40 citations · 92 across the 9 of their papers we have counts for
10 papers
TurboProm: Low Cost and High Performance Metrics Observability at Scale
Milind Srivastava, Zeying Zhu, Peilin Xin +3
Many large enterprise and cloud deployments rely on metrics observability solutions (e.g., Prometheus) to monitor key performance indicators of system health (e.g., CPU, memory, ne…
ASAP: Reimagining the Data Lifecycle using Application Semantic-Aware Processing
Milind Srivastava, Zeying Zhu, Yajie Zhou +5
Across many domains (e.g., observability, networking, security), data processing pipelines face what we refer to as the CSP problem: achieving low Cost at large Scale, while mainta…
Enabling Efficient and General Subpopulation Analytics in Multidimensional Data Streams
Antonis Manousis, Zhuo Cheng, Ran Ben Basat +2
Today's large-scale services (e.g., video streaming platforms, data centers, sensor grids) need diverse real-time summary statistics across multiple subpopulations of multidimensio…
Sketchy With a Chance of Adoption: Can Sketch-Based Telemetry Be Ready for Prime Time?
Zaoxing Liu, Hun Namkung, Anup Agarwal +4
Sketching algorithms or sketches have emerged as a promising alternative to the traditional packet sampling-based network telemetry solutions. At a high level, they are attractive…
Unleashing In-network Computing on Scientific Workloads
Daehyeok Kim, Ankush Jain, Zaoxing Liu +4
Many recent efforts have shown that in-network computing can benefit various datacenter applications. In this paper, we explore a relatively less-explored domain which we argue can…
Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches
Tian Li, Zaoxing Liu, Vyas Sekar +1
Communication and privacy are two critical concerns in distributed learning. Many existing works treat these concerns separately. In this work, we argue that a natural connection e…
Memory-Efficient Performance Monitoring on Programmable Switches with Lean Algorithms
Zaoxing Liu, Samson Zhou, Ori Rottenstreich +2
Network performance problems are notoriously difficult to diagnose. Prior profiling systems collect performance statistics by keeping information about each network flow, but maint…
Enhancing the Privacy of Federated Learning with Sketching
Zaoxing Liu, Tian Li, Virginia Smith +1
In response to growing concerns about user privacy, federated learning has emerged as a promising tool to train statistical models over networks of devices while keeping data local…
DistCache: Provable Load Balancing for Large-Scale Storage Systems with Distributed Caching
Zaoxing Liu, Zhihao Bai, Zhenming Liu +5
Load balancing is critical for distributed storage to meet strict service-level objectives (SLOs). It has been shown that a fast cache can guarantee load balancing for a clustered…
Scalable Streaming Tools for Analyzing -body Simulations: Finding Halos and Investigating Excursion Sets in One Pass
Nikita Ivkin, Zaoxing Liu, Lin F. Yang +6
Cosmological -body simulations play a vital role in studying models for the evolution of the Universe. To compare to observations and make a scientific inference, statistic anal…