INTEGRATING SERVERLESS AND EDGE COMPUTING: A FRAMEWORK FOR IMPROVED QOS AND RESOURCE OPTIMIZATION

With the launch of Google App Engine in 2008, serverless computing emerged as a popular cloud computing paradigm, simplifying application deployment by delegating infrastructure management to cloud providers. These providers handle server and resource management through automated provisioning and scaling. In addition, the ephemeral nature of serverless functions allows for the de-provisioning of resources and offers a granular pay-per-use pricing model, charging users only based on the invocations, which facilitates cost savings. This research explores the intersection of serverless and edge computing, leveraging the lower latency, reduced resource consumption, and improved energy efficiency of edge environments to enhance the performance of serverless functions and maintain service continuity in a Multi-access Edge Computing (MEC) environment. We propose a framework that proactively spawns multiple instances of functions based on predicted user movements, increasing solution reliability. To further optimize function deployment and relocation times, we introduce server selection criteria, a caching mechanism, and a distributed image registry to improve image pulling and layer sharing processes. Numerical results and experiments show that these strategies effectively reduce relocation times and frequency, lower energy consumption, and optimize network usage.