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[[File:Infocepo-picture.png|thumb|right|Discover cloud | [[File:Infocepo-picture.png|thumb|right|Discover cloud and AI on infocepo.com]] | ||
= | = infocepo.com – Cloud, AI & Labs = | ||
Welcome | Welcome to the '''infocepo.com''' portal. | ||
This wiki is intended for system administrators, cloud engineers, developers, students, and enthusiasts who want to: | |||
* Understand modern architectures (Kubernetes, OpenStack, bare-metal, HPC…) | |||
* Deploy private AI assistants and productivity tools | |||
* Build hands-on labs to learn by doing | |||
* Prepare large-scale audits, migrations, and automations | |||
The goal: turn theory into '''reusable scripts, diagrams, and architectures'''. | |||
__TOC__ | __TOC__ | ||
== | ---- | ||
* ''' | |||
* ''' | = Getting started quickly = | ||
* ''' | |||
* ''' | == Recommended paths == | ||
; 1. Build a private AI assistant | |||
* Deploy a typical stack: '''Open WebUI + Ollama + GPU''' (H100 or consumer-grade GPU) | |||
* Add a chat model and a summarization model | |||
* Integrate internal data (RAG, embeddings) | |||
; 2. Launch a Cloud lab | |||
* Create a small cluster (Kubernetes, OpenStack, or bare-metal) | |||
* Set up a deployment pipeline (Helm, Ansible, Terraform…) | |||
* Add an AI service (transcription, summarization, chatbot…) | |||
; 3. Prepare an audit / migration | |||
* Inventory servers with '''ServerDiff.sh''' | |||
* Design the target architecture (cloud diagrams) | |||
* Automate the migration with reproducible scripts | |||
== Content overview == | |||
* '''AI guides & tools''' : assistants, models, evaluations, GPUs | |||
* '''Cloud & infrastructure''' : HA, HPC, web-scale, DevSecOps | |||
* '''Labs & scripts''' : audit, migration, automation | |||
* '''Comparison tables''' : Kubernetes vs OpenStack vs AWS vs bare-metal, etc. | |||
---- | ---- | ||
= AI & Cloud Tools = | = future = | ||
[[File:Automation-full-vs-humans.png|thumb|right|The world after automation]] | |||
= AI Assistants & Cloud Tools = | |||
== AI Assistants == | |||
; '''ChatGPT''' | |||
* https://chatgpt.com ChatGPT – Public conversational assistant, suited for exploration, writing, and rapid experimentation. | |||
; '''Self-hosted AI assistants''' | |||
* https://github.com/open-webui/open-webui Open WebUI + https://www.scaleway.com/en/h100-pcie-try-it-now/ H100 GPU + https://ollama.com Ollama | |||
: Typical stack for private assistants, self-hosted LLMs, and OpenAI-compatible APIs. | |||
* https://github.com/ynotopec/summarize Private summary – Local, fast, offline summarizer for your own data. | |||
== Development, models & tracking == | |||
; ''' | ; '''Discovering and tracking models''' | ||
* | * https://ollama.com/library LLM Trending – Model library (chat, code, RAG…) for local deployment. | ||
* | * https://huggingface.co/models Models Trending – Model marketplace, filterable by task, size, and license. | ||
* | * https://huggingface.co/models?pipeline_tag=image-text-to-text&sort=trending Img2txt Trending – Vision-language models (image → text). | ||
* https://huggingface.co/spaces/TIGER-Lab/GenAI-Arena Txt2img Evaluation – Image generation model comparisons. | |||
; ''' | ; '''Evaluation & benchmarks''' | ||
* | * https://lmarena.ai/leaderboard ChatBot Evaluation – Chatbot rankings (open-source and proprietary models). | ||
* | * https://huggingface.co/spaces/mteb/leaderboard Embedding Leaderboard – Benchmark of embedding models for RAG and semantic search. | ||
* https://ann-benchmarks.com Vectors DB Ranking – Vector database comparison (latency, memory, features). | |||
* https://top500.org/lists/green500/ HPC Efficiency – Ranking of the most energy-efficient supercomputers. | |||
; '''Development & fine-tuning tools''' | |||
* | * https://github.com/search?q=stars%3A%3E15000+forks%3A%3E1500+created%3A%3E2022-06-01&type=repositories&s=updated&o=desc Project Trending – Major recent open-source projects, sorted by popularity and activity. | ||
* | * https://github.com/hiyouga/LLaMA-Factory LLM Fine Tuning – Advanced framework for LLM fine-tuning (instruction tuning, LoRA, etc.). | ||
* https://www.perplexity.ai Perplexity AI – Advanced research and synthesis oriented as a “research copilot”. | |||
* | |||
* | == AI Hardware & GPUs == | ||
* | |||
; '''GPUs & accelerators''' | |||
* https://www.nvidia.com/en-us/data-center/h100/ NVIDIA H100 – Datacenter GPU for Kubernetes clusters and intensive AI workloads. | |||
* NVIDIA 5080 – Consumer GPU for lower-cost private LLM deployments. | |||
* https://www.mouser.fr/ProductDetail/BittWare/RS-GQ-GC1-0109?qs=ST9lo4GX8V2eGrFMeVQmFw%3D%3D GROQ LLM accelerator – Hardware accelerator dedicated to LLM inference. | |||
---- | ---- | ||
= | = Open models & internal endpoints = | ||
''(Last | |||
''(Last update: 2026-02-13)'' | |||
The models below correspond to '''logical endpoints''' (for example via a proxy or gateway), selected for specific use cases. | |||
{| class="wikitable" | {| class="wikitable" | ||
! | ! Endpoint !! Description / Primary use case | ||
|- | |- | ||
| '''ai-chat''' || | | '''ai-chat''' || Based on '''gpt-oss-20b''' – General-purpose chat, good cost / quality balance. | ||
|- | |- | ||
| '''ai- | | '''ai-translate''' || gpt-oss-20b, temperature = 0 – Deterministic, reproducible translation (FR, EN, other languages). | ||
|- | |- | ||
| '''ai- | | '''ai-summary''' || qwen3 – Model optimized for summarizing long texts (reports, documents, transcriptions). | ||
|- | |- | ||
| '''ai- | | '''ai-code''' || gpt-oss-20b – Code reasoning, explanation, and refactoring. | ||
|- | |- | ||
| '''ai-code''' || | | '''ai-code-completion''' || gpt-oss-20b – Fast code completion, designed for IDE auto-completion. | ||
|- | |- | ||
| '''ai- | | '''ai-parse''' || qwen3 – Structured extraction, log / JSON / table parsing. | ||
|- | |- | ||
| '''ai- | | '''ai-RAG-FR''' || qwen3 – RAG usage in French (business knowledge, internal FAQs). | ||
|- | |- | ||
| ''' | | '''gpt-oss-20b''' || Agentic tasks. | ||
|} | |} | ||
Usage idea: each endpoint is associated with one or more labs (chat, summary, parsing, RAG, etc.) in the Cloud Lab section. | |||
---- | ---- | ||
= | = News & Trends = | ||
* | * https://www.youtube.com/@lev-selector/videos Top AI News – Curated AI news videos. | ||
* | * https://betterprogramming.pub/color-your-captions-streamlining-live-transcriptions-with-diart-and-openais-whisper-6203350234ef Real-time transcription with Diart + Whisper – Example of real-time transcription with speaker detection. | ||
* | * https://github.com/openai-translator/openai-translator OpenAI Translator – Modern extension / client for LLM-assisted translation. | ||
* https://opensearch.org/docs/latest/search-plugins/conversational-search Opensearch with LLM – Conversational search based on LLMs and OpenSearch. | |||
* | |||
---- | ---- | ||
| Line 80: | Line 130: | ||
= Training & Learning = | = Training & Learning = | ||
* | * https://www.youtube.com/watch?v=4Bdc55j80l8 Transformers Explained – Introduction to Transformers, the core architecture of LLMs. | ||
* Hands-on labs and | * Hands-on labs, scripts, and real-world feedback in the [[LAB project|CLOUD LAB]] project below. | ||
---- | ---- | ||
| Line 87: | Line 137: | ||
= Cloud Lab & Audit Projects = | = Cloud Lab & Audit Projects = | ||
[[File:Infocepo.drawio.png|400px|Cloud Lab | [[File:Infocepo.drawio.png|400px|Cloud Lab reference diagram]] | ||
The '''Cloud Lab''' provides reproducible scenarios: infrastructure audits, cloud migration, automation, high availability. | |||
== Audit project – Cloud Audit == | |||
; '''[[ServerDiff.sh]]''' | |||
Bash audit script to: | |||
* detect configuration drift, | |||
* compare multiple environments, | |||
* prepare a migration or remediation plan. | |||
== | == Example of Cloud migration == | ||
[[File:Diagram-migration-ORACLE-KVM-v2.drawio.png|400px|Cloud migration diagram]] | |||
[[File:Diagram-migration-ORACLE-KVM-v2.drawio.png|400px|Cloud | |||
Example: migration of virtual environments to a modernized cloud, including audit, architecture design, and automation. | |||
{| class="wikitable" | {| class="wikitable" | ||
! Task !! Description !! Duration (days) | ! Task !! Description !! Duration (days) | ||
|- | |- | ||
| | | Infrastructure audit || 82 services, automated audit via '''ServerDiff.sh''' || 1.5 | ||
|- | |- | ||
| | | Cloud architecture diagram || Visual design and documentation || 1.5 | ||
|- | |- | ||
| Compliance | | Compliance checks || 2 clouds, 6 hypervisors, 6 TB of RAM || 1.5 | ||
|- | |- | ||
| | | Cloud platform installation || Deployment of main target environments || 1.0 | ||
|- | |- | ||
| Stability | | Stability verification || Early functional tests || 0.5 | ||
|- | |- | ||
| Automation study || | | Automation study || Identification and automation of repetitive tasks || 1.5 | ||
|- | |- | ||
| | | Template development || 6 templates, 8 environments, 2 clouds / OS || 1.5 | ||
|- | |- | ||
| Migration diagram || | | Migration diagram || Illustration of the migration process || 1.0 | ||
|- | |- | ||
| | | Migration code writing || 138 lines (see '''MigrationApp.sh''') || 1.5 | ||
|- | |- | ||
| Process stabilization || | | Process stabilization || Validation that migration is reproducible || 1.5 | ||
|- | |- | ||
| Cloud benchmarking || Performance | | Cloud benchmarking || Performance comparison vs legacy infrastructure || 1.5 | ||
|- | |- | ||
| Downtime | | Downtime tuning || Calculation of outage time per migration || 0.5 | ||
|- | |- | ||
| VM loading || 82 VMs: OS, code, 2 IPs | | VM loading || 82 VMs: OS, code, 2 IPs per VM || 0.1 | ||
|- | |- | ||
! colspan=2 align="right"| '''Total''' !! 15 | ! colspan=2 align="right"| '''Total''' !! 15 person-days | ||
|} | |} | ||
=== Stability checks (minimal HA) === | |||
{| class="wikitable" | {| class="wikitable" | ||
! Action !! Expected | ! Action !! Expected result | ||
|- | |- | ||
| | | Shutdown of one node || All services must automatically restart on remaining nodes. | ||
|- | |- | ||
| | | Simultaneous shutdown / restart of all nodes || All services must recover correctly after reboot. | ||
|} | |} | ||
---- | ---- | ||
= Web | = Web Architecture & Best Practices = | ||
[[File:WebModelDiagram.drawio.png|400px| | [[File:WebModelDiagram.drawio.png|400px|Reference web architecture]] | ||
* Favor | Principles for designing scalable and portable web architectures: | ||
* | |||
* Use network load balancers (LVS, IPVS) for | * Favor '''simple, modular, and flexible''' infrastructure. | ||
* | * Follow client location (GDNS or equivalent) to bring content closer. | ||
* | * Use network load balancers (LVS, IPVS) for scalability. | ||
* Caching: Varnish, Apache Traffic Server for large content | * Systematically compare costs and beware of '''vendor lock-in'''. | ||
* | * TLS: | ||
* Use message queues and | ** HAProxy for fast frontends, | ||
* For | ** Envoy for compatibility and advanced use cases (mTLS, HTTP/2/3). | ||
* Caching: | |||
** Varnish, Apache Traffic Server for large content volumes. | |||
* Favor open-source stacks and database caches (e.g., Memcached). | |||
* Use message queues, buffers, and quotas to smooth traffic spikes. | |||
* For complete architectures: | |||
** https://wikitech.wikimedia.org/wiki/Wikimedia_infrastructure Wikimedia Cloud Architecture | |||
** https://github.com/systemdesign42/system-design System Design GitHub | |||
---- | ---- | ||
= | = Comparison of major Cloud platforms = | ||
{| class="wikitable" | {| class="wikitable" | ||
! | ! Feature !! Kubernetes !! OpenStack !! AWS !! Bare-metal !! HPC !! CRM !! oVirt | ||
|- | |- | ||
| '''Deployment | | '''Deployment tools''' || Helm, YAML, ArgoCD, Juju || Ansible, Terraform, Juju || CloudFormation, Terraform, Juju || Ansible, Shell || xCAT, Clush || Ansible, Shell || Ansible, Python | ||
|- | |- | ||
| '''Bootstrap | | '''Bootstrap method''' || API || API, PXE || API || PXE, IPMI || PXE, IPMI || PXE, IPMI || PXE, API | ||
|- | |- | ||
| '''Router | | '''Router control''' || Kube-router || Router/Subnet API || Route Table / Subnet API || Linux, OVS || xCAT || Linux || API | ||
|- | |- | ||
| '''Firewall | | '''Firewall control''' || Istio, NetworkPolicy || Security Groups API || Security Group API || Linux firewall || Linux firewall || Linux firewall || API | ||
|- | |- | ||
| '''Network | | '''Network virtualization''' || VLAN, VxLAN, others || VPC || VPC || OVS, Linux || xCAT || Linux || API | ||
|- | |- | ||
| '''DNS''' || CoreDNS || DNS-Nameserver || Route 53 || GDNS || xCAT || Linux || API | | '''DNS''' || CoreDNS || DNS-Nameserver || Route 53 || GDNS || xCAT || Linux || API | ||
| Line 177: | Line 242: | ||
| '''Load Balancer''' || Kube-proxy, LVS || LVS || Network Load Balancer || LVS || SLURM || Ldirectord || N/A | | '''Load Balancer''' || Kube-proxy, LVS || LVS || Network Load Balancer || LVS || SLURM || Ldirectord || N/A | ||
|- | |- | ||
| '''Storage | | '''Storage options''' || Local, Cloud, PVC || Swift, Cinder, Nova || S3, EFS, EBS, FSx || Swift, XFS, EXT4, RAID10 || GPFS || SAN || NFS, SAN | ||
|} | |} | ||
This table serves as a starting point for choosing the right stack based on: | |||
* Desired level of control (API vs bare-metal), | |||
* Context (on-prem, public cloud, HPC, CRM…), | |||
* Existing automation tooling. | |||
---- | ---- | ||
= Useful Cloud & IT | = Useful Cloud & IT links = | ||
* | * https://cloud.google.com/free/docs/aws-azure-gcp-service-comparison Cloud Providers Compared – AWS / Azure / GCP service mapping. | ||
* | * https://global-internet-map-2021.telegeography.com/ Global Internet Topology Map – Global Internet mapping. | ||
* | * https://landscape.cncf.io/?fullscreen=yes CNCF Official Landscape – Overview of cloud-native projects (CNCF). | ||
* | * https://wikitech.wikimedia.org/wiki/Wikimedia_infrastructure Wikimedia Cloud Wiki – Wikimedia infrastructure, real large-scale example. | ||
* | * https://openapm.io OpenAPM – SRE Tools – APM / observability tooling. | ||
* | * https://access.redhat.com/downloads/content/package-browser RedHat Package Browser – Package and version search at Red Hat. | ||
* | * https://www.silkhom.com/barometre-2021-des-tjm-dans-informatique-digital Barometer of IT freelance daily rates. | ||
* | * https://www.glassdoor.fr/salaire/Hays-Salaires-E10166.htm IT Salaries (Glassdoor) – Salary indicators. | ||
---- | ---- | ||
= Advanced: High | = Advanced: High Availability, HPC & DevSecOps = | ||
== High Availability with Corosync & Pacemaker == | == High Availability with Corosync & Pacemaker == | ||
[[File:HA-REF.drawio.png|400px|HA cluster architecture]] | |||
Basic principles: | |||
=== Common | * Multi-node or multi-site clusters for redundancy. | ||
* Multipath storage, | * Use of IPMI for fencing, provisioning via PXE/NTP/DNS/TFTP. | ||
* User and | * For a 2-node cluster: | ||
* | – carefully sequence fencing to avoid split-brain, | ||
– 3 or more nodes remain recommended for production. | |||
=== Common resource patterns === | |||
* Multipath storage, LUNs, LVM, NFS. | |||
* User resources and application processes. | |||
* Virtual IPs, DNS records, network listeners. | |||
== HPC == | == HPC == | ||
[[File:HPC.drawio.png|400px|HPC | |||
[[File:HPC.drawio.png|400px|Overview of an HPC cluster]] | |||
* Job orchestration (SLURM or equivalent). | |||
* High-performance shared storage (GPFS, Lustre…). | |||
* Possible integration with AI workloads (large-scale training, GPU inference). | |||
== DevSecOps == | == DevSecOps == | ||
[[File:DSO-POC-V3.drawio.png|400px|DevSecOps | |||
[[File:DSO-POC-V3.drawio.png|400px|DevSecOps reference design]] | |||
* CI/CD pipelines with built-in security checks (linting, SAST, DAST, SBOM). | |||
* Observability (logs, metrics, traces) integrated from design time. | |||
* Automated vulnerability scanning, secret management, policy-as-code. | |||
---- | ---- | ||
= About & Contributions = | |||
For more examples, scripts, diagrams, and feedback, see: | |||
* https://infocepo.com infocepo.com | |||
Suggestions for corrections, diagram improvements, or new labs are welcome. | |||
This wiki aims to remain a '''living laboratory''' for AI, cloud, and automation. | |||
Latest revision as of 01:24, 13 February 2026
infocepo.com – Cloud, AI & Labs
Welcome to the infocepo.com portal.
This wiki is intended for system administrators, cloud engineers, developers, students, and enthusiasts who want to:
- Understand modern architectures (Kubernetes, OpenStack, bare-metal, HPC…)
- Deploy private AI assistants and productivity tools
- Build hands-on labs to learn by doing
- Prepare large-scale audits, migrations, and automations
The goal: turn theory into reusable scripts, diagrams, and architectures.
Getting started quickly
Recommended paths
- 1. Build a private AI assistant
- Deploy a typical stack: Open WebUI + Ollama + GPU (H100 or consumer-grade GPU)
- Add a chat model and a summarization model
- Integrate internal data (RAG, embeddings)
- 2. Launch a Cloud lab
- Create a small cluster (Kubernetes, OpenStack, or bare-metal)
- Set up a deployment pipeline (Helm, Ansible, Terraform…)
- Add an AI service (transcription, summarization, chatbot…)
- 3. Prepare an audit / migration
- Inventory servers with ServerDiff.sh
- Design the target architecture (cloud diagrams)
- Automate the migration with reproducible scripts
Content overview
- AI guides & tools : assistants, models, evaluations, GPUs
- Cloud & infrastructure : HA, HPC, web-scale, DevSecOps
- Labs & scripts : audit, migration, automation
- Comparison tables : Kubernetes vs OpenStack vs AWS vs bare-metal, etc.
future
AI Assistants & Cloud Tools
AI Assistants
- ChatGPT
- https://chatgpt.com ChatGPT – Public conversational assistant, suited for exploration, writing, and rapid experimentation.
- Self-hosted AI assistants
- https://github.com/open-webui/open-webui Open WebUI + https://www.scaleway.com/en/h100-pcie-try-it-now/ H100 GPU + https://ollama.com Ollama
- Typical stack for private assistants, self-hosted LLMs, and OpenAI-compatible APIs.
- https://github.com/ynotopec/summarize Private summary – Local, fast, offline summarizer for your own data.
Development, models & tracking
- Discovering and tracking models
- https://ollama.com/library LLM Trending – Model library (chat, code, RAG…) for local deployment.
- https://huggingface.co/models Models Trending – Model marketplace, filterable by task, size, and license.
- https://huggingface.co/models?pipeline_tag=image-text-to-text&sort=trending Img2txt Trending – Vision-language models (image → text).
- https://huggingface.co/spaces/TIGER-Lab/GenAI-Arena Txt2img Evaluation – Image generation model comparisons.
- Evaluation & benchmarks
- https://lmarena.ai/leaderboard ChatBot Evaluation – Chatbot rankings (open-source and proprietary models).
- https://huggingface.co/spaces/mteb/leaderboard Embedding Leaderboard – Benchmark of embedding models for RAG and semantic search.
- https://ann-benchmarks.com Vectors DB Ranking – Vector database comparison (latency, memory, features).
- https://top500.org/lists/green500/ HPC Efficiency – Ranking of the most energy-efficient supercomputers.
- Development & fine-tuning tools
- https://github.com/search?q=stars%3A%3E15000+forks%3A%3E1500+created%3A%3E2022-06-01&type=repositories&s=updated&o=desc Project Trending – Major recent open-source projects, sorted by popularity and activity.
- https://github.com/hiyouga/LLaMA-Factory LLM Fine Tuning – Advanced framework for LLM fine-tuning (instruction tuning, LoRA, etc.).
- https://www.perplexity.ai Perplexity AI – Advanced research and synthesis oriented as a “research copilot”.
AI Hardware & GPUs
- GPUs & accelerators
- https://www.nvidia.com/en-us/data-center/h100/ NVIDIA H100 – Datacenter GPU for Kubernetes clusters and intensive AI workloads.
- NVIDIA 5080 – Consumer GPU for lower-cost private LLM deployments.
- https://www.mouser.fr/ProductDetail/BittWare/RS-GQ-GC1-0109?qs=ST9lo4GX8V2eGrFMeVQmFw%3D%3D GROQ LLM accelerator – Hardware accelerator dedicated to LLM inference.
Open models & internal endpoints
(Last update: 2026-02-13)
The models below correspond to logical endpoints (for example via a proxy or gateway), selected for specific use cases.
| Endpoint | Description / Primary use case |
|---|---|
| ai-chat | Based on gpt-oss-20b – General-purpose chat, good cost / quality balance. |
| ai-translate | gpt-oss-20b, temperature = 0 – Deterministic, reproducible translation (FR, EN, other languages). |
| ai-summary | qwen3 – Model optimized for summarizing long texts (reports, documents, transcriptions). |
| ai-code | gpt-oss-20b – Code reasoning, explanation, and refactoring. |
| ai-code-completion | gpt-oss-20b – Fast code completion, designed for IDE auto-completion. |
| ai-parse | qwen3 – Structured extraction, log / JSON / table parsing. |
| ai-RAG-FR | qwen3 – RAG usage in French (business knowledge, internal FAQs). |
| gpt-oss-20b | Agentic tasks. |
Usage idea: each endpoint is associated with one or more labs (chat, summary, parsing, RAG, etc.) in the Cloud Lab section.
News & Trends
- https://www.youtube.com/@lev-selector/videos Top AI News – Curated AI news videos.
- https://betterprogramming.pub/color-your-captions-streamlining-live-transcriptions-with-diart-and-openais-whisper-6203350234ef Real-time transcription with Diart + Whisper – Example of real-time transcription with speaker detection.
- https://github.com/openai-translator/openai-translator OpenAI Translator – Modern extension / client for LLM-assisted translation.
- https://opensearch.org/docs/latest/search-plugins/conversational-search Opensearch with LLM – Conversational search based on LLMs and OpenSearch.
Training & Learning
- https://www.youtube.com/watch?v=4Bdc55j80l8 Transformers Explained – Introduction to Transformers, the core architecture of LLMs.
- Hands-on labs, scripts, and real-world feedback in the CLOUD LAB project below.
Cloud Lab & Audit Projects
The Cloud Lab provides reproducible scenarios: infrastructure audits, cloud migration, automation, high availability.
Audit project – Cloud Audit
Bash audit script to:
- detect configuration drift,
- compare multiple environments,
- prepare a migration or remediation plan.
Example of Cloud migration
Example: migration of virtual environments to a modernized cloud, including audit, architecture design, and automation.
| Task | Description | Duration (days) |
|---|---|---|
| Infrastructure audit | 82 services, automated audit via ServerDiff.sh | 1.5 |
| Cloud architecture diagram | Visual design and documentation | 1.5 |
| Compliance checks | 2 clouds, 6 hypervisors, 6 TB of RAM | 1.5 |
| Cloud platform installation | Deployment of main target environments | 1.0 |
| Stability verification | Early functional tests | 0.5 |
| Automation study | Identification and automation of repetitive tasks | 1.5 |
| Template development | 6 templates, 8 environments, 2 clouds / OS | 1.5 |
| Migration diagram | Illustration of the migration process | 1.0 |
| Migration code writing | 138 lines (see MigrationApp.sh) | 1.5 |
| Process stabilization | Validation that migration is reproducible | 1.5 |
| Cloud benchmarking | Performance comparison vs legacy infrastructure | 1.5 |
| Downtime tuning | Calculation of outage time per migration | 0.5 |
| VM loading | 82 VMs: OS, code, 2 IPs per VM | 0.1 |
| Total | 15 person-days | |
Stability checks (minimal HA)
| Action | Expected result |
|---|---|
| Shutdown of one node | All services must automatically restart on remaining nodes. |
| Simultaneous shutdown / restart of all nodes | All services must recover correctly after reboot. |
Web Architecture & Best Practices
Principles for designing scalable and portable web architectures:
- Favor simple, modular, and flexible infrastructure.
- Follow client location (GDNS or equivalent) to bring content closer.
- Use network load balancers (LVS, IPVS) for scalability.
- Systematically compare costs and beware of vendor lock-in.
- TLS:
- HAProxy for fast frontends,
- Envoy for compatibility and advanced use cases (mTLS, HTTP/2/3).
- Caching:
- Varnish, Apache Traffic Server for large content volumes.
- Favor open-source stacks and database caches (e.g., Memcached).
- Use message queues, buffers, and quotas to smooth traffic spikes.
- For complete architectures:
- https://wikitech.wikimedia.org/wiki/Wikimedia_infrastructure Wikimedia Cloud Architecture
- https://github.com/systemdesign42/system-design System Design GitHub
Comparison of major Cloud platforms
| Feature | Kubernetes | OpenStack | AWS | Bare-metal | HPC | CRM | oVirt |
|---|---|---|---|---|---|---|---|
| Deployment tools | Helm, YAML, ArgoCD, Juju | Ansible, Terraform, Juju | CloudFormation, Terraform, Juju | Ansible, Shell | xCAT, Clush | Ansible, Shell | Ansible, Python |
| Bootstrap method | API | API, PXE | API | PXE, IPMI | PXE, IPMI | PXE, IPMI | PXE, API |
| Router control | Kube-router | Router/Subnet API | Route Table / Subnet API | Linux, OVS | xCAT | Linux | API |
| Firewall control | Istio, NetworkPolicy | Security Groups API | Security Group API | Linux firewall | Linux firewall | Linux firewall | API |
| Network virtualization | VLAN, VxLAN, others | VPC | VPC | OVS, Linux | xCAT | Linux | API |
| DNS | CoreDNS | DNS-Nameserver | Route 53 | GDNS | xCAT | Linux | API |
| Load Balancer | Kube-proxy, LVS | LVS | Network Load Balancer | LVS | SLURM | Ldirectord | N/A |
| Storage options | Local, Cloud, PVC | Swift, Cinder, Nova | S3, EFS, EBS, FSx | Swift, XFS, EXT4, RAID10 | GPFS | SAN | NFS, SAN |
This table serves as a starting point for choosing the right stack based on:
- Desired level of control (API vs bare-metal),
- Context (on-prem, public cloud, HPC, CRM…),
- Existing automation tooling.
Useful Cloud & IT links
- https://cloud.google.com/free/docs/aws-azure-gcp-service-comparison Cloud Providers Compared – AWS / Azure / GCP service mapping.
- https://global-internet-map-2021.telegeography.com/ Global Internet Topology Map – Global Internet mapping.
- https://landscape.cncf.io/?fullscreen=yes CNCF Official Landscape – Overview of cloud-native projects (CNCF).
- https://wikitech.wikimedia.org/wiki/Wikimedia_infrastructure Wikimedia Cloud Wiki – Wikimedia infrastructure, real large-scale example.
- https://openapm.io OpenAPM – SRE Tools – APM / observability tooling.
- https://access.redhat.com/downloads/content/package-browser RedHat Package Browser – Package and version search at Red Hat.
- https://www.silkhom.com/barometre-2021-des-tjm-dans-informatique-digital Barometer of IT freelance daily rates.
- https://www.glassdoor.fr/salaire/Hays-Salaires-E10166.htm IT Salaries (Glassdoor) – Salary indicators.
Advanced: High Availability, HPC & DevSecOps
High Availability with Corosync & Pacemaker
Basic principles:
- Multi-node or multi-site clusters for redundancy.
- Use of IPMI for fencing, provisioning via PXE/NTP/DNS/TFTP.
- For a 2-node cluster:
– carefully sequence fencing to avoid split-brain, – 3 or more nodes remain recommended for production.
Common resource patterns
- Multipath storage, LUNs, LVM, NFS.
- User resources and application processes.
- Virtual IPs, DNS records, network listeners.
HPC
- Job orchestration (SLURM or equivalent).
- High-performance shared storage (GPFS, Lustre…).
- Possible integration with AI workloads (large-scale training, GPU inference).
DevSecOps
- CI/CD pipelines with built-in security checks (linting, SAST, DAST, SBOM).
- Observability (logs, metrics, traces) integrated from design time.
- Automated vulnerability scanning, secret management, policy-as-code.
About & Contributions
For more examples, scripts, diagrams, and feedback, see:
- https://infocepo.com infocepo.com
Suggestions for corrections, diagram improvements, or new labs are welcome. This wiki aims to remain a living laboratory for AI, cloud, and automation.