ClairvoyantEdge: Prescient Prefetching of On-demand Video at the Edge of the Network
ClairvoyantEdge: Prescient Prefetching of On-demand Video at the Edge of the Network
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DOI：
10.1109/sec54971.2022.00010
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发表时间：
2022-12
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影响因子：
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通讯作者：
Manasvini Sethuraman;Anirudh Sarma;Adwait Bauskar;Ashutosh Dhekne;U. Ramachandran
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作者：
Manasvini Sethuraman;Anirudh Sarma;Adwait Bauskar;Ashutosh Dhekne;U. Ramachandran
点播视频占移动的网络上数据流量的很大一部分。预计这一比例在未来几年将进一步大幅增加。虽然蜂窝基础设施不断发展以跟上这一不断增长的需求，但有必要确保为其他延迟敏感的实时应用（如视频会议和多人视频游戏）保留足够的带宽。一种切实可行的方法是减少蜂窝网络上的点播视频负载，特别是来自移动用户的视频负载。基于两个观察结果，我们看到了使用边缘节点降低蜂窝负载的机会：（1）视频流主要是一种仅下载操作，具有顺序数据访问;（2）短程毫米波链路可以为附近的数据接收者提供极高的吞吐量。用户计划的行驶路线的知识为在车辆通过时及时使用途中边缘节点上的毫米波设备来预先获取和交付内容创造了机会。ClairvoyantEdge是一种新型的网络化系统基础设施，它利用边缘节点间的通信和用户轨迹的知识来规划和向经过的车辆提供缓冲的视频片段。为了评估ClairvoyantEdge，我们构建了一个全面的端到端基于仿真的工作流程，将毫米波链路的现场测量纳入我们自己的仿真框架。通过使用分布在该区域的20个边缘节点对46平方公里的地理区域进行0.12%的覆盖，为过往车辆提供短程毫米波接入，我们使用包括758辆汽车的真实工作负载，实现了视频下载的蜂窝带宽使用平均减少高达21%。我们的结果验证了ClairvoyantEdge融入未来边缘基础设施发展的承诺。
On-demand video contributes a large fraction of the data traffic on mobile networks. This share is expected to increase even more drastically in the coming years. While the cellular infrastructure is continuously evolving to keep pace with this increasing demand, it is necessary to ensure that sufficient bandwidth is reserved for other latency-sensitive realtime applications like video conferencing and multiplayer video games. A tangible approach involves reducing on-demand video load on cellular networks, especially from users on the move. We see an opportunity for cellular load reduction using edge nodes based on two observations: (1) video streaming is mostly a download-only operation with sequential data access; and (2) short-range mmWave links can deliver an extremely high throughput for nearby recipients of data. The knowledge of the user's planned travel route creates opportunities for prescient prefetching and delivering the content as the vehicle passes through just in time, using mmWave devices on en route edge nodes. ClairvoyantEdge is a novel networked system infrastructure that leverages inter-edge node communication and the knowledge of users' trajectories to plan and deliver buffered video segments to the vehicles passing by. To evaluate ClairvoyantEdge, we built a comprehensive end-to-end emulation-based workflow that incorporates in situ field measurements of mmWave links into our own homegrown emulation framework. With a minuscule 0.12% coverage of a 46km2 geographical area employing 20 edge nodes distributed in that area providing short-range mmWave access to passing vehicles, we achieve an average reduction of up to 21% in cellular bandwidth usage for video downloads, using a real-world workload comprising 758 vehicles. Our results validate the promise of ClairvoyantEdge for incorporation in future edge infrastructure evolution.