AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
Infrastructure management for cloud native and traditional workloads. Get your cloud running in weeks with Kubernetes-native orchestration and OpenStack-influenced services, fully open source.
Developer toolsGo · ShellGitHub @cobaltcore-devOnlineOwner unconfirmed
Have you tried it? One tap is enough. No sign-in needed.
No updates detected yet. GitHub releases will appear here.
Owners of public repositories under a personal GitHub account can sign in to verify ownership and manage the introduction themselves. No administrator approval is needed.
Sign in, generate a verification file, and commit it to this repository’s default branch within 30 minutes. Files on other branches or forks do not count. Then check the file below to manage the project without administrator approval. Keep the file to retain your access.
If needed, you will go to sign-in. After signing in, return here and press your chosen verification button again.
Run /nomorevibe verify in your project folder to verify ownership and manage the introduction yourself.
Verify in one minuteAsk about other verification options
Unified initial placement and scheduling of workloads within cloud and edge environments
Translation pending
Taken from the repository README
Loading the full README. The saved excerpt is shown below.
Cortex
======
Cortex is a modular and extensible service for initial placement and scheduling in cloud-native environments covering workloads such as compute, storage, network, and other scheduling domains.
It improves resource utilization and operational performance by making smart placement decisions based on the current state of the environment and defined constraints and objectives.Read the full README ↗Cortex is a modular and extensible service for initial placement and scheduling in cloud-native environments covering workloads such as compute, storage, network, and other scheduling domains. It improves resource utilization and operational performance by making smart placement decisions based on the current state of the environment and defined constraints and objectives.
Cortex provides unified initial placement and scheduling across multiple resource domains by composing scheduling logic via pipelines. It handles workloads such as virtual machines (VM) as well as reservations (RES) for customer commitments and failover capacity.
As part of the CobaltCore project, it complements the platform with advanced placement and scheduling capabilities.
Learn more about CobaltCore and the broader Apeiro Reference Architecture ecosystem.
Modular and extensible design Cortex consists of a minimal core framework that can be extended with various plugins to support different data sources and scheduling algorithms. This provides flexibility and enables adapting Cortex to various environments and requirements.
Centralized knowledge database Cortex provides a holistic knowledge database that stores enriched data from various sources. This enables efficient and consistent access to the infrastructure state for placement and scheduling decisions.
Integrated placement and scheduling Cortex combines initial placement and continuous scheduling into a single service.
Cross-domain support Cortex supports a wide range of workloads from various scheduling domains, including compute, storage, network, and Kubernetes. The architecture allows handling the domains either independently or through coordinated multi-domain decisions.
Performance and scalability Cortex is designed for production-scale deployments using algorithmic approaches to balance decision quality, execution efficiency, and maintaining a low resource footprint. It is battle-tested in large-scale, production cloud computing environments and can handle thousands of placement requests per second.
Read the full documentation at docs/readme.md.
This project is open to feature requests/suggestions, bug reports etc. via GitHub issues. Contribution and feedback are encouraged and always welcome. For more information about how to contribute, the project structure, as well as additional contribution information, see our Contribution Guidelines.
If you find any bug that may be a security problem, please follow our instructions at in our security policy on how to report it. Please do not create GitHub issues for security-related doubts or problems.
We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone. By participating in this project, you agree to abide by its Code of Conduct at all times.
Please see our LICENSE for copyright and license information. Detailed information including third-party components and their licensing/copyright information is available via the REUSE tool.