Microsoft Azure Virtual Machines — Technology Profile & Architecture

Core Information

#core_information

Name, description, vendor website, logo, technology type and tags.

Overview & Role

Azure Virtual Machines (VMs) are on-demand, scalable computing resources that provide the flexibility of virtualization without the need to buy and maintain hardware. They offer a wide range of operating systems, including Windows and Linux, and can be used for various workloads, from development and testing to running enterprise applications.

Domain Classification & Tags

Cloud Infrastructure

Performance Profile

#performance_profile

Typical read and write throughput, payload size and processing latency.

Read Performance Profile

Throughput: Varies significantly by VM size, disk type (HDD/SSD/Ultra Disk), and network configuration. Can range from hundreds of MB/s to tens of GB/s for disk I/O, and 1 Gbps to 100 Gbps for network I/O.

Payload Size: Varies widely depending on application and workload. Can range from small blocks (e.g., 4KB for database reads) to large files (e.g., several GB for data analytics).

Processing Latency: Varies significantly by VM size, disk type, and network configuration. Disk latency can be sub-millisecond for Ultra Disks, 1-5 ms for Premium SSDs, and 10-100+ ms for Standard HDDs. Network latency typically 1-5 ms within a region.

Write Performance Profile

Throughput: Varies significantly by VM size, disk type, and network configuration. Can range from hundreds of MB/s to tens of GB/s for disk I/O, and 1 Gbps to 100 Gbps for network I/O.

Payload Size: Varies widely depending on application and workload. Can range from small blocks (e.g., 4KB for database writes) to large files (e.g., several GB for data analytics).

Processing Latency: Varies significantly by VM size, disk type, and network configuration. Disk latency can be sub-millisecond for Ultra Disks, 1-5 ms for Premium SSDs, and 10-100+ ms for Standard HDDs. Network latency typically 1-5 ms within a region.

Architecture Diagram

#architecture_diagram

Reference diagram of the technology's internal architecture.

Component Architecture & Topology

open_in_new Standalone Tab

Features

#features

Catalogued product capabilities and what each one does.

Empirical data for features is currently being compiled in the global catalog.

Typical Use Cases

#typical_use_cases

Scenarios the technology is commonly chosen for.

Empirical data for typical use cases is currently being compiled in the global catalog.

Known Customers

#known_customers

Publicly referenced organisations using the technology.

Empirical data for known customers is currently being compiled in the global catalog.

Known Integrations

#known_integrations

Other products and services it is documented to work with.

Empirical data for known integrations is currently being compiled in the global catalog.

Connectors

#connectors

Directional data connections to other technologies, with direction and maturity.

Empirical data for connectors is currently being compiled in the global catalog.

Reference Architectures

#reference_architectures

Published architectures where the technology is used or mentioned.

No published reference architecture blueprints currently link to Microsoft Azure Virtual Machines.

Security Features

#security_features

Built-in security and access-control capabilities.

Empirical data for security features is currently being compiled in the global catalog.

Known Issues

#known_issues

Documented limitations, defects and operational pitfalls.

Empirical data for known issues is currently being compiled in the global catalog.

Guidelines

#guidelines

Recommended practices for adopting and operating the technology.

Empirical data for guidelines is currently being compiled in the global catalog.

Standards & Compliance

#standards_and_compliance

Standards, certifications and control requirements it maps to.

Empirical data for standards & compliance is currently being compiled in the global catalog.

Sources

#sources

Documentation and research references behind the recorded information.

Empirical data for sources is currently being compiled in the global catalog.

Expert Validation

#expert_validation

Whether domain experts reviewed and confirmed the content.

Empirical data for expert validation is currently being compiled in the global catalog.