DevOps & SRE notes

kubectl plugin to list allocations (cpu, memory, gpu,... X utilization, requested, limit, allocatable,...) https://github.com/davidB/kubectl-view-allocations

Find vulnerabilities, misconfigurations, secrets, SBOM in containers, Kubernetes, code repositories, clouds and more https://github.com/aquasecurity/trivy

Rhel compatible distribution in danger. RedHat change their policy and licenses agreements https://www.jeffgeerling.com/blog/2023/dear-red-hat-are-you-dumb

This is a Helm plugin which map deprecated or removed Kubernetes APIs in a release to supported APIs https://github.com/helm/helm-mapkubeapis

Debug a target container in a Kubernetes cluster by automatically creating a new, non-invasive, 'debug' container in the same pid, network, user, and ipc namespace as the target container without disrupting the target container. https://github.com/JamesTGrant/kubectl-debug

Adrien "ZeratoR" Nougaret's annual charity event, Zevent, returned this year with a new addition called Zevent Place. Inspired by Reddit's r/place, Zevent Place is a collaborative canvas where donors can draw pixels based on the amount they donate. Developers William Traoré and Alexandre Moghrabi created the platform with several features, such as Pixel Upgrade system and real-time updates, to protect community creations and enhance user experience. The team utilized various technologies like GraphQL, NestJS, Redis, and MinIO, and managed to handle massive amounts of updates while maintaining a low CPU and bandwidth footprint. Although there were challenges, such as unexpected rate limit errors with Cloudflare, the event achieved 98.4% uptime, with the downtime being addressed and resolved promptly. Overall, Zevent Place was a successful project, and valuable lessons were learned throughout its development and implementation. https://medium.com/@alexmogfr/zevent-place-how-we-handled-100k-ccu-on-a-real-time-collective-canvas-71d3d346e0ab

In this post, the author explores various load balancing algorithms, including round robin, weighted round robin, dynamic weighted round robin, and least connections. The simulations demonstrate how these algorithms perform in different scenarios, highlighting their strengths and weaknesses. Round robin performs well in terms of median latency but struggles with higher percentiles. Least connections offer a good balance between simplicity and performance but may not be optimal in terms of latency. The PEWMA algorithm, which combines techniques from dynamic weighted round robin and least connections, shows significant improvements across all latency percentiles but has additional complexity and may not handle dropped requests as well as least connections. Ultimately, the choice of load balancing algorithm depends on the specific requirements of a workload and the performance characteristics that need to be optimized. https://samwho.dev/load-balancing/

macOS and Linux VMs on Apple Silicon to use in CI and other automations https://github.com/cirruslabs/tart

Have you ever heard that company migrate from microservice architecture to monolith? Moving our service to a monolith reduced our infrastructure cost by over 90%. It also increased our scaling capabilities. Today, we’re able to handle thousands of streams and we still have capacity to scale the service even further. Moving the solution to Amazon EC2 and Amazon ECS also allowed us to use the Amazon EC2 compute saving plans that will help drive costs down even further. https://www.primevideotech.com/video-streaming/scaling-up-the-prime-video-audio-video-monitoring-service-and-reducing-costs-by-90

rustic - fast, encrypted, deduplicated backups powered by Rust https://github.com/rustic-rs/rustic

Open-Source Tracing Platform https://github.com/teletrace/teletrace

Roadmapper - A Roadmap as Code (Rac) python library. Generate professional roadmap diagram using python code. https://github.com/csgoh/roadmapper

Efficient GPU utilization is crucial for minimizing infrastructure expenses, especially in large Kubernetes clusters running AI and HPC workloads. NVIDIA MIG enables partitioning GPUs into smaller slices, but using MIG in Kubernetes through the NVIDIA GPU Operator alone has limitations due to static configurations. Dynamic MIG Partitioning addresses these limitations by automating the creation and deletion of MIG profiles based on real-time workload requirements, ensuring optimal GPU utilization. The nos module works alongside the NVIDIA GPU Operator to implement dynamic MIG partitioning, simplifying the management of MIG configurations and reducing operational costs. https://towardsdatascience.com/dynamic-mig-partitioning-in-kubernetes-89db6cdde7a3

f you like this channel, you can support it. If you don't have an account in DO and what to have a simple cloud platform for development and running your project, you can register here by my referral link, https://m.do.co/c/0f8bec835d26 . By this link, you get $100 in credit over 60 days.