- What is CREODIAS?
- Computing & Cloud
- Data & Processing
- Pricing Plans
- Fight with COVID-19
- Examples of usage
- Processing Sentinel-5P data using HARP and Python
- EO Data Access (R)evolution
- Land cover classification using remote sensing and AI/ML technology
- AI-based satellite image enhancer and mosaicking tools
- Monitoring air pollution using Sentinel-5P data
- Species classification of forests
- Enabling AI / ML workflows with CREODIAS vGPUs
- Satellite remote sensing analyses of the forest
- Satellite-based Urban Heat Island Mapping on CREODIAS
- Old but gold - historical EO data immediately available and widely used on CREODIAS
- CREODIAS for emergency fire management
- AgroTech project as an example of how CREODIAS can be used for food and environmental research
- Monitoring Air Quality of Germany in Pre vs During COVID Lockdown Period
- EO4UA
- Common Agricultural Policy monitoring with Earth Observation
- Applications of CREODIAS data
- Meteorological data usage on the CREODIAS platform
- Building added value under Horizon Europe with CREODIAS
- CREODIAS: Introduction to SAR Sentinel-1 data
- Land subsidence and landslides monitoring based on satellite data
- Satellite imagery in support of the Common Agriculture Policy (CAP) and crop statistics
- Useful tools for data processing, available on CREODIAS platform
- CREODIAS for hydrological drought modelling
- CREODIAS for managing Urban Heat Islands
- CREODIAS for Digitising Green Spaces
- CREODIAS for Air Quality
- Advanced data processors on CREODIAS
- CREODIAS for your application
- Solutions for agriculture with CREODIAS
- Earth Observation data for Emergency response
- Security Applications with Satellite Data
- Climate Monitoring with Satellite Data
- Water Analysis on CREODIAS
- CREODIAS for land and agriculture monitoring
- Solutions for atmospheric analysis
- Example of tool usage
- Processing EO Data and Serving www services
- Processing and Storing EO
- Embedding OGC WMS Services into Your website
- GPU Use Case
- Using the EO Browser
- EO Data Finder API Manual
- Use of SNAP and QGIS on a CREODIAS Virtual Machine
- Use of WMS Configurator
- DNS as a Service - user documentation
- Use of Sinergise Sentinel Hub on the CREODIAS EO Data Hub
- Load Balancer as a Service
- Jupyter Hub
- Use of CREODIAS Finder for ordering data
- ESRI ArcGIS on CREODIAS
- Use of CEMS data through CREODIAS
- Searching, processing and analysis of Sentinel-5P data on CREODIAS
- ASAR data available on CREODIAS
- Satellite remote sensing analyses of the forest
- EO Data Catalogue API Manual
- Public Reporting Dashboards
- Sentinel Hub Documentation
- Legal Matters
- FAQ
- News
- Partner Services
- About Us
- Forum
- Knowledgebase
Computing & Cloud
Computing Services
Creodias operates on two independent public cloud regions, CF2 and WAW3-1 with plans to expand to two more location in the coming year. Although each of these clouds is fully separated and can operate independently they share the same billing and user management. All public cloud instances have the same level of access to EODATA. In each cloud user can run similar services but they may have different prices depending on instance location.
Virtual Machines
Description
Virtual Machines (VMs) are fully functional computational instances. They operate as if they were real physical entities with all the elements of a physical server. A user obtains his VM with full root access. He can fully manage it and install any software he has and needs.
In the EO Cloud Users can use Virtual Machines (VMs) by defining their different parameters and characteristics, including machine type (physical or virtual), RAM, CPU (vCores), Storage quantity and type, Operating System, middleware components, Virtual Networks connected to the machine.
Users determine the characteristics of a newly provisioned VM by selecting its Flavor and base image. Currently available flavours are presented in the following table:
Virtual Machines | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Available VMs | #vCores | RAM (GB) | SSD Network Storage (GB) | NVMe Local Storage (GB) | ||||||||
eo1 | ||||||||||||
eo1.xsmall | 1 | 1 | 8 | 0 | ||||||||
eo1.small | 2 | 2 | 16 | 0 | ||||||||
eo1.xmedium | 1 | 2 | 8 | 0 | ||||||||
eo1.medium | 2 | 4 | 16 | 0 | ||||||||
eo1.large | 4 | 8 | 32 | 0 | ||||||||
eo2 | ||||||||||||
eo2.medium | 1 | 4 | 16 | 0 | ||||||||
eo2.large | 2 | 8 | 32 | 0 | ||||||||
eo2.xlarge | 4 | 16 | 64 | 0 | ||||||||
eo2.2xlarge | 8 | 32 | 128 | 0 | ||||||||
eo2a* | ||||||||||||
eo2a.medium | 1 | 4 | 16 | 0 | ||||||||
eo2a.large | 2 | 8 | 32 | 0 | ||||||||
eo2a.xlarge | 4 | 16 | 64 | 0 | ||||||||
eo2a.2xlarge | 8 | 32 | 128 | 0 | ||||||||
eo2a.3xlarge | 16 | 64 | 256 | 0 | ||||||||
eo2a.4xlarge | 32 | 128 | 512 | 0 | ||||||||
eo2a.5xlarge | 64 | 256 | 1024 | 0 | ||||||||
hm | ||||||||||||
hm.medium | 2 | 16 | 64 | 0 | ||||||||
hm.large | 4 | 32 | 128 | 0 | ||||||||
hm.xlarge | 8 | 64 | 256 | 0 | ||||||||
hm.2xlarge | 16 | 128 | 384 | 0 | ||||||||
hm.3xlarge | 32 | 256 | 384 | 0 | ||||||||
hm.4xlarge | 48 | 496 | 384 | 0 | ||||||||
hmd***** | ||||||||||||
hmd.medium | 2 | 16 | 0 | 50 | ||||||||
hmd.large | 4 | 32 | 0 | 100 | ||||||||
hmd.xlarge | 8 | 64 | 0 | 200 | ||||||||
hmd.2xlarge | 16 | 128 | 0 | 400 | ||||||||
hmd.3xlarge | 32 | 256 | 0 | 800 | ||||||||
gpu** | ||||||||||||
gpu.medium | 12 | 117 | 64 | |||||||||
Software ready*** | ||||||||||||
ArcGIS.eo2.xlarge | 4 | 16 | 64 | 0 | ||||||||
ArcGIS.eo2.2xlarge | 8 | 32 | 128 | 0 | ||||||||
ArcGIS.hm.xlarge | 8 | 64 | 256 | 0 | ||||||||
ArcGIS.hm.2xlarge | 16 | 128 | 384 | 0 | ||||||||
ArcGIS.ds.large.gpu**** | 40 (20 cores) | 112 | 128 | 2 x 1000 |
* Flavours with AMD processors.
** gpu Virtual Machines are equipped with GeForce RTX 2080TI (4352 CUDA Cores, 11GB GDDR6).
*** All Software ready Cloud Servers are available only with Windows Server Standard in bundle with preconfigured Esri ArcGIS Pro Desktop.
**** Arc.GIS.ds.large.gpu is equipped with GeForce RTX 2080Ti (4352 CUDA Cores, 11GB GDDR6).
*****HMD VM come with only one local drive based on NVMe physical drive located in compute server
The list of currently available operating system images is presented below:
- CentOS 6, 7
- Ubuntu 16.04 LTS, 18.04 LTS
- Windows 2019
- RHEL 6,7 mini / full
- OSGeo 11.0
- App Catalog Image
Virtual Machines | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Available VMs | #vCores | RAM (GB) | SSD Network Storage (GB) | NVMe Local Storage (GB) | ||||||||
eo1 | ||||||||||||
eo1.xsmall | 1 | 1 | 8 | 0 | ||||||||
eo1.small | 2 | 2 | 16 | 0 | ||||||||
eo1.xmedium | 1 | 2 | 8 | 0 | ||||||||
eo1.medium | 2 | 4 | 16 | 0 | ||||||||
eo1.large | 4 | 8 | 32 | 0 | ||||||||
eo2 | ||||||||||||
eo2.medium | 1 | 4 | 16 | 0 | ||||||||
eo2.large | 2 | 8 | 32 | 0 | ||||||||
eo2.xlarge | 4 | 16 | 64 | 0 | ||||||||
eo2.2xlarge | 8 | 32 | 128 | 0 | ||||||||
eo2.3xlarge | 16 | 64 | 256 | 0 | ||||||||
eo2.4xlarge | 32 | 128 | 512 | 0 | ||||||||
eo2a* | ||||||||||||
eo2a.medium | 1 | 4 | 16 | 0 | ||||||||
eo2a.large | 2 | 8 | 32 | 0 | ||||||||
eo2a.xlarge | 4 | 16 | 64 | 0 | ||||||||
eo2a.2xlarge | 8 | 32 | 128 | 0 | ||||||||
eo2a.3xlarge | 16 | 64 | 256 | 0 | ||||||||
eo2a.4xlarge | 32 | 128 | 512 | 0 | ||||||||
eo2a.5xlarge | 64 | 256 | 1024 | 0 | ||||||||
hm | ||||||||||||
hm.medium | 2 | 16 | 64 | 0 | ||||||||
hm.large | 4 | 32 | 128 | 0 | ||||||||
hm.xlarge | 8 | 64 | 256 | 0 | ||||||||
hm.2xlarge | 16 | 128 | 384 | 0 | ||||||||
hm.3xlarge | 32 | 256 | 384 | 0 | ||||||||
hm.4xlarge | 48 | 496 | 384 | 0 |
* Flavours with AMD processors.
Figure 3 - GPU AI Cloud computing WAW3-1
Virtual Machines | |||||
---|---|---|---|---|---|
Available VMs | #vCores | RAM (GB) | Virtual GPU Type | vGPU RAM (GB) | SSD Network Storage (GB) |
vm.gpu | |||||
vm.a6000.1 | 2 | 14 | RTXA6000-6C | 6 | 40 |
vm.a6000.1 | 4 | 28 | RTXA6000-12C | 12 | 80 |
vm.a6000.1 | 8 | 56 | RTXA6000-24C | 24 | 160 |
vm.a6000.1 | 16 | 112 | RTXA6000-48C | 48 | 320 |
The list of currently available operating system images is presented below:
- CentOS 6, 7
- Ubuntu 14.04 LTS, 16.04 LTS, 18.04 LTS
- Windows 2016 mini / full
- RHEL 6,7 mini / full
- SLES 12 mini / full
- OSGeo 11.0
- App Catalog Image
All the VMs come fully configured (based on the image selected) and ready-for-use, with an administrative User account, network access, preconfigured toolboxes and software components. Volume Storage may be attached to running VM-s to extend the storage space available. VMs can be started, stopped, rebooted, paused, suspended and snapshotted. Live backup functionality is also available, including server quiescing. VMs may also be attached to Virtual Networks. Virtual Networks may be system-defined or User-defined.
System defined networks include:
- Internet network used to access the global internet
- the EO Storage network available in Projects/Environments that are allowed access the EO Storage
Provisioning
Users may utilize VM-s and other cloud Resources using the EO Cloud Dashboard, the REST API, a command line client or the Openstack Orchestration scripts (Heat). VMs can be connected to the network using virtual interfaces.
Billing
The VMs can be billed in monthly or longer quanta (Fixed Term Mode) or can be billed per hours of usage (Per Usage Mode). Users may also temporarily shelve their VMs based on persistent storage, paying only for the persistent storage space they occupy.
Figure 4 - Cloud Dashboard - Instances
The instances as seen in the Cloud Dashboard are presented in a picture above.
Dedicated Server Virtual Machine
Description
DSs are special VMs. Each DS is a virtual machine that fills the full computing machine (hypervisor server). There are no other VMs in this server. So a User of an DS has a full physical server for his own use. Additionally SVMs are equipped with very efficient SSD disks installed in pass through mode. This way full capacity and speed of those disks can be utilized. DSs are a perfect solution for everybody who wants to have efficiency and independence of Baremetal Servers but simultaneously wants to utilize all the elements of the OpenStack cloud platform. For details – see DS flavor list below.
Figure 5 - DS Flavours CF2
Dedicated Servers* | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Available DSs | #vCores | RAM (GB) | SSD NVMe Local Storage (GB) | ||||||||
ds | |||||||||||
ds.medium | 40 (20 cores) | 48 | 2 x 500 | ||||||||
ds.large | 40 (20 cores) | 112 | 2x 1000 | ||||||||
ds.2xlarge | 40 (20 cores) | 368 | 2x 1920 | ||||||||
ds.3xlarge | 48 (24 cores) | 496 | 2x 1920 | ||||||||
ds.large.gpu** | 40 (20 cores) | 112 | 2x 1000 |
Figure 5 - DS Flavours WAW3-1
Dedicated Servers* | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Available DSs | #vCores | RAM (GB) | SSD NVMe Local Storage (GB) | ||||||||
ds | |||||||||||
ds.3xlarge | 48 (24 cores) | 496 | 2x 1920 |
* If the above configurations do not fit your project, please contact our sales team (sales@creodias.eu) to design a custom solution.
** ds.large.gpu is equipped with GeForce GTX 2080 Ti (4352 CUDA Cores, 11GB GDDR6)
Provisioning
DS can be provisioned in exactly the same way as standard VMs. Users may utilize DS-s and other cloud Resources using the CREODIAS Dashboard, the REST API, a command line client or the Openstack Orchestration scripts (Heat). DSs can be connected to the network using virtual interfaces.
Billing
The DSs are billed in exactly the same way as standard VMs.
Containers
Description
Containers are isolated, portable environments where one can run applications along with the libraries and dependencies they need. Containers are not VMs. In some ways they are similar, but there are even more ways that they are different. Like VMs Containers share system resources for access to compute, networking and storage. They are different because all containers on the same host share the same Operating System kernel and keep applications, runtimes and various other services separated from each other using kernel features known as namespaces and groups. Docker added the concept of a container image which allows containers to be used on any host with a modern Linux Kernel. The Container image allows for much more rapid deployment of applications than if they were packaged in a VM image.
Provisioning
You can run Kubernetes on CloudFerro WAW3-1 infrastructure, which has Kubernetes support built-in (OpenStack Magnum module).
If your CREODIAS account has access only to CF2 infrastructure, please contact support to get access to WAW3-1.
Billing
The Containers billing depends on the billing of the underlying VMs. The VMs creating a cluster are billed as described in the section on VMs.