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World Congress 2023 β€’ Nov 10, 2023

Fifty Shades of Kubernetes Autoscaling

Mario-Leander Reimer

Simple Kubernetes pod replication isn't enough for hyperscale traffic. Discover how to leverage VPA, Carpenter, and KEDA to perfectly right-size operations and eliminate resource waste.

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#1 about 4 min

Transitioning to cloud native hyperscale applications

Building fault-tolerant applications requires modern infrastructure mechanisms to support continuous delivery and extreme workload scalability.

#2 about 3 min

Understanding elasticity in scaling workloads and clusters

Handling varying system demands involves horizontal workload spreading, vertical resource upgrades, and dynamic host defragmentation.

#3 about 5 min

Leveraging events and multi-tier metrics for scaling

Modern autoscalers utilize continuous node state events bridging standard object properties with custom infrastructure registries.

#4 about 5 min

Configuring horizontal pod autoscaling with diverse metrics

Horizontal pod configurations dynamically manipulate container replica counts based on defined threshold endpoints and traffic fluctuations.

#5 about 3 min

Using vertical pod autoscalers for workload rightsizing

Running resource-hungry dependencies in recommendation mode identifies actual processing thresholds instead of relying on baseline guesses.

#6 about 4 min

Managing node capacity with default cluster autoscaling

Aggressive scaling profiles automatically spin up virtual machine segments exactly when deployment demands exceed existing capacity blocks.

#7 about 6 min

Accelerating node provisioning with rapid spot instances

Adopting an agnostic bare-metal autoscaler isolates unpredictable batch costs strictly to immediately expendable spot hardware.

#8 about 2 min

Implementing event-driven component scaling for message queues

Event-based scalers allow background application listeners to entirely drop capacity down to zero against empty transmission pipes.

#9 about 2 min

Essential components for robust infrastructure capacity planning

A comprehensive cloud footprint perfectly meshes endpoint metric servers, specific application scalers, and node balancing tools.

#10 about 3 min

Graceful workload termination and cluster autoscaler reliability

Safely trapping pod shutdown signals ensures active transactions naturally drain before automated hardware consolidations destroy processes.

Matching moments

3:01 min

Evaluating auto-scalable infrastructure and capacity resource requirements

LuΓ­s Ventura LuΓ­s Ventura Β· World Congress 2024

3:07 min

Analyzing cluster right-sizing and scaling experiments inside Kubernetes

Michael Mueller Michael Mueller Β· World Congress 2024

1:10 min

Optimizing Kubernetes clusters for resource and cost efficiency

Christian Grieger Christian Grieger Β· Europe 2026 Virtual

1:09 min

Deploying intelligent cluster node auto-provisioning software

PaweΕ‚ Siwek PaweΕ‚ Siwek Β· Europe 2026 Virtual

1:00 min

Aligning auto scaling infrastructure costs with real business value

Simone Desantis Simone Desantis Β· World Congress 2026 Europe

3:49 min

Configuring horizontal autoscaling for language models with KServe

Aarno Aukia Β· LIVE

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September 25, 2026 Β· 16:10–16:40

Stage 1

Kubernetes Is Not Your Sandbox

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Ivan Burazin CEO and Co-Founder at Daytona

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Senior Systems Engineer at ESET Cybersecurity

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Managing GPUs by Just Asking, Infrastructure in the Age of MCP

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Developer Relations Lead, Community at Runpod

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VP of AI at TensorWave

Kyle Bell
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September 24, 2026 Β· 17:30–18:00

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Boring Failover: Predictable Region Recovery Across 5,000 Microservices

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Garvit Kataria
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