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David Lo
Researcher at Google
Publications - 18
Citations - 1100
David Lo is an academic researcher from Google. The author has contributed to research in topics: Microservices & Cloud computing. The author has an hindex of 8, co-authored 18 publications receiving 845 citations. Previous affiliations of David Lo include Stanford University.
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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.
Proceedings ArticleDOI
Rethinking DRAM Power Modes for Energy Proportionality
TL;DR: MemBlaze is presented, an architecture with DRAMs and links that are capable of fast power up, which provides more opportunities to power down memories and MemCorrect which detects timing errors while MemDrowsy lowers transfer rates and widens sampling margins to accommodate timing uncertainty in situations where the interface circuitry must recalibrate after exit from power down state.
Proceedings ArticleDOI
Sage: practical and scalable ML-driven performance debugging in microservices
TL;DR: Sage as mentioned in this paper leverages unsupervised ML models to circumvent the overhead of trace labeling, captures the impact of dependencies between microservices to determine the root cause of unpredictable performance online, and applies corrective actions to recover a cloud service's QoS.
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.