Marc de Kruijf
Marc develops advanced network and operating system technologies at Google. His interests include computer architecture, network systems, and distributed software infrastructure. Marc received a PhD in computer science from the University of Wisconsin-Madison.
Authored Publications
Google Publications
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Understanding Host Interconnect Congestion
Khaled Elmeleegy
Masoud Moshref
Rachit Agarwal
Saksham Agarwal
Sylvia Ratnasamy
Association for Computing Machinery, New York, NY, USA (2022), 198–204
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We present evidence and characterization of host congestion in production clusters: adoption of high-bandwidth access links leading to emergence of bottlenecks within the host interconnect (NIC-to-CPU data path). We demonstrate that contention on existing IO memory management units and/or the memory subsystem can significantly reduce the available NIC-to-CPU bandwidth, resulting in hundreds of microseconds of queueing delays and eventual packet drops at hosts (even when running a state-of-the-art congestion control protocol that accounts for CPU-induced host congestion). We also discuss implications of host interconnect congestion to design of future host architecture, network stacks and network protocols.
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Snap: a Microkernel Approach to Host Networking
Jacob Adriaens
Sean Bauer
Carlo Contavalli
Mike Dalton
William C. Evans
Nicholas Kidd
Roman Kononov
Carl Mauer
Emily Musick
Lena Olson
Mike Ryan
Erik Rubow
Kevin Springborn
Valas Valancius
In ACM SIGOPS 27th Symposium on Operating Systems Principles, ACM, New York, NY, USA (2019) (to appear)
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This paper presents our design and experience with a microkernel-inspired approach to host networking called Snap. Snap is a userspace networking system that supports Google’s rapidly evolving needs with flexible modules that implement a range of network functions, including edge packet switching, virtualization for our cloud platform, traffic shaping policy enforcement, and a high-performance reliable messaging and RDMA-like service. Snap has been running in production for over three years, supporting the extensible communication needs of several large and critical systems.
Snap enables fast development and deployment of new networking features, leveraging the benefits of address space isolation and the productivity of userspace software development together with support for transparently upgrading networking services without migrating applications off of a machine. At the same time, Snap achieves compelling performance through a modular architecture that promotes principled synchronization with minimal state sharing, and supports real-time scheduling with dynamic scaling of CPU resources through a novel kernel/userspace CPU scheduler co-design. Our evaluation demonstrates over 3x Gbps/core improvement compared to a kernel networking stack for RPC workloads, software-based RDMA-like performance of up to 5M IOPS/core, and transparent upgrades that are largely imperceptible to user applications. Snap is deployed to over half of our fleet of machines and supports the needs of numerous teams.
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Andromeda: Performance, Isolation, and Velocity at Scale in Cloud Network Virtualization
Mike Dalton
David Schultz
Ahsan Arefin
Alex Docauer
Anshuman Gupta
Brian Matthew Fahs
Dima Rubinstein
Enrique Cauich Zermeno
Erik Rubow
Jake Adriaens
Jesse L Alpert
Jing Ai
Jon Olson
Kevin P. DeCabooter
Nan Hua
Nathan Lewis
Nikhil Kasinadhuni
Riccardo Crepaldi
Srinivas Krishnan
Subbaiah Venkata
Yossi Richter
15th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2018
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This paper presents our design and experience with Andromeda, Google Cloud Platform’s network virtualization stack. Our production deployment poses several challenging requirements, including performance isolation among customer virtual networks, scalability, rapid provisioning of large numbers of virtual hosts, bandwidth and latency largely indistinguishable from the underlying hardware, and high feature velocity combined with high availability.
Andromeda is designed around a flexible hierarchy of flow processing paths. Flows are mapped to a programming path dynamically based on feature and performance requirements. We introduce the Hoverboard programming model, which uses gateways for the long tail of low bandwidth flows, and enables the control plane to program network connectivity for tens of thousands of VMs in seconds. The on-host dataplane is based around a high-performance OS bypass software packet processing path. CPU-intensive per packet operations with higher latency targets are executed on coprocessor threads. This architecture allows Andromeda to decouple feature growth from fast path performance, as many features can be implemented solely on the coprocessor path. We demonstrate that the Andromeda datapath achieves performance that is competitive with hardware while maintaining the flexibility and velocity of a software-based architecture.
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