[Cloud Computing]Patterns: Dynamic Data Normalization

Dynamic Data Normalization (Erl, Naserpour)

How can redundant data within cloud storage devices be automatically avoided?

Problem

Cloud consumers may store large volumes of redundant data within cloud storage devices, thereby bloating the storage architecture and compromising data access performance.

Solution

Data received by cloud consumers is automatically normalized so that redundant data is avoided and cloud storage device capacity and performance is optimized.

Application

Data de-duplication technology is used to detect and eliminate redundant data at block or file-based levels.

Mechanisms

Cloud Storage Device

Compound Patterns

Burst In,  Burst Out to Private Cloud,  Burst Out to Public Cloud,  Cloud Authentication,  Elastic Environment, Infrastructure-as-a-Service (IaaS),  Isolated Trust Boundary,  Multitenant Environment,  Platform-as-a-Service (PaaS),  Private Cloud,  Public Cloud,  Resilient Environment,  Resource Workload Management,  Secure Burst Out to Private Cloud/Public Cloud,  Software-as-a-Service (SaaS)

In Part A, datasets containing redundant data unnecessarily bloat data storage. The Dynamic Data Normalization pattern results in the constant and automatic streamlining of data as shown in Part B, regardless of how denormalized the data received from the cloud consumer is.

NIST Reference Architecture Mapping

This pattern relates to the highlighted parts of the NIST reference architecture, as follows: