An Internet-Wide Analysis of Traffic Policing

Flach, Tobias; Papageorge, Pavlos; Terzis, Andreas; Pedrosa, Luis; Cheng, Yuchung; Karim, Tayeb; Katz-Bassett, Ethan; Govindan, Ramesh

doi:10.1145/2934872.2934873

Cited by 56 publications

(34 citation statements)

References 45 publications

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“…These results were gathered from video playbacks. We note that this graph shows the retransmission rate averaged within 10 ms RTT buckets, and the actual rate experienced by a connection can be much higher [22]. Across all RTTs, retransmission rates are 0%, 2%, 8% and 18% at the 50th, 80th, 90th and 95th percentiles.…”

Section: Search Latencymentioning

confidence: 90%

The QUIC Transport Protocol

Langley

Riddoch

Wilk

et al. 2017

Proceedings of the Conference of the ACM Special Interest Group on Data Communication

570

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We present our experience with QUIC, an encrypted, multiplexed, and low-latency transport protocol designed from the ground up to improve transport performance for HTTPS traffic and to enable rapid deployment and continued evolution of transport mechanisms. QUIC has been globally deployed at Google on thousands of servers and is used to serve traffic to a range of clients including a widely-used web browser (Chrome) and a popular mobile video streaming app (YouTube). We estimate that 7% of Internet traffic is now QUIC. We describe our motivations for developing a new transport, the principles that guided our design, the Internet-scale process that we used to perform iterative experiments on QUIC, performance improvements seen by our various services, and our experience deploying QUIC globally. We also share lessons about transport design and the Internet ecosystem that we learned from our deployment.

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Section: Search Latencymentioning

confidence: 90%

The QUIC Transport Protocol

Langley

Riddoch

Wilk

et al. 2017

Proceedings of the Conference of the ACM Special Interest Group on Data Communication

570

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“…On a basic level, QoS enforcement relies on two options of treating packets in the network: They can be either dropped or enqueued. Mechanisms that decide how packets are treated form the fundamentals of QoS control techniques, e.g., flow prioritization or rate allocation with weighted fair queuing are widely applied in today's communication networks [14]. Table 1 summarizes and Table 1.…”

Section: Network Qos Control Mechanismsmentioning

confidence: 99%

“…Buffer space and scheduling QoS options are limited on the devices. Discarding packets along the way from the sender to receiver interacts badly with the sender's congestion control [14] and increases the network load due to the retransmission of discarded packets.…”

Section: Introductionmentioning

confidence: 99%

Scalable Application- and User-aware Resource Allocation in Enterprise Networks Using End-Host Pacing

Sieber

Schwarzmann

Blenk

et al. 2020

ACM Trans. Model. Perform. Eval. Comput. Syst.

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Providing scalable user-and application-aware resource allocation for heterogeneous applications sharing an enterprise network is still an unresolved problem. The main challenges are as follows: (i) How do we define user-and application-aware shares of resources? (ii) How do we determine an allocation of shares of network resources to applications? (iii) How do we allocate the shares per application in heterogeneous networks at scale? In this article, we propose solutions to the three challenges and introduce a system design for enterprise deployment. Defining the necessary resource shares per application is hard, as the intended use case, the user's environment, e.g., big or small display, and the user's preferences influence the resource demand. We tackle the challenge by associating application flows with utility functions from subjective user experience models, selected Key Performance Indicators, and measurements. The specific utility functions then enable a mapping of network resources in terms of throughput and latency budget to a common user-level utility scale. A sensible distribution of the resources is determined by formulating a multi-objective mixed integer linear program to solve the throughput-and delay-aware embedding of each utility function in the network for a max-min fairness criteria. The allocation of resources in traditional networks with policing and scheduling cannot distinguish large numbers of classes and interacts badly with congestion control algorithms. We propose a resource allocation system design for enterprise networks based on Software-Defined Networking principles to achieve delay-constrained routing in the network and application pacing at the end-hosts. The system design is evaluated against best effort networks in a proof-of-concept setup for scenarios with increasing number of parallel applications competing for the throughput of a constrained link. The competing applications belong to the five application classes web browsing, file download, remote terminal work, video streaming, and Voice-over-IP. The results show that the proposed methodology improves the minimum and total utility, minimizes packet loss and queuing delay at bottlenecks, establishes fairness in terms of utility between applications, and achieves predictable application performance at high link utilization.

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“…Policers deviate by 10x from the target rate for two reasons: first by dropping non-conformant packets, Policers lose the opportunity to schedule them at a future time, and resort to emulating a target rate with on/off behavior. Second, Policers trigger poor behavior from TCP, where even modest packet loss leads to low throughput [21] and wasted upstream work.…”

Section: The Cost Of Shapingmentioning

confidence: 99%

Carousel

Ahmed

Dukkipati

Valancius

et al. 2017

Proceedings of the Conference of the ACM Special Interest Group on Data Communication

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Traffic shaping, including pacing and rate limiting, is fundamental to the correct and efficient operation of both datacenter and wide area networks. Sample use cases include policy-based bandwidth allocation to flow aggregates, rate-based congestion control algorithms, and packet pacing to avoid bursty transmissions that can overwhelm router buffers. Driven by the need to scale to millions of flows and to apply complex policies, traffic shaping is moving from network switches into the end hosts, typically implemented in software in the kernel networking stack. In this paper, we show that the performance overhead of end-host traffic shaping is substantial limits overall system scalability as we move to thousands of individual traffic classes per server. Measurements from production servers show that shaping at hosts consumes considerable CPU and memory, unnecessarily drops packets, suffers from head of line blocking and inaccuracy, and does not provide backpressure up the stack. We present Carousel, a framework that scales to tens of thousands of policies and flows per server, built from the synthesis of three key ideas: i) a single queue shaper using time as the basis for releasing packets, ii) fine-grained, just-in-time freeing of resources in higher layers coupled to actual packet departures, and iii) one shaper per CPU core, with lock-free coordination. Our production experience in serving video traffic at a Cloud service provider shows that Carousel shapes traffic accurately while improving overall machine CPU utilization by 8% (an improvement of 20% in the CPU utilization attributed to networking) relative to state-of-art deployments. It also conforms 10 times more accurately to target rates, and consumes two orders of magnitude less memory than existing approaches.

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An Internet-Wide Analysis of Traffic Policing

Cited by 56 publications

References 45 publications

The QUIC Transport Protocol

The QUIC Transport Protocol

Scalable Application- and User-aware Resource Allocation in Enterprise Networks Using End-Host Pacing

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