So far, this work focuses on a single user and a single, specific application but we do have some key learnings.

  • For applications with high levels of user interactivity, low latency networking to “backend” computing is critical. In many of our experiments, connection to cloud infrastructure outside of our metro area caused unacceptable E2E latency of >150ms.
  • Carrier interconnect points also caused similar E2E latency challenges. Without local metro carrier interconnect, application sessions from an LTE mobile device to an edge node on local wired carrier connected through an interconnect point outside the metro area resulting in the same 150ms+ E2E latency.
  • When the network path stays within the metro area, E2E latency begins to be driven as much by application compute latency as from network latency. This effect is, of course, highly application specific. But it suggests that attention to the CPU and GPU compute resources available from edge nodes is important for experience acceptability.

Going forward, we’ll focus on diversifying the number and types of applications under study to include multi-user interactive, IOT and edge analytics applications. We also plan to test in more real and simulated network environments to further validate and extend our learnings.

This work shows the value of using application benchmarking and system simulation to better understand what’s behind the network and computing curtain imposed by infrastructure opaqueness. These are not the only tools developers will need to design and characterize their applications but they can provide key insights into application acceptability in the face of widely varying environments.

For more information on our work in this area, see the following.


  1. S. George et al., "OpenRTiST: End-to-End Benchmarking for Edge Computing," in IEEE Pervasive Computing, vol. 19, no. 4, pp. 10-18, 1 Oct.-Dec. 2020, doi: 10.1109/MPRV.2020.3028781.
  2. J. Blakley et al., “Simulating Edge Computing Environments to Optimize Application Experience”, Carnegie Mellon University Technical Report, CMU-CS-20-135, November 2020.