Literature on Cloud Capacity Planning

This release captures the state-of-the-art (as of 2020) in cloud capacity planning literature and provides a set of complementary scripts to analyze this literature. The dataset which is central to this release (publications.yaml) maps 57 cloud capacity planning approaches as published in literature to the taxonomy on cloud capacity planning which the authors of this release have proposed. The approaches were gathered with a systematic literature survey process, aggregating multiple common sources and executing a set of automated and manual filtering steps.

Taxonomy

The taxonomy and the process used to derive it is described in detail in the MSc Thesis of Georgios Andreadis at Delft University of Technology (to be published end of August 2020), on cloud capacity planning. We describe the taxonomy here to provide context to the raw data.

The taxonomy divides the process underlying capacity planning systems into the following categories:

1. System Model
   • Workloads
   • Resources
   • Model Inputs
2. Forecast Model
   • Modeling Strategy
   • Model Structure
3. Decision Support
   • Role
   • Type of Advice
   • Advice Method

For each of these categories, the taxonomy prescribes a set of possible classes (possible instantiations of the category). We list these for each category, below, preceded by its abbrevation as appearing in the dataset:

1. System Model
   • Workloads
     • VM: Virtual Machines
     • DB: Databases
     • S: Streaming Workloads
     • BD: Big Data Frameworks
     • WS: Web Service
     • B: Batch Jobs
   • Resources
     • C: Compute Hardware
     • S: Storage Hardware
     • N: Network Hardware
     • E: Energy Hardware (Storage and Supply)
     • H: Heat Control Hardware
     • V: Virtualized Resources (VM, containers, etc.)
   • Model Inputs
     • H: Historical Data
     • RS: Resource Specifications
     • B: (Micro)Benchmarks or Systematic Performance Tests
     • S: SLAs
     • P: Pricing Data
     • LC: Lease Contracts
     • HP: Human Personnel-related Factors
2. Forecast Model
   • Modeling Strategy
     • A: Analytical
     • S: Simulation
     • E: Real-world Experimentation
   • Model Structure
     • U: Unconditional Extrapolation
     • W: What-if Scenarios
3. Decision Support
   • Role
     • F: Forecast
     • A: Adaptation Advice
   • Type of Advice
     • N: Number of Resources
     • T: Type of Resources
     • L: Locality of Resources
   • Advice Method
     • H: Heuristic
     • R: Regression
     • L: Local Search
     • SS: Stochastic Search
     • SP: Stochastic Programming
     • NN: Neural Network
     • GT: Game Theory
     • GA: Genetic Algorithm
     • NLP: (Non)Linear Programming

File Structure

This release is structured as follows:

• publications.yaml: This is the dataset of mappings of publications to the taxonomy. Each item in the array represents a publication, with a set of true-false classifications per category for each class.
  • The id field of each publication identifies the publication (first-author and publication year).
  • The summary field of each publication summarizes the publication in a short sentence.
  • The classification field contains a set of true-false classifications per category for each class.
  • The notes field is an optional field containing any additional notes kept by the author of this dataset on their classification, in the case where doubts arose during the classification process.
• taxonomy.py: Script which parses the YAML dataset into different CSV views per category, to facilitate meta-analysis. Also prints out a full (long-table) representation of the mappings.
• taxonomy_analysis.py: Jupyter notebook which contains several meta-analysis processing steps, including trend, cluster, and correlation analysis.
• README.md: A file containing this description.