CRS: A Cost-Aware Resource Scheduling Framework for Deep Learning Task Orchestration in Mobile Clouds

Deep learning (DL) has found extensive application in supporting various mobile applications. The efficient execution of DL tasks is paramount for ensuring the effectiveness of AI-driven mobile applications. While previous research has predominantly focused on minimizing the completion time of DL tasks, the associated cost of execution has often been overlooked. Nonetheless, cost becomes a critical factor, particularly when utilizing DL infrastructure rented from third-party cloud service providers. In this paper, we propose a cost-aware resource scheduling framework named CRS for orchestrating DL task execution in mobile cloud systems. Our aim is to minimize server rental costs by strategically orchestrating DL jobs with diverse deadlines and workload scales across rented cloud servers. We formally define the problem and prove its NP-hardness by reducing it to a multiple knapsack problem (MKP). To solve this problem, we devise an approximation algorithm with a guaranteed upper bound performance ratio of <inline-formula><tex-math notation="LaTeX">$1+\frac{1}{e-1}$</tex-math></inline-formula>. We evaluate CRS against state-of-the-art baselines through simulations of various job arrival scenarios in a real elastic mobile cloud system. The results demonstrate that CRS, on average, reduces rental costs by 45.1% compared to other baselines, while simultaneously achieving a shorter average job completion time (JCT) and maximum job completion time (i.e., makespan).