Edge-Mapping of Service Function Trees for Sensor Event Processing

TL;DR

This work addresses real-time IIoT event processing by recasting microservice placement as mapping a Service Function Tree ($SFT$) onto a fog network substrate ($PN$) with ROI-aware sensor access. It introduces a novel bottom-up $SFT$ mapping algorithm that uses backtracking and an extended BFS-based search to satisfy sensor availability, resource, connectivity, and latency constraints, ensuring feasible composite deployments when single-task placement fails. Demonstrations via simulations across simple to complex concrete-pouring scenarios show that the approach can identify valid mappings while minimizing hop-based latency and respecting ROI constraints. The proposed $SFT$ embedding enables scalable, near-real-time analytics by clustering computation near sensors and supports complex event-processing pipelines beyond traditional linear service chains, with practical implications for ROI-focused IIoT deployments.

Abstract

Fog computing offers increased performance and efficiency for Industrial Internet of Things (IIoT) applications through distributed data processing in nearby proximity to sensors. Given resource constraints and their contentious use in IoT networks, current strategies strive to optimise which data processing tasks should be selected to run on fog devices. In this paper, we advance a more effective data processing architecture for optimisation purposes. Specifically, we consider the distinct functions of sensor data streaming, multi-stream data aggregation and event handling, required by IoT applications for identifying actionable events. We retrofit this event processing pipeline into a logical architecture, structured as a service function tree (SFT), comprising service function chains. We present a novel algorithm for mapping the SFT into a fog network topology in which nodes selected to process SFT functions (microservices) have the requisite resource capacity and network speed to meet their event processing deadlines. We used simulations to validate the algorithm's effectiveness in finding a successful SFT mapping to a physical network. Overall, our approach overcomes the bottlenecks of single service placement strategies for fog computing through composite service placements of SFTs.

Figures (7)

• Figure 1: Fog-Cloud Architecture
• Figure 2: Physical Network in an IIoT Site
• Figure 3: Service Function Tree Designs in an IIoT Site Scenario
• Figure 4: Valid Mapping of Simple SFT to a Physical Network
• Figure 5: Valid Mapping of Complex SFT to a Physical Network