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Run-Time Models for Online Performance and Resource Management in Data Centers

  • Simon SpinnerEmail author
  • Antonio Filieri
  • Samuel Kounev
  • Martina Maggio
  • Anders Robertsson
Chapter

Abstract

In this chapter, we introduce run-time models that a system may use for self-aware performance and resource management during operation. We focus on models that have been successfully used at run-time by a system itself or a system controller to reason about resource allocations and performance management in an online setting. This chapter provides an overview of existing classes of run-time models, including statistical regression models, queueing networks, control-theoretical models, and descriptive models. This chapter contributes to the state of the art, by creating a classification scheme, which we use to compare the different run-time model types. The aim of the scheme is to deepen the knowledge about the purpose, assumptions, and structure of each model class. We describe in detail two modeling case studies chosen because they are considered to be representative for a specific class of models. The description shows how these models can be used in a self-aware system for performance and resource management.

Keywords

Abstraction Level Multivariate Adaptive Regression Spline Model Inference Service Demand Hybrid Automaton 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Simon Spinner
    • 1
    Email author
  • Antonio Filieri
    • 2
  • Samuel Kounev
    • 1
  • Martina Maggio
    • 3
  • Anders Robertsson
    • 3
  1. 1.Department of Computer ScienceUniversity of Würzburg, Am HublandWürzburgGermany
  2. 2.Department of ComputingImperial College LondonLondonUK
  3. 3.Department of Automatic ControlLund UniversityLundSweden

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