distributed-systems - Concepts

Explore concepts tagged with "distributed-systems"

Total concepts: 13

Concepts

Microservices Architecture - A distributed architecture style that structures an application as a collection of small, autonomous services organized around business capabilities.
Agent Orchestration - The coordination and management of multiple AI agents, including their workflows, communication, task delegation, and error handling to achieve complex goals.
Kubernetes - An open source container orchestration platform for automating deployment, scaling, and management of containerized applications.
AI Agent Swarms - Systems where multiple AI agents work together to accomplish complex tasks through collaboration, communication, and coordination.
CAP Theorem - A theorem stating that distributed data stores can only guarantee two of three properties: Consistency, Availability, and Partition tolerance.
Byzantine Generals Problem - A fundamental problem in distributed computing about achieving consensus among distributed components when some may be faulty or malicious, named after a metaphor involving generals coordinating an attack.
Determinism - The principle that given the same inputs and initial conditions, a system or process will always produce the same outputs.
Idempotency - A property where an operation can be applied multiple times without changing the result beyond the initial application.
Chaos Engineering - The discipline of experimenting on distributed systems to build confidence in their ability to withstand turbulent conditions.
Statelessness - A design principle where components do not retain information between requests or operations, treating each interaction independently.
HTTP - The foundational protocol of the World Wide Web that enables the transfer of hypertext documents and data between clients and servers.
Client-Server Architecture - A distributed computing model where client applications request services and resources from centralized servers over a network.
Federated Learning - A distributed machine learning approach where models are trained across multiple decentralized devices or servers holding local data, without exchanging raw data.

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