CoNEXT 2019 Conference Program

Our solution has been deployed in production by a Demand Side Platform (DSP) to measure the viewability rate of the ad campaigns. Leveraging the infrastructure of this DSP, we compare the performance of our methodology with a commercial solution. Both techniques report a similar overall viewability rate of 50%. However, our solution measured the viewability in 93% of the ads served by the DSP, unlike to 74% of the ads measured by the commercial solution. A rough estimation indicates that this increase in the measured rate may lead to a revenue increase of $3.5 million per year for a mid-sized DSP serving 100M of ads per day.

ADS proposes an estimation technique that is highly accurate and scalable. ADS uses multiple frequencies in the excitation signal to receive packets from tags. It helps us to utilize channel independence across several frequencies but reduces the communication range. By exploiting the channel independence, ADS reduces the number of parameters for the estimation technique, which improves the performance. ADS provides 1.6x better scalability and 1.5x higher accuracy. In our evaluations, ADS achieves 1.7x better throughput on average and a maximum of 3x better throughput than the existing methods.

In this paper, we reveal that the current practice of using a fixed high-percentile RTT for ECN threshold calculation can lead to persistent queue buildups, significantly increasing packet latency. On the other hand, directly adopting lower percentile RTTs results in throughput degradation. To handle the problem, we introduce ECN^♯, a simple yet effective solution to enable ECN for RTT variations. At its heart, ECN^♯ inherits the current instantaneous ECN marking (based on a high-percentile RTT) to achieve high throughput and burst tolerance, while further marking packets (conservatively) upon detecting long-term queue buildups to eliminate unnecessary queueing delay without degrading throughput. We implement ECN^♯ on a Barefoot Tofino switch and evaluate it through extensive testbed experiments and large-scale simulations. Our evaluation confirms that ECN^♯ can effectively reduce latency without hurting throughput. For example, compared to the current practice, ECN^♯ achieves up to 23.4% (31.2%) lower average (99th percentile) flow completion time (FCT) for short flows while delivering similar FCT for large flows under production workloads.

Due to the fundamental complexity characteristics of TSS, unfortunately, there seems to be no complete mitigation to the problem. As a long-term solution, we suggest the use of other algorithms (e.g., HaRP) that are not vulnerable to the TSE attack. As a short-term countermeasure, we propose MFCguard that carefully manages the tuple space and keeps packet classification fast.

RSS++ has been implemented for both (i) DPDK and (ii) the Linux kernel. Additionally, we implement a new state migration technique, which facilitates sharding and reduces contention between CPU cores accessing per-flow data. RSS++ keeps the flow-state by groups that can be migrated at once, leading to a 20% higher efficiency than a state of the art shared flow table.

We propose one such method, explicitly aimed at tackling the high temporal dynamism inherent to low-Earth orbit satellites. We exploit repetitive patterns in the network topology such that we avoid expensive link changes over time, while still providing near-minimal latencies at nearly 2x the throughput of standard past methods. Further, we observe that the geometry of satellite constellations admits more efficient designs, if a small, controlled amount of dynamism in links is permissible. For the leading Starlink constellation, our approach enables an efficiency improvement of 54%.