Packet scheduling is a fundamental networking task that recently received renewed attention in the context of programmable data planes. Programmable packet scheduling systems such as those based on Push-In First-Out (PIFO) abstraction enabled flexible scheduling policies, but are too resource-expensive for large-scale line rate operation. This prompted research into practical programmable schedulers (e.g., SP-PIFO, AIFO) approximating PIFO behavior on regular hardware. Yet, their scalability remains limited due to extensive number of memory operations. To address this, we design an effective yet resource-efficient packet scheduler, Range-In First-Out (RIFO), which uses only three mutable memory cells and one FIFO queue per PIFO queue. RIFO is based on multi-criteria decision-making principles and uses small guaranteed admission buffers. Our large-scale simulations in Netbench demonstrate that despite using fewer resources, RIFO generally achieves competitive flow completion times across all studied workloads, and is especially effective in workloads with a significant share of large flows, reducing flow completion time up to 2.9x in Datamining workloads compared to state-of-the-art solutions. Our prototype implementation using P4 on Tofino switches requires only 650 lines of code, is scalable, and runs at line rate.

翻译：数据包调度是一项基础网络任务，近期在可编程数据平面的背景下重新受到关注。基于先进先出（PIFO）抽象的可编程数据包调度系统实现了灵活的调度策略，但资源开销过高，难以支持大规模线速运行。这促使研究人员探索在常规硬件上近似PIFO行为的实用可编程调度器（如SP-PIFO、AIFO）。然而，由于需要大量内存操作，其可扩展性仍然受限。为解决此问题，我们设计了一种高效且资源节约的数据包调度器——范围先进先出（RIFO），每个PIFO队列仅使用三个可变内存单元和一个FIFO队列。RIFO基于多准则决策原理，并采用小型有保证接纳缓冲区。在Netbench中的大规模仿真表明，尽管使用更少的资源，RIFO在所有研究的工作负载下通常能实现具有竞争力的流完成时间，尤其在包含大量大流的工作负载中效果显著，在数据挖掘工作负载中相比现有技术可将流完成时间降低至2.9倍。我们在Tofino交换机上基于P4的原型实现仅需650行代码，具备可扩展性，并能以线速运行。