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
σ-order on the set of coflows. With extensive simulation, we demonstrate
the effectiveness of our algorithm in improving up to 3× more coflows
that meet their deadline in comparison the best SoA solution, namely
CS-MHA. Furthermore, when weights are used to differentiate
coflow classes, \wdcoflow~ is able to improve the admission per class up to
4×, while increasing the average weighted coflow admission rate.Comment: Submitted to IEEE Transactions on Cloud Computing. Parts of this work
have been presented at IFIP Networking 202