Carnegie Mellon University

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September 30, 2021

New Technical Report: Improving Edge Elasticity via Decode Offload

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A New Carnegie Mellon University Technical Report Published, Feng, Z., George, S., Turki, H., Iyengar, R., Pillai, P., Harkes, J., & Satyanarayanan, M. (2021). Improving Edge Elasticity via Decode Offload.

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.

Read the Report