This tutorial presents techniques for self-adaptive software systems that use performance models in order to achieve desired quality-of-service objectives. Main hindrances with the state of the art are the assumption of a steady-state regime to be able to use analytical solutions and the explosion of the state space which occurs when modeling software systems with stochastic processes such as Markov chains. This makes their online use difficult because the system under consideration may be in a transient regime, and the typically large cost of the analysis does not permit fast tracking of performance dynamics. We will introduce fluid models based on nonlinear ordinary differential equations as a key enabling technique to effectively approximate large-scale stochastic processes. This representation makes it possible to employ online optimization methods based on model-predictive control in order to find an assignment of the values of tunable parameters of the model steering the system toward a given performance goal. We will also show how, dually, the same techniques can be used for the online estimation of software service demands. In this tutorial we will focus on software performance models based on queuing networks, with applications to runtime auto-scaling in virtualized environments.

Model-based Performance Self-adaptation: A Tutorial

Incerto E.;Tribastone M.
2019

Abstract

This tutorial presents techniques for self-adaptive software systems that use performance models in order to achieve desired quality-of-service objectives. Main hindrances with the state of the art are the assumption of a steady-state regime to be able to use analytical solutions and the explosion of the state space which occurs when modeling software systems with stochastic processes such as Markov chains. This makes their online use difficult because the system under consideration may be in a transient regime, and the typically large cost of the analysis does not permit fast tracking of performance dynamics. We will introduce fluid models based on nonlinear ordinary differential equations as a key enabling technique to effectively approximate large-scale stochastic processes. This representation makes it possible to employ online optimization methods based on model-predictive control in order to find an assignment of the values of tunable parameters of the model steering the system toward a given performance goal. We will also show how, dually, the same techniques can be used for the online estimation of software service demands. In this tutorial we will focus on software performance models based on queuing networks, with applications to runtime auto-scaling in virtualized environments.
9781450362863
adaptive software
control-theory
model predictive control
performance engineering
resource sharing
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11771/16511
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