Yuchung Cheng 鄭又中
Yuchung Cheng is a software engineer at Google. He works on TCP performance for Web and Google services. He obtained a Ph.D. from University of California, San Diego and a B.S from National Taiwan University.
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Fathom: Understanding Datacenter Application Network Performance
Junhua Yan
Mubashir Adnan Qureshi
Van Jacobson
Yousuk Seung
Proceedings of ACM SIGCOMM 2023
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We describe our experience with Fathom, a system for identifying the network performance bottlenecks of any service running in the Google fleet. Fathom passively samples RPCs, the principal unit of work for services. It segments the overall latency into host and network components with kernel and RPC stack instrumentation. It records these detailed latency metrics, along with detailed transport connection state, for every sampled RPC. This lets us determine if the completion is constrained by the client, network or server. To scale while enabling analysis, we also aggregate samples into distributions that retain multi-dimensional breakdowns. This provides us with a macroscopic view of individual services. Fathom runs globally in our datacenters for all production traffic, where it monitors billions of TCP connections 24x7. For five years Fathom has been our primary tool for troubleshooting service network issues and assessing network infrastructure changes. We present case studies to show how it has helped us improve our production services.
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Improving Network Availability with Protective ReRoute
Abdul Kabbani
Van Jacobson
Jim Winget
Brad Morrey
Uma Parthavi Moravapalle
Steven Knight
SIGCOMM 2023
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We present PRR (Protective ReRoute), a transport technique for shortening user-visible outages that complements routing repair. It can be added to any transport to provide benefits in multipath networks. PRR responds to flow connectivity failure signals, e.g., retransmission timeouts, by changing the FlowLabel on packets of the flow, which causes switches and hosts to choose a different network path that may avoid the outage. To enable it, we shifted our IPv6 network architecture to use the FlowLabel, so that hosts can change the paths of their flows without application involvement. PRR is deployed fleetwide at Google for TCP and Pony Express, where it has been protecting all production traffic for several years. It is also available to our Cloud customers. We find it highly effective for real outages. In a measurement study on our network backbones, adding PRR reduced the cumulative region-pair outage time for RPC traffic by 63--84%. This is the equivalent of adding 0.4--0.8 "nines'" of availability.
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PLB: Congestion Signals are Simple and Effective for Network Load Balancing
Abdul Kabbani
Junhua Yan
Kira Yin
Masoud Moshref
Mubashir Adnan Qureshi
Qiaobin Fu
Van Jacobson
SIGCOMM(2022)
Preview abstract
We describe our experience with PLB, a host-based load balancing design for modern networks. PLB randomly changes the paths of connections that experience congestion, preferring idle periods to minimize transport interactions. It does so by changing the IPv6 FlowLabel on the packets of a connection, which switches include as part of the ECMP flow hash. Across many hosts, this action drives down the number of hotspots in the network, while separating short RPCs from elephant flows to keep their completion time low.
We use PLB fleetwide in Google networks for TCP and PonyExpress (RDMA-like) traffic. We find it to be simple, general, and effective. It was easy to deploy, co-existing with other traffic, requiring only small transport modifications and little of switches, and needing no application changes. And it has produced large gains across the board, for multiple transports and from datacenter through backbone networks. After deploying PLB, the median utilization imbalance of busy switches in Google datacenter networks fell by 60\% and packet drops correspondingly fell by 33\%. At hosts, the tail latency (99$^{th}$ percentile) of short RPCs fell by up to 25\%.
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This document presents the RACK-TLP loss detection algorithm for TCP. RACK-TLP uses per-segment transmit timestamps and selective acknowledgments (SACKs) and has two parts. Recent Acknowledgment (RACK) starts fast recovery quickly using time-based inferences derived from acknowledgment (ACK) feedback, and Tail Loss Probe (TLP) leverages RACK and sends a probe packet to trigger ACK feedback to avoid retransmission timeout (RTO) events. Compared to the widely used duplicate acknowledgment (DupAck) threshold approach, RACK-TLP detects losses more efficiently when there are application-limited flights of data, lost retransmissions, or data packet reordering events. It is intended to be an alternative to the DupAck threshold approach.
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BBR: Congestion-Based Congestion Control
C. Stephen Gunn
Van Jacobson
Communications of the ACM, 60(2017), pp. 58-66
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By all accounts, today’s Internet is not moving data as well as it should. Most of the world’s cellular users experience delays of seconds to minutes; public Wi-Fi in airports and conference venues is often worse. Physics and climate researchers need to exchange petabytes of data with global collaborators but find their carefully engineered multi-Gbps infrastructure often delivers at only a few Mbps over intercontinental distances.6 These problems result from a design choice made when TCP congestion control was created in the 1980s—interpreting packet loss as “congestion.”13 This equivalence was true at the time but was because of technology limitations, not first principles. As NICs (network interface controllers) evolved from Mbps to Gbps and memory chips from KB to GB, the relationship between packet loss and congestion became more tenuous. Today TCP’s loss-based congestion control—even with the current best of breed, CUBIC11—is the primary cause of these problems. When bottleneck buffers are large, loss-based congestion control keeps them full, causing bufferbloat. When bottleneck buffers are small, loss-based congestion control misinterprets loss as a signal of congestion, leading to low throughput. Fixing these problems requires an alternative to loss-based congestion control. Finding this alternative requires an understanding of where and how network congestion originates.
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BBR: Congestion-Based Congestion Control
C. Stephen Gunn
Van Jacobson
ACM Queue, 14, September-October(2016), pp. 20 - 53
Preview abstract
By all accounts, today’s Internet is not moving data as well as it should. Most of the world’s cellular users experience delays of seconds to minutes; public Wi-Fi in airports and conference venues is often worse. Physics and climate researchers need to exchange petabytes of data with global collaborators but find their carefully engineered multi-Gbps infrastructure often delivers at only a few Mbps over intercontinental distances.6
These problems result from a design choice made when TCP congestion control was created in the 1980s—interpreting packet loss as “congestion.”13 This equivalence was true at the time but was because of technology limitations, not first principles. As NICs (network interface controllers) evolved from Mbps to Gbps and memory chips from KB to GB, the relationship between packet loss and congestion became more tenuous.
Today TCP’s loss-based congestion control—even with the current best of breed, CUBIC11—is the primary cause of these problems. When bottleneck buffers are large, loss-based congestion control keeps them full, causing bufferbloat. When bottleneck buffers are small, loss-based congestion control misinterprets loss as a signal of congestion, leading to low throughput. Fixing these problems requires an alternative to loss-based congestion control. Finding this alternative requires an understanding of where and how network congestion originates.
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An Internet-Wide Analysis of Traffic Policing
Tobias Flach
Luis Pedrosa
Tayeb Karim
Ethan Katz-Bassett
Ramesh Govindan
SIGCOMM(2016)
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Large flows like videos consume significant
bandwidth. Some ISPs actively manage these high volume
flows with techniques like policing, which enforces a flow
rate by dropping excess traffic. While the existence of policing
is well known, our contribution is an Internet-wide study
quantifying its prevalence and impact on video quality metrics.
We developed a heuristic to identify policing from
server-side traces and built a pipeline to deploy it at scale on
hundreds of servers worldwide within one of the largest online
content providers. Using a dataset of 270 billion packets
served to 28,400 client ASes, we find that, depending on region,
up to 7% of lossy transfers are policed. Loss rates are
on average 6× higher when a trace is policed, and it impacts
video playback quality. We show that alternatives to policing,
like pacing and shaping, can achieve traffic management
goals while avoiding the deleterious effects of policing.
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RFC 7413 - TCP Fast Open
Jerry Chu
Sivasankar Radhakrishnan
Arvind Jain
Internet Engineering Task Force (IETF)(2014)
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This document describes an experimental TCP mechanism called TCP Fast Open (TFO). TFO allows data to be carried in the SYN and SYN-ACK packets and consumed by the receiving end during the initial connection handshake, and saves up to one full round-trip time (RTT) compared to the standard TCP, which requires a three-way handshake (3WHS) to complete before data can be exchanged. However, TFO deviates from the standard TCP semantics, since the data in the SYN could be replayed to an application in some rare circumstances.Applications should not use TFO unless they can tolerate this issue, as detailed in the Applicability section.
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This document describes an experimental Proportional Rate Reduction (PRR) algorithm as an alternative to the widely deployed Fast Recovery and Rate-Halving algorithms. These algorithms determine the amount of data sent by TCP during loss recovery. PRR minimizes excess window adjustments, and the actual window size at the end of recovery will be as close as possible to the ssthresh, as determined by the congestion control algorithm.
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packetdrill: Scriptable Network Stack Testing, from Sockets to Packets
Lawrence Brakmo
Matt Mathis
Barath Raghavan
Hsiao-keng Jerry Chu
Tom Herbert
Proceedings of the USENIX Annual Technical Conference (USENIX ATC 2013), USENIX, 2560 Ninth Street, Suite 215, Berkeley, CA, 94710 USA, pp. 213-218
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Testing today’s increasingly complex network protocol implementations can be a painstaking process. To help meet this challenge, we developed packetdrill, a portable, open-source scripting tool that enables testing the correctness and performance of entire TCP/UDP/IP network stack implementations, from the system call layer to the hardware network interface, for both IPv4 and IPv6. We describe the design and implementation of the tool, and our experiences using it to execute 657 test cases. The tool was instrumental in our development of three new features for Linux TCP—Early Retransmit, Fast Open, and Loss Probes—and allowed us to find and fix 10 bugs in Linux. Our team uses packetdrill in all phases of the development process for the kernel used in one of the world’s largest Linux installations.
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