Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Arzani, Behnaz"'
Autor:
Namyar, Pooria, Arzani, Behnaz, Beckett, Ryan, Segarra, Santiago, Raj, Himanshu, Krishnaswamy, Umesh, Govindan, Ramesh, Kandula, Srikanth
Production systems use heuristics because they are faster or scale better than their optimal counterparts. Yet, practitioners are often unaware of the performance gap between a heuristic and the optimum or between two heuristics in realistic scenario
Externí odkaz:
http://arxiv.org/abs/2311.12779
Autor:
Namyar, Pooria, Arzani, Behnaz, Kandula, Srikanth, Segarra, Santiago, Crankshaw, Daniel, Krishnaswamy, Umesh, Govindan, Ramesh, Raj, Himanshu
We consider the max-min fair resource allocation problem. The best-known solutions use either a sequence of optimizations or waterfilling, which only applies to a narrow set of cases. These solutions have become a practical bottleneck in WAN traffic
Externí odkaz:
http://arxiv.org/abs/2310.09699
Autor:
Namyar, Pooria, Arzani, Behnaz, Crankshaw, Daniel, Berger, Daniel S., Hsieh, Kevin, Kandula, Srikanth, Govindan, Ramesh
Some faults in data center networks require hours to days to repair because they may need reboots, re-imaging, or manual work by technicians. To reduce traffic impact, cloud providers \textit{mitigate} the effect of faults, for example, by steering t
Externí odkaz:
http://arxiv.org/abs/2305.13792
Autor:
Arzani, Behnaz, Kakarla, Siva Kesava Reddy, Castro, Miguel, Kandula, Srikanth, Maleki, Saeed, Marshall, Luke
We show communication schedulers' recent work proposed for ML collectives does not scale to the increasing problem sizes that arise from training larger models. These works also often produce suboptimal schedules. We make a connection with similar pr
Externí odkaz:
http://arxiv.org/abs/2305.13479
Autor:
So, Jinhyun, Hsieh, Kevin, Arzani, Behnaz, Noghabi, Shadi, Avestimehr, Salman, Chandra, Ranveer
Large-scale deployments of low Earth orbit (LEO) satellites collect massive amount of Earth imageries and sensor data, which can empower machine learning (ML) to address global challenges such as real-time disaster navigation and mitigation. However,
Externí odkaz:
http://arxiv.org/abs/2202.01267
Autor:
Yaseen, Nofel, Arzani, Behnaz, Chintalapudi, Krishna, Ranganathan, Vaishnavi, Frujeri, Felipe, Hsieh, Kevin, Berger, Daniel, Liu, Vincent, Kandula, Srikanth
Continuously monitoring a wide variety of performance and fault metrics has become a crucial part of operating large-scale datacenter networks. In this work, we ask whether we can reduce the costs to monitor -- in terms of collection, storage and ana
Externí odkaz:
http://arxiv.org/abs/2110.05554
Automated machine learning (AutoML) systems aim to enable training machine learning (ML) models for non-ML experts. A shortcoming of these systems is that when they fail to produce a model with high accuracy, the user has no path to improve the model
Externí odkaz:
http://arxiv.org/abs/2102.11267
Autor:
Arzani, Behnaz, Rouhani, Bita
Clouds gather a vast volume of telemetry from their networked systems which contain valuable information that can help solve many of the problems that continue to plague them. However, it is hard to extract useful information from such raw data. Mach
Externí odkaz:
http://arxiv.org/abs/2004.11931
Autor:
Arzani, Behnaz, Iodice, Nicholas, Hwang, Steven, Venkataramanan, Prahalad, Geurin, Roch, Loo, Boon Thau
In spite of much progress and many advances, cost-effective, high-quality video delivery over the internet remains elusive. To address this ongoing challenge, we propose Sunstar, a solution that leverages simultaneous downloads from multiple servers
Externí odkaz:
http://arxiv.org/abs/1812.00109
Autor:
Arzani, Behnaz, Ciraci, Selim, Chamon, Luiz, Zhu, Yibo, Liu, Hingqiang, Padhye, Jitu, Loo, Boon Thau, Outhred, Geoff
Network failures continue to plague datacenter operators as their symptoms may not have direct correlation with where or why they occur. We introduce 007, a lightweight, always-on diagnosis application that can find problematic links and also pinpoin
Externí odkaz:
http://arxiv.org/abs/1802.07222