Improving the interoperability of a Function-as-a-Service platform using an orchestration framework with a cloud-agnostic approach

Function-as-a-Service (FaaS) applications suffer from cold-start latency owing to their high execution time and memory usage. We propose a FaaS orchestration framework based on a cloud-agnostic approach for application and data interoperability. The framework's performance is evaluated on the web application of a student attendance system for online learning. The test system comprises 300 records from 50 students across five courses. Through optional file elimination, entry recognition, optional function generation, and function-level rewriting, the proposed orchestration framework suitably manages the storage of the student attendance records. The framework notably contributes to the student attendance web application by ensuring scalable, reliable, and efficient operations. Various evaluation measures confirm the effectiveness of the orchestration framework. These measures include cold/warm-start execution time, cold/warm-start memory usage, and zip execution time. An empirical analysis reveals that our framework reduces the execution time by 2 s –15 s and memory usage by 1MB–3 MB for the evaluated application.