HOTS

Application for testing a hybrid resource allocation method using machine learning and optimization.

Contents

• Introduction to hots
  • Intended audience
  • What is hots
  • Usage example
• User manual
  • Preparing the parameters
  • Preparing data
  • Running the app
  • Output explanation
• Installation
  • Requirements
  • Production
  • Development
  • Docker
• The developer and maintainer corner
  • Git workflow and life cycle
  • Coding style
  • Running the tests
• Process explanation
  • Preliminary information
  • Analysis period
  • Loop period
• Business use
  • Optimization models modularity
  • Business components modularity
• Pyomo use
  • What is pyomo ?
  • How is it used ?
  • Optimization models description
• Streaming data
  • Overview
  • Connector plugin interface
  • Built-in connectors
  • Integration in the main loop
• Python API
  • hots.evaluation.evaluator
  • hots.plugins.clustering.*
  • hots.config.loader API
  • hots API
  • hots.core.instance
  • hots.cli
  • hots.plugins.optimization.pyomo_model
  • Core app & interfaces
  • hots.plugins.problem.placement
  • hots.visualization.plot API
  • Utility & logging helpers
• HOTS KPIs Reference
  • Conventions
  • Step Metadata
  • clst__* — Clustering Change KPIs
  • clst_struct__* — Clustering Structure KPIs
  • plc__* — Placement Change KPIs
  • plc_delta__* — Placement Delta Counts
  • moves__* — Applied Moves KPIs
  • Conflict Graph KPIs
  • load__* — Host Load / Balance KPIs
  • Practical Notes
• Legal information
  • Credits
  • License
• Glossary
• Release history

Indices and tables

• Index

• Module Index

• Search Page