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497 lines
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<a href="/20241210233721-brain_moc.html"> <img src="/assets/gr.png" alt="Site Logo" class="banner-logo" /> </a>
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<div id="updated">Updated: 2025-12-28 Sun 19:48</div>
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<h1 class="title">ise<sub>week</sub><sub>5</sub></h1>
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<div class="title-metadata">
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<span class="metadata-item">
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<span class="metadata-label">planted:</span>
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<span class="metadata-value">2025-03-31</span>
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</span>
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<span class="metadata-item">
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<span class="metadata-label">last tended to:</span>
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<span class="metadata-value">2025-12-28</span>
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</div>
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<div id="outline-container-org958f870" class="outline-2">
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<h2 id="org958f870"><a href="#org958f870"><span class="todo TODO">TODO</span> Model free Tuning (for ORM) (pages 9-17)</a></h2>
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</div>
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||
<div id="outline-container-org3b61b9b" class="outline-2">
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<h2 id="org3b61b9b"><a href="#org3b61b9b"><span class="todo TODO">TODO</span> Model free Tuning (BestConfig) (pages 18-23)</a></h2>
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</div>
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<div id="outline-container-org3046aac" class="outline-2">
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<h2 id="org3046aac"><a href="#org3046aac">5.1 Model-Free Tuning for ORM Systems - Intelligent Software Engineering</a></h2>
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<div class="outline-text-2" id="text-org3046aac">
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||
</div>
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<div id="outline-container-org9196a2e" class="outline-3">
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<h3 id="org9196a2e"><a href="#org9196a2e">1. Introduction and Background</a></h3>
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<div class="outline-text-3" id="text-org9196a2e">
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<ul class="org-ul">
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<li>This approach was proposed by Singh et al. (2016).</li>
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<li>The goal is to optimize Object-Relational Mapping (ORM) systems without relying on models.</li>
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<li>Uses <b><b>NSGA-II</b></b>, a multi-objective evolutionary algorithm, to handle <b><b>multiple performance concerns</b></b>.</li>
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</ul>
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<p>
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Reference: Singh, Ravjot et al. <b>“Optimizing the performance-related configurations of object-relational mapping frameworks using a multi-objective genetic algorithm.”</b> ACM/SPEC ICPE 2016.
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</p>
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||
</div>
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||
</div>
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||
<div id="outline-container-org7ad873e" class="outline-3">
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<h3 id="org7ad873e"><a href="#org7ad873e">2. Architecture and Setup</a></h3>
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<div class="outline-text-3" id="text-org7ad873e">
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<ul class="org-ul">
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<li>Focuses exclusively on <b><b>binary configuration options</b></b>.</li>
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<li>Example of a configuration: `{0011}` – a binary vector where each bit represents a configuration toggle (on/off).</li>
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<li>Evaluates performance using three objective metrics:
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<ul class="org-ul">
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<li><b><b>Execution time</b></b></li>
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<li><b><b>CPU load</b></b></li>
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<li><b><b>Memory consumption</b></b></li>
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</ul></li>
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<li>Other components follow the standard NSGA-II flow: selection, crossover, mutation, and fitness evaluation.</li>
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||
</ul>
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||
</div>
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||
</div>
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||
<div id="outline-container-org4229c33" class="outline-3">
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||
<h3 id="org4229c33"><a href="#org4229c33">3. Stopping Criteria</a></h3>
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<div class="outline-text-3" id="text-org4229c33">
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<p>
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||
Two specific stopping rules are proposed for determining when to terminate the evolutionary process:
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</p>
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||
</div>
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||
<div id="outline-container-org93f0342" class="outline-4">
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||
<h4 id="org93f0342"><a href="#org93f0342">a. Setting 1: t-test-based Stopping</a></h4>
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||
<div class="outline-text-4" id="text-org93f0342">
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||
<ul class="org-ul">
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<li>Conducts statistical <b><b>t-tests</b></b> to compare changes in objective values between generations.</li>
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<li>For each pair of consecutive generations \( g_i \) and \( g_j \):
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<ul class="org-ul">
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<li>Run a t-test on ∆CPU and ∆MEM between all configurations in both generations.</li>
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</ul></li>
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<li>If <b><b>all p-values > 0.05</b></b> for <b><b>two consecutive generations</b></b>, it indicates <b><b>no statistically significant improvement</b></b>, and the algorithm is stopped.</li>
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||
</ul>
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||
</div>
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||
</div>
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||
<div id="outline-container-orgc722cfe" class="outline-4">
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||
<h4 id="orgc722cfe"><a href="#orgc722cfe">b. Setting 2: Mutual Dominance Rate (MDR)</a></h4>
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<div class="outline-text-4" id="text-orgc722cfe">
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||
<ul class="org-ul">
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||
<li>Measures how much progress is made by comparing the current and previous generation.</li>
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<li>Let:
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<ul class="org-ul">
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<li>Set A = configurations from the previous generation</li>
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<li>Set B = configurations from the current generation</li>
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</ul></li>
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<li>Define \( dom(A,B) \) as the number of configurations in A that are <b><b>dominated</b></b> by any configuration in B.</li>
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</ul>
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<p>
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Interpretation:
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</p>
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<ul class="org-ul">
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<li><b><b>MDR = 0</b></b>: No progress — performance plateau</li>
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<li><b><b>MDR < 0</b></b>: Regression — performance is deteriorating</li>
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<li><b><b>MDR > 0</b></b>: Improvement — current generation is better than the last</li>
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</ul>
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||
</div>
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||
</div>
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||
</div>
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||
<div id="outline-container-orgdabead0" class="outline-3">
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<h3 id="orgdabead0"><a href="#orgdabead0">4. Termination Condition</a></h3>
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<div class="outline-text-3" id="text-orgdabead0">
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<ul class="org-ul">
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<li>The tuning process should stop if <b><b>any</b></b> of the defined stopping conditions (t-test or MDR) are met.</li>
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||
</ul>
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||
</div>
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||
</div>
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||
<div id="outline-container-org75b7607" class="outline-3">
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||
<h3 id="org75b7607"><a href="#org75b7607">5. Experimental Results</a></h3>
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<div class="outline-text-3" id="text-org75b7607">
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<ul class="org-ul">
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<li>NSGA-II consistently found configurations that ranked within the <b><b>top 25%</b></b> of all possible configurations across tested applications.</li>
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<li>Results were obtained by combining different <b><b>aggregation functions</b></b> and <b><b>stopping rules</b></b>, demonstrating strong generalization and effectiveness.</li>
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</ul>
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||
</div>
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</div>
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</div>
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||
<div id="outline-container-org5e9c0bc" class="outline-2">
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||
<h2 id="org5e9c0bc"><a href="#org5e9c0bc">5.2 Model-Free Tuning with BestConfig - Intelligent Software Engineering</a></h2>
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||
<div class="outline-text-2" id="text-org5e9c0bc">
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||
</div>
|
||
<div id="outline-container-org200b5fc" class="outline-3">
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||
<h3 id="org200b5fc"><a href="#org200b5fc">1. Introduction and Background</a></h3>
|
||
<div class="outline-text-3" id="text-org200b5fc">
|
||
<ul class="org-ul">
|
||
<li>BestConfig is a model-free configuration tuning system proposed by <b><b>Zhu et al. (2017)</b></b>.</li>
|
||
<li>It focuses on tuning for <b><b>a single performance objective</b></b> (e.g., throughput, latency).</li>
|
||
<li>Utilizes <b><b>local search techniques</b></b> rather than global evolutionary approaches.</li>
|
||
<li>Employs <b><b>label encoding</b></b> for parameters (e.g., {0, 23, 100}).</li>
|
||
<li>Key strategy: aggressively explore <b><b>promising regions</b></b> of the configuration space.</li>
|
||
</ul>
|
||
|
||
<p>
|
||
Reference: Zhu, Yuqing et al. <b>“BestConfig: tapping the performance potential of systems via automatic configuration tuning.”</b> SoCC 2017.
|
||
</p>
|
||
</div>
|
||
</div>
|
||
<div id="outline-container-org92fae6e" class="outline-3">
|
||
<h3 id="org92fae6e"><a href="#org92fae6e">2. Architecture Overview</a></h3>
|
||
<div class="outline-text-3" id="text-org92fae6e">
|
||
<ul class="org-ul">
|
||
<li>BestConfig is designed to intelligently search a high-dimensional configuration space.</li>
|
||
<li>Architecture relies on two core components:
|
||
<ul class="org-ul">
|
||
<li>DDS (Divide & Diverge Sampling)</li>
|
||
<li>RBS (Recursive Bound & Search)</li>
|
||
</ul></li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
<div id="outline-container-orgd75c60f" class="outline-3">
|
||
<h3 id="orgd75c60f"><a href="#orgd75c60f">3. DDS: Divide & Diverge Sampling</a></h3>
|
||
<div class="outline-text-3" id="text-orgd75c60f">
|
||
<ul class="org-ul">
|
||
<li>Purpose: Ensures <b><b>coverage</b></b> of the entire configuration space by dividing it into <b><b>subspaces</b></b>.</li>
|
||
<li>Process:
|
||
<ol class="org-ol">
|
||
<li>Each configuration parameter’s range is divided into <b><b>k intervals</b></b>.</li>
|
||
<li>These intervals are combined across all parameters, forming \( k^n \) subspaces.</li>
|
||
<li><b><b>One random sample</b></b> is taken from each subspace.</li>
|
||
</ol></li>
|
||
|
||
<li>Advantages:
|
||
<ul class="org-ul">
|
||
<li>Avoids bias in sampling (common in uniform random search).</li>
|
||
<li>More likely to sample from all areas of the space.</li>
|
||
<li>Especially useful in <b><b>high-dimensional spaces</b></b>.</li>
|
||
</ul></li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
<div id="outline-container-org0edda77" class="outline-3">
|
||
<h3 id="org0edda77"><a href="#org0edda77">4. RBS: Recursive Bound & Search</a></h3>
|
||
<div class="outline-text-3" id="text-org0edda77">
|
||
<ul class="org-ul">
|
||
<li>Purpose: Locally refines and improves the best-known configuration.</li>
|
||
<li>Steps:
|
||
<ol class="org-ol">
|
||
<li>Identify the best-performing configuration \( C_0 \) from the initial samples.</li>
|
||
<li>Define <b><b>bounds</b></b> for each parameter based on neighboring values around \( C_0 \).</li>
|
||
<li>Sample new points within this bounded space to find a better configuration \( C_1 \).</li>
|
||
<li>Repeat the bounding and sampling process <b><b>recursively</b></b> until no improvement is found.</li>
|
||
</ol></li>
|
||
|
||
<li>Bound Definition:
|
||
<ul class="org-ul">
|
||
<li>For each parameter value in \( C_0 \), the closest lower and higher values in the dataset are chosen as bounds.</li>
|
||
</ul></li>
|
||
|
||
<li>Termination Conditions:
|
||
<ul class="org-ul">
|
||
<li>If no better configuration is found in a recursive round, the search <b><b>restarts from a broader space</b></b>.</li>
|
||
<li>The entire tuning process <b><b>stops</b></b> only when a <b><b>predefined resource budget</b></b> (e.g., time, evaluations) is exhausted.</li>
|
||
</ul></li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
<div id="outline-container-org2f2d133" class="outline-3">
|
||
<h3 id="org2f2d133"><a href="#org2f2d133">5. Results and Observations</a></h3>
|
||
<div class="outline-text-3" id="text-org2f2d133">
|
||
<ul class="org-ul">
|
||
<li>BestConfig consistently finds configurations <b><b>significantly better than the system’s default settings</b></b>.</li>
|
||
<li>Achieves these improvements within a <b><b>reasonable time frame</b></b>, making it practical for real-world use.</li>
|
||
</ul>
|
||
|
||
|
||
<p class="backlinks-section" id="backlinks">
|
||
Backlinks
|
||
</p>
|
||
<ul class="org-ul backlinks-list">
|
||
<li><a href="20250329114733-ise.html#ID-c69e4c4d-2fb4-4cf1-a835-a235cf6db8e9">ise</a></li>
|
||
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|
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|
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