DOCSLIB.ORG

Deep Shredder 13 Free Download Immortalchessforum

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Deep Shredder 13 Free Download Immortalchessforum deep shredder 13 free download ImmortalChessForum. Thank You Posts are strictly not allowed. You will click Thumbs Up button instead URLs might be in encrypted text. You will read the tutorial on how to encrypt/decrypt text if needed You will search before posting Multiple Accounts are not allowed You will not get into unrequired conversations. You will report posts to Moderators incase needed You realize that if there is a 15 days read-only access period where you will be unable to post anything for users who new members You agree not to share these files without proper credits anywehre else You agree you will not try and make money through any posts available on this forum You agree you will read the Rules and Regulations if you have not went through immedietely after logging in. Deep shredder 13 free download. You can download a slightly restricted test version of the world champion chess program Shredder Classic free of charge and try it out for 30 days without obligation. Shredder Classic runs on various platforms. Windows Download Macintosh Download Linux Download. Chess Tutor Demo Download. You can download a restricted demo version of our interactive chess teaching program Chess Tutor free of charge without obligation. Windows Download Step 1 Windows Download Step 2 Windows Download Step 3. Download Purchased Versions. If you have already bought Shredder and would like to download your purchased software again you can simply download your program version again via the link in your order confirmation email. Download UCI Chess Engine Protocol. You can download specifications explaining how in Shredder the chess user-interface communicates with the chess engine. UCI stands for Universal Chess Interface. This download is already included in the Shredder chess download. Start Download. Shredder8 for Windows 10/8.1. A user-friendly program that helps you delete sensitive data permanently from the computer and wipe free space using different algorithms, such as U.S DoD 5220.22-M and British HMG IS5. What's new in Shredder8 for Windows 10/8.1 2.2.0.0: Version 2.2.0 fixes some bugs, adds direct links to privacy policy, help and developer contact. Shredder8 for Windows 10/8.1 is a small software application designed specifically for Windows 8 users in order to help them delete sensitive data permanently from the computer. Simple interface. The tool implements an intuitive behavior, so even less experienced users can easily discover and tweak its features. It reveals two animated icons in the primary panel: the first one is used for wiping the free space from a device while the second enables you to shred files and folders permanently from your system. For those users who are not familiar with secure deletion operations, the program displays useful information in the main pane. Wiping free space. Shredder8 for Windows 8 gives you the possibility to clean the free space that may still contain old data which was previously deleted. This mode does not remove or modify the existing files but it only shreds deleted data. What’s more, you can pick the storage device and choose the desired deletion algorithm from several options, such as U.S DoD 5220.22-M, British HMG IS5, Russian GOST P50739-95, or others. Additionally, you may enable the quick erasing mode for overwriting files with zero bytes. When it comes to wiping out files and folders, you may build up a list with the items that you want to purge, make use of batch operations for processing multiple files at the same time, and choose the deletion algorithm. Performance. Tests revealed that the program is not quite fast when it comes to wiping free space, so it needs extra time in order to finish the process. However, it proves to be pretty fast when deleting files and folders from your computer using secure file deletion operations. Bottom line. All in all, if you are looking for a straightforward software solution that helps you delete sensitive data permanently from the computer and wipe free space, you may give Shredder8 for Windows 10/8.1 a try and see what it can do for you. Deep Shredder 13 - Chess Playing Software Download. Deep Shredder 13 is the best Shredder for Windows computers with multiple processors or dual core processors. Deep Shredder 13 Windows works also fine on computers with one processor and one core, but cannot develop his full power there and is comparable to Shredder 13 Windows on those machines. Shredder Chess Software for Windows. The chess programs Shredder Classic 5 Windows, Shredder 13 Windows and Deep Shredder 13 Windows combine extremely powerful chess play with easy handling. The level of play of the program can be adjusted to any chess playing strength, so that everyone can find an adequate chess partner. Many functions are available for analyzing one's own chess games, including a built-in chess coach, who is alert to mistakes and helps improve your chess play. Click here to find out more about the differences between those three versions. Whats New in Shredder: Improved chess engine, more than 330(!) Elo stronger than the predecessor More than 1000 built-in chess puzzles Learn and practice openings Practice technical and practical endgames Play handicap games New hires piece sets and boards Modern design Improved opening book Many more improvements. Shredder's Other Features: User interface that is easy to handle and can be set up individually Many different sets of pieces and chessboards both in 2D and 3D Chess engine with extremely powerful play, that can be individually adjusted and configured Simulates a human player with any playing strength Makes typical human mistakes on lower levels Built-in Chess coach, who is alert of mistakes and suggests better moves Own games can be automatically examined for mistakes Daily updated chess puzzles with three levels of difficulty The program always keeps up-to-date with automatic online updates Opening advisor allows Shredder to play much better without opening book Ability to play Chess960, also know as Fischer Random Chess Direct access to huge online endgame database with more than 1200 GB data User interface in German, English, French, Dutch, Spanish and Italian. Moves can easily be entered by mouse or keyboard Extensive documentation Tip of the day Games can be provided with alternative lines and comments Many features for training and analysis Manifold possibilities to systematically search and examine the chess endgame databases Free, open protocol between chess user interface and chess engine (UCI protocol) Possibility to simultaneously load up to ten chess engines Many possibilities for automatic tests and comparisons of different chess engines Shredderbases: new extremely fast and compact chess endgame databases Computer assisted game analysis Direct access to online chess opening database with more than 16 million moves Rated games against different computer opponents to measure one’s own play strength Automatic game level where Shredder will always play with your strength Voice output of the moves Arrows to show threats, hints, best engine moves and the last move Graphical display of the legal moves and the attacked pieces Automatic histogram generation. Deep Shredder 13 Windows runs on all Windows PCs with at least Windows Vista including Windows 7, Windows 8 and Windows 10. 18 Best Chess Engines of 2021 | Based On Their Ratings. A chess engine usually analyzes thousands of outcomes before making an efficient move. Since the hardware and programming techniques are getting better year by year, chess engines are becoming more intelligent. Modern engines are more selective and have a better positional understanding. If you construct a complete tree of all possible moves in a chessboard, you will get a total of 10 120 moves. That’s an extremely large number. To put this into perspective, there have been only 10 26 nanoseconds since the Big Bang and estimated 10 75 atoms in the entire universe. These numbers are dwarfed by the number of possible moves in chess, making it one of the most complex board games. There are literally hundreds of ‘rating lists’ that measure the relative strength of chess engines, based on how many moves they make per minute. In addition to ranking chess engines from best to worst, these lists also provide margins of errors on the given ratings. Among these rating lists, the most famous are CCRL (Computer Chess Rating Lists) and CEGT (Chess Engines Grand Tournament). Keeping both these ratings in mind, we are presenting the most advanced Chess Engines that demonstrate the machine’s domination over humanity. 18. Hannibal. CCRL Rating: 3229 CEGT Rating : 3094. Hannibal is a Universal Chess Interface (UCI) engine that incorporates ideas from earlier engines, Twisted Logic, and LearningLemming. It uses the alpha-beta technique with many other chess specific heuristics and relies on a selective search method. Besides incredible endgame knowledge, the engine has a good understanding of material imbalances. It also understands the fortresses and trapped pieces and can sacrifice material for the initiative on king attacks. Furthermore, Hannibal’s time management is tuned for the Fischer time controls. 17. Critter. CCRL Rating: 3232 CEGT Rating : 3098. Critter is the UCI chess engine available for Windows, Mac, Android, and Linux. You can use it for private purposes only. It was initially written in Delphi but later converted to C++ using Bitboard technology. This was done to enhance its performance on 64-bit processors. This chess engine features null move pruning, forward pruning, principal variation search, parallel search with up to 8 threads, and blockage detection in the endgames. 16. Suger XPro. CCRL Rating: 3533. SugaR engine is derived from Stockfish and supports up to 128 cores.
Load more

Recommended publications
  • Algorithmic Progress in Six Domains
    MIRI MACHINE INTELLIGENCE RESEARCH INSTITUTE Algorithmic Progress in Six Domains Katja Grace MIRI Visiting Fellow Abstract We examine evidence of progress in six areas of algorithms research, with an eye to understanding likely algorithmic trajectories after the advent of artificial general intelli- gence. Many of these areas appear to experience fast improvement, though the data are often noisy. For tasks in these areas, gains from algorithmic progress have been roughly fifty to one hundred percent as large as those from hardware progress. Improvements tend to be incremental, forming a relatively smooth curve on the scale of years. Grace, Katja. 2013. Algorithmic Progress in Six Domains. Technical report 2013-3. Berkeley, CA: Machine Intelligence Research Institute. Last modified December 9, 2013. Contents 1 Introduction 1 2 Summary 2 3 A Few General Points 3 3.1 On Measures of Progress . 3 3.2 Inputs and Outputs . 3 3.3 On Selection . 4 4 Boolean Satisfiability (SAT) 5 4.1 SAT Solving Competition . 5 4.1.1 Industrial and Handcrafted SAT Instances . 6 Speedup Distribution . 6 Two-Year Improvements . 8 Difficulty and Progress . 9 4.1.2 Random SAT Instances . 11 Overall Picture . 11 Speedups for Individual Problem Types . 11 Two-Year Improvements . 11 Difficulty and Progress . 14 4.2 João Marques-Silva’s Records . 14 5 Game Playing 17 5.1 Chess . 17 5.1.1 The Effect of Hardware . 17 5.1.2 Computer Chess Ratings List (CCRL) . 18 5.1.3 Swedish Chess Computer Association (SSDF) Ratings . 19 Archived Versions . 19 Wikipedia Records . 20 SSDF via ChessBase 2003 . 20 5.1.4 Chess Engine Grand Tournament (CEGT) Records .
    [Show full text]
  • Move Similarity Analysis in Chess Programs
    Move similarity analysis in chess programs D. Dailey, A. Hair, M. Watkins Abstract In June 2011, the International Computer Games Association (ICGA) disqual- ified Vasik Rajlich and his Rybka chess program for plagiarism and breaking their rules on originality in their events from 2006-10. One primary basis for this came from a painstaking code comparison, using the source code of Fruit and the object code of Rybka, which found the selection of evaluation features in the programs to be almost the same, much more than expected by chance. In his brief defense, Rajlich indicated his opinion that move similarity testing was a superior method of detecting misappropriated entries. Later commentary by both Rajlich and his defenders reiterated the same, and indeed the ICGA Rules themselves specify move similarity as an example reason for why the tournament director would have warrant to request a source code examination. We report on data obtained from move-similarity testing. The principal dataset here consists of over 8000 positions and nearly 100 independent engines. We comment on such issues as: the robustness of the methods (upon modifying the experimental conditions), whether strong engines tend to play more similarly than weak ones, and the observed Fruit/Rybka move-similarity data. 1. History and background on derivative programs in computer chess Computer chess has seen a number of derivative programs over the years. One of the first was the incident in the 1989 World Microcomputer Chess Cham- pionship (WMCCC), in which Quickstep was disqualified due to the program being \a copy of the program Mephisto Almeria" in all important areas.
    [Show full text]
  • A Comparison of Rybka 1.0 Beta and Fruit 2.1 2 Outline of the Evidence
    A comparison of Rybka 1.0 Beta and Fruit 2.1 This document is designed as a reference guide to various technical elements in the discussions with Rybka 1.0 Beta and Fruit 2.1. In some sense, it is a prequel to the Rybka/IPPOLIT analysis. However, the issues considered are not really the same. The claims made about Rybka/IPPOLIT were of a sufficiently different nature to those about Rybka/Fruit that a different type of discussion seems necessary. [This document is the version of March 11, 2011]. 1.1 Evidence, and standards therein This document shall outline the evidence regarding Rybka 1.0 Beta and Fruit 2.1, This document is and try (at times) to put it into context. In particular, one must make a choice not intended to be a of a standard of comparison. Many have been suggested, such as \code" copy- \statement for the prosection" per se, ing, or \copyright" considerations. It is my opinion, however, that the proper but could presum- standard to use is that which is commonly used in the context of computer ably prove useful to either side, de- chess (or more generally, computer boardgames). This has, at least historically, pending upon what been firstly construed to mean that no code which has a demonstrative influence standard is used. on the performance of the program in question may be borrowed from a com- petitor.1 The previous decisions in this genre include that of the case between Berliner and Hsu, where the latter agreed to remove/rewrite approximately 0.3% of his codebase (some sort of simulator of the Cray Blitz evaluation) due to the fact that it had been taken from the HITECH project at an earlier time.
    [Show full text]
  • Draft – Not for Circulation
    Søren Riis is a Computer Scientist at Queen Mary University of London. He has a PhD in Maths from University of Oxford. He used to play competitive chess (Elo 2300). A Gross Miscarriage of Justice in Computer Chess by Dr. Søren Riis Introduction In June 2011 it was widely reported in the global media that the International Computer Games Association (ICGA) had found chess programmer International Master Vasik Rajlich in breach of the ICGA’s annual World Computer Chess Championship (WCCC) tournament rule related to program originality. In the ICGA’s accompanying report it was asserted that Rajlich’s chess program Rybka contained “plagiarized” code from Fruit, a program authored by Fabien Letouzey of France. Some of the headlines reporting the charges and ruling in the media were “Computer Chess Champion Caught Injecting Performance-Enhancing Code”, “Computer Chess Reels from Biggest Sporting Scandal Since Ben Johnson” and “Czech Mate, Mr. Cheat”, accompanied by a photo of Rajlich and his wife at their wedding. In response, Rajlich claimed complete innocence and made it clear that he found the ICGA’s investigatory process and conclusions to be biased and unprofessional, and the charges baseless and unworthy. He refused to be drawn into a protracted dispute with his accusers or mount a comprehensive defense. In his only public statement on the matter to date he said Rybka does not include game-playing code written by others, aside from standard exceptions which wouldn’t count as ‘game-playing’. Rajlich added that the ICGA’s action would not deter him from continuing to improve Rybka and sell it commercially, as he has done successfully for the past five years, and that his program would be willing to compete in a match or tournament with any worthy challenger.
    [Show full text]
  • Review of the Rybka Case 1 the ICGA Process
    Review of the Rybka case Version of September 2, 2013. This is a brief recapitulation of the underlying elements of the evidence in the Rybka case considered by the ICGA. 1 The ICGA process The International Computer Games Association was asked by Fabien Letouzey (the author of Fruit), as joined by an open letter of 15 other programmers, to investigate the \origins" of Vasik Rajlich's entries into its tournaments, and whether in particular said entries misappropriated Letouzey's work. The ICGA responded by convening a Panel of computer chess experts, to consider particularly the specific Rybka/Fruit question, but not limited therein. In fact, during the investigation of Rybka, a prior version (privately circulated, though presumably a similar engine participated in CCT 6 in Jan 2004) was analyzed for its re-use of Crafty code. The Panel was advisory in nature, with any decisions being taken by the Board (see below for the membership and credentials of the Board). On a broader level, said Panel has also advised the ICGA on how to deal with the recurrent problem of \clones" in computer games. One specific purpose of the Panel with Rybka was to provide the ICGA Board with an advisory opinion regarding the (voluminous) technical evidence. The discussions of said Panel were done on a private wiki. Approximately 34 persons were admitted to this, some of whom were merely observers. As the Panel was advisory and investigatory in nature, persons with close connections to competitors (and indeed, direct principals of some competitors of Rybka) were also allowed. This was in part due to the fact that the world of computer chess is quite small, and persons with adequate technical knowledge have often been associated with one chess program or another over the years.
    [Show full text]
  • ARS ELECTRONICA 2017 FESTIVAL for ART, TECHNOLOGY, and SOCIETY Aiartificial Intelligence Das Andere Ich
    AIArtificial Intelligence Das andere Ich ARS ELECTRONICA 2017 FESTIVAL FOR ART, TECHNOLOGY, AND SOCIETY AIArtificial Intelligence Das andere Ich Edited by Gerfried Stocker / Christine Schöpf / Hannes Leopoldseder Ars Electronica 2017 Festival for Art, Technology, and Society September 7—11, 2017 Ars Electronica, Linz Editors: Hannes Leopoldseder, Christine Schöpf, Gerfried Stocker Editing: Magdalena Götz Translations: German–English, all texts (unless otherwise indicated): Mel Greenwald English–German: Ingrid Fischer-Schreiber German text writers: Teresa Brandstetter, Maria Sigl Copyediting: David Westacott Graphic design and production: Cornelia Prokop, Lunart Werbeagentur Typeface: Klavika Printed by: Friedrich Druck & Medien, Linz Paper: Magno Volume 1,1 Vol., 135 g/m2, 300 g/m2 © 2017 Ars Electronica © 2017 for the reproduced works by the artists, or their legal successors Published by Hatje Cantz Verlag GmbH Mommsenstrasse 27 10629 Berlin Germany Tel. +49 30 3464678-00 www.hatjecantz.de A Ganske Publishing Group company Hatje Cantz books are available internationally at selected bookstores. For more information about our distribution partners please visit our homepage at www.hatjecantz.com ISBN 978-3-7757-4335-8 Printed in Austria Cover illustration: Ramiro Joly-Mascheroni & Aline Sardin-Dalmasso Ars Electronica 2017 Lehramt Bildnerische Erziehung—Kunstuniversität Linz Festival für Kunst, Technologie und Gesellschaft LENTOS Kunstmuseum Linz Festival for Art, Technology, and Society Mariendom Linz mb21 Organization MIT Media Lab Ars Electronica Linz GmbH & Co KG Moviemento Managing Directors OÖ Familienbund Gerfried Stocker, Diethard Schwarzmair QUT Queensland University of Technology Ars-Electronica-Straße 1, 4040 Linz, Austria Resonate Tel: +4373272720 Salon 2000 Fax: +4373272722 Science Gallery Dublin [email protected] Stadtbibliothek Linz Stadtwerkstatt Co-organizers STAIR Lab (Chiba Institute of Technology) ORF Oberösterreich Südwind Director: Kurt Rammerstorfer Tagtool Tangible Media Group LIVA—Veranstaltungsgesellschaft m.b.H.
    [Show full text]
  • Ph.D Thesis Proposal Multi-Tier Internet Service Management
    UNIVERSITY OF COLORADO AT COLORADO SPRINGS COLLEGE OF ENGINEERING AND APPLIED SCIENCE COMPUTER SCIENCE DEPARTMENT Ph.D Thesis Proposal Multi-tier Internet Service Management: Statistical Learning Approaches May 2011 Sireesha Muppala 1 Respectfully submitted by: Sireesha Muppala Ph.D. Candidate 3132 Greenmoor Ct Colorado Springs, CO 80920 [email protected] [email protected] mobile 719-640-3274 home 719-282-0277 Date: May 6 2011 Advisor: Dr. Xiaobo Zhou Committee Members: Dr. Xiaobo Zhou (chair) Dr. Edward Chow Dr. Chaun Yue Dr. Rory Lewis Dr. Liqiang Zhang 2 Abstract Modern Internet services employ a multi-tier architecture. While facilitating flexible service deployment, the tiered architectures introduce several significant challenges. Com- plex inter-tier dependencies and dynamic bottleneck tier shift are challenges inherent to multi-tier architectures. Highly dynamics of session-oriented Internet workloads further magnifies the complexity of multi-tier Internet services. Managing these complex services for session-oriented performance improvement and quality of service provisioning is an important but challenging undertaking. Three critical Internet service management mech- anisms are: admission control, dynamic resource provisioning and service differentiation. Significant research literature studies the three mechanisms using a variety of techniques. Queuing based analytical models of the multi-tier systems have been proposed to achieve request-oriented performance guarantees. However, queuing models often lack the abil- ity to capture session workload dynamics in multi-tier systems and queueing-theoretic ap- proaches are often not effective in resource management for quality-of-service provisioning in highly complex multi-tier systems with highly complex session-based workloads. This thesis concentrates on applying statistical learning based approaches for multi-tier Internet service management to achieve efficient, balanced and scalable services.
    [Show full text]
  • Multi-Tier Internet Service Management
    MULTI-TIER INTERNET SERVICE MANAGEMENT : STATISTICAL LEARNING APPROACHES by SIREESHA MUPPALA B.E., Nagarjuna University, India, 1992 A dissertation submitted to the Graduate Faculty of the University of Colorado at Colorado Springs in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Computer Science 2013 This dissertation for the Doctor of Philosophy degree by Sireesha Muppala has been approved for the Department of Computer Science by Xiaobo Zhou, Chair Edward Chow Chaun Yue Rory Lewis Jia Rao Liqiang Zhang Date ii Muppala, Sireesha (Ph.D., Computer Science) Multi-tier Internet Service Management: Statistical Learning Approaches Dissertation directed by Associate Professor, Chair Xiaobo Zhou Modern Internet services are multi-tiered and are typically hosted in virtualized shared platforms. While facilitating flexible service deployment, multi-tier architecture introduces significant challenges for Quality of Service (QoS) provisioning in hosted Internet services. Complex inter-tier dependencies and dynamic bottleneck tier shift are challenges inherent to tiered architectures. Hard-to-predict and bursty session-based Internet workloads further magnify this complexity. Virtualization of shared platforms adds yet another layer of complication in managing the hosted multi-tier Internet services. We consider three critical aspects of Internet service management for improved performance and qual- ity of service provisioning : admission control, dynamic resource provisioning and service differentiation. This thesis concentrates on statistical learning based approaches for multi-tier Internet service management to achieve efficient, balanced and scalable services. Statistical learning techniques are capable of solving complex dynamic problems through learning and adaptation with no priori domain-specific knowledge. We explore the effectiveness of supervised and unsupervised learning in managing multi-tier Internet services.
    [Show full text]
  • Algorithmic Progress in Six Domains
    MMIRIACHINE INTELLIGENCE RESEARCH INSTITUTE Algorithmic Progress in Six Domains Katja Grace MIRI Visiting Fellow Abstract We examine evidence of progress in six areas of algorithms research, with an eye to understanding likely algorithmic trajectories after the advent of artificial general intelli- gence. Many of these areas appear to experience fast improvement, though the data are often noisy. For tasks in these areas, gains from algorithmic progress have been roughly fifty to one hundred percent as large as those from hardware progress. Improvements tend to be incremental, forming a relatively smooth curve on the scale of years. Grace, Katja. 2013. Algorithmic Progress in Six Domains. Technical report 2013-3. Berkeley, CA: Machine Intelligence Research Institute. Last modified December 9, 2013. Contents 1 Introduction 1 2 Summary 2 3 A Few General Points 3 3.1 On Measures of Progress . 3 3.2 Inputs and Outputs . 3 3.3 On Selection . 4 4 Boolean Satisfiability (SAT) 5 4.1 SAT Solving Competition . 5 4.1.1 Industrial and Handcrafted SAT Instances . 6 Speedup Distribution . 6 Two-Year Improvements . 8 Difficulty and Progress . 9 4.1.2 Random SAT Instances . 11 Overall Picture . 11 Speedups for Individual Problem Types . 11 Two-Year Improvements . 11 Difficulty and Progress . 14 4.2 João Marques-Silva’s Records . 14 5 Game Playing 17 5.1 Chess . 17 5.1.1 The Effect of Hardware . 17 5.1.2 Computer Chess Ratings List (CCRL) . 18 5.1.3 Swedish Chess Computer Association (SSDF) Ratings . 19 Archived Versions . 19 Wikipedia Records . 20 SSDF via ChessBase 2003 . 20 5.1.4 Chess Engine Grand Tournament (CEGT) Records .
    [Show full text]
  • Autonomic Performance and Power Control in Virtualized Datacenters
    AUTONOMIC PERFORMANCE AND POWER CONTROL IN VIRTUALIZED DATACENTERS by PALDEN LAMA B.Tech., Indian Institute of Technology, India, 2003 A dissertation submitted to the Graduate Faculty of the University of Colorado at Colorado Springs in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Computer Science 2013 This dissertation for the Doctor of Philosophy degree by Palden Lama has been approved for the Department of Computer Science by Xiaobo Zhou, Chair Terry Boult Edward Chow Chuan Yue Jia Rao Liqiang Zhang Date Lama, Palden (Ph.D., Computer Science) Autonomic Performance and Power Control in Virtualized Datacenters Dissertation directed by Associate Professor, Chair Xiaobo Zhou Virtualized datacenters, the platform for supporting Cloud computing, allow diverse applications to share the underlying server resources. Due to the highly dynamic nature of Internet workloads, increas- ing complexity of applications, and complex dynamics of shared infrastructure, datacenters face significant challenges in managing application performance while maintaining resource utilization efficiency and re- ducing power consumption costs. This thesis presents middleware approaches to autonomic performance and power control in virtualized datacenters. To this end, we designed self-adaptive resource management techniques based on queuing models, machine learning and feedback control theory. Firstly, we designed an efficient server provisioning mechanism based on end-to-end resource allocation optimization for client perceived response time guarantee in a multi-tier server cluster. To guarantee an important percentile-based performance in the face of highly dynamic workloads, we developed a self- adaptive and model-independent neural fuzzy controller, which is capable of self-constructing and adapting its server allocation policies.
    [Show full text]