Optimizing Microservices Placement in the Cloud-to-Edge Continuum: A Comparative Analysis of App and Service Based Approaches

TL;DR

The paper addresses the challenge of placing microservices across the cloud-to-edge continuum to minimize latency in real-time processing scenarios. It compares app-based and service-based placement strategies using four algorithms (Greedy Latency, Greedy Free RAM, Near Gateway, Round-robin IPT) within the YAFS fog simulator, assessing both latency and load balance. Findings indicate that service-based placement generally outperforms or matches app-based placement in latency across most algorithms, with similar or improved load distribution, providing practical guidance for deploying microservices in fog and edge environments. The work advances understanding of placement trade-offs in the cloud-to-edge continuum and informs design choices for robust, low-latency edge deployments.

Abstract

In the ever-evolving landscape of computing, the advent of edge and fog computing has revolutionized data processing by bringing it closer to end-users. While cloud computing offers numerous advantages, including mobility, flexibility and scalability, it introduces challenges such as latency. Fog and edge computing emerge as complementary solutions, bridging the gap and enhancing services' proximity to users. The pivotal challenge addressed in this paper revolves around optimizing the placement of application microservices to minimize latency in the cloud-to-edge continuum, where a proper node selection may influence the app's performance. Therefore, this task gains complexity due to the paradigm shift from monolithic to microservices-based architectures. Two distinct placement approaches, app-based and service-based, are compared through four different placement algorithms based on criteria such as link latency, node resources, and gateway proximity. App-based allocates all the services of one app sequentially, while service-based allocates one service of each app at a time. The study, conducted using YAFS (Yet Another Fog Simulator), evaluates the impact of these approaches on latency and load balance. The findings consistently confirm the hypothesis that strategies utilizing a service-based approach outperformed or performed equally well compared to app-based approaches, offering valuable insights into trade-offs and performance differences among the algorithms and each approach in the context of efficient microservices placement in cloud-to-edge environments.

Figures (2)

• Figure 1: Average App Latency per Algorithm
• Figure 2: Average Latency per Algorithm