Anirudh Sarma

selected publications

1. SEC

   ClairvoyantEdge: Prescient Prefetching of On-demand Video at the Edge of the Network

   Manasvini Sethuraman, Anirudh Sarma, Ashutosh Dhekne, and 1 more author

   In Proceedings of the 7th ACM/IEEE Symposium on Edge Computing, Dec 2022

   Abs Bib PDF

   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 real-time 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 \textitprescient prefetching and delivering the content as the vehicle passes through \textitjust in time, using mmWave devices on \textiten 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 \textitin situ field measurements of mmWave links into our own homegrown emulation framework. With a minuscule 0.12% coverage of a 46 km^2 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.

   @inproceedings{sethuraman_clairvoyantedge_2022,
     series = {{SEC} '22},
     title = {ClairvoyantEdge: Prescient Prefetching of On-demand Video at the Edge of the Network},
     copyright = {All rights reserved},
     %isbn = {978-1-4503-8434-6},
     shorttitle = {ClairvoyantEdge},
     %url = {https://doi.org/10.1145/3458305.3463384},
     %doi = {10.1145/3458305.3463384},
     booktitle = {Proceedings of the 7th {ACM}/{IEEE} {Symposium on Edge Computing}},
     publisher = {Association for Computing Machinery},
     author = {Sethuraman, Manasvini and Sarma, Anirudh and Dhekne, Ashutosh and Ramachandran, Umakishore},
     month = dec,
     year = {2022},
     keywords = {computing, mmwave, video streaming},
   }

2. arXiv

   NFSlicer: Data Movement Optimization for Shallow Network Functions

   Anirudh Sarma, Hamed Seyedroudbari, Harshit Gupta, and 2 more authors

   Mar 2022

   Abs Bib PDF

   Network Function (NF) deployments on commodity servers have become ubiquitous in datacenters and enterprise settings. Many commonly used NFs such as firewalls, load balancers and NATs are shallow—i.e., they only examine the packet’s header, despite the entire packet being transferred on and off the server. As a result, the gap between moved and inspected data when handling large packets exceeds 20×. At modern network rates, such excess data movement is detrimental to performance, hurting both the average and 90% tail latency of large packets by up to 1.7×. Our thorough performance analysis identifies high contention on the NIC-server PCIe interface and in the server’s memory hierarchy as the main bottlenecks.

   @article{sarma_nfslicer_2022,
     title = {{NFSlicer}: {Data} {Movement} {Optimization} for {Shallow} {Network} {Functions}},
     language = {en},
     author = {Sarma, Anirudh and Seyedroudbari, Hamed and Gupta, Harshit and Ramachandran, Umakishore and Daglis, Alexandros},
     month = mar,
     year = {2022},
   }