activity
20192026
most citedServing DNNs like Clockwork: Performance Predictability from the Bottom Up

71 citations · 78 across the 6 of their papers we have counts for

collaborators

7 papers

cs.DC2026

Enabling Multi-Dimensional Distributed Trace Comparison with Contrast

Vaastav Anand, Rodrigo Fonseca, Jonathan Mace +1

Diagnosis using distributed traces is fundamentally a comparative task: operators seek to understand how an anomalous execution differs from expected behavior, how a deployment cha…

cs.CR2022★ 1 cited

Groundhog: Efficient Request Isolation in FaaS

Mohamed Alzayat, Jonathan Mace, Peter Druschel +1

Security is a core responsibility for Function-as-a-Service (FaaS) providers. The prevailing approach has each function execute in its own container to isolate concurrent execution…

cs.DC2022

ACT now: Aggregate Comparison of Traces for Incident Localization

Kamala Ramasubramanian, Ashutosh Raina, Jonathan Mace +1

Incidents in production systems are common and downtime is expensive. Applying an appropriate mitigating action quickly, such as changing a specific firewall rule, reverting a chan…

cs.DC2022★ 3 cited

The Benefit of Hindsight: Tracing Edge-Cases in Distributed Systems

Lei Zhang, Vaastav Anand, Zhiqiang Xie +2

Today's distributed tracing frameworks are ill-equipped to troubleshoot rare edge-case requests. The crux of the problem is a trade-off between specificity and overhead. On the one…

cs.DC2020★ 1 cited

Aggregate-Driven Trace Visualizations for Performance Debugging

Vaastav Anand, Matheus Stolet, Thomas Davidson +3

Performance issues in cloud systems are hard to debug. Distributed tracing is a widely adopted approach that gives engineers visibility into cloud systems. Existing trace analysis…

cs.DC2020★ 71 cited

Serving DNNs like Clockwork: Performance Predictability from the Bottom Up

Arpan Gujarati, Reza Karimi, Safya Alzayat +4

Machine learning inference is becoming a core building block for interactive web applications. As a result, the underlying model serving systems on which these applications depend…

cs.DC2019★ 2 cited

No DNN Left Behind: Improving Inference in the Cloud with Multi-Tenancy

Amit Samanta, Suhas Shrinivasan, Antoine Kaufmann +1

With the rise of machine learning, inference on deep neural networks (DNNs) has become a core building block on the critical path for many cloud applications. Applications today re…