The Internet community faces an explosion in new congestion control algorithms such as Copa, Sprout, PCC, and BBR. In this paper, we discuss considerations for deploying new algorithms on the Internet. While past efforts have focused on achieving 'fairness'or 'friendliness' between new algorithms and deployed algorithms, we instead advocate for an approach centered on quantifying and limiting harm caused by the new algorithm on the status quo. We argue that a harm-based approach is more practical, more future proof, and handles a wider range of quality metrics than traditional notions of fairness and friendliness.

BBR is a new congestion control algorithm (CCA) deployed for Chromium QUIC and the Linux kernel. As the default CCA for YouTube (which commands 11+% of Internet traffic), BBR has rapidly become a major player in Internet congestion control. BBR's fairness or friendliness to other connections has recently come under scrutiny as measurements from multiple research groups have shown undesirable outcomes when BBR competes with traditional CCAs. One such outcome is a fixed, 40% proportion of link capacity consumed by a single BBR flow when competing with as many as 16 loss-based algorithms like Cubic or Reno. In this short paper, we provide the first model capturing BBR's behavior in competition with loss-based CCAs. Our model is coupled with practical experiments to validate its implications. The key lesson is this: under competition, BBR becomes window-limited by its 'in-flight cap' which then determines BBR's bandwidth consumption. By modeling the value of BBR's in-flight cap under varying network conditions, we can predict BBR's throughput when competing against Cubic flows with a median error of 5%, and against Reno with a median of 8%.

Increasingly, proposals for new datacenter networking fabrics employ some form of traffic scheduling-often to avoid congestion, mitigate queuing delays, or avoid timeouts. Fundamentally, practical implementations require estimating upcoming traffic demand. Unfortunately, as our results show, it is difficult to accurately predict demand in typical datacenter applications more than a few milliseconds ahead of time. We explore the impact of errors in demand estimation on traffic scheduling in circuit-switched networks. We show that even relatively small estimation errors such as shifting the arrival time of at most 30% of traffic by a few milliseconds can lead to suboptimal schedules that dramatically reduce network efficiency. Existing systems cope by provisioning extra capacity-either on each circuit, or through the addition of a separate packet-switched fabric. We show through simulation that indirect traffic routing is a powerful technique for recovering from the inefficiencies of suboptimal scheduling under common datacenter workloads, performing as well as networks with 16% extra circuit bandwidth or a packet switch with 6% of the circuit bandwidth.

Various trends are reshaping Internet video delivery: exponential growth in video traffic, rising expectations of high video quality of experience (QoE), and the proliferation of varied content delivery network (CDN) deployments (e.g., cloud computing-based, content provider-owned datacenters, and ISP-owned CDNs). More fundamentally though, content providers are shifting delivery from a single CDN to multiple CDNs, through the use of a content broker. Brokers have been shown to invalidate many traditional delivery assumptions (e.g., shifting traffic invalidates short- and long-term traffic prediction) by not communicating their decisions with CDNs. In this work, we analyze these problems using data from a CDN and a broker. We examine the design space of potential solutions, finding that a marketplace design (inspired by advertising exchanges) potentially provides interesting tradeoffs. A marketplace allows all CDNs to profit on video delivery through fine-grained pricing and optimization, where CDNs learn risk-adverse bidding strategies to aid in traffic prediction. We implement a marketplace-based system (which we dub Video Delivery eXchange or VDX) in CDN and broker data-driven simulation, finding significant improvements in cost and data-path distance.

Various trends are reshaping content delivery on the Internet: the explosive growth of traffic due to video, users' increasing expectations for higher quality of experience (QoE), and the proliferation of server capacity from a variety of sources (e.g., cloud computing, content provider-owned datacenters, and ISP-owned CDNs). In order to meet the scale and quality demands imposed by users, content providers have started to spread demand across a variety of CDNs using a broker. Brokers break many traditional CDN assumptions (e.g., unexpected traffic skew, significant variance in demand over short timescales, etc.). Through an analysis of data from a leading broker and a leading CDN, we show the potential challenges and opportunities that brokers impart on content delivery. We take the first steps towards improvement through a redesigned broker-CDN interface.

A range of new datacenter switch designs combine wireless or optical circuit technologies with electrical packet switching to deliver higher performance at lower cost than traditional packet-switched networks. These "hybrid" networks schedule large traffic demands via a high-rate circuits and remaining traffic with a lower-rate, traditional packet-switches. Achieving high utilization requires an efficient scheduling algorithm that can compute proper circuit configurations and balance traffic across the switches. Recent proposals, however, provide no such algorithm and rely on an omniscient oracle to compute optimal switch configurations.

Most wireless solutions today are centered around people-centric devices like laptops and cell phones that are insufficient for mobile robots. The key difference is that people-centric devices use wireless connectivity in bursts under primarily stationary settings while mobile robots continuously transmit data even while moving. When mobile robots use existing wireless solutions, it results in intolerable and seemingly random interruptions in wireless connectivity when moving [1]. These wireless issues stem from suboptimal switching across wireless infrastructure access points (APs), also called AP handoffs. These poor handoff decisions are due to stateless handoff algorithms that make wireless decisions solely from immediate and noisy scans of surrounding wireless conditions. In this paper, we propose to overcome these motion-based wireless connectivity issues for autonomous robots using highly informed handoff algorithms that combine fine-grain wireless maps with accurate robot localization. Our results show significant wireless performance improvements for continuously moving robots in real environments without any modifications to the wireless infrastructure.

Live video delivery is expected to reach a peak of 50 Tbps this year. This surging popularity is fundamentally changing the Internet video delivery landscape. CDNs must meet users' demands for fast join times, high bitrates, and low buffering ratios, while minimizing their own cost of delivery and responding to issues in real-time. Wide-area latency, loss, and failures, as well as varied workloads ("mega-events" to long-tail), make meeting these demands challenging.

User-created live video streaming is marking a fundamental shift in the workload of live video delivery. However, live-video-specific challenges and the viral nature of user-created content makes it difficult for current CDNs to deliver 1) high-quality, 2) highly-scalable, and 3) highly-responsive service. We present the design and implementation of VDN, a new control plane for CDNs designed to optimize the delivery of live streams within the CDN. VDN satisfies these requirements by using two approaches: 1) optimizing directly for video quality (not just throughput) and 2) combining centralized control with local control, allowing VDN to adapt to traffic dynamics and network failures at fine timescales.

Though most would agree that accountability and privacy are both valuable, today's Internet provides little support for either. Previous efforts have explored ways to offer stronger guarantees for one of the two, typically at the expense of the other; indeed, at first glance accountability and privacy appear mutually exclusive. At the center of the tussle is the source address: in an accountable Internet, source addresses undeniably link packets and senders so hosts can be punished for bad behavior. In a privacy-preserving Internet, source addresses are hidden as much as possible.

Despite the plethora of incremental deployment mechanisms proposed, rapid adoption of new network-layer protocols and architectures remains difficult as reflected by the widespread lack of IPv6 traffic on the Internet. We show that all deployment mechanisms must address four key questions: How to select an egress from the source network, how to select an ingress into the destination network, how to reach that egress, and how to reach that ingress. By creating a design space that maps all existing mechanisms by how they answer these questions, we identify the lack of existing mechanisms in part of this design space and propose two novel approaches: the "4ID" and the "Smart 4ID". The 4ID mechanism utilizes new data plane technology to flexibly decide when to encapsulate packets at forwarding time. The Smart 4ID mechanism additionally adopts an SDN-style control plane to intelligently pick ingress/egress pairs based on a wider view of the local network. We implement these mechanisms along with two widely used IPv6 deployment mechanisms and conduct wide-area deployment experiments over PlanetLab. We conclude that Smart 4ID provide better overall performance and failure semantics, and that innovations in the data plane and control plane enable straightforward incremental deployment.

eXpressive Internet Architecture (XIA) [1] is an architecture that natively supports multiple communication types and allows networks to evolve their abstractions and functionality to accommodate new styles of communication over time. XIA embeds an elegant mechanism for handling unforeseen communication types for legacy routers.