If you’re running Kubernetes workloads on Amazon EKS backed by Intel-based instances, you’re leaving significant savings on the table. In this blog, we will look at how many Rafay customers have been able to immediately cut compute costs by ~20-30% with minimal effort and quickly comply with internal cost saving mandates.
In the previous blog, we discussed how Project Slinky bridges the gap between Slurm, the de facto job scheduler in HPC, and Kubernetes, the standard for modern container orchestration.
Project Slinky and Rafay’s GPU Platform-as-a-Service (PaaS) combined provide enterprises and cloud providers with a transformative combination that enables secure, multi-tenant, self-service access to Slurm-based HPC environments on shared Kubernetes clusters. Together, they allow cloud providers and enterprise platform teams to offer Slurm-as-a-Service on Kubernetes—without compromising on performance, usability, or control.
As high-performance computing (HPC) environments evolve, there’s an increasing demand to bridge the gap between traditional HPC job schedulers and modern cloud-native infrastructure. Project Slinky is an open-source project that integrates Slurm, the industry-standard workload manager for HPC, with Kubernetes, the de facto orchestration platform for containers.
This enables organizations to deploy and operate Slurm-based workloads on Kubernetes clusters allowing them to leverage the best of both worlds: Slurm’s mature, job-centric HPC scheduling model and Kubernetes’s scalable, cloud-native runtime environment.
Organizations deploying Kubernetes in on-premises data centers or hybrid cloud environments often face challenges with exposing services externally. Unlike public cloud providers that offer managed load balancers out of the box, bare metal environments require custom solutions. This is where Cilium steps in as a powerful alternative, offering native load balancing capabilities using BGP (Border Gateway Protocol).
Cilium is more than just a CNI plugin. It enables advanced networking features, such as observability, security, and load balancing—all integrated deeply with the Kubernetes networking model. Specifically, Cilium can advertise Kubernetes LoadBalancer service IPs to external routers using BGP, making these services reachable directly from external networks without needing to rely on cloud-native load balancers or manual proxy setups. This is ideal for enterprises running bare metal Kubernetes clusters, air-gapped environments, or hybrid cloud setups.
Want to dive deeper? Check out our introductory blog on Cilium’s Kubernetes load balancing capabilities. Navigate to the detailed step-by-step instructions for additional information.
In Kubernetes, exposing services of type LoadBalancer in on-prem or bare-metal environments typically requires a dedicated "Layer 2" or "BGP-based" software load balancer—such as MetalLB. While MetalLB has been the go-to solution for this use case, recent advances in Cilium, a powerful eBPF-based Kubernetes networking stack, offer a modern and more integrated alternative.
Cilium isn’t just a fast, scalable Container Network Interface (CNI). It also includes cilium-lb, a built-in eBPF-powered load balancer that can replace MetalLB with a more performant, secure, and cloud-native approach.
Enterprises are increasingly leveraging Amazon SageMaker AI to empower their data science teams with scalable, managed machine learning (ML) infrastructure. However, without proper administrative controls, SageMaker AI usage can lead to unexpected cost overruns and significant waste.
In large organizations where dozens or hundreds of data scientists may be experimenting concurrently, this risk compounds quickly.
In this step-by-step guide, the Bioinformatics data scientist will use Rafay's end user portal to launch a well resourced remote VM and run a series of BioContainers with Docker.
In today's fast-paced world of bioinformatics, the constant evolution of tools, dependencies, and operating system environments presents a significant challenge. Researchers often spend countless hours grappling with software installation, configuration, and version conflicts, hindering their ability to focus on scientific discovery. Enter biocontainers – a revolutionary approach that leverages containerization technology to package bioinformatics software and its entire environment into self-contained, portable units.
Imagine a meticulously organized lab where every experiment, regardless of its complexity, can be instantly replicated with identical results.
This is the promise of biocontainers. Built upon established container platforms like Docker and Singularity, biocontainers encapsulate everything a bioinformatics tool needs to run: the application itself, its libraries, dependencies, and even specific operating system configurations.
In the world of GPU clouds, where speed, scalability, and efficiency are paramount, it’s surprising how many “Neo cloud” providers still manage their infrastructure the old-fashioned way—through spreadsheets.
As laughable as it sounds, this is the harsh reality. Inventory management, one of the most foundational aspects of a reliable cloud platform, is often overlooked or under built. And for modern GPU clouds, that’s a deal breaker.
In Kubernetes, autoscaling is key to ensuring application performance while managing infrastructure costs. Two powerful tools that help achieve this are the Horizontal Pod Autoscaler (HPA) and Kubernetes Event-Driven Autoscaling (KEDA). While they share the goal of scaling workloads, their approaches and capabilities are actually very different and distinct.
In this introductory blog, we will provide a bird's eye view of how they compare, and when you might choose one over the other.
