Replication Package for "Predictive Autoscaling in Kubernetes via Machine Learning Time Series Forecasting"

Note: Links do not work in Zenodo but only from the README.MD file when downloaded locally. Contents The repository contains the following: INSTALL: Thorough installation guide describing how to test the system and install necessary tools and components of the system. DATASETS: Contains all test datasets extracted from testing phase GroundTruth: Zero autoscaling enabled Baseline: Default Kubernetes Horizontal Pod Autoscaler enabled Intermediate Study: The proposed Autoscaler solution enabled agg_minute.csv: Training dataset used for training the models COMPONENTS: Contains the components and source code which constitute the solution Autoscaler: Backend and frontend for the solution Forecaster: Model training and prediction Workload: Workload generation for testing FIGURES: Contains all figures from the paper NB. Workload 0 defines the generated data which closely resembles the traning data, while workload 1 is the sinusoidally generated data. Paper figures: Ground Truth 10x10 workload 0 Ground Truth 10x10 workload 0 Baseline workload 0 Baseline workload 0 Autoscaler workload 0 Autoscaler workload 0 Architectural Diagram: Architectural diagram describing the proposed system Preprocessing pipeline: Preprossing pipeline flow Figures with startup-time infixed e.g intermediate-study-v2.2-startup-time-workload-0.pdf displays the extended tests using applications with longer startup times. MODELS: Contains all the baseline models and selected hyperparameters from tuning phase. The baseline models used are present in the p9 folder. RESULTS: Contains results from system tests extracted from datasets using scripts SCRIPTS: Contains scripts to generate figures plot_module.py: Reads csv, aggregates the input, formats figures and outputs to results in .svg, .png and .pdf format make_figures.py: Uses above module to iterate through files in --input_dir to create a plot for each input file. plot_training_data.py: Used to plot the training data figure from agg_minute.csv