低成本大规模直播流量工程

Low-cost traffic engineering for large-scale live streaming

近年来,基于直播的网络应用大量出现,此类应用对互联网服务质量的要求更严格。目前,虽然一些专用骨干网可以为此类应用提供优质服务,但是服务价格昂贵,且无法覆盖世界各地的所有用户。因此,服务提供商选择依赖Overlay网络或云计算、雾计算和边缘计算等技术提升网络性能,改善用户体验。该文研究了用于大规模直播的Overlay网络中基于成本敏感的流量工程问题。经实际调研可知,成本由服务器的峰值数据速率决定,因此该流量工程问题涉及时间序列的路由决策。首先,将流量工程问题形式化,转化为一系列基于时间序列的整数规划。其次,提出了以可微函数逼近不可微函数的方法,并使用Lagrange乘子法和梯度下降算法有效求解该整数规划。最后,提出基于成本敏感的方案——在线路由算法LiveTE,从运行的Overlay网络收集真实数据,并通过数值模拟评估了LiveTE。结果表明:与现有方案相比,LiveTE的总成本降低幅度达52%,平均传输延时降低幅度达6%以上。

[Objective]Recent years have witnessed a remarkable rise in popularity among livestream-based network applications,prompting higher expectations for the quality of internet services.However,the public internet infrastructure's highest quality services and shared resources often fail to meet the demanding data transmission requirements of livestreaming applications.The objective of this research is to address the problem of cost-sensitive traffic engineering(TE)in Overlay networks for large-scale livestreaming,providing economically efficient livestreaming flow transmission services while minimizing costs and adhering to quality of service(QoS)requirements.By achieving these objectives,service providers can enhance network performance,optimize resource allocation,and deliver high-quality livestreaming to a wide user base.[Methods] To address the cost-sensitive traffic engineering problem,a comprehensive approach based on time-series optimization and approximate differentiable modeling is adopted.The scenario considered in this research is an application-layer transport network comprising forwarding nodes and virtual links.Business flows are transmitted along paths between forwarding nodes,and these paths may include multiple virtual paths to enhance performance.The problem is formulated as a time-series optimization problem,necessitating the decomposition into a series of time-series-based integer programming problems to simplify the solution process.To handle the nondifferentiable aspects of the problem,an innovative approximating differentiable model is proposed.Path selection is approximated with the Gumbel-Softmax function,a technique allowing for differentiable path selection.Moreover,differentiable functions are employed to approximate the cost and transmission delay functions,ensuring smooth optimization.The Lagrange multiplier method is utilized to transform the problem into an efficient optimization framework.An online routing algorithm(LiveTE)is developed to solve for the set of decision paths,utilizing a gradient descent algorithm to solve the optimization problem iteratively.[Results]The effectiveness of the LiveTE algorithm was evaluated via extensive experimentation and numerical simulations using real data obtained from a running-overlay livestreaming network.The results exhibited considerable cost reductions and improved transmission delay compared to existing methods.LiveTE achieved a remarkable total cost reduction of 52%while simultaneously lowering the average transmission delay by over 6%,highlighting its efficacy in enabling economically efficient livestreaming flow transmission services in overlay networks.The algorithm's ability to optimize resource allocation and improve QoS in large-scale livestreaming scenarios was evident,allowing service providers to enhance network performance and deliver high-quality livestreaming experiences to a diverse user base.[Conclusions]In summary,a comprehensive approach is presented to address the cost-sensitive traffic engineering problem in overlay networks for large-scale livestreaming applications.By formulating the problem as a time-series optimization problem and employing an approximating differentiable model,the proposed LiveTE algorithm achieves remarkable cost reductions while simultaneously improving transmission delay and QoS.The results contribute to the economically efficient delivery of high-quality livestreaming services,allowing service providers to optimize resource allocation and enhance network performance.Furthermore,the proposed LiveTE algorithm provides a valuable solution for service providers seeking to enhance user experience,mitigate costs,and maximize the utilization of overlay networks in livestreaming-based applications.