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With a high frame-rate and high bit-rate, ultra-high definition (UHD) real-time communication (RTC) users could sometimes suffer from severe service degradation. Due to the fluctuations of frames incoming and decoding at the client side, a decoder queue could be formulated before the streaming decoder at the client side. Those fluctuations could easily overload the decoder queue and introduce a noticeable delay for those queued frames. In this paper, we propose a Frame-Skipping mechanism to effectively reduce the queuing delay by actively managing the frames inside the decoder queue. We jointly optimize the frames with skipping to maintain the end-to-end delay while ensuring the decoding quality of video codec. We also mathematically quantify the potential performance with a Markovian chain. We evaluate the Frame-Skipping mechanism with our trace-driven simulation with real word UHD RTC traces. Our experiments demonstrate that Frame-Skipping can reduce the ratio of severe decoder queue delay by up to 23x and the ratio of severe total delay by up to 2.6x.

Congestion control algorithms (CCA) are devoted to improve their performance on long-term metrics (e.g., throughput, fairness), whereas the increasing low-latency application demand poses strict requirements on and motivates us to evaluate the transient performance of CCAs. However, due to the complexity of existing CCAs and changeable network conditions, it is challenging to quantify the transient performance. Our observation is that the transient performance could be modelled as damped oscillations. In this poster, we propose a framework to quantify the transient performance by calculating the extent of the damped oscillation. We use our framework to evaluate 3 widely-deployed CCAs and offer a comparison between their transient performance.

Major commercial client-side video players employ adaptive bitrate (ABR) algorithms to improve user quality of experience (QoE). With the evolvement of ABR algorithms, increasingly complex methods such as neural networks have been adopted to pursue better performance. However, these complex methods are too heavyweight to be directly implemented in client devices, especially mobile phones with very limited resources. Existing solutions suffer from a trade-off between algorithm performance and deployment overhead. To make the implementation of sophisticated ABR algorithms practical, we propose PiTree, a general, high-performance and scalable framework that can faithfully convert sophisticated ABR algorithms into lightweight decision trees to reduce deployment overhead. We also provide a theoretical upper bound on the optimization loss during the conversion. Evaluation results on three representative ABR algorithms demonstrate that PiTree could faithfully convert ABR algorithms into decision trees with

Recently SmartNICs are widely used to accelerate service chains in NFV. However, when the SmartNIC is overloaded, casually migrating vNFs away from SmartNIC to CPU may lead to additional packet transmissions between SmartNIC and CPU. To address this problem, we present PAM, push aside migration to effectively alleviate the hot spot on SmartNIC with no performance overhead. Our key novelty is to push vNFs on the border of SmartNIC and CPU aside to release resources for the bottleneck vNF. Evaluation shows that PAM could efficiently alleviate the hot spot on SmartNIC and generate a service chain with much lower latency compared with the naive solution., In Proceedings of ACM SIGCOMM 2018 Conference Posters and Demos

We propose PRAM, a priority-aware flow migrating scheme to address the problem of which flows to migrate during dynamic scale in or out of network functions in NFV networks. We introduce the concept of Priority-Weighted Migration Time (PWMT), and use a greedy algorithm to minimize the PWMT, in order to exert the minimum impact on latency-sensitive flows. Simulation results show that PRAM can reduce the PWMT by 5.61x to 18.48x compared with the random strategy.