Deploying to gh-pages from @ 1708dba 🚀

feed.xml

@@ -1,4 +1,4 @@
-<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"><generator uri="https://jekyllrb.com/" version="4.3.4">Jekyll</generator><link href="https://shaoleiren.github.io/feed.xml" rel="self" type="application/atom+xml"/><link href="https://shaoleiren.github.io/" rel="alternate" type="text/html" hreflang="en"/><updated>2024-11-22T08:38:44+00:00</updated><id>https://shaoleiren.github.io/feed.xml</id><title type="html">blank</title><subtitle></subtitle><entry><title type="html">a post with bibliography</title><link href="https://shaoleiren.github.io/blog/2023/post-bibliography/" rel="alternate" type="text/html" title="a post with bibliography"/><published>2023-07-12T13:56:00+00:00</published><updated>2023-07-12T13:56:00+00:00</updated><id>https://shaoleiren.github.io/blog/2023/post-bibliography</id><content type="html" xml:base="https://shaoleiren.github.io/blog/2023/post-bibliography/"><![CDATA[<p>This post shows how to add bibliography to simple blog posts. If you would like something more academic, check the <a href="/blog/2021/distill/">distill style post</a>.</p>]]></content><author><name></name></author><category term="sample-posts"/><category term="formatting"/><category term="bib"/><summary type="html"><![CDATA[an example of a blog post with bibliography]]></summary></entry><entry><title type="html">a post with jupyter notebook</title><link href="https://shaoleiren.github.io/blog/2023/jupyter-notebook/" rel="alternate" type="text/html" title="a post with jupyter notebook"/><published>2023-07-04T12:57:00+00:00</published><updated>2023-07-04T12:57:00+00:00</updated><id>https://shaoleiren.github.io/blog/2023/jupyter-notebook</id><content type="html" xml:base="https://shaoleiren.github.io/blog/2023/jupyter-notebook/"><![CDATA[<p>To include a jupyter notebook in a post, you can use the following code:</p> <div class="language-html highlighter-rouge"><div class="highlight"><pre class="highlight"><code>{::nomarkdown}
+<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"><generator uri="https://jekyllrb.com/" version="4.3.4">Jekyll</generator><link href="https://shaoleiren.github.io/feed.xml" rel="self" type="application/atom+xml"/><link href="https://shaoleiren.github.io/" rel="alternate" type="text/html" hreflang="en"/><updated>2024-11-23T06:10:21+00:00</updated><id>https://shaoleiren.github.io/feed.xml</id><title type="html">blank</title><subtitle></subtitle><entry><title type="html">a post with bibliography</title><link href="https://shaoleiren.github.io/blog/2023/post-bibliography/" rel="alternate" type="text/html" title="a post with bibliography"/><published>2023-07-12T13:56:00+00:00</published><updated>2023-07-12T13:56:00+00:00</updated><id>https://shaoleiren.github.io/blog/2023/post-bibliography</id><content type="html" xml:base="https://shaoleiren.github.io/blog/2023/post-bibliography/"><![CDATA[<p>This post shows how to add bibliography to simple blog posts. If you would like something more academic, check the <a href="/blog/2021/distill/">distill style post</a>.</p>]]></content><author><name></name></author><category term="sample-posts"/><category term="formatting"/><category term="bib"/><summary type="html"><![CDATA[an example of a blog post with bibliography]]></summary></entry><entry><title type="html">a post with jupyter notebook</title><link href="https://shaoleiren.github.io/blog/2023/jupyter-notebook/" rel="alternate" type="text/html" title="a post with jupyter notebook"/><published>2023-07-04T12:57:00+00:00</published><updated>2023-07-04T12:57:00+00:00</updated><id>https://shaoleiren.github.io/blog/2023/jupyter-notebook</id><content type="html" xml:base="https://shaoleiren.github.io/blog/2023/jupyter-notebook/"><![CDATA[<p>To include a jupyter notebook in a post, you can use the following code:</p> <div class="language-html highlighter-rouge"><div class="highlight"><pre class="highlight"><code>{::nomarkdown}
 {% assign jupyter_path = "assets/jupyter/blog.ipynb" | relative_url %}
 {% capture notebook_exists %}{% file_exists assets/jupyter/blog.ipynb %}{% endcapture %}
 {% if notebook_exists == "true" %}

index.html

@@ -40,6 +40,13 @@
   <span class="na">month</span> <span class="p">=</span> <span class="s">{}</span><span class="p">,</span>
   <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span>
   <span class="na">url</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2411.04204}</span><span class="p">,</span>
+<span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <div class="col-sm-2 abbr"><abbr class="badge">CACM</abbr></div> <div id="Environmentally_Equitable_AI_CACM_2024" class="col-sm-10"> <div class="title"><a href="https://arxiv.org/abs/2307.05494" rel="external nofollow noopener" target="_blank">Towards Environmentally Equitable AI</a></div> <div class="author"> Mohammad Hajiesmaili, <em>Shaolei Ren</em>, Ramesh Sitaraman, and Adam Wierman</div> <div class="periodical"> <em>Communications of the ACM (accepted)</em>, 2024 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/abs/2307.05494" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">HTML</a> </div> <div class="badges"> </div> <div class="abstract hidden"> <p>The skyrocketing demand for artificial intelligence (AI) has created an enormous appetite for globally deployed power-hungry servers. As a result, the environmental footprint of AI systems has come under increasing scrutiny. More crucially, the current way that we exploit AI workloads’ flexibility and manage AI systems can lead to wildly different environmental impacts across locations, increasingly raising environmental inequity concerns and creating unintended sociotechnical consequences. In this paper, we advocate environmental equity as a priority for the management of future AI systems, advancing the boundaries of existing resource management for sustainable AI and also adding a unique dimension to AI fairness. Concretely, we uncover the potential of equity-aware geographical load balancing to fairly re-distribute the environmental cost across different regions, followed by algorithmic challenges. We conclude by discussing a few future directions to exploit the full potential of system management approaches to mitigate AI’s environmental inequity.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">Environmentally_Equitable_AI_CACM_2024</span><span class="p">,</span>
+  <span class="na">title</span> <span class="p">=</span> <span class="s">{Towards Environmentally Equitable AI}</span><span class="p">,</span>
+  <span class="na">author</span> <span class="p">=</span> <span class="s">{Hajiesmaili, Mohammad and Ren, Shaolei and Sitaraman, Ramesh and Wierman, Adam}</span><span class="p">,</span>
+  <span class="na">journal</span> <span class="p">=</span> <span class="s">{Communications of the ACM (accepted)}</span><span class="p">,</span>
+  <span class="na">month</span> <span class="p">=</span> <span class="s">{}</span><span class="p">,</span>
+  <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span>
+  <span class="na">url</span> <span class="p">=</span> <span class="s">{https://arxiv.org/abs/2307.05494}</span><span class="p">,</span>
 <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <div class="col-sm-2 abbr"><abbr class="badge">e-Energy</abbr></div> <div id="Environmentally_Equitable_AI_eEnergy_2024" class="col-sm-10"> <div class="title"><a href="https://arxiv.org/abs/2307.05494" rel="external nofollow noopener" target="_blank">Towards Environmentally Equitable AI via Geographical Load Balancing</a></div> <div class="author"> Pengfei Li, Jianyi Yang, Adam Wierman, and <em>Shaolei Ren</em> </div> <div class="periodical"> <em>e-Energy</em>, 2024 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/abs/2307.05494" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">HTML</a> </div> <div class="badges"> </div> <div class="abstract hidden"> <p>Fueled by the soaring popularity of large language and foundation models, the accelerated growth of artificial intelligence (AI) models’ enormous environmental footprint has come under increased scrutiny. While many approaches have been proposed to make AI more energy-efficient and environmentally friendly, environmental inequity – the fact that AI’s environmental footprint can be disproportionately higher in certain regions than in others – has emerged, raising social-ecological justice concerns. This paper takes a first step toward addressing AI’s environmental inequity by balancing its regional negative environmental impact. Concretely, we focus on the carbon and water footprints of AI model inference and propose equity-aware geographical load balancing (GLB) to explicitly address AI’s environmental impacts on the most disadvantaged regions. We run trace-based simulations by considering a set of 10 geographically-distributed data centers that serve inference requests for a large language AI model. The results demonstrate that existing GLB approaches may amplify environmental inequity while our proposed equity-aware GLB can significantly reduce the regional disparity in terms of carbon and water footprints.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">Environmentally_Equitable_AI_eEnergy_2024</span><span class="p">,</span>
   <span class="na">title</span> <span class="p">=</span> <span class="s">{Towards Environmentally Equitable AI via Geographical Load Balancing}</span><span class="p">,</span>
   <span class="na">author</span> <span class="p">=</span> <span class="s">{Li, Pengfei and Yang, Jianyi and Wierman, Adam and Ren, Shaolei}</span><span class="p">,</span>