| Title | : | Restructuring endpoint congestion control |
| Speaker | : | Srinivas Narayana (Rutgers University) |
| Details | : | Tue, 6 Aug, 2019 3:00 PM @ AM Turing Hall |
| Abstract: | : | This talk describes a system to implement complex congestion control
functions by placing them in a separate agent outside the datapath.
Each datapath—such as the Linux kernel TCP, UDP-based QUIC, or
kernel-bypass transports like mTCP-on-DPDK—summarizes information
about packet round-trip times, receptions, losses, and ECN via a
well-defined interface to algorithms running in the off-datapath
Congestion Control Plane (CCP). The algorithms use this information to
control the datapath’s congestion window or pacing rate. Algorithms
written in CCP can run on multiple datapaths. CCP improves both the
pace of development and ease of maintenance of congestion control
algorithms by providing better, modular abstractions, and supports
aggregation capabilities of the Congestion Manager, all with one-time
changes to datapaths. CCP also enables new capabilities, such as Copa
in Linux TCP, several algorithms running on QUIC and mTCP/DPDK, and
the use of signal processing algorithms to detect whether
cross-traffic is ACK-clocked. Experiments with our user-level Linux
CCP implementation show that CCP algorithms behave similarly to kernel
algorithms, and incur modest CPU overhead of a few percent.
Bio: Srinivas Narayana (N.G. Srinivas during IITM days) is an Assistant Professor in the Department of Computer Science at Rutgers University, New Jersey, USA. Srinivas's research aims to make computer networks easier to program, troubleshoot, and develop novel applications atop of. Srinivas received his M.A/Ph.D. in Computer Science from Princeton University (adv: Jennifer Rexford and David Walker) in 2016 and a B.Tech from Indian Institute of Technology Madras in 2010. Srinivas completed a Post-Doctoral Fellowship at Massachusetts Institute of Technology in 2018 (adv: Hari Balakrishnan and Mohammad Alizadeh). Srinivas's research has been recognized with the best paper award at the 2017 ACM SIGCOMM conference. |
