Modern Internet traffic is increasingly dominated by interactive video applications (e.g. cloud gaming, conferencing, etc) that require high throughput, extremely low delay, and near-zero drop rates. Meeting these requirements is inherently challenging for such applications because video encoders often need to overshoot the target bitrates, causing transient traffic bursts. These bursts lead to inevitable delays and drops on traditional Internet bottlenecks with fixed capacities and finite physical queues. However, we observe that modern Internet bottlenecks are often artificial, where ISPs intentionally limit user's traffic to subscribed rates. Our work exploits the flexibility afforded in these artificial bottlenecks to achieve zero queuing and drops for interactive applications. Specifically, we make a case for using traffic policers (implemented as token bucket filters) at the ISPs to enforce desired rate limits on an average. Policers can absorb traffic bursts without any queuing, but incur packet drops. Our system, QLDL, avoids packet drops by augmenting the policer and the endpoints with a light-weight explicit rate control mechanism. Our prototype evaluation using WebRTC applications shows how QLDL thus achieves zero queuing and drops, thereby improving interactive applications' QoE on all fronts: 2× higher bitrate, 2 – 3× lower frame delays, and 20× lower playback freezes, when compared to state-of-the-art baselines.

Emerging farming techniques rely on smart devices such as multi-spectral cameras that collect fine-grained data, and tele-operated robots that perform tasks such as de-weeding, berry-picking, etc. These networked farm applications (requiring 10s of Mbps of throughput per device to the edge servers, with tens to hundreds of devices in a typical farm) must be supported on a wireless mesh network with limited capacity. In this work, we use these networked farm applications as a compelling case-study to design FarmNetes, a centralized traffic engineering (TE) system for wireless mesh networks. FarmNetes leverages explicit control over farm workloads to make centralized TE decisions (temporal flow schedules, sending rates, load-aware routes, and channel configurations) from an edge server, so as to best meet task requirements. FarmNetes' centralized TE decisions enable it to work with commodity devices and control how the network is shared across flows based on the desired policies (prioritization and fairness) irrespective of the underlying MAC layer link sharing mechanisms. This further enables MAC-agnostic reasoning of wireless network behavior when making TE decisions. Our evaluation, using testbeds in a farm and trace-driven simulations, shows how FarmNetes achieves 3 × higher end-end network throughput and better meets application demands, compared to status-quo wireless mesh strategies.

ISPs routinely rate-limit user traffic. In addition to correctly enforcing the desired rates, rate-limiting mechanisms must be able to support rich rate-sharing policies within each traffic aggregate (e.g. per-flow fairness, weighted fairness, and prioritization). This must be done at scale to support the vast magnitude of users efficiently. There are two primary rate-limiting mechanisms - traffic shaping (that buffers packets in queues to enforce the desired rates and policies) and traffic policing (that filters packets as per the desired rates without buffering them). Policers are lightweight and scalable but don't support rich policy enforcement and often provide poor rate enforcement (being notoriously hard to configure). Shapers, on the other hand, achieve desired rates and policies, but at the cost of high system resource (memory and CPU) utilization impacting scalability. This paper explores whether we can get the best of both worlds. We present our system BC-PQP, which augments a policer with (i) multiple phantom queues that simulate buffer occupancy using counters and enable rich policy enforcement, and (ii) a novel burst-control mechanism that enables auto-configuration of the queues for correct rate enforcement. Our system achieves the rate and policy enforcement properties close to that of a shaper with 7× higher efficiency.

It is challenging to meet the bandwidth and latency requirements of interactive real-time applications (e.g., virtual reality, cloud gaming, etc.) on time-varying 5G cellular links. Today's feedback-based congestion controllers try to match the sending rate at the endhost with the estimated network capacity. However, such controllers cannot precisely estimate the cellular link capacity that changes at timescales smaller than the feedback delay. We instead propose a different approach for controlling congestion on 5G links. We send real-time data streams using an imprecise controller (that errs on the side of overestimating network capacity) to ensure high throughput, and then adapt the transmitted content by dropping appropriate packets in the cellular base stations to match the actual capacity and minimize delay. We build a system called Octopus to realize this approach. Octopus provides parameterized primitives that applications at the endhost can configure differently to express different content adaptation policies. Octopus transport encodes the corresponding app-specified parameters in packet header fields, which the base-station logic can parse to execute the desired dropping behavior. Our evaluation shows how real-time applications involving standard and volumetric videos can be designed to exploit Octopus, and achieve 1.5--18× better performance than state-of-the-art schemes.

Web Light is a transcoding service introduced by Google to show lighter and faster webpages to users searching on slow mobile clients. The service detects slow clients (e.g., users on 2G) and tries to convert webpages on the fly into a version optimized for these clients. Web Light claims to significantly reduce page load times, save user data, and substantially increase traffic to such webpages. However, there are several concerns around this service, including, its effectiveness in, preserving relevant content on a page, showing third-party advertisements, improving user performance as well as privacy concerns for users and publishers.