Datacenter networks commonly facilitate the transmission of data in distributed computing frameworks through coflows, which are collections of parallel flows associated with a common task. Most of the existing research has concentrated on scheduling coflows to minimize the time required for their completion, i.e., to optimize the average dispatch rate of coflows in the network fabric. Nevertheless, modern applications often produce coflows that are specifically intended for online services and mission-crucial computational tasks, necessitating adherence to specific deadlines for their completion. In this paper, we introduce \wdcoflow,~ a new algorithm to maximize the weighted number of coflows that complete before their deadline. By combining a dynamic programming algorithm along with parallel inequalities, our heuristic solution performs at once coflow admission control and coflow prioritization, imposing a $\sigma$-order on the set of coflows. With extensive simulation, we demonstrate the effectiveness of our algorithm in improving up to $3\times$ more coflows that meet their deadline in comparison the best SoA solution, namely $\mathtt{CS\text{-}MHA}$. Furthermore, when weights are used to differentiate coflow classes, \wdcoflow~ is able to improve the admission per class up to $4\times$, while increasing the average weighted coflow admission rate.

翻译：数据中心网络通常通过共流（即与同一任务关联的并行流集合）促进分布式计算框架中的数据传输。现有研究主要聚焦于调度共流以最小化其完成所需时间，即优化网络架构中共流的平均调度速率。然而，现代应用往往产生专门面向在线服务和关键计算任务的共流，这要求其必须在特定截止时间前完成。本文提出一种新算法 \wdcoflow，用于最大化在截止时间前完成加权的共流数量。通过结合动态规划算法与并行不等式，我们的启发式方法同时实现了共流接纳控制与优先级排序，对共流集合施加 $\sigma$ 序。大量仿真结果表明，相较于最优现有解决方案 $\mathtt{CS\text{-}MHA}$，我们的算法在满足截止时间要求的共流数量上可提升高达3倍。此外，当使用权重区分共流类别时，\wdcoflow 可将每类共流的接纳率提升至4倍，同时提高加权平均共流接纳率。