Cloud Computing Glossary

Service for declaratively managing and deploying cloud resources on Google Cloud Platform.

Automated, end-to-end pipelines for software delivery in cloud environments, streamlining development and deployment processes.

Collaborative approach integrating development and operations teams for efficient cloud-based software delivery.

Measurements for assessing the performance and efficiency of DevOps processes in cloud environments.

Integration of security practices into DevOps processes using automated tools in cloud environments.

Integrated tools for implementing security throughout the DevOps lifecycle in cloud environments.

Tools and practices for configuring, monitoring, and securing IoT devices connected to cloud platforms.

Process of setting up and configuring IoT devices for secure connection to cloud platforms.

Cloud-based tools for adding noise to data to protect individual privacy while maintaining overall accuracy.

Tools for tracking and optimizing user interactions with cloud-based applications and services.

Virtual representations of physical objects or systems, often leveraging cloud resources for simulation and analysis.

Virtual models of physical assets or processes, often using cloud computing for data processing and simulation.

Cloud-based virtual representations of physical entities for monitoring, simulation, and optimization.

Dedicated network connection between on-premises infrastructure and cloud service providers.

Cloud infrastructure designs separating compute, storage, and networking for flexible resource allocation.

Cloud storage architecture separating storage management functions from physical storage devices.

Strategies and tools for recovering data and systems after a catastrophic event in cloud environments.

Process of training machine learning models across multiple cloud nodes for improved speed and efficiency.

Cloud services distributed across multiple physical locations but managed centrally.

Techniques for solving complex problems across multiple cloud nodes while satisfying various constraints.

Cloud-based systems for running data analysis and machine learning tasks across multiple nodes.

Cloud-based security systems deploying decoys across multiple points to detect and mislead attackers.

Process of training AI models across multiple edge devices and cloud resources, enabling decentralized learning at scale.

Technique for tuning machine learning models across multiple cloud nodes for improved performance.

Managing and coordinating blockchain networks across multiple cloud environments.

Tracking and analyzing requests as they flow through cloud-based microservices architectures.

Specialized programming languages designed for efficient cloud resource management and deployment.

Monitoring and identifying unauthorized or unintended changes in cloud infrastructure configurations.

Extension of Azure Functions that enables stateful operations in serverless compute environments.

Adaptive security measures that adjust permissions based on context in cloud environments.

Responsible disposal and recycling of outdated or decommissioned cloud hardware.

Strategies for sustainably disposing of and recycling obsolete cloud computing hardware.

Process of moving data from source systems into a data warehouse in cloud environments.

Approaches to moving to the cloud that minimize environmental impact and energy consumption.

Artificial intelligence algorithms running on devices at the network edge, often integrated with cloud backends.

Methods for reducing the size of AI models to run efficiently on edge devices with cloud support.

Managing and updating AI model versions across distributed edge devices and cloud systems.

Measures to protect AI models and data on edge devices connected to cloud networks.

Artificial intelligence and machine learning capabilities deployed on edge devices, often with cloud integration.

Processing and analyzing data near its source on edge devices before sending insights to the cloud.

Software platforms for developing and deploying analytics applications on edge devices with cloud connectivity.

Identifying unusual patterns or behaviors in data at the network edge before sending alerts to the cloud.

Storing frequently accessed data on edge devices or servers to reduce latency and cloud bandwidth usage.

Network architectures optimized for connecting edge computing devices to cloud resources.

Processing and analyzing visual data on edge devices before sending results to the cloud.

Distributing and caching content on edge servers to reduce latency and cloud bandwidth consumption.

Database systems designed to run on edge devices with intermittent connectivity to cloud backends.

Creating AI-generated content on edge devices with potential cloud-based model updates and synchronization.

Techniques to improve AI model performance on edge devices, reducing latency and cloud dependence.

Distributed sites hosting cloud services closer to end-users for reduced latency and improved performance.