Weighted Scheduling of Time-Sensitive Coflows

Datacenter networks commonly facilitate the trans-mission 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 WD Coflow, 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 to3xmore coflows that meet their deadline in comparison the best SoA solution, namely CS-MHA. Furthermore, when weights are used to differentiate coflow classes, WD Coflowis able to improve the admission per class up to4x, while increasing the average weighted coflow admission rate.