Cost-Aware Edge Resource Probing for Infrastructure-Free Edge Computing: From Optimal Stopping to Layered Learning

To meet the stringent requirement of artificial intelligence applications, such as face recognition and video streaming analytics, a resource-constrained device can offload its task to nearby resource-rich devices in edge computing. Resource awareness, as a prime prerequisite for offloading decision-making, is critical for achieving efficient collaborative computation performance. In this paper, we consider cost-aware edge resource probing (CERP) framework design for infrastructure-free edge computing wherein a task device self-organizes its resource probing for informed computation offloading. We first propose a multi-stage optimal stopping formulation for the problem, and derive the optimal probing strategy which reveals a nice multi-threshold structure. Accordingly, we then devise a data-driven layered learning mechanism for more practical and complicated application environments. Layered learning enables the task device to adaptively learn the optimal probing sequence and decision thresholds at runtime, aiming at deriving a good balance between the gain of choosing the best edge device and the accumulated cost of deep resource probing. We further conduct thorough performance evaluation of the proposed CERP schemes using both extensive numerical simulations and realistic system prototype implementation, which demonstrate the superior performance of CERP in the diverse application scenarios.