Name
Abstract | ||
|---|---|---|
As datacenter speeds scale to 100 Gb/s and beyond, traditional congestion control algorithms like TCP and RCP converge slowly to steady sending rates, which leads to poorer and less predictable user performance. These reactive algorithms use congestion signals to perform gradient descent to approach ideal sending rates, causing poor convergence times. In this paper, we propose a proactive congestion control algorithm called PERC, which explicitly computes rates independently of congestion signals in a decentralized fashion. Inspired by message-passing algorithms with traction in other fields (e.g., modern Low Density Parity Check decoding algorithms), PERC improves convergence times by a factor of 7 compared to reactive explicit rate control protocols such as RCP. This fast convergence reduces tail flow completion time (FCT) significantly in high speed networks; for example, simulations of a realistic workloads in a 100 Gb/s network show that PERC achieves up to 4x lower 99th percentile FCT compared to RCP. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2834050.2834096 | The Workshop on Hot Topics in Networks |
Field | DocType | Citations |
Convergence (routing),Gradient descent,Low-density parity-check code,Computer science,Computer network,Congestion control algorithm,Functional testing (manufacturing),Network congestion,Decoding methods,Percentile | Conference | 20 |
PageRank | References | Authors |
0.79 | 20 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lavanya Jose | 1 | 44 | 2.16 |
| Lisa Yan | 2 | 62 | 4.52 |
| Mohammad Alizadeh | 3 | 1482 | 77.16 |
| George Varghese | 4 | 8149 | 727.66 |
| Nick McKeown | 5 | 13247 | 1201.05 |
| Sachin Katti | 6 | 5775 | 344.82 |