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<h1 id="global-scheduler-multi-instance-load-balancing">Global Scheduler: Multi-Instance Load Balancing</h1>
<h2 id="design-overview">Design Overview</h2>
<p>Cluster nodes autonomously pull tasks from other nodes during idle periods based on real-time workload, then push execution results back to the originating node.</p>
<h3 id="what-problem-does-the-global-scheduler-solve">What Problem Does the Global Scheduler Solve?</h3>
<p><img alt="Local Scheduler" src="../images/LocalScheduler.png" /></p>
<p>Standard load balancing distributes requests using round-robin strategy, ensuring equal request counts per inference instance.</p>
<p>In LLM scenarios, request processing time varies significantly based on input/output token counts. Even with equal request distribution, inference completion times differ substantially across instances.</p>
<p>The global scheduler solves this imbalance through cluster-level optimization.</p>
<h2 id="how-the-global-scheduler-works">How the Global Scheduler Works</h2>
<p><img alt="Global Scheduler" src="../images/GlobalScheduler.png" /></p>
<p>As shown:
* Nodes 1, 2, and n each receive 3 requests
* At time T:
* Node 1 completes all tasks
* Nodes 2 and n process their second requests (1 request remaining each)
* Node 1 steals a task from Node 2, processes it, and pushes results to Node 2’s response queue</p>
<p>This secondary load balancing strategy will
✅ Maximizes cluster resource utilization
✅ Reduces Time-To-First-Token (TTFT)</p>
<h1 id="how-to-use-global-scheduler">How to use Global Scheduler</h1>
<h2 id="prerequisite-redis-setup">Prerequisite: Redis Setup</h2>
<h3 id="conda-installation">conda installation</h3>
<pre><code class="language-bash">conda install redis
# Launch
nohup redis-server &gt; redis.log 2&gt;&amp;1 &amp;
</code></pre>
<h3 id="apt-installation-debianubuntu">apt installation (Debian/Ubuntu)</h3>
<pre><code class="language-bash"># Install
sudo apt install redis-server -y
# Launch
sudo systemctl start redis-server
</code></pre>
<h3 id="yum-installation-rhelcentos">yum installation (RHEL/CentOS)</h3>
<pre><code class="language-bash"># Install
sudo yum install redis -y
# Launch
sudo systemctl start redis
</code></pre>
<h2 id="launching-fastdeploy">Launching FastDeploy</h2>
<pre><code class="language-bash">python -m fastdeploy.entrypoints.openai.api_server \
--port 8801 \
--metrics-port 8802 \
--engine-worker-queue-port 8803 \
--model baidu/ERNIE-4.5-0.3B-Paddle \
--scheduler-name global \
--scheduler-ttl 900 \
--scheduler-host &quot;127.0.0.1&quot; \
--scheduler-port 6379 \
--scheduler-db 0 \
--scheduler-password &quot;&quot; \
--scheduler-topic &quot;default&quot; \
--scheduler-min-load-score 3 \
--scheduler-load-shards-num 1
</code></pre>
<p><a href="../../online_serving/scheduler/">Scheduler Launching Parameter</a></p>
<h3 id="deployment-notes">Deployment notes:</h3>
<ol>
<li>Execute this command on multiple machines to create inference instances</li>
<li>For single-machine multi-instance deployments: ensure port uniqueness</li>
<li>Use Nginx for external load balancing alongside the global scheduler’s internal balancing</li>
</ol>
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