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Rate enforcement is routinely employed in modern networks (e.g. ISPs rate-limiting users traffic to the subscribed rates). 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). And all of this must be done at scale to efficiently support the vast magnitude of users. 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 light-weight and scalable, but do not 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 which impacts scalability. In this paper, we explore whether we can get the best of both worlds -- the scalability of a policer with the rate and policy enforcement properties of a shaper. We answer this question in the affirmative with our system BC-PQP. BC-PQP 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. We implement BC-PQP as a middlebox over DPDK. Our evaluation shows how it achieves the rate and policy enforcement properties close to that of a shaper with 7x higher efficiency. 

It is challenging to meet the bandwidth and latency requirements of interactive real-time applications (e.g., virtual reality, cloud gam- ing, 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 can- not 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 through- put, 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 ap- proach. 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. 

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 basestation 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.