14 Repos
Tools for orchestrating application deployments across multiple remote environments simultaneously.
Distinguishing note: Focuses on the distribution of stacks to edge device groups.
Explore 14 awesome GitHub repositories matching devops & infrastructure · Distributed Deployment. Refine with filters or upvote what's useful.
This project is a satirical software development framework and conceptual parody of modern DevOps. It functions as an empty-project generator and non-functional deployment tool designed to automate the total absence of code and infrastructure. The framework distinguishes itself by providing a zero-code application building process that removes the need for source code authoring. It includes a nowhere deployment capability, which distributes applications to non-existent environments to eliminate hosting requirements and technical liability. Additional capabilities include a build pipeline tha
Distributes application logic to non-existent environments to eliminate hosting requirements.
Portainer is a unified infrastructure management platform that provides a centralized control plane for deploying, monitoring, and managing containerized applications. It functions as an orchestration-abstraction layer, translating user actions into platform-specific API calls to maintain consistency across diverse container runtimes and cluster technologies. By organizing users, teams, and resources into a single interface, it enables granular role-based access control and lifecycle management for containerized services and stacks. The platform distinguishes itself through its support for di
Distributes applications to multiple remote environments from a single interface by assigning stacks to specific groups.
This project is a curated directory and reference library of open-source Python applications. It serves as a comprehensive index designed to help developers study real-world software architecture, design patterns, and practical implementation strategies through a diverse collection of community-driven projects. The repository distinguishes itself by focusing on the analysis of production-ready software patterns rather than providing a single tool. It offers a structured way to explore how complex features, such as modular plugin systems, configuration management, and various deployment strate
Ships software pre-installed on dedicated hardware to provide a controlled and secure infrastructure.
F Prime ist ein komponentenbasiertes Framework für die Entwicklung und Bereitstellung von Embedded- und Raumfahrtsoftware. Es bietet eine modulare Architektur, die Softwarelogik von Kommunikationsschnittstellen entkoppelt und es Entwicklern ermöglicht, Systemstrukturen über eine domänenspezifische Modellierungssprache zu definieren. Dieser modellbasierte Ansatz ermöglicht eine automatisierte Codegenerierung, die Konsistenz über komplexe Systemtopologien hinweg gewährleistet und gleichzeitig strikte Schnittstellenverträge zwischen Softwaremodulen aufrechterhält. Das Framework zeichnet sich durch sein integriertes Build-System und eine Suite für Bodendatenoperationen aus. Es automatisiert den gesamten Lebenszyklus von Embedded-Software, von der Cross-Kompilierung und dem Abhängigkeitsmanagement bis hin zur Generierung von Telemetrie- und Befehlsschnittstellen. Durch die Bereitstellung einer einheitlichen Umgebung für Onboard-Flugsoftware und bodengestützte Überwachung erleichtert es die nahtlose Integration, das Testen sowie die Steuerung und Überwachung verteilter Embedded-Systeme über verschiedene Hardwareplattformen hinweg. Über die Kernarchitektur hinaus enthält das Projekt umfassende Werkzeuge für die Systembeobachtbarkeit, einschließlich Echtzeit-Telemetrie-Visualisierung, Ereignisprotokollierung und diagnostischer Tracing-Funktionen. Es unterstützt eine breite Palette von Bereitstellungsszenarien, von Bare-Metal-Umgebungen bis hin zu Echtzeitbetriebssystemen, und bietet Mechanismen für Speicherverwaltung, zustandsgesteuerte Verhaltensmodellierung und asynchrone Aufgabenausführung. Das Projekt wird als C++-Repository mit umfangreicher Dokumentation und Build-System-Unterstützung für die plattformübergreifende Entwicklung gepflegt.
Links independent software deployments across physical devices to enable unified communication and data exchange.
T-Pot is a multi-honeypot platform and threat intelligence framework that deploys a collection of containerized decoy services to capture attacker behavior and network telemetry. It functions as a Docker-based deception system, simulating vulnerable network environments to gather intelligence on threat actors. The system features a distributed sensor network using a hub-and-spoke architecture, allowing remote sensors to transmit logs back to a central management hub. It integrates large language models to create a dynamic deception engine capable of adaptive interactions with attackers. The
Implements a hub-and-spoke architecture where remote sensors host services and transmit telemetry to a central hub.
T-Pot is a multi-honeypot orchestration platform and threat intelligence collector. It utilizes a Docker-based security sandbox to deploy and manage a collection of diverse decoy services that simulate vulnerable targets to lure attackers and record their activity. The system features a distributed sensor network where remote nodes capture attack logs and transmit them via encrypted communication to a central hub. This central hub employs an analytics stack to transform raw logs into geographic maps and interactive dashboards for adversary behavior visualization. To increase the realism of si
Coordinates a network of remote sensors that relay captured intruder activity back to a central server.
Scrutiny is a distributed hardware monitoring system and predictive drive failure analyzer. It provides a centralized management platform and web-based dashboard for tracking hard drive health and S.M.A.R.T. metrics across multiple remote servers. The system functions as a S.M.A.R.T. alerting gateway and storage health trend visualizer. It estimates hardware risk by comparing drive attributes against real-world failure thresholds and records historical data to identify gradual degradation patterns that may not trigger immediate alerts. Capabilities include distributed data collection via rem
Uses a hub-and-spoke deployment model to distribute data collection across multiple remote servers.
Coroot is an observability platform and Kubernetes performance monitor that utilizes eBPF to automatically collect metrics, logs, and traces without requiring manual code instrumentation. It functions as an OpenTelemetry trace analyzer and an LLM observability gateway, exposing system health data to large language models through the Model Context Protocol. The platform differentiates itself by combining automated root cause analysis and AI-driven diagnostics to investigate performance regressions. It also includes a cloud cost monitoring tool that attributes infrastructure spending to specifi
Implements a hub-and-spoke architecture where distributed node agents report observability data to a central server.
NetAlertX is a distributed network scanner and asset discovery tool designed to identify connected devices and track unauthorized hardware. It aggregates discovery results from multiple remote monitoring nodes into a single centralized inventory hub to provide unified network visibility. The project distinguishes itself by integrating as a bridge to MQTT brokers for smart home automation and providing a dedicated interface for AI agents to query system data. It employs multi-protocol identity resolution using DNS, mDNS, and NetBIOS to identify hardware and generates synthetic identifiers to e
Aggregates discovery data from distributed remote monitoring nodes into a single central hub for unified visibility.
Karmada ist ein Kubernetes-Multi-Cluster-Orchestrator und Multi-Cloud-Cluster-Manager für das Deployment und Management Cloud-nativer Anwendungen über mehrere Cluster und Cloud-Provider hinweg. Er fungiert als zentralisierte Control Plane, die als Ressourcen-Propagator und Workload-Scheduler arbeitet und Ressourcen über Public Clouds, On-Premises-Rechenzentren und Edge-Standorte hinweg koordiniert. Das Projekt zeichnet sich durch eine richtlinienbasierte Engine aus, die Anwendungen unter Verwendung von Affinity, Topologie-Constraints und Ressourcen-Quotas verteilt. Es bietet spezifische Funktionen für Multi-Region-Disaster-Recovery, einschließlich automatisiertem Application-Failover und geo-redundanten Deployments zur Aufrechterhaltung der Serviceverfügbarkeit. Zudem ermöglicht es cluster-bewusstes Ressourcen-Overriding, um Konfigurationsparameter basierend auf der Ziel-Cluster-Region oder dem Provider zu spezialisieren. Das System deckt ein breites Spektrum operativer Bereiche ab, einschließlich bidirektionaler State-Synchronisation, aggregiertem API-Proxying und mehrdimensionalem Scheduling. Es enthält Tools für Cluster-Lifecycle-Management, globale Ressourcensuche und Cross-Cluster-Traffic-Balancing. Installation und Management können über einen Operator-basierten Installationsprozess und ein dediziertes CLI für Administration und Control-Plane-Operationen durchgeführt werden.
Uses a central management cluster to coordinate and distribute resource state to multiple registered member clusters.
P4wnP1_aloa is a physical security framework designed to transform a Raspberry Pi into a dedicated appliance for red teaming and penetration testing. It functions as a USB gadget emulation tool, a wireless network spoofing utility, and a GPIO automation controller. The system enables the emulation of composite USB peripherals, such as keyboards, mice, and storage devices, without requiring a reboot. It further provides capabilities for broadcasting fake access point beacons and spoofed responses to emulate diverse wireless network environments. The framework includes a remote management inte
Transforms a Raspberry Pi into a dedicated physical security appliance for red teaming engagements.
StreamPark ist eine zentralisierte Managementplattform, die darauf ausgelegt ist, das Deployment, Monitoring und den operativen Lebenszyklus verteilter Stream-Processing- und Batch-Anwendungen zu koordinieren. Sie fungiert als Control-Plane und Orchestrator für Datenpipelines und bietet spezifisch Managementfunktionen für Apache Flink- und Hadoop YARN-Umgebungen. Die Plattform zeichnet sich durch einen Low-Code-Ansatz für das Task-Deployment und einen Multi-Engine-Execution-Adapter aus, der diverse Verarbeitungs-Runtimes unterstützt. Sie erleichtert das Echtzeit-Datenpipeline-Management durch die Kombination von Streaming-SQL-Analytics mit einer ressourcenbasierten Deployment-Pipeline, die Versionierung, Binär-Uploads und Savepoint-basierte Zustands-Wiederherstellung handhabt. Das System deckt ein breites Spektrum an Funktionen ab, einschließlich verteilter Job-Orchestrierung, Echtzeit-Datenintegration über vorgefertigte Connectors und Identitätsintegration via LDAP oder SSO. Es bietet zudem Observability-Tools für sekundengenaue Anwendungsüberwachung und automatisierte operative Fehlerbenachrichtigungen.
Implements a centralized control plane to manage the deployment and monitoring of batch and stream tasks.
Gatekeeper is a Kubernetes admission control and policy enforcement engine used to ensure cluster resources comply with organizational security and configuration standards. It intercepts API requests to validate or reject non-compliant resources before they are persisted in the cluster. The project uses a parameterized policy library and custom resource definitions to create reusable templates and enforcement rules. It distinguishes itself through a hub-and-spoke management model, allowing a controller in a management cluster to enforce policies across separate target clusters. Beyond admiss
Employs a hub-and-spoke control plane model to manage policies from a central cluster across multiple target clusters.
RLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the infrastructure to train vision-language-action models and robotic policies using a combination of reinforcement learning and supervised fine-tuning. The system is designed for scaling workloads across GPU clusters, managing the placement of actors, rollout workers, and environment components. It features a specialized robotics data collection pipeline for gathering teleoperated demonstrations and simulation trajectories into standardized replay buffers, alongside a hardware interface
Launches computational units across multiple nodes and GPUs, automatically configuring necessary environment variables.