2023/03/30 by Olivier Brun, Rachid El-Azouzi, Brun, Olivier +9
Computer Science · #Cloud Computing and Resource Management #Distributed #Distributed systems and fault tolerance #FOS: Computer and information sciences #Parallel #Software-Defined Networks and 5G #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2303.17175
openalex publication_date 2023/03/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 \mathttCS-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.