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.
|Title of host publication||2021 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2021|
|Number of pages||8|
|State||Published - 2021|
|Event||2021 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2021 - Virtual, Online, United States|
Duration: Dec 13 2021 → Dec 15 2021
|Name||2021 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2021|
|Conference||2021 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2021|
|Period||12/13/21 → 12/15/21|
Bibliographical noteFunding Information:
This material is based upon work supported by the National Science Foundation under grant no. CSR-1948387. This work was also partially funded by Cisco Systems Inc. under the research grant number 1215519250. We thank both our sponsors for their generous support.
© 2021 IEEE.
- Deep Learning
- Edge Computing
- Industrial Internet-of-Things
- Network Optimization
- Task Offloading
ASJC Scopus subject areas
- Computer Networks and Communications
- Hardware and Architecture
- Signal Processing