/3utools-233-download.html. Dec 10, 2015 Users may not be familiar with a new temperature environment. In this case, its advisable to use the PID auto-tuning functionality to improve the PID parameters settings. To do so, selecting auto-tuning (S3+4 = K3) for an initial adjustment is suggested. After initial tuning is completed, the instruction will auto modify control mode to the mode. A new car tuning kit for the Jaguar XF has been released by the guys at Loder1899. With some tweaks to the ECU, they were able to boost the 3.0-liter powerplant from the factory 275 bhp and 600 Nm of torque to 310 bhp with 680 Nm of torque. Jun 22, 2011  Hi guys, Just set to finish my BTEC First Diploma in Motorsport Engineering (Yes I know not great, but I messed about in school so now going from the bottom) I live away from home to do this. I used to be on the fence regarding whether or not Automatic Tuning should be on as the default when creating Azure SQL Databases. A part of me never liked the idea of Azure creating/dropping indexes or forcing plans without my prior approval but then again if it happens to do the right thing at the right time then it’s a pleasurable experience. Sep 30, 2014  The first single from Aretha Franklin’s new album went live on Monday: It’s a cover of Adele’s hit “Rolling in the Deep.” And a lot of people are excited about it.

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Garlicoin Stuck On Auto-tuning Today

In control theory a self-tuning system is capable of optimizing its own internal running parameters in order to maximize or minimize the fulfilment 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-linearadaptive control. Self-tuning systems have been a hallmark of the aerospace industry for decades, as this sort of feedback is necessary to generate optimal multi-variable control for non-linear processes. In the telecommunications industry, adaptive communications are often used to dynamically modify operational system parameters to maximize efficiency and robustness.

Examples[edit]

Examples of self-tuning systems in computing include:

• TCP (Transmission Control Protocol)
• Microsoft SQL Server (Newer implementations only)
• FFTW (Fastest Fourier Transform in the West)
• ATLAS (Automatically Tuned Linear Algebra Software)
• libtune (Tunables library for Linux)
• PhiPAC (Self Tuning Linear Algebra Software for RISC)
• MILEPOST GCC (Machine learning based self-tuning compiler)

Performance benefits can be substantial. Professor Jack Dongarra, an American computer scientist, claims self-tuning boosts performance, often on the order of 300%^[1].

Digital self-tuning controllers are an example of self-tuning systems at the hardware level.

Architecture[edit]

Self-tuning 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 behaviour. 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 ^[2]. In any non-self-tuning 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 (minimum or maximum) 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 para-metrical 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 specialised hardware or adaptive software algorithms. Giving software the ability to self-tune (adapt):

1. Facilitates controlling critical processes of systems;
2. Approaches optimum operation regimes;
3. Facilitates design unification of control systems;
4. Shortens the lead times of system testing and tuning;
5. Lowers the criticality of technological requirements on control systems by making the systems more robust;
6. Saves personnel time for system tuning.

Literature[edit]

1. ^http://appliedmathematician.org/pdf/news/781.pdf Faster than a Speeding Algorithm
2. ^http://bse.sci-lib.com/article099233.html Big Soviet Encyclopedia, Self-Tuning Systems (in Russian)

External links[edit]

• 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.

Azure SQL Database automatic tuning provides peak performance and stable workloads through continuous performance tuning based on AI and machine learning.

Automatic tuning is a fully managed intelligent performance service that uses built-in intelligence to continuously monitor queries executed on a database, and it automatically improves their performance. This is achieved through dynamically adapting database to the changing workloads and applying tuning recommendations. Automatic tuning learns horizontally from all databases on Azure through AI and it dynamically improves its tuning actions. The longer a database runs with automatic tuning on, the better it performs.

Azure SQL Database automatic tuning might be one of the most important features that you can enable to provide stable and peak performing database workloads.

What can automatic tuning do for you

• Automated performance tuning of Azure SQL databases
• Automated verification of performance gains
• Automated rollback and self-correction
• Tuning history
• Tuning action T-SQL scripts for manual deployments
• Proactive workload performance monitoring
• Scale out capability on hundreds of thousands of databases
• Positive impact to DevOps resources and the total cost of ownership

Safe, Reliable, and Proven

Tuning operations applied to databases in Azure SQL Database are fully safe for the performance of your most intense workloads. The system has been designed with care not to interfere with the user workloads. Automated tuning recommendations are applied only at the times of a low utilization. The system can also temporarily disable automatic tuning operations to protect the workload performance. In such case, 'Disabled by the system' message will be shown in Azure portal. Automatic tuning regards workloads with the highest resource priority.

Automatic tuning mechanisms are mature and have been perfected on several million databases running on Azure. Automated tuning operations applied are verified automatically to ensure there is a positive improvement to the workload performance. Regressed performance recommendations are dynamically detected and promptly reverted. Through the tuning history recorded, there exists a clear trace of tuning improvements made to each Azure SQL Database.

Azure SQL Database automatic tuning is sharing its core logic with the SQL Server automatic tuning engine. For additional technical information on the built-in intelligence mechanism, see SQL Server automatic tuning.

For an overview of how automatic tuning works and for typical usage scenarios, see the embedded video:

Enable automatic tuning

You can enable automatic tuning for single and pooled databases in the Azure portal or using the ALTER DATABASE T-SQL statement. You enable automatic tuning for instance databases in a managed instance deployment using the ALTER DATABASE T-SQL statement.

Automatic tuning options

Automatic tuning options available in Azure SQL Database are:

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Automatic tuning for single and pooled databases

Automatic tuning for single and pooled databases uses the CREATE INDEX, DROP INDEX, and FORCE LAST GOOD PLAN database advisor recommendations to optimize your database performance. For more information, see Database advisor recommendations in the Azure portal, in PowerShell, and in the REST API.

You can either manually apply tuning recommendations using the Azure portal or you can let automatic tuning autonomously apply tuning recommendations for you. The benefits of letting the system autonomously apply tuning recommendations for you is that it automatically validates there exists a positive gain to the workload performance, and if there is no significant performance improvement detected, it will automatically revert the tuning recommendation. Please note that in case of queries affected by tuning recommendations that are not executed frequently, the validation phase can take up to 72 hrs by design.

In case you are applying tuning recommendations through T-SQL, the automatic performance validation, and reversal mechanisms are not available. Recommendations applied in such way will remain active and shown in the list of tuning recommendations for 24-48 hrs. before the system automatically withdraws them. If you would like to remove a recommendation sooner, you can discard it from Azure portal.

Automatic tuning options can be independently enabled or disabled per database, or they can be configured on SQL Database servers and applied on every database that inherits settings from the server. SQL Database servers can inherit Azure defaults for automatic tuning settings. Azure defaults at this time are set to FORCE_LAST_GOOD_PLAN is enabled, CREATE_INDEX is enabled, and DROP_INDEX is disabled.

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Important

As of March, 2020 changes to Azure defaults for automatic tuning will take effect as follows:

• New Azure defaults will be FORCE_LAST_GOOD_PLAN = enabled, CREATE_INDEX = disabled, and DROP_INDEX = disabled.
• Existing servers with no automatic tuning preferences configured will be automatically configured to INHERIT the new Azure defaults. This applies to all customers currently having server settings for automatic tuning in an undefined state.
• New servers created will automatically be configured to INHERIT the new Azure defaults (unlike earlier when automatic tuning configuration was in an undefined state upon new server creation).

Configuring automatic tuning options on a server and inheriting settings for databases belonging to the parent server is a recommended method for configuring automatic tuning as it simplifies management of automatic tuning options for a large number of databases.

To learn about building email notifications for automatic tuning recommendations, see Email notifications for automatic tuning.

Automatic tuning for instance databases

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Automatic tuning for instance databases in a managed instance deployment only supports FORCE LAST GOOD PLAN. For more information about configuring automatic tuning options through T-SQL, see Automatic tuning introduces automatic plan correction and Automatic plan correction.

Next steps

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• To learn about built-in intelligence used in automatic tuning, see Artificial Intelligence tunes Azure SQL databases.
• To learn how automatic tuning works under the hood, see Automatically indexing millions of databases in Microsoft Azure SQL Database.