@inproceedings{katebzadeh2023saba,
author = {Katebzadeh, M.R. Siavash and Costa, Paolo and Grot, Boris},
title = {Saba: Rethinking Datacenter Network Allocation from Application’s Perspective},
booktitle = {Eighteenth European Conference on Computer Systems (EuroSys),},
year = {2023},
month = {May},
abstract = {Today's datacenter workloads increasingly comprise distributed data-intensive applications, including data analytics, graph processing, and machine-learning training. These applications are bandwidth-hungry and often congest the datacenter network, resulting in poor network performance, which hurts application completion time. Efforts made to address this problem generally aim to achieve max-min fairness at the flow or application level.
We observe that splitting the bandwidth equally among workloads is sub-optimal for aggregate application-level performance because various workloads exhibit different sensitivity to network bandwidth: for some workloads, even a small reduction in the available bandwidth yields a significant increase in completion time; for others, the completion time is largely insensitive to the available bandwidth.

Building on this insight, we propose Saba, an application-aware bandwidth allocation framework that distributes network bandwidth based on application-level sensitivity. Saba combines ahead-of-time application profiling to determine bandwidth sensitivity with runtime bandwidth allocation using lightweight software support with no modifications to network hardware or protocols. Experiments with a 32-server hardware testbed show that Saba improves average completion time by 1.88x (and by 1.27x in a simulated 1,944-server cluster).},
publisher = {ACM},
url = {http://approjects.co.za/?big=en-us/research/publication/saba-rethinking-datacenter-network-allocation-from-applications-perspective/},
}