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    <title>InfoQ - Performance</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ Performance feed</description>
    <item>
      <title>Article: The Mathematics of Backlogs: Capacity Planning for Queue Recovery</title>
      <link>https://www.infoq.com/articles/capacity-planning-queue-recovery/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Performance</link>
      <description>&lt;img src="https://res.infoq.com/articles/capacity-planning-queue-recovery/en/headerimage/The-Mathematics-of-Backlogs-Capacity-Planning-for-Queue-Recovery-header-1778227922596.jpg"/&gt;&lt;p&gt;Backlogs in distributed systems are arithmetic problems, not mysteries. This article provides practical formulas for calculating backlog drain time, sizing consumer headroom, and setting auto-scaling triggers. It covers key failure modes — retry amplification, metastable states, and cascading pipeline bottlenecks — plus when to shed load instead of draining.&lt;/p&gt; &lt;i&gt;By Rajesh Kumar Pandey&lt;/i&gt;</description>
      <category>Capacity Planning</category>
      <category>Failure</category>
      <category>Load Testing</category>
      <category>Queue</category>
      <category>DevOps</category>
      <category>Architecture &amp; Design</category>
      <category>article</category>
      <pubDate>Thu, 21 May 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/capacity-planning-queue-recovery/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Performance</guid>
      <dc:creator>Rajesh Kumar Pandey</dc:creator>
      <dc:date>2026-05-21T09:00:00Z</dc:date>
      <dc:identifier>/articles/capacity-planning-queue-recovery/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: The AI Gateway: Scaling Centralized Inference across Decentralized Teams</title>
      <link>https://www.infoq.com/presentations/ai-gateway-scalability/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Performance</link>
      <description>&lt;img src="https://res.infoq.com/presentations/ai-gateway-scalability/en/mediumimage/medium-1778663382364.jpg"/&gt;&lt;p&gt;Meryem Arik discusses why modern engineering teams face "inference chaos" and how AI model gateways provide a critical control layer. She explains the balance between empowering decentralized teams to choose the best models and maintaining centralized oversight for security, RBAC, and cost control.  Explore open-source solutions like LiteLLM and Doubleword to streamline your AI infra.&lt;/p&gt; &lt;i&gt;By Meryem Arik&lt;/i&gt;</description>
      <category>QCon AI 2025</category>
      <category>Scalability</category>
      <category>Artificial Intelligence</category>
      <category>Transcripts</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Wed, 20 May 2026 12:40:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-gateway-scalability/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Performance</guid>
      <dc:creator>Meryem Arik</dc:creator>
      <dc:date>2026-05-20T12:40:00Z</dc:date>
      <dc:identifier>/presentations/ai-gateway-scalability/en</dc:identifier>
    </item>
    <item>
      <title>Article: Kernel-Level Ground Truth: Why eBPF is Replacing User-Space Agents for Security Observability</title>
      <link>https://www.infoq.com/articles/ebpf-for-security-observability/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Performance</link>
      <description>&lt;img src="https://res.infoq.com/articles/ebpf-for-security-observability/en/headerimage/ebpf-for-security-observability-header-1778674557176.jpg"/&gt;&lt;p&gt;eBPF is emerging as a preferred method for security observability over traditional user-space agents. By attaching probes directly to the Linux kernel's syscall interface, it provides consistent visibility even during container-level compromises. eBPF reduces security-related CPU consumption and limits data volume by performing filtering at the kernel level, enhancing operational efficiency.&lt;/p&gt; &lt;i&gt;By Niranjan Sharma&lt;/i&gt;</description>
      <category>eBPF</category>
      <category>Application Security</category>
      <category>Observability</category>
      <category>DevOps</category>
      <category>article</category>
      <pubDate>Tue, 19 May 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/ebpf-for-security-observability/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Performance</guid>
      <dc:creator>Niranjan Sharma</dc:creator>
      <dc:date>2026-05-19T09:00:00Z</dc:date>
      <dc:identifier>/articles/ebpf-for-security-observability/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Using AI as a Thinking Partner for Large-Scale Engineering Systems</title>
      <link>https://www.infoq.com/presentations/ai-large-scale-engineering-systems/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Performance</link>
      <description>&lt;img src="https://res.infoq.com/presentations/ai-large-scale-engineering-systems/en/mediumimage/medium-1778069080461.jpeg"/&gt;&lt;p&gt;Julie Qiu explains how AI serves as a "thinking partner" for engineering leaders. She discusses five distinct roles - Archaeologist, Experimenter, Critic, Author, and Reviewer - to manage the cognitive load of 400+ repositories. She shares how AI provides the "RAM" needed to synthesize legacy context, pressure-test designs, and accelerate high-level architectural decisions.&lt;/p&gt; &lt;i&gt;By Julie Qiu&lt;/i&gt;</description>
      <category>QCon AI 2025</category>
      <category>Scalability</category>
      <category>Artificial Intelligence</category>
      <category>Transcripts</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Culture &amp; Methods</category>
      <category>presentation</category>
      <pubDate>Fri, 15 May 2026 13:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-large-scale-engineering-systems/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Performance</guid>
      <dc:creator>Julie Qiu</dc:creator>
      <dc:date>2026-05-15T13:00:00Z</dc:date>
      <dc:identifier>/presentations/ai-large-scale-engineering-systems/en</dc:identifier>
    </item>
    <item>
      <title>Mini book: Architecting Autonomy: Decentralising Architecture Inside an Organization</title>
      <link>https://www.infoq.com/minibooks/architecting-autonomy/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Performance</link>
      <description>&lt;img src="https://res.infoq.com/minibooks/architecting-autonomy/en/smallimage/emag-124-Architecting-Autonomy-thumb-image-1778565056506.jpg"/&gt;&lt;p&gt;As AI accelerates delivery cycles, traditional centralized architecture becomes a bottleneck. This eMag brings together practitioner insights on decentralizing decision-making and moving from approval chains to guardrails. Discover frameworks for rethinking the architect’s role, creating enabling platforms, and balancing edge autonomy with the strategic coherence needed to scale effectively.&lt;/p&gt; &lt;i&gt;By InfoQ&lt;/i&gt;</description>
      <category>Scalability</category>
      <category>InfoQ Certification Program</category>
      <category>Architecture ICSAET</category>
      <category>Generative AI</category>
      <category>Architecture Decision Records</category>
      <category>Sociotechnical Architecture</category>
      <category>Artificial Intelligence</category>
      <category>AI Architecture</category>
      <category>Governance</category>
      <category>Leadership</category>
      <category>Platforms</category>
      <category>Emergent Architecture</category>
      <category>Architecture &amp; Design</category>
      <category>minibook</category>
      <pubDate>Fri, 15 May 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/minibooks/architecting-autonomy/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Performance</guid>
      <dc:creator>InfoQ</dc:creator>
      <dc:date>2026-05-15T11:00:00Z</dc:date>
      <dc:identifier>/minibooks/architecting-autonomy/en</dc:identifier>
    </item>
    <item>
      <title>Pinterest Engineers Eliminate CPU Zombies to Resolve Production Bottlenecks</title>
      <link>https://www.infoq.com/news/2026/05/pinterest-cpu-zombies-bottleneck/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Performance</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/05/pinterest-cpu-zombies-bottleneck/en/headerimage/header-1778308038640.jpeg"/&gt;&lt;p&gt;Pinterest identified and resolved CPU starvation issues that affected machine learning training jobs on its Kubernetes-based platform, PinCompute. The engineers traced the problem to an unused Amazon ECS agent, which caused memory cgroup leaks. By disabling the agent, they stabilised performance. This case illustrates the importance of understanding system defaults for effective troubleshooting.&lt;/p&gt; &lt;i&gt;By Mark Silvester&lt;/i&gt;</description>
      <category>Performance Tuning</category>
      <category>Debugging</category>
      <category>Performance</category>
      <category>Observability</category>
      <category>DevOps</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Thu, 14 May 2026 10:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/05/pinterest-cpu-zombies-bottleneck/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Performance</guid>
      <dc:creator>Mark Silvester</dc:creator>
      <dc:date>2026-05-14T10:00:00Z</dc:date>
      <dc:identifier>/news/2026/05/pinterest-cpu-zombies-bottleneck/en</dc:identifier>
    </item>
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