Changes in network state are a common source of instability in networks. An update event typically involves multiple flows that compete for network resources at the cost of rescheduling and migrating some existing flows. Previous network updating schemes tackle such flows independently, rather than as the entity of an update event. They only optimize the flow-level metrics for the flows involved in an update event. In this paper, we present an event-level abstraction of network update that groups flows of an update event and schedules them together to minimize the event completion time (ECT). We then study the scheduling problem of multiple update events for achieving high scheduling efficiency and preserving fairness. The designed least migration traffic first (LMTF) method schedules all update events in the FIFO order, but it avoids head-of-line blocking by randomly fine-tuning the queue order of some events. It can considerably reduce the update cost, the average, and tail ECTs of update events. In addition, we design a general parallel-LMTF (P-LMTF) method to guarantee fairness and further improve scheduling efficiency among update events. This improves the LMTF method by opportunistically updating multiple events simultaneously. The comprehensive evaluation results indicate that the average ECT of our approach is up to $10 \times $ faster than the flow-level scheduling method for network update events, and its tail ECT is up to $6 \times $ faster. Our P-LMTF method incurs a 75% reduction in the average ECT compared with FIFO when the network utilization exceeds 70%, and it achieves a 42% reduction in tail ECT.

中文翻译：

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网络更新的高效事件调度

网络状态的变化是网络不稳定的常见来源。更新事件通常涉及以重新调度和迁移一些现有流为代价争夺网络资源的多个流。以前的网络更新方案独立处理此类流，而不是作为更新事件的实体。它们仅针对更新事件中涉及的流优化流级指标。在本文中，我们提出了网络更新的事件级抽象，它将更新事件的流分组并将它们一起调度以最小化事件完成时间 (ECT)。然后我们研究了多个更新事件的调度问题，以实现高调度效率和保持公平性。设计的最小迁移流量优先 (LMTF) 方法按 FIFO 顺序调度所有更新事件，但它通过随机微调某些事件的队列顺序来避免队头阻塞。它可以显着降低更新成本、更新事件的平均和尾部 ECT。此外，我们设计了一种通用的并行LMTF（P-LMTF）方法来保证公平性并进一步提高更新事件之间的调度效率。这通过机会性地同时更新多个事件来改进 LMTF 方法。综合评估结果表明，我们的方法的平均 ECT 比网络更新事件的流级调度方法快 10 倍，其尾部 ECT 快 6 倍。当网络利用率超过 70% 时，我们的 P-LMTF 方法与 FIFO 相比，平均 ECT 减少了 75%，并且它实现了尾 ECT 减少了 42%。