Datacenter networks routinely support the data transfers of distributed computing frameworks in the form of coflows, i.e., sets of concurrent flows related to a common task. The vast majority of the literature has focused on the problem of scheduling coflows for completion time minimization, i.e., to maximize the average rate at which coflows are dispatched in the network fabric. However, many modern applications generate coflows dedicated to online services and mission-critical computing tasks which have to comply with specific completion deadlines. In this paper, we introduce $\mathtt{WDCoflow}$, 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 SotA solution, namely $\mathtt{CS\text{-}MHA}$. Furthermore, when weights are used to differentiate coflow classes, $\mathtt{WDCoflow}$ is able to improve the admission per class up to $4\times$, while increasing the average weighted coflow admission rate.

翻译：数据中心网络通常以协同流的形式支持分布式计算框架的数据传输，协同流即与同一任务相关的一组并发流。现有文献大多集中于解决协同流调度问题以实现完成时间最小化，即最大化网络中协同流分发的平均速率。然而，许多现代应用产生的协同流专用于在线服务和关键任务计算，这些任务必须遵守特定的完成截止时间。本文提出$\mathtt{WDCoflow}$这一新算法，旨在最大化在截止时间前完成的加权协同流数量。通过结合动态规划算法与并行不等式，我们的启发式解法同时实现了协同流准入控制与优先级排序，并对协同流集合施加$\sigma$序。通过广泛仿真，我们证明了该算法在满足截止时间的协同流数量上相较最优现有方案$\mathtt{CS\text{-}MHA}$可提升高达3倍的有效性。此外，当使用权重区分协同流类别时，$\mathtt{WDCoflow}$能够将每类准入率提升高达4倍，同时提高平均加权协同流准入率。