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Elastic Scaling of Real-Time Communication Services
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DOI：10.1109/TNSM.2026.3674598.png)
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Abstract
En 中文
Real-time Communications (RTC) services, including multiparty conferencing, live streaming, and cloud-gaming, rely on a large-scale media plane infrastructure that provides real-time audio/video processing to clients. Unfortunately, off-the-shelf RTC services are not elastically scalable. As a result, operators must provision media servers to meet peak demand, resulting in resource under-utilization and high cost. Given that today microservice orchestrators like Kubernetes allow web-services to scale transparently and econimically, this paper looks at applying the same approach to scale large-scale RTC services. We find that this is challenging for two reasons: 1) default network dataplane underlying Kubernetes does not meet the compelling traffic management, performance and real-time requirements of RTC; and 2) current autoscaling policies are ill-suited to RTC. We address these challenges by designing a RTC-specific service mesh that pushes media traffic processing into the OS kernel and designing new RTC-specific Kubernetes autoscaling policies. Our evaluation on a functional VoIP test-bed shows that this combination allows to deploy elatically scalable RTC services with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100\times $ </tex-math></inline-formula> lower-jitter and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$700\times $ </tex-math></inline-formula> lower RTT than the current state-of-the art.
Keywords:
Real-time systems
WebRTC
web conferencing
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