Visual analytics on recently-captured data from video cameras has emerged as an important class of workloads in edge computing. These workloads make intense processing demands on cloudlets, whose elasticity is limited by their smaller physical and electrical footprint relative to exascale cloud data centers. In this paper, we show how cloudlet elasticity can be improved by offloading visual data decoding. We define a new data access API that embodies decode offload, thereby avoiding application-level decoding of visual data. Using thermal, power density and data copying considerations, we identify cloudlet storage as the optimal location for placement of the decode function. Using a proof-of-concept implementation, we show that this approach can lower cloudlet CPU utilization by up to 50–80%, and deliver up to 3.5x improvement in the elapsed time of a typical visual analytics pipeline.
Carnegie Mellon University Technical Report
Feng, Z., George, S., Turki, H., Iyengar, R., Pillai, P., Harkes, J., & Satyanarayanan, M. (2021). Improving Edge Elasticity via Decode Offload.