Key Flow First Prioritized Flow Scheduling Strategy in Multi-Tenant Data Centers

• Published in: IEEE eTransactions on network and service management Vol. 21; no. 3; pp. 3264 - 3277
• Main Authors: Tao, Xudong, Qian, Xiaoyan, Han, Lei, Fan, Weibei, Shi, Yuzhou, Zhu, Xinrui, Li, Zhiyu, Wei, Shuwen, Xu, Rui
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Communication system traffic; Completion time; Computer centers; Computer networks; Data center networks; Data centers; fair queueing; flow scheduling; Heuristic algorithms; Priority scheduling; Quality of service; Real time; Scheduling; Scheduling algorithms; traffic engineering
• ISSN: 1932-4537; 1932-4537
• DOI: 10.1109/TNSM.2024.3364149

The mixed flow in multi-tenant data centers presents a challenge for priority flow scheduling due to the coexistence of various requirements such as latency and throughput. To address this issue, we propose Key Flow First (KFF), a balanced scheduling algorithm suitable for mixed flows in multi-tenant data centers. Firstly, KFF categorizes flows into Latency-Sensitive Flows (LS Flow) and Throughput-Demanding Flows (TD Flow) based on the Quality of Service (QoS) of their application sources. Secondly, it further differentiates flows into Mice Flows and Elephants Flows based on the amount of already sent bytes. Thirdly, KFF employs the Multi-Level Feedback Queue (MLFQ) threshold update algorithm and a priority-based strict forwarding mechanism. By avoiding reliance on complex flow priors, KFF consistently maintains reasonable scheduling of mixed flows under different load scenarios. Experimental results demonstrate that KFF effectively reduces the real-time load on the network and achieves good performance in terms of MAX (Shortest Job First (SJF), Earliest Deadline First (EDF)) performance under diverse load conditions. Compared to PIAS, KFF reduces the FCT slow down of deadline flows by nearly 60% under high TD loads; compared to Karuma and Time Deadline Aware pFabric (TDA-pFabric), KFF reduces the flow completion time (FCT) slow down of non-deadline Mice flows by over 90% under high LS loads and meanwhile guaranteeing nearly 0 deadline miss rate.