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@laura_garcia shared a post, 1 day, 4 hours ago

Software Developer, RELIANOID

Network Load Balancer

🌐 𝘎𝘳𝘦𝘢𝘵 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦 𝘴𝘵𝘢𝘳𝘵𝘴 𝘸𝘪𝘵𝘩 𝘪𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘵 𝘵𝘳𝘢𝘧𝘧𝘪𝘤 𝘥𝘪𝘴𝘵𝘳𝘪𝘣𝘶𝘵𝘪𝘰𝘯. A 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗟𝗼𝗮𝗱 𝗕𝗮𝗹𝗮𝗻𝗰𝗲𝗿 helps organizations maximize availability, eliminate bottlenecks, and keep critical applications running smoothly—even under heavy traffic or unexpected failures. In our latest article, we explain how Layer 4 l..

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@laura_garcia shared a post, 2 days ago

Software Developer, RELIANOID

SOC2 compliance

🔐 𝗦𝗢𝗖 𝟮 alignment is about trust, resilience, and doing security right by design. At 𝗥𝗘𝗟𝗜𝗔𝗡𝗢𝗜𝗗, our load balancing and application delivery platform is aligned with the 𝗦𝗢𝗖 𝟮 𝗧𝗿𝘂𝘀𝘁 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗖𝗿𝗶𝘁𝗲𝗿𝗶𝗮—𝗰𝗼𝘃𝗲𝗿𝗶𝗻𝗴 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝘁𝗶𝗮𝗹𝗶𝘁𝘆, 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗣𝗿𝗶𝘃𝗮𝗰𝘆. From encryption ..

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@kevin-faun started using tool BOOM , 2 days, 3 hours ago.

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@goutham-annem started using tool vLLM , 2 days, 9 hours ago.

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@goutham-annem started using tool Kubernetes , 2 days, 9 hours ago.

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@goutham-annem started using tool Istio , 2 days, 9 hours ago.

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@goutham-annem started using tool GPT-5.3-Codex , 2 days, 9 hours ago.

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@goutham-annem started using tool Google Kubernetes Engine (GKE) , 2 days, 9 hours ago.

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@goutham-annem started using tool Claude Code , 2 days, 9 hours ago.

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@goutham-annem started using tool Azure Kubernetes Service (AKS) , 2 days, 9 hours ago.

Kueue is a Kubernetes-native job queueing and workload management system designed for large-scale, mixed compute environments such as AI/ML training, batch workloads, and HPC workflows. Instead of scheduling individual Pods, Kueue operates at the job level, deciding when a job should run based on resource quotas, fair-sharing policies, cluster availability, and workload priorities.

Kueue integrates tightly with Kubernetes, working alongside the default scheduler rather than replacing it. It provides features such as all-or-nothing (gang) admission, workload preemption, quota-based sharing across teams or tenants, and support for advanced frameworks like JobSet and Ray. Its goal is to help Kubernetes clusters run efficiently under heavy load while ensuring that critical, latency-sensitive, or large training jobs receive the resources they need without starving lower-priority workloads.