Boosting Edge Intelligence With Collaborative Cross-Edge Analytics

Abstract

Edge intelligence is emerging as a new interdiscipline to accelerate the convergence of artificial intelligence (AI) and Internet of Things (IoT). By applying edge computing, edge intelligence enables resource-limited IoT devices to offload computation-intensive AI applications to the network edge for execution. While existing efforts have focused on optimizing the model training and inference phases of edge intelligence, the initial phase of edge intelligence-collaborative cross-edge analytics that preprocess the unlabeled raw data dispersed at multiple edge sites to obtain labeled and trainable data samples-has greatly overlooked. To bridge this gap, in this article, we study how to jointly optimize the input data and task placement, with the goal of speeding up collaborative cross-edge analytics at low network traffic cost. This problem is by no means trivial since it is nonconvex and involving future uncertainty of the query characteristics. To accommodate these dual challenges, we blend the advantages of the convex relaxation method and a two-stage optimization. Specifically, based on a prediction of the query characteristics, the input data placement is first determined when it is generated, and then when the query job arrives, the actual value of the query characteristics is used to optimize the task placement. The problem becomes more complicated when we have multiple queries simultaneously. In response, we develop an efficient flow scheduling for the intermediate data transfers of competing queries by extending the classic shortest remaining processing time (SRPT) policy. Extensive trace-driven simulations verify the efficacy of our proposed solution.