In this exercise, students will deploy a Kubernetes cluster locally to manage an application that retrieves and stores electrical consumption data, forecasts future consumption, and presents both historical and projected consumption trends.
In this exercise, students will locally deploy a Kubernetes cluster. This K8S cluster will be used to manage an application which retrieves and stores electrical consumption data, forecasts future consumption, and presents both historical and projected consumption trends.
The electrical consumption is reprsented by a a CSV file stored on S3. This CSV file has 11 columns. The first column is the time stamp. The other ten columns each represent the power measurements (P) of a smart meter's electricity consumption. Measurements are taken every 15 minutes. A row in the CSV file therefore corresponds to the power measurement at a given time t (HH:00, HH:15, HH:30. HH:45) for the 10 smart meters. The measures cover the period 01.01.2021 - 31.05.2022.
The electrical consumption is represented by a a CSV file stored on S3. This CSV file has 11 columns. The first column is the time stamp. The other ten columns each represent the power measurements (P) of a smart meter's electricity consumption. Measurements are taken every 15 minutes. A row in the CSV file therefore corresponds the power measurement at a given time t (HH:00, HH:15, HH:30. HH:45) for the 10 smart meters. The measures cover the period 01.01.2021 - 31.05.2022.
The application will be deployed on a local kubernetes cluster created using the [kind] (https://kind.sigs.k8s.io/) tool.
The application is to be deployed on a local kubernetes cluster created using the [kind] (https://kind.sigs.k8s.io/) tool.
