Distributed computing (cloud) networks, e.g., mobile edge computing (MEC), are playing an increasingly important role in the efficient hosting, running, and delivery of real-time stream-processing applications such as industrial automation, immersive video, and augmented reality. While such applications require timely processing of real-time streams that are simultaneously useful for multiple users/devices, existing technologies lack efficient mechanisms to handle their increasingly multicast nature, leading to unnecessary traffic redundancy and associated network congestion. In this paper, we address the design of distributed packet processing, routing, and duplication policies for optimal control of multicast stream-processing services. We present a characterization of the enlarged capacity region that results from efficient packet duplication, and design the first fully distributed multicast traffic management policy that stabilizes any input rate in the interior of the capacity region while minimizing overall operational cost. Numerical results demonstrate the effectiveness of the proposed policy to achieve throughput- and cost-optimal delivery of stream-processing services over distributed computing networks.
翻译:分布式计算(cloud)网络,例如移动边缘计算(MEC),在有效托管、运行和提供实时流处理应用程序方面正在发挥越来越重要的作用,这些应用程序包括工业自动化、浸泡式视频和扩大现实。虽然这些应用程序需要及时处理同时对多个用户/装置有用的实时流,但现有技术缺乏有效的机制来处理其日益多播的性质,导致不必要的交通冗余和相关的网络拥堵。在本文件中,我们处理的是设计分布式袋处理、路由和重复政策,以便最佳控制多播流处理服务。我们介绍了因高效的包件重复而扩大的能力区域的特点,并设计了第一个完全分布式多流管理政策,以稳定能力区域内部的任何投入率,同时最大限度地降低总体业务成本。数字结果表明,拟议的政策对于在分布式计算网络上实现流处理服务的吞吐量和成本最佳交付的有效性。