Cloud-Based Infrastructure for Data-Intensive e-Science Applications: Requirements and Architecture - Cloud Computing with e-Science Applications - page 31

The following support data security and access control to scientific data
during their life cycle: data acquisition (experimental data), initial data filter-
ing, specialist processing, research data storage and secondary data mining,
data and research information archiving.

2.5 Scientific Data Infrastructure Architecture Model
The proposed generic SDI architecture model provides a basis for building
interoperable data or project-centric SDI using modern technologies and
best practices. Figure 2.4 shows the multilayer SDI architecture for e-science
(e-SDI) that contains the following layers:

Layer D1: Network infrastructure layer represented by either the general-
purpose Internet infrastructure or dedicated network infrastructure
Layer D2: Data centers and computing resources/facilities
Layer D3: Infrastructure virtualization layer represented by the cloud/grid
infrastructure services and middleware supporting specialized scien-
tific platform deployment and operation

Layers

Scientific/User Applications

Layer B6
Scientific
Applications

Layer B5
Access and
Delivery Layer

Federated Access and Delivery Infrastructure (FADI)
Policy Management and collaborative Groups Support

Layer B4
Scientific Platform
and Instruments

Shared Scientific Platform and Instruments
(specific for scientific area, also Grid based)

Cloud/Grid Infrastructure
Virtualization and Management
Middleware

Layer B3
Infrastructure
Virtualisation

Middleware
security

Layer B2
Datacenter and
Computing Facility

Compute
Resources

Storage
Resources

Layer B1
Network
Infrastructure

Network infrastructure

FIGURE 2.4
The proposed SDI architecture model.