QoS-Aware Priority-Based Task Offloading for Deep Learning Services at the Edge

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.