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<p>本文主要是为ML inference进行serverless的适配。设计点有以下</p>
<ul>
<li>batch处理时的速率讨论</li>
<li>基于五元组和distinct operator的性能预测</li>
<li>贪心解决带约束多项式的调度NP问题</li>
<li>两个不同时间尺度直方图加权来预测pre-warm和keep alive时间</li>
</ul>
<p>基于openfaas，开源：<a
target="_blank" rel="noopener" href="https://github.com/TankLabTJU/INFless/">https://github.com/TankLabTJU/INFless/</a></p>
<h1 id="background">background</h1>
<p>现有的问题</p>
<ul>
<li>High latency：在aws做了实验，小内存跑得很慢，大内存也不快</li>
<li>Batching在commercial平台上没用</li>
<li>resource over-provisioning：CPU-Memory的这种配制方法让那些CPU
intensive的应用浪费了memory</li>
<li>one-to-one
mapping在ML过程中可能会导致因为bursty的workload起过多的instance</li>
<li>OTP batching只能够产生有限的提升，因为没有关于batch config- uration,
instance scheduling and resource allocation的设计</li>
</ul>
<h1 id="design">Design</h1>
<h2 id="workflow">Workflow</h2>
<p>整个工作流程</p>
<ol type="1">
<li>部署函数</li>
<li>根据DAG建立performance prediction model</li>
<li>为了建立performance prediction model，还需要事先profile
operator</li>
<li>用户发送请求</li>
<li>到达的请求打包成内置的批次，并分配给相应的实例</li>
<li>如果工作负载或集群资源状态发生变化，自动扩展引擎会通过从优化的批次-资源决策中挑选出非统一的扩展策略来自适应地调整新的实例配置</li>
<li>使用LSTH避免冷启动</li>
</ol>
<h2 id="built-in-non-uniformed-batching">built-in, non-uniformed
batching</h2>
<p>解释：</p>
<ul>
<li>built-in：内置在平台内的</li>
<li>non-uniformed：每个instance有自己的batching queue，batch
size和resource quotas都是独立配置的</li>
</ul>
<p>本节的design点：
batch有严格的速率限制，既要不丢包，又要不违反SLO（在队列里等太久）</p>
<h2 id="combined-operator-profiling">combined operator profiling</h2>
<p>虽然有很多的call，但是distinct operator，因此采用5-tuple based
profiling进行预测，并combine不同的operator</p>
<ul>
<li>p：输入数据大小</li>
<li>b：batch size</li>
<li>c：cpu相关资源配置</li>
<li>g：gpu相关资源配置</li>
<li>t：完成时间</li>
</ul>
<h2 id="scheduling">scheduling</h2>
<p>这是一个带约束多项式的NP问题。最后用贪心去做。</p>
<h2 id="managing-cold-start-with-lsth">managing cold start with
lsth</h2>
<p>负载的两个特点：</p>
<ul>
<li>长期周期性(LTP)：请求负载总体上呈现出昼夜交替的用户访问模式</li>
<li>短期爆发性(STB)：在短时间内有许多突然变化（包括增加和减少）。</li>
</ul>
<p>解决方法：</p>
<ol type="1">
<li>维护两个窗口，一个是long-term（1天），一个是short-term（一小时）。</li>
<li>5th作为prewarm，99th作为keep alive。</li>
<li>long-term和short-term加权得到一个值</li>
</ol>
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