L
Liqun Cheng
Researcher at Google
Publications - 16
Citations - 842
Liqun Cheng is an academic researcher from Google. The author has contributed to research in topics: Asynchronous communication & Service level objective. The author has an hindex of 5, co-authored 16 publications receiving 672 citations.
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Proceedings ArticleDOI
Heracles: improving resource efficiency at scale
TL;DR: Heracles is presented, a feedback-based controller that enables the safe colocation of best-effort tasks alongside a latency-critical service and dynamically manages multiple hardware and software isolation mechanisms to ensure that the latency-sensitive job meets latency targets while maximizing the resources given to best- Effort tasks.
Journal ArticleDOI
Towards energy proportionality for large-scale latency-critical workloads
TL;DR: PEGASUS is presented, a feedback-based controller that significantly improves the energy proportionality of WSC systems, as demonstrated by a real implementation in a Google search cluster.
Journal ArticleDOI
Improving Resource Efficiency at Scale with Heracles
TL;DR: Heracles is presented, a feedback-based controller that enables the safe colocation of best-effort tasks alongside a latency-critical service and dynamically manages multiple hardware and software isolation mechanisms to ensure that the latency-sensitive job meets latency targets while maximizing the resources given to best- Effort tasks.
Proceedings ArticleDOI
WSMeter: A Performance Evaluation Methodology for Google's Production Warehouse-Scale Computers
TL;DR: This work defines a new metric which accurately represents a WSC's overall performance, taking a wide variety of unevenly distributed jobs into account, and proposes a model to statistically embrace the performance variance inherent in WSCs, to conduct an evaluation with minimal costs and risks.
Proceedings ArticleDOI
Kelp: QoS for Accelerated Machine Learning Systems
TL;DR: Kelp, a software runtime that isolates high priority accelerated ML tasks from memory resource interference, is designed and implemented and evaluated with both production and artificial aggressor workloads, and its effectiveness is evaluated.