Performance Predictors Project This repository contains components for managing and evaluating Round-Trip Time (RTT) and variability predictors used in edge computing experiments. The system is modular, with separate containers for managing predictors, running individual prediction services, and post-processing experimental results. Repository Structure prediction_manager Purpose:Hosts the predictor manager responsible for orchestrating both RTT and variability predictors. Container:Runs as a standalone Docker container. Execution:Built using the provided Dockerfile and launches src/main.py. rtt_predictor Purpose:Contains the RTT prediction processes. Container:Runs independently in a Docker container. Execution:Built using the provided Dockerfile and launches src/main.py. variability_predictor Purpose:Contains the variability prediction processes. Container:Runs independently in a Docker container. Execution:Built using the provided Dockerfile and launches src/main.py. postprocessing Purpose:Provides scripts for analyzing predictor performance at the end of experiments. Functionality: Evaluates predictor accuracy, overhead, and configuration changes over the experiment duration. Uses raw data and stores results in the data/ subdirectory. Data The raw experiment data and analysis results are stored in the postprocessing/data/ directory. This directory is structured to separate raw logs from processed results. Relevant articles: RTT predictors: P. Giannakopoulos, B. van Knippenberg, C. K. Joshi, N. Calabretta, and G. Exarchakos, “Morpheus: Lightweight RTT prediction for performance-aware load balancing,” Future Generation Computer Systems, 2026, Art. no. 108452, ISSN 0167-739X, doi: 10.1016/j.future.2026.108452. Variability predictors: P. Giannakopoulos, B. van Knippenberg, C. K. Joshi, N. Calabretta, and G. Exarchakos, “Runtime RTT variability predictors for performance-aware scheduling in edge computing,” in Proc. 8th Conference on Cloud and Internet of Things (CIoT), London, UK, 2025, doi: 10.1109/CIoT67574.2025.11410133