data snapshots within the same overall schedule cycle, thus increasing parallelism through pipelining. Consequently, the effective data collection time of an individual snapshot may span over multiple, successive, schedule cycles. To this end, we modify the aggregation convergecast model, decoupling schedule length, and data collection delay, by relaxing its precedence constraints. Our solution for this new problem involves the unconventional approach of constructing the schedule before finalizing the exact form of the data aggregation tree, which, in turn, requires that the schedule construction phase guarantees that every node can reach the sink. We compare our results using snapshot pipelining against a previously proposed algorithm that also uses a form of pipelining, as well as against an algorithm that though lacking pipelining, exhibits the ability to produce very short schedules. The results confirm the potential to achieve a substantial throughput increase, at the cost of some increase in latency.
机译：我们考虑的问题，增加了数据收集频率聚合收敛。以前的研究试图通过缩短单个数据收集周期来增加数据收集频率。我们的目标是通过使用流水线提高数据收集更新的频率，从而提高总体数据收集频率和吞吐量。为了实现这一点，我们在同一个总体计划周期内重叠多个数据快照的传播计划，从而通过流水线提高并行性。因此，单个快照的有效数据收集时间可能跨越多个连续的计划周期。为此，我们通过放松优先约束来修改聚合聚合聚合模型、解耦调度长度和数据收集延迟。我们对这个新问题的解决方案涉及一种非常规的方法，即在最终确定数据聚合树的确切形式之前构造调度，这反过来要求调度构造阶段确保每个节点都能到达接收器。我们将使用快照流水线的结果与之前提出的算法进行了比较，该算法也使用了一种流水线形式，并且与一种算法进行了比较，该算法虽然缺乏流水线，但能够生成非常短的时间表。结果证实了以延迟增加为代价实现吞吐量大幅增加的潜力。

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