Joint Compression and Offloading Decisions for Deep Learning Services in 3-Tier Edge Systems

TY - GEN

T1 - Joint Compression and Offloading Decisions for Deep Learning Services in 3-Tier Edge Systems

AU - Hosseinzadeh, Minoo

AU - Hudson, Nathaniel

AU - Zhao, Xiaobo

AU - Khamfroush, Hana

AU - Lucani Rötter, Daniel Enrique

PY - 2021

Y1 - 2021

N2 - Task offloading in edge computing infrastructure remains a challenge for dynamic and complex environments, such as Industrial Internet-of-Things. The hardware resource constraints of edge servers must be explicitly considered to ensure that system resources are not overloaded. Many works have studied task offloading while focusing primarily on ensuring system resilience. However, in the face of deep learning-based services, model performance with respect to loss/accuracy must also be considered. Deep learning services with different implementations may provide varying amounts of loss/accuracy while also being more complex to run inference on. That said, communication latency can be reduced to improve overall Quality-of-Service by employing compression techniques. However, such techniques can also have the side-effect of reducing the loss/accuracy provided by deep learning-based service. As such, this work studies a joint optimization problem for task offloading decisions in 3-Tier edge computing platforms where decisions regarding task offloading are made in tandem with compression decisions. The objective is to optimally offload requests with compression such that the trade-off between latency-Accuracy is not greatly jeopardized. We cast this problem as a mixed integer nonlinear program. Due to its nonlinear nature, we then decompose it into separate subproblems for offloading and compression. An efficient algorithm is proposed to solve the problem. Empirically, we show that our algorithm attains roughly a 0.958-Approximation of the optimal solution provided by a block coordinate descent method for solving the two sub-problems back-To-back.

AB - Task offloading in edge computing infrastructure remains a challenge for dynamic and complex environments, such as Industrial Internet-of-Things. The hardware resource constraints of edge servers must be explicitly considered to ensure that system resources are not overloaded. Many works have studied task offloading while focusing primarily on ensuring system resilience. However, in the face of deep learning-based services, model performance with respect to loss/accuracy must also be considered. Deep learning services with different implementations may provide varying amounts of loss/accuracy while also being more complex to run inference on. That said, communication latency can be reduced to improve overall Quality-of-Service by employing compression techniques. However, such techniques can also have the side-effect of reducing the loss/accuracy provided by deep learning-based service. As such, this work studies a joint optimization problem for task offloading decisions in 3-Tier edge computing platforms where decisions regarding task offloading are made in tandem with compression decisions. The objective is to optimally offload requests with compression such that the trade-off between latency-Accuracy is not greatly jeopardized. We cast this problem as a mixed integer nonlinear program. Due to its nonlinear nature, we then decompose it into separate subproblems for offloading and compression. An efficient algorithm is proposed to solve the problem. Empirically, we show that our algorithm attains roughly a 0.958-Approximation of the optimal solution provided by a block coordinate descent method for solving the two sub-problems back-To-back.

KW - Compression

KW - Deep Learning

KW - Edge Computing

KW - Industrial Internet-of-Things

KW - Network Optimization

KW - Task Offloading

UR - http://www.scopus.com/inward/record.url?scp=85125333691&partnerID=8YFLogxK

U2 - 10.1109/DySPAN53946.2021.9677398

DO - 10.1109/DySPAN53946.2021.9677398

M3 - Article in proceedings

SP - 254

EP - 261

BT - 2021 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2021

PB - IEEE

T2 - 2021 IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN

Y2 - 13 December 2021 through 15 December 2021

ER -