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Predictive CDN Selection for Video Delivery Based on LSTM Network Performance Forecasts and Cost-Effective Trade-Offs
IEEE Transactions on Broadcasting ( IF 4.5 ) Pub Date : 2020-11-03 , DOI: 10.1109/tbc.2020.3031724
Roberto Viola , Angel Martin , Javier Morgade , Stefano Masneri , Mikel Zorrilla , Pablo Angueira , Jon Montalban

Owing to increasing consumption of video streams and demand for higher quality content and more advanced displays, future telecommunication networks are expected to outperform current networks in terms of key performance indicators (KPIs). Currently, content delivery networks (CDNs) are used to enhance media availability and delivery performance across the Internet in a cost-effective manner. The proliferation of CDN vendors and business models allows the content provider (CP) to use multiple CDN providers simultaneously. However, extreme concurrency dynamics can affect CDN capacity, causing performance degradation and outages, while overestimated demand affects costs. 5G standardization communities envision advanced network functions executing video analytics to enhance or boost media services. Network accelerators are required to enforce CDN resilience and efficient utilization of CDN assets. In this regard, this study investigates a cost-effective service to dynamically select the CDN for each session and video segment at the Media Server, without any modification to the video streaming pipeline being required. This service performs time series forecasts by employing a Long Short-Term Memory (LSTM) network to process real time measurements coming from connected video players. This service also ensures reliable and cost-effective content delivery through proactive selection of the CDN that fits with performance and business constraints. To this end, the proposed service predicts the number of players that can be served by each CDN at each time; then, it switches the required players between CDNs to keep the (Quality of Service) QoS rates or to reduce the CP’s operational expenditure (OPEX). The proposed solution is evaluated by a real server, CDNs, and players and delivering dynamic adaptive streaming over HTTP (MPEG-DASH), where clients are notified to switch to another CDN through a standard MPEG-DASH media presentation description (MPD) update mechanism.

中文翻译:

基于LSTM网络性能预测和成本有效权衡的视频交付CDN预测选择

由于视频流的消耗增加,以及对更高质量的内容和更高级的显示的需求,在关键性能指标(KPI)方面,未来的电信网络有望胜过当前的网络。当前,内容交付网络(CDN)用于以经济高效的方式增强Internet上的媒体可用性和交付性能。CDN供应商和业务模型的激增使内容提供商(CP)可以同时使用多个CDN提供商。但是,极端的并发动态可能会影响CDN容量,从而导致性能下降和中断,而高估的需求会影响成本。5G标准化社区设想了执行视频分析以增强或增强媒体服务的高级网络功能。需要网络加速器来增强CDN的弹性和CDN资产的有效利用。在这方面,本研究调查了一种经济高效的服务,可以在媒体服务器上为每个会话和视频段动态选择CDN,而无需对视频流传输管道进行任何修改。该服务通过使用长短期记忆(LSTM)网络来处理来自连接的视频播放器的实时测量,从而执行时间序列预测。通过主动选择适合性能和业务限制的CDN,该服务还可以确保可靠且具有成本效益的内容交付。为此,建议的服务将预测每个CDN每次可以服务的玩家数量;然后,它在CDN之间切换所需的播放器,以保持(服务质量)QoS速率或减少CP的运营支出(OPEX)。所提出的解决方案由真实的服务器,CDN和播放器进行评估,并通过HTTP(MPEG-DASH)传递动态自适应流,其中通过标准MPEG-DASH媒体表示描述(MPD)更新机制通知客户端切换到另一个CDN 。
更新日期:2020-11-03
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