Skip to content

Commit 0cc10e7

Browse files
authored
Update pooria.bib
1 parent 7d47f59 commit 0cc10e7

File tree

1 file changed

+2
-2
lines changed

1 file changed

+2
-2
lines changed

_bibliography/people/pooria.bib

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -4,8 +4,8 @@ @inproceedings{namyar2025mitigation
44
booktitle = {22nd USENIX Symposium on Networked Systems Design and Implementation (NSDI 25)},
55
year={2025},
66
abbr={NSDI},
7-
url={https://arxiv.org/abs/2305.13792},
8-
abstract={Cloud providers install mitigations to reduce the impact of network failures in their datacenters. To determine the best action, existing automatic network mitigation systems rely on simple local criteria or global proxy metrics. In this paper, we show that we can explicitly optimize end-to-end flow-level metrics and analyze actions holistically to support a broader range of actions and select much more effective mitigations. To this end, we develop novel techniques to quickly estimate the impact of different mitigations and rank them with high fidelity. Our results on incidents from a large cloud provider show orders of magnitude improvements in flow completion time and throughput. We also show our approach scales to large datacenters.}
7+
url={https://drive.google.com/file/d/1kXOCrUBKsEAAyjfDI8fCCt-7poA3hPuP/view},
8+
abstract={Cloud providers install mitigations to reduce the impact of network failures within their datacenters. Existing network mitigation systems rely on simple local criteria or global proxy metrics to determine the best action. In this paper, we show that we can support a broader range of actions and select more effective mitigations by directly optimizing end-to-end flow-level metrics and analyzing actions holistically. To achieve this, we develop novel techniques to quickly estimate the impact of different mitigations and rank them with high fidelity. Our results on incidents from a large cloud provider show orders of magnitude improvements in flow completion time and throughput. We also show our approach scales to large datacenters.}
99
}
1010

1111
@inproceedings{alcoz2025packs,

0 commit comments

Comments
 (0)