Overall Delay of Task Processing in Resource- Constrained Industrial Edge Computing: Model and Optimization

With the advancement of the industrial Internet of Things, minimizing delay has become a critical performance metric for many industrial applications. Edge computing effectively addresses this requirement by offloading tasks to nearby edge servers, significantly reducing task processing delay, including both transmission and computation delays at local or edge servers. However, current researches often focus on isolated aspects of the task processing delay and typically handle multiple simultaneous tasks by dividing computing capacity for concurrent processing, which can lead to increased delays. In this article, we propose a comprehensive delay model that captures the entire process from task generation to completion, termed the overall delay of task processing (ODTP), along with a computing resource allocation strategy that sequentially allocates computing resource based on an optimized scheduling order (SAOS). To minimize the ODTP in resource-constrained, container-based industrial edge computing environments, we introduce an optimization problem termed ODTP-M and a corresponding solution, ODTP-O, which optimizes task offloading, computing resource scheduling order, container caching, and image caching. Due to the nonlinear coupling of variables, which makes solving ODTP-M directly challenging, we transform it as an equivalent linear problem, termed l-ODTP-M, using a series of mathematical techniques. Numerical simulation results demonstrate that this transformation quickly achieve the global optimal solution of ODTP-M. Furthermore, we developed an environment to simulate the task process in resource-constrained container-based industrial edge computing. Compared to concurrent task processing and random order sequential processing, SAOS achieves the lowest task processing delay. In addition, ODTP-M consistently minimizes task processing delay across various scenarios, including resource-constrained conditions, outperforming recent studies that focus on specific aspects of task delay.