activity
20182022
most citedAutomating In-Network Machine Learning

36 citations · 37 across the 3 of their papers we have counts for

collaborators

9 papers

cs.NI202236 cited

Automating In-Network Machine Learning

Changgang Zheng, Mingyuan Zang, Xinpeng Hong +4

Using programmable network devices to aid in-network machine learning has been the focus of significant research. However, most of the research was of a limited scope, providing a…

cs.DC2021

Stochastic Coordination in Heterogeneous Load Balancing Systems

Guy Goren, Shay Vargaftik, Yoram Moses

Current-day data centers and high-volume cloud services employ a broad set of heterogeneous servers. In such settings, client requests typically arrive at multiple entry points, an…

math.PR20211 cited

On the Persistent-Idle Load Distribution Policy Under Batch Arrivals and Random Service Capacity

Rami Atar, Isaac Keslassy, Gal Mendelson +2

The Persistent-Idle (PI) load distribution policy was recently introduced as an appealing alternative to current low-communication load balancing techniques. In PI, servers only up…

cs.DS2021

SALSA: Self-Adjusting Lean Streaming Analytics

Ran Ben Basat, Gil Einziger, Michael Mitzenmacher +1

Counters are the fundamental building block of many data sketching schemes, which hash items to a small number of counters and account for collisions to provide good approximations…

cs.DS2020

Faster and More Accurate Measurement through Additive-Error Counters

Ran Ben Basat, Gil Einziger, Michael Mitzenmacher +1

Counters are a fundamental building block for networking applications such as load balancing, traffic engineering, and intrusion detection, which require estimating flow sizes and…

cs.NI2020

LSQ: Load Balancing in Large-Scale Heterogeneous Systems with Multiple Dispatchers

Shay Vargaftik, Isaac Keslassy, Ariel Orda

Nowadays, the efficiency and even the feasibility of traditional load-balancing policies are challenged by the rapid growth of cloud infrastructure and the increasing levels of ser…