Emerging Edge Computing (EC) technology has shown promise for many delay-sensitive Deep Learning (DL) based applications of smart cities in terms of improved Quality-of-Service (QoS). EC requires judicious decisions which jointly consider the limited capacity of the edge servers and provided QoS of DL-dependent services. In a smart city environment, tasks may have varying priorities in terms of when and how to serve them; thus, priorities of the tasks have to be considered when making resource management decisions. In this paper, we focus on finding optimal offloading decisions in a three-tier user-edge-cloud architecture while considering different priority classes for the DL-based services and making a trade-off between a task’s completion time and the provided accuracy by the DL-based service. We cast the optimization problem as an Integer Linear Program (ILP) where the objective is to maximize a function called gain of system (GoS) defined based on provided QoS and priority of the tasks. We prove the problem is NP-hard. We then propose an efficient offloading algorithm, called PGUS, that is shown to achieve near-optimal results in terms of the provided GoS. Finally, we compare our proposed algorithm, PGUS, with heuristics and a state-of-the-art algorithm, called GUS, using both numerical analysis and real-world implementation. Our results show that PGUS outperforms GUS by a factor of 45% in average in terms of serving the top 25% higher priority classes of the tasks while still keeping the overall percentage of the dropped tasks minimal and the overall gain of system maximized.
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Joint Compression and Offloading Decisions for Deep Learning Services in 3-Tier Edge Systems
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.
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- Award ID(s):
- 1948387
- NSF-PAR ID:
- 10317548
- Date Published:
- Journal Name:
- 2021 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/DySPAN53946.2021.9677398
