Sundar Dev
Sundar is a performance engineer in the Systems and Services Infrastructure Org at Google. He works on improving efficiency and increasing performance of the distributed compute infrastructure that enables Google's user facing software services like Websearch, Gmail, YouTube, Maps, Ads, Workspace, Google Cloud, etc. His technical interests include computer architecture, distributed and parallel processing systems, hardware/software co-design, and machine learning. He joined Google in 2015 after receiving his M.S. in Electrical and Computer Engineering from Georgia Tech.
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Cloud applications are increasingly shifting from large monolithic services to complex graphs of loosely-coupled microservices. Despite the advantages of modularity and elasticity microservices offer, they also complicate cluster management and performance debugging, as dependencies between tiers introduce backpressure and cascading QoS violations.
We present Sage, a machine learning-driven root cause analysis system for interactive cloud microservices. Sage 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. In experiments on both dedicated local clusters and large clusters on Google Compute Engine we show that Sage consistently achieves over 93% accuracy in correctly identifying the root cause of QoS violations, and improves performance predictability.
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Autonomous Warehouse-Scale Computers
Proceedings of the 57th Annual Design Automation Conference 2020, Association for Computing Machinery, New York, NY United States
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Modern Warehouse-Scale Computers (WSCs), composed of many generations of servers and a myriad of domain specific accelerators, are becoming increasingly heterogeneous. Meanwhile, WSC workloads are also becoming incredibly diverse with different communication patterns, latency requirements, and service level objectives (SLOs). Insufficient understanding of the interactions between workload characteristics and the underlying machine architecture leads to resource over-provisioning, thereby significantly impacting the utilization of WSCs.
We present Autonomous Warehouse-Scale Computers, a new WSC design that leverages machine learning techniques and automation to improve job scheduling, resource management, and hardware-software co-optimization to address the increasing heterogeneity in WSC hardware and workloads. Our new design introduces two new layers in the WSC stack, namely: (a) a Software-Defined Server (SDS) Abstraction Layer which redefines the hardware-software boundary and provides greater control of the hardware to higher layers of the software stack through stable abstractions; and (b) a WSC Efficiency Layer which regularly monitors the resource usage of workloads on different hardware types, autonomously quantifies the performance sensitivity of workloads to key system configurations, and continuously improves scheduling decisions and hardware resource QoS policies to maximize cluster level performance. Our new WSC design has been successfully deployed across all WSCs at Google for several years now. The new WSC design improves throughput of workloads (by 7-10%, on
average), increases utilization of hardware resources (up to 2x), and reduces performance variance for critical workloads (up to 25%).
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