NAS Storage Optimization Using Cloud Tiering Appliance

Managing unstructured data growth is a key challenge for many organizations. However, data growth is only one part of the problem. Most data that is created sits idle, barely accessed over long periods of time. Regulatory and compliance requirements add to the problem, forcing organizations to retain inactive but still potentially valuable data for longer periods of time.

Optimization strategies such as Hierarchical Storage Management (HSM) can help organizations optimize their primary storage and ensure that inactive data moves across different storage tiers during its lifecycle to free up precious storage space. The HSM solution can be on-premise, cloud-based (public), or a combination of both. However, cloud adoption has been slow within organizations due to concerns about data security and privacy; also a thorough ROI analysis would reveal that public cloud as an archiving tier for the long term can be a costly affair. Besides the complexities associated with the deployment model there is another set of challenges associated with manual HSM or archiving techniques:

• identification of inactive data

• movement of data across tiers when it is being accessed

In this Knowledge Sharing article, Brijesh Das Mangadan Kamnat  posits that what is needed is an automated HSM solution—such as EMC Cloud Tiering Appliance (CTA) —that can identify inactive data and move them through different storage tiers completely online, with minimum user intervention, and without disruptions to end users.

Brijesh describes a HSM solution using CTA which allows for moving inactive NAS data based on user-defined policy to secondary tier storage,  including public cloud service providers such as AWS. Key points covered in this article include:

• Evaluation of typical use cases for CTA deployment

• Solution Architecture for deployment

• Configuration and Execution workflow

• Using CTA for NAS migration

• Sharing key observations related to time taken for archiving and recall for different data sets

Read the full article.