Self-tuning
Encyclopedia
In control theory
a self-tuning system is capable of optimizing its own internal running parameters in order to maximize or minimize the fulfillment of an objective function; typically the maximization of efficiency
or error minimization.
Self-tuning and auto-tuning often refer to the same concept. Many software research groups consider auto-tuning the proper nomenclature.
Self-tuning systems typically exhibit non-linear adaptive control
. Self-tuning systems have been a hallmark of the aerospace industry for decades, as this sort of feedback is necessary to generate optimal multivariable control for nonlinear processes. In the telecommunications industry, adaptive communications
are often used to dynamically modify operational system parameters to maximize efficiency and robustness.
Examples=
Examples of self-tuning systems in computing include:
Performance benefits can be substantial. Professor Jack Dorr, an American computer scientist, claims self-tuning boosts performance
often on the order of 300%.
Digital Self-tuning Controllers are an example of self-tuning systems at the hardware level.
Measurements gather data about the conditions and behavior. Analysis helps determine whether the expectations are being met- and which subsequent actions should be performed. Common actions are gathering more data and performing dynamic reconfiguration of the system.
Self-tuning (self-adapting) systems of automatic control are systems whereby adaptation to randomly changing conditions is performed by means of automatically changing parameters or via automatically determining their optimum configuration [1]. In any non-selftuning automatic control system there are parameters which have an influence on system stability and control quality and which can be tuned. If these parameters remain constant whilst operating conditions (such as input signals or different characteristics of controlled objects) are substantially varying, control can degrade or even become unstable. Manual tuning is often cumbersome and sometimes impossible. In such cases, not only is using self-tuning systems technically and economically worthwhile, but it could be the only means of robust control. Self-tuning systems can be with or without parameter determination.
In systems with parameter determination the required level of control quality is achieved by automatically searching for an optimum (in some sense) set of parameter values. Control quality is described by a generalised characteristic which is usually a complex and not completely known or stable function of the primary parameters. This characteristic is either measured directly or computed based on the primary parameter values. The parameters are then tentatively varied. An analysis of the control quality characteristic oscillations caused by the varying of the parameters makes it possible to figure out if the parameters have optimum values, i.e. if those values deliver extreme (min or max) values of the control quality characteristic. If the characteristic values deviate from an extremum, the parameters need to be varied until optimum values are found. Self-tuning systems with parameter determination can reliably operate in environments characterised by wide variations of exogenous conditions.
In practice systems with parameter determination require considerable time to find an optimum tuning, i.e. time necessary for self-tuning in such systems is bounded from below. Self-tuning systems without parameter determination do not have this disadvantage. In such systems, some characteristic of control quality is used (e.g., the first time derivative of a controlled parameter). Automatic tuning makes sure that this characteristic is kept within given bounds. Different self-tuning systems without parameter determination exist that are based on controlling transitional processes, frequency characteristics, etc. All of those are examples of closed-circuit self-tuning systems, whereby parameters are automatically corrected every time the quality characteristic value falls outside the allowable bounds. In contrast, open-circuit self-tuning systems are systems with parametrical compensation, whereby input signal itself is controlled and system parameters are changed according to a specified procedure. This type of self-tuning can be close to instantaneous. However, in order to realise such self-tuning one needs to control the environment in which the system operates and a good enough understanding of how the environment influences the controlled system is required.
In practice self-tuning is done through the use of specialsed hardware or adaptive software algorithms. Giving software the ability to self-tune (adapt):
Control theory
Control theory is an interdisciplinary branch of engineering and mathematics that deals with the behavior of dynamical systems. The desired output of a system is called the reference...
a self-tuning system is capable of optimizing its own internal running parameters in order to maximize or minimize the fulfillment of an objective function; typically the maximization of efficiency
Efficiency
Efficiency in general describes the extent to which time or effort is well used for the intended task or purpose. It is often used with the specific purpose of relaying the capability of a specific application of effort to produce a specific outcome effectively with a minimum amount or quantity of...
or error minimization.
Self-tuning and auto-tuning often refer to the same concept. Many software research groups consider auto-tuning the proper nomenclature.
Self-tuning systems typically exhibit non-linear adaptive control
Adaptive control
Adaptive control is the control method used by a controller which must adapt to a controlled system with parameters which vary, or are initially uncertain. For example, as an aircraft flies, its mass will slowly decrease as a result of fuel consumption; a control law is needed that adapts itself...
. Self-tuning systems have been a hallmark of the aerospace industry for decades, as this sort of feedback is necessary to generate optimal multivariable control for nonlinear processes. In the telecommunications industry, adaptive communications
Adaptive communications
Adaptive communications can mean any communications system, or portion thereof, that automatically uses feedback information obtained from the system itself or from the signals carried by the system to modify dynamically one or more of the system operational parameters to improve system performance...
are often used to dynamically modify operational system parameters to maximize efficiency and robustness.
Examples=
Examples of self-tuning systems in computing include:
- TCPTransmission Control ProtocolThe Transmission Control Protocol is one of the core protocols of the Internet Protocol Suite. TCP is one of the two original components of the suite, complementing the Internet Protocol , and therefore the entire suite is commonly referred to as TCP/IP...
(Transfer Control Protocol) - Microsoft SQL ServerMicrosoft SQL ServerMicrosoft SQL Server is a relational database server, developed by Microsoft: It is a software product whose primary function is to store and retrieve data as requested by other software applications, be it those on the same computer or those running on another computer across a network...
(Newer implementations only) - FFTWFFTWFFTW, for "Fastest Fourier Transform in the West", is a software library for computing discrete Fourier transforms , developed by Matteo Frigo and Steven G. Johnson at the Massachusetts Institute of Technology....
(Fastest Fourier TransformFourier transformIn mathematics, Fourier analysis is a subject area which grew from the study of Fourier series. The subject began with the study of the way general functions may be represented by sums of simpler trigonometric functions...
in the West) - ATLAS (Automatically Tuned Linear Algebra SoftwareAutomatically Tuned Linear Algebra SoftwareAutomatically Tuned Linear Algebra Software is a software library for linear algebra. It provides a mature open source implementation of BLAS APIs for C and Fortran77....
) - libtune (Tunables library for LinuxLinuxLinux is a Unix-like computer operating system assembled under the model of free and open source software development and distribution. The defining component of any Linux system is the Linux kernel, an operating system kernel first released October 5, 1991 by Linus Torvalds...
) - PhiPAC (Self Tuning Linear Algebra Software for RISC)
- MILEPOST GCCMILEPOST GCCis free community-driven open-source adaptive self-tuning compiler that combinesstable production-quality GCC, Interactive Compilation Interface and machine learning plugins to...
(Machine learning based self-tuning compiler)
Performance benefits can be substantial. Professor Jack Dorr, an American computer scientist, claims self-tuning boosts performance
Performance
A performance, in performing arts, generally comprises an event in which a performer or group of performers behave in a particular way for another group of people, the audience. Choral music and ballet are examples. Usually the performers participate in rehearsals beforehand. Afterwards audience...
often on the order of 300%.
Digital Self-tuning Controllers are an example of self-tuning systems at the hardware level.
Architecture
Selftuning systems are typically composed of four components::: expectations, measurement, analysis, and actions. The expectations describe how the system should behave given exogenous conditions.Measurements gather data about the conditions and behavior. Analysis helps determine whether the expectations are being met- and which subsequent actions should be performed. Common actions are gathering more data and performing dynamic reconfiguration of the system.
Self-tuning (self-adapting) systems of automatic control are systems whereby adaptation to randomly changing conditions is performed by means of automatically changing parameters or via automatically determining their optimum configuration [1]. In any non-selftuning automatic control system there are parameters which have an influence on system stability and control quality and which can be tuned. If these parameters remain constant whilst operating conditions (such as input signals or different characteristics of controlled objects) are substantially varying, control can degrade or even become unstable. Manual tuning is often cumbersome and sometimes impossible. In such cases, not only is using self-tuning systems technically and economically worthwhile, but it could be the only means of robust control. Self-tuning systems can be with or without parameter determination.
In systems with parameter determination the required level of control quality is achieved by automatically searching for an optimum (in some sense) set of parameter values. Control quality is described by a generalised characteristic which is usually a complex and not completely known or stable function of the primary parameters. This characteristic is either measured directly or computed based on the primary parameter values. The parameters are then tentatively varied. An analysis of the control quality characteristic oscillations caused by the varying of the parameters makes it possible to figure out if the parameters have optimum values, i.e. if those values deliver extreme (min or max) values of the control quality characteristic. If the characteristic values deviate from an extremum, the parameters need to be varied until optimum values are found. Self-tuning systems with parameter determination can reliably operate in environments characterised by wide variations of exogenous conditions.
In practice systems with parameter determination require considerable time to find an optimum tuning, i.e. time necessary for self-tuning in such systems is bounded from below. Self-tuning systems without parameter determination do not have this disadvantage. In such systems, some characteristic of control quality is used (e.g., the first time derivative of a controlled parameter). Automatic tuning makes sure that this characteristic is kept within given bounds. Different self-tuning systems without parameter determination exist that are based on controlling transitional processes, frequency characteristics, etc. All of those are examples of closed-circuit self-tuning systems, whereby parameters are automatically corrected every time the quality characteristic value falls outside the allowable bounds. In contrast, open-circuit self-tuning systems are systems with parametrical compensation, whereby input signal itself is controlled and system parameters are changed according to a specified procedure. This type of self-tuning can be close to instantaneous. However, in order to realise such self-tuning one needs to control the environment in which the system operates and a good enough understanding of how the environment influences the controlled system is required.
In practice self-tuning is done through the use of specialsed hardware or adaptive software algorithms. Giving software the ability to self-tune (adapt):
- Facilitates controlling critical processes of systems;
- Approaches optimum operation regimes;
- Facilitates design unification of control systems;
- Shortens the lead times of system testing and tuning;
- Lowers the criticality of technological requirements on control systems by making the systems more robust;
- Saves personnel time for system tuning.
Literature
External links
- Using Probabilistic Reasoning to Automate Software Tuning
- Frigo, M. and Johnson, S. G., "The design and implementation of FFTW3", Proceedings of the IEEE, 93(2), February 2005, 216 - 231. DOI:10.1109/JPROC.2004.840301.
- A Collaborative guide to ATLAS Development
- Optimizing Matrix Multiply using PHiPAC: a Portable, High-Performance, ANSI C Coding Methodology
- Faster than a Speeding Algorithm
- Rethinking Database System Architecture: Towards a Self-tuning RISC-style Database System
- Self-Tuning Systems Software
- Microsoft Research Adds Data Mining and Self-tuning Technology to SQL Server 2000
- A Comparison of TCP Automatic Tuning Techniques for Distributed Computing
- Tunables library for Linux
- Digital Self-tuning Controllers