Show HN: Pebble Falcon – 自主资源优化与碳感知 Kubernetes 调度器
1 分•作者: kevalshah90•6 个月前
大家好，我是Keval，Pebble Falcon背后的AI团队成员之一。<p>什么是Pebble Falcon：<p>两个长期运行的agent，部署在您的k8s集群内部。<p>> PerfectFit:<p>- 实时监控pod / VM / GPU的利用率<p>- 计算剩余资源<p>- 生成JSON补丁，用于调整CPU、内存和存储的大小<p>- 可选的人工审批步骤<p>> EcoAgent<p>- 轮询实时电网碳排放数据，重新配置GPU节点和pod，以最大限度地利用清洁能源。<p>- 将批处理作业重新排队到gCO₂/kWh最低的区域，同时满足您的延迟SLA要求<p>一个轻量级的Web UI显示节省的成本、避免的功耗以及30天预测。<p>数据不出集群；唯一的输入是公共碳排放强度数据。<p>我们为什么构建它：<p>我们一直看到GPU节点空闲率低于20%，而ETL作业却在碳排放高的地区运行。手动“调整大小冲刺”永远无法跟上需求。<p>试点结果（40个节点GPU集群，30天）<p>消除了70%的空闲计算资源<p>避免了4160公斤CO₂排放（≈六次纽约到伦敦的航班）<p>Pebble Falcon需要访问集群，因此目前还没有公开的沙盒。我们录制了一个3分钟的agent概述。<p>视频演示：<a href="https://youtu.be/DTpHxAmVrAo" rel="nofollow">https://youtu.be/DTpHxAmVrAo</a><p>如果您有兴趣试用，请留言，我们会与您联系，并将您添加到我们的试点测试中：<a href="https://www.gopebble.com/sign-up" rel="nofollow">https://www.gopebble.com/sign-up</a><p>欢迎提问，关于AI工作负载的能源、成本和计算感知调度。
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Hi HN — I’m Keval, part of the AI team behind Pebble Falcon.<p>What it Pebble Falcon:<p>Two long-running agents you deploy inside your k8s cluster.<p>> PerfectFit:<p>- Streams pod / VM / GPU utilization<p>- Calculates head-room<p>- Generates JSON patches to right-size CPU, memory and storage<p>- Optional human-approval step<p>> EcoAgent<p>- Polls real-time grid-carbon feed and re-configures GPU nodes and pods to maximize clean energy use.<p>- Re-queues batch jobs to the region with the lowest gCO₂/kWh that still meets your latency SLA<p>A lightweight web UI shows cost saved, watts avoided and a 30-day projection.<p>No data leaves the cluster; the only inbound feed is public carbon intensity.<p>Why we built it:<p>We kept seeing GPU nodes idling below 20 % while ETL jobs ran in high-carbon regions. Manual “rightsizing sprints” never caught up.<p>Pilot result (40-node GPU cluster, 30 days)
70% idle compute removed<p>4160 kg CO₂ avoided (≈ six NYC→London flights)<p>Pebble Falcon needs access to clusters so no public sandbox yet. Instead we recorded a 3-min overview of the agents.<p>Video Demo: <a href="https://youtu.be/DTpHxAmVrAo" rel="nofollow">https://youtu.be/DTpHxAmVrAo</a><p>However if you’re interested in trying it out, comment and we’ll get in contact and can add you to our pilot test: <a href="https://www.gopebble.com/sign-up" rel="nofollow">https://www.gopebble.com/sign-up</a><p>AMA about energy, cost and compute aware scheduling for AI workloads.