EC2 instance types
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| EC2 instance types | |
|---|---|
| Name | EC2 instance types |
| Developer | Amazon Web Services |
| First release | 2006 |
| Website | Amazon EC2 |
EC2 instance types are virtualized compute configurations offered by Amazon Web Services for running workloads on the Amazon Web Services cloud. They provide combinations of central processing unit performance, random-access memory capacity, network bandwidth, and storage options tailored to diverse applications from high performance computing to web serving. EC2 instance types are grouped into families and sizes to simplify selection and are integral to architectures used by organizations such as Netflix (company), Airbnb, NASA projects, and research at institutions like California Institute of Technology and Massachusetts Institute of Technology.
Overview
EC2 instance types originated as part of the initial services launched by Amazon.com's cloud division and evolved alongside competitors including Google Cloud Platform and Microsoft Azure. The taxonomy separates variants by purpose, reflecting advances in Intel Corporation and Advanced Micro Devices processors, innovations from NVIDIA for accelerator-backed instances, and networking features driven by partnerships with firms like Mellanox Technologies (now Nvidia Mellanox). Adoption spans industries such as streaming by Hulu, e-commerce by Shopify, genomics at Broad Institute, and finance at firms like Goldman Sachs.
Instance Families and Use Cases
Families categorize instances into purpose-built groups: general-purpose, compute-optimized, memory-optimized, storage-optimized, accelerated computing, and inference accelerators. General-purpose families serve companies like LinkedIn and Dropbox for web applications and middleware. Compute-optimized instances support workloads seen in research groups at Lawrence Berkeley National Laboratory and financial simulation teams at JPMorgan Chase. Memory-optimized families support databases used by MongoDB, Inc. and Oracle Corporation deployments. Storage-optimized choices underpin analytics at Cloudera and archival scenarios similar to systems at European Organization for Nuclear Research. Accelerated instances with GPUs are favored by machine learning groups such as OpenAI collaborators and autonomous vehicle labs at Waymo; inference accelerators are used by edge AI initiatives in companies like Tesla, Inc..
Instance Sizes and Specifications
Each family offers sizes like large and xlarge to scale compute capability; sizes map to vCPU counts, RAM amounts, and network throughput. Hardware specifications reflect chip developments from Intel Xeon generations and AMD EPYC microarchitecture advances, while NVIDIA CUDA cores and AMD Radeon Instinct accelerators influence GPU-backed instance specs used by teams at Stanford University and Carnegie Mellon University. Storage options include ephemeral instance store backed by NVMe from vendors such as Samsung Electronics and persistent volumes like Amazon Elastic Block Store used by enterprise customers including Siemens and General Electric.
Pricing and Purchasing Options
Pricing models include on-demand, reserved instances, savings plans, and spot instances, enabling cost strategies used by corporates like Procter & Gamble and startups funded by firms such as Sequoia Capital. On-demand billing resembles utility models used by cloud adopters at The New York Times for variable traffic. Reserved and savings plans parallel capital allocation decisions made by companies like Intel Corporation and Cisco Systems when planning infrastructure. Spot instances are used for batch workloads in research at National Institutes of Health and rendering farms for studios like Industrial Light & Magic.
Performance and Benchmarks
Benchmarking EC2 instance performance engages tools and organizations including SPEC (organisation), Phoronix Test Suite, and research groups at University of California, Berkeley. Benchmarks compare single-thread and multi-thread metrics, memory bandwidth, and networking latency; these metrics inform deployments at companies such as Bloomberg L.P. for low-latency trading and scientific computing centers at Oak Ridge National Laboratory. GPU instances are profiled using frameworks like TensorFlow and PyTorch in collaborations with research labs at University of Toronto.
Launching and Managing Instances
Launching involves selecting an Amazon Machine Image, instance type, networking configuration in a Virtual Private Cloud (Amazon) subnet, and storage. Management integrates with services including Amazon CloudWatch for monitoring, AWS Identity and Access Management for access control, and automation via HashiCorp Terraform or configuration tools used by Puppet (software) and Chef (software). Organizations such as Capital One and Pinterest use orchestration patterns and autoscaling groups to manage fleets of instances.
Security and Compliance Considerations
Security controls include network security groups, IAM roles, and encryption for EBS volumes; compliance programs align with standards observed by institutions like U.S. Department of Defense contractors and healthcare providers adhering to frameworks influenced by Health Insurance Portability and Accountability Act and certifications recognized by International Organization for Standardization. Customers often combine instance-level hardening guided by best practices from Center for Internet Security and logging to services used in audits by firms such as Deloitte and KPMG.