Zobrazeno 1 - 10
of 259
pro vyhledávání: '"GUPTA, INDRANIL"'
This paper tackles the challenge of running multiple ML inference jobs (models) under time-varying workloads, on a constrained on-premises production cluster. Our system Faro takes in latency Service Level Objectives (SLOs) for each job, auto-distill
Externí odkaz:
http://arxiv.org/abs/2409.19488
We propose Dirigo, a distributed stream processing service built atop virtual actors. Dirigo achieves both a high level of resource efficiency and performance isolation driven by user intent (SLO). To improve resource efficiency, Dirigo adopts a serv
Externí odkaz:
http://arxiv.org/abs/2308.03615
Autor:
Liu, Si, Ganhotra, Jatin, Rahman, Muntasir Raihan, Nguyen, Son, Gupta, Indranil, Meseguer, José
Publikováno v:
Leibniz Transactions on Embedded Systems, Vol 4, Iss 1, Pp 03:1-03:26 (2017)
The promise of high scalability and availability has prompted many companies to replace traditional relational database management systems (RDBMS) with NoSQL key-value stores. This comes at the cost of relaxed consistency guarantees: key-value stores
Externí odkaz:
https://doaj.org/article/7d16402990b14d9d88cdebda4ebc7fe8
While mesh networking for edge settings (e.g., smart buildings, farms, battlefields, etc.) has received much attention, the layer of control over such meshes remains largely centralized and cloud-based. This paper focuses on applications with sense-t
Externí odkaz:
http://arxiv.org/abs/2303.00207
Many tools empower analysts and data scientists to consume analysis results in a visual interface, such as a dashboard. When the underlying data changes, these results need to be updated, but this update can take a long time -- all while the user con
Externí odkaz:
http://arxiv.org/abs/2302.05476
Autor:
Jeon, Beomyeol, Cai, Linda, Shetty, Chirag, Srivastava, Pallavi, Jiang, Jintao, Ke, Xiaolan, Meng, Yitao, Xie, Cong, Gupta, Indranil
Machine Learning graphs (or models) can be challenging or impossible to train when either devices have limited memory, or models are large. To split the model across devices, learning-based approaches are still popular. While these result in model pl
Externí odkaz:
http://arxiv.org/abs/2301.08695
Autor:
Su, Li, Qin, Xiaoming, Zhang, Zichao, Yang, Rui, Xu, Le, Gupta, Indranil, Yu, Wenyuan, Zeng, Kai, Zhou, Jingren
Graph query services (GQS) are widely used today to interactively answer graph traversal queries on large-scale graph data. Existing graph query engines focus largely on optimizing the latency of a single query. This ignores significant challenges po
Externí odkaz:
http://arxiv.org/abs/2202.12530
Autor:
Mendoza, Trisha, Trevino, Casey L., Shrey, Daniel W., Lin, Jack J., Sen-Gupta, Indranil, Lopour, Beth A.
Publikováno v:
In Clinical Neurophysiology August 2024 164:30-39
Resource provisioning in multi-tenant stream processing systems faces the dual challenges of keeping resource utilization high (without over-provisioning), and ensuring performance isolation. In our common production use cases, where streaming worklo
Externí odkaz:
http://arxiv.org/abs/2010.03035
The scalability of Distributed Stochastic Gradient Descent (SGD) is today limited by communication bottlenecks. We propose a novel SGD variant: Communication-efficient SGD with Error Reset, or CSER. The key idea in CSER is first a new technique calle
Externí odkaz:
http://arxiv.org/abs/2007.13221