Improving the Accuracy-Latency Trade-off of Edge-Cloud Computation Offloading for Deep Learning Services

TY - GEN

T1 - Improving the Accuracy-Latency Trade-off of Edge-Cloud Computation Offloading for Deep Learning Services

AU - Zhao, Xiaobo

AU - Hosseinzadeh, Minoo

AU - Hudson, Nathaniel

AU - Khamfroush, Hana

AU - Lucani Rötter, Daniel Enrique

PY - 2020/12

Y1 - 2020/12

N2 - Offloading tasks to the edge or the Cloud has the potential to improve accuracy of classification and detection tasks as more powerful hardware and machine learning models can be used. The downside is the added delay introduced for sending the data to the Edge/Cloud. In delay-sensitive applications, it is usually necessary to strike a balance between accuracy and latency. However, the state of the art typically considers offloading all-or-nothing decisions, e.g., process locally or send all available data to the Edge (Cloud). Our goal is to expand the options in the accuracy-latency trade-off by allowing the source to send a fraction of the total data for processing. We evaluate the performance of image classifiers when faced with images that have been purposely reduced in quality in order to reduce traffic costs. Using three common models (SqueezeNet, GoogleNet, ResNet) and two data sets (Caltech101, ImageNet) we show that the Gompertz function provides a good approximation to determine the accuracy of a model given the fraction of the data of the image that is actually conveyed to the model. We formulate the offloading decision process using this new flexibility and show that a better overall accuracy-latency tradeoff is attained: 58% traffic reduction, 25% latency reduction, as well as 12% accuracy improvement.

AB - Offloading tasks to the edge or the Cloud has the potential to improve accuracy of classification and detection tasks as more powerful hardware and machine learning models can be used. The downside is the added delay introduced for sending the data to the Edge/Cloud. In delay-sensitive applications, it is usually necessary to strike a balance between accuracy and latency. However, the state of the art typically considers offloading all-or-nothing decisions, e.g., process locally or send all available data to the Edge (Cloud). Our goal is to expand the options in the accuracy-latency trade-off by allowing the source to send a fraction of the total data for processing. We evaluate the performance of image classifiers when faced with images that have been purposely reduced in quality in order to reduce traffic costs. Using three common models (SqueezeNet, GoogleNet, ResNet) and two data sets (Caltech101, ImageNet) we show that the Gompertz function provides a good approximation to determine the accuracy of a model given the fraction of the data of the image that is actually conveyed to the model. We formulate the offloading decision process using this new flexibility and show that a better overall accuracy-latency tradeoff is attained: 58% traffic reduction, 25% latency reduction, as well as 12% accuracy improvement.

UR - http://www.scopus.com/inward/record.url?scp=85102929059&partnerID=8YFLogxK

U2 - 10.1109/GCWkshps50303.2020.9367470

DO - 10.1109/GCWkshps50303.2020.9367470

M3 - Article in proceedings

AN - SCOPUS:85102929059

T3 - 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings

BT - 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings

PB - IEEE

T2 - 2020 IEEE Globecom Workshops, GC Wkshps 2020

Y2 - 7 December 2020 through 11 December 2020

ER -