Flowtune: Flowlet Control for Datacenter Networks
Name
flowtune-nsdi17.pdf
Description
Accepted version
Size
494.27 KB
Format
Adobe PDF
Checksum (MD5)
1ba8274948eacaab41707cf79181534c
Author(s)
Perry, Jonathan
Balakrishnan, Hari
Shah, Devavrat
Date Issued
2017
Citation
Perry, Jonathan, Balakrishnan, Hari and Shah, Devavrat. 2017. "Flowtune: Flowlet Control for Datacenter Networks."
Version
Author's final manuscript
Abstract
Rapid convergence to a desired allocation of network
resources to endpoint traffic is a difficult problem. The
reason is that congestion control decisions are distributed
across the endpoints, which vary their offered load in
response to changes in application demand and network
feedback on a packet-by-packet basis. We propose a different approach for datacenter networks, flowlet control,
in which congestion control decisions are made at the
granularity of a flowlet, not a packet. With flowlet control, allocations have to change only when flowlets arrive
or leave. We have implemented this idea in a system
called Flowtune using a centralized allocator that receives
flowlet start and end notifications from endpoints. The
allocator computes optimal rates using a new, fast method
for network utility maximization, and updates endpoint
congestion-control parameters. Experiments show that
Flowtune outperforms DCTCP, pFabric, sfqCoDel, and
XCP on tail packet delays in various settings, converging
to optimal rates within a few packets rather than over
several RTTs. Benchmarks on an EC2 deployment show
a fairer rate allocation than Linux’s Cubic. A data aggregation benchmark shows 1.61× lower p95 coflow completion time.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://www.usenix.org/system/files/conference/nsdi17/nsdi17-perry.pdf
