5 research outputs found
Anableps: Adapting Bitrate for Real-Time Communication Using VBR-encoded Video
Content providers increasingly replace traditional constant bitrate with
variable bitrate (VBR) encoding in real-time video communication systems for
better video quality. However, VBR encoding often leads to large and frequent
bitrate fluctuation, inevitably deteriorating the efficiency of existing
adaptive bitrate (ABR) methods. To tackle it, we propose the Anableps to
consider the network dynamics and VBR-encoding-induced video bitrate
fluctuations jointly for deploying the best ABR policy. With this aim, Anableps
uses sender-side information from the past to predict the video bitrate range
of upcoming frames. Such bitrate range is then combined with the receiver-side
observations to set the proper bitrate target for video encoding using a
reinforcement-learning-based ABR model. As revealed by extensive experiments on
a real-world trace-driven testbed, our Anableps outperforms the GCC with
significant improvement of quality of experience, e.g., 1.88x video quality,
57% less bitrate consumption, 85% less stalling, and 74% shorter interaction
delay.Comment: This paper will be presented at IEEE ICME 202
Network Contention-Aware Cluster Scheduling with Reinforcement Learning
With continuous advances in deep learning, distributed training is becoming
common in GPU clusters. Specifically, for emerging workloads with diverse
amounts, ratios, and patterns of communication, we observe that network
contention can significantly degrade training throughput. However, widely used
scheduling policies often face limitations as they are agnostic to network
contention between jobs. In this paper, we present a new approach to mitigate
network contention in GPU clusters using reinforcement learning. We formulate
GPU cluster scheduling as a reinforcement learning problem and opt to learn a
network contention-aware scheduling policy that efficiently captures contention
sensitivities and dynamically adapts scheduling decisions through continuous
evaluation and improvement. We show that compared to widely used scheduling
policies, our approach reduces average job completion time by up to 18.2\% and
effectively cuts the tail job completion time by up to 20.7\% while allowing a
preferable trade-off between average job completion time and resource
utilization
Improving Adaptive Real-Time Video Communication Via Cross-layer Optimization
Effective Adaptive BitRate (ABR) algorithm or policy is of paramount
importance for Real-Time Video Communication (RTVC) amid this pandemic to
pursue uncompromised quality of experience (QoE). Existing ABR methods mainly
separate the network bandwidth estimation and video encoder control, and
fine-tune video bitrate towards estimated bandwidth, assuming the maximization
of bandwidth utilization yields the optimal QoE. However, the QoE of a RTVC
system is jointly determined by the quality of compressed video, fluency of
video playback, and interaction delay. Solely maximizing the bandwidth
utilization without comprehensively considering compound impacts incurred by
both network and video application layers, does not assure the satisfactory
QoE. And the decoupling of network and video layer further exacerbates the user
experience due to network-codec incoordination. This work therefore proposes
the Palette, a reinforcement learning based ABR scheme that unifies the
processing of network and video application layers to directly maximize the QoE
formulated as the weighted function of video quality, stalling rate and delay.
To this aim, a cross-layer optimization is proposed to derive fine-grained
compression factor of upcoming frame(s) using cross-layer observations like
network conditions, video encoding parameters, and video content complexity. As
a result, Palette manages to resolve the network-codec incoordination and to
best catch up with the network fluctuation. Compared with state-of-the-art
schemes in real-world tests, Palette not only reduces 3.1%-46.3% of the
stalling rate, 20.2%-50.8% of the delay, but also improves 0.2%-7.2% of the
video quality with comparable bandwidth consumption, under a variety of
application scenarios