Latency-Aware and Proactive Service Placement for Edge Computing

Smart IoT devices and applications in smart cities exchange important real-time information with their environment. However, a subset of these systems may face limitations in analyzing and processing the required large amounts of data to meet ultra-low-latency criteria. This limitation could be attributed to factors such as constrained CPU and battery resources. Thanks to the 5G and edge computing capabilities, a viable solution involves migrating a subset of these latency-sensitive and computationally intensive tasks to edge nodes and servers. This strategic service placement ensures a safe continuity of the application. In this paper, autonomous cars operating in smart cities, engaging in continuous data exchange with their external environment to meet real-time and latency-sensitive requirements, serve as an illustrative example of smart applications. The car's decision service is strategically placed on edge nodes through a proactive (re)placement approach designed for dynamic and mobile environments. This approach uses a quality of service (QoS) metric prediction degradation module, which leverages Exponential smoothing methods to identify a suitable edge node for hosting the car's decision module, with latency as a key criterion. Multiple configurations for outlier detection techniques are evaluated. A proof-of-concept validates the chosen model by comparing it to the AutoRegressive Integrated Moving Average (ARIMA) and the proposed proactive service (re)placement approach. This approach ensures the continuity of the placed module, suggesting the feasibility of locating non-critical modules on edge nodes.