Multipath transport, as embodied in MPTCP, is deployed to improve throughput and reliability in mobile and residential access networks, with additional use-cases including spreading load in data centers and WANs. However, MPTCP is fundamentally tied to TCP Reno's legacy AIMD algorithm, and significantly lags behind the performance of modern single-path designs. Consequently, MPTCP fails to achieve high performance in many real-world environments. We present MPCC, a high-performance multipath congestion control architecture. To achieve our combined goals of fairness and high performance in challenging environments, MPCC employs online convex optimization (a.k.a. online learning). In experiments with a kernel implementation on emulated and live networks, MPCC significantly outperforms MPTCP.
|Original language||American English|
|Title of host publication||CoNEXT 2020 - Proceedings of the 16th International Conference on Emerging Networking EXperiments and Technologies|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||15|
|State||Published - 23 Nov 2020|
|Event||16th ACM Conference on Emerging Networking Experiment and Technologies, CoNEXT 2020 - Barcelona, Spain|
Duration: 1 Dec 2020 → 4 Dec 2020
|Name||CoNEXT 2020 - Proceedings of the 16th International Conference on Emerging Networking EXperiments and Technologies|
|Conference||16th ACM Conference on Emerging Networking Experiment and Technologies, CoNEXT 2020|
|Period||1/12/20 → 4/12/20|
Bibliographical noteFunding Information:
We thank the anonymous shepherds and reviewers for many valuable comments. Brighten Godfrey and Michael Schapira were partly supported by a an NSF-BSF CNS Award (NSF 2008971, BSF 2019798). Costin Raiciu was partly supported by research gift funding from Huawei. Michael Schapira was partly supported by the ISF.
© 2020 ACM.
- congestion control