Cloud spend is anticipated to continue its massive growth trajectory for the foreseeable future. However, the spend does not have to match that same growth curve! To help our Clients maintain sound Cloud Optimization programs, we've assembled a series of articles and tips called: 12 for '21 Cloud Optimization Series (note: request a download of all 21 here).
Today we review the fourth installment in the series: Set Up Hierarchical Storage Management.
Data sets have specific lifecycles. Early on, your workload might often access the data. But overtime, data access, in particular the aging one, drops drastically. Some data remains idle in the cloud and is rarely accessed once stored for regulation purpose. Some data can be deleted as they become obsolete weeks or months after creation, while other data sets are read and modified throughout their lifetimes. AWS, GCP and Azure all offer storage lifecycle management service whereas you set rules to automatically leverage the different storage classes based on your business requirements.
Examples of Lifecycle Management Rules:
Enable access time tracking.
Time tracking allows you to get insights on access and usage frequency of your different data set. With that information, you could then set up time-based rules for the data storage.
Move aging data to a cooler storage class.
A common real world scenario is to automatically move your data set from one storage class to a cooler/cheaper class when data has been accessed for a certain period of time. As discussed in our step 9 article (‘use the appropriate storage class’), you could set rules based on storage access per storage class:
- From hot storage class (‘AWS S3, Azure Blob Storage’) to infrequent storage class (‘AWS S3-SIA, GCP nearline’) based on a 30-day storage duration without access.
- From infrequent storage class to archiving storage class (‘GCP Coldline, AWS Glacier’) based on a 90-day storage duration without access.
- From archiving storage class to deep archiving storage class (‘AWS Deep glacier, Azure Archive’) based on a 180-day storage duration without access.
Delete data based on age.
Some data is expected to expire days or months after creation. You can configure a lifecycle management policy to expire data by deletion based on data age.
Archiving Class and read access.
Be cautious on the rule setting for the archiving class. Once archived, the data retrieval options are time-consuming (standard retrieval 3-to-5 hours) or an expensive process (expedite retrieval 1-to-5mn). It is imperative that you have sufficient historical data on your storage or a thorough storage planning to identify appropriate data sets for archiving.
Update and change to Lifecycle Management Rules.
Your cloud spend and workloads storage requirements will evolve over time. As such, it is critical to keep monitoring your storage costs and from time to time, refine your policies rules toward the best storage classes.
The Object Lifecycle Management feature is a powerful tool that, when understood and used properly, can bring down the overall cost of your storage consumption. For best outcome, we suggest you start setting up your storage lifecycle management policy first with your storage buckets with predictable consumption patterns then expand once you have a good grasp of the rules settings and it storage cost impact.
As with any strategic technology issue, problem, question, or concern, give NET(net) a call to help you better understand and optimize your supplier agreements, relationships, and investments. Contact us today, and we’ll get you in touch with the appropriate subject matter experts to help you.
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