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Posts Tagged ‘nas

EMC FAST Fully Automated Storage Tiering for storage savings

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Chuck Hollis, VP Global Marketing, CTO, EMC, describes FAST over 3 blog posts. The technology has been in Beta usage by several customers in 2009.

The premise

When you analyze the vast majority of application I/O profiles, you’ll realize that a small amount of data is responsible for the majority of I/Os; almost all of it is infrequently accessed. 

The principle

Watch how the data is being accessed, and dynamically cache the most popular/ frequently accessed data on flash drives, usually the small amount, and the vast majority of infrequently accessed data on big, slow SATA drives.

The storage savings solution

FAST Place  the right information on the right media based on frequency of access
Thin This (virtual) provisioning allocate physical storage when it is actually being used, rather than when it is provisioned.
Small Compression, single-instancing and data deduplication technologies eliminate information redundancies.
Green A significant amount of enterprise information is used *very* infrequently.  So infrequently, in fact, that the disk drives can be spun down, or at the least  be made semi-idle. 
Gone Policy-based lifecycle management – Archiving and Deletion, Federation to the cloud through private and public cloud integration.
The information can get shopped to a specialized service provider as an option

 

… and life goes on!

One thing hasn’t changed, though. The information beast continues to grow

Written by paule1s

December 11, 2009 at 9:29 am

A sysadmin’s DAS to Netapp NAS migration experience

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This is from Andy Leonard’s post

I work for a relatively small, but growing, research non-profit. When last I measured it, our data use was growing at a compound rate of about 8% each month; in other words, we double our storage use every nine months or so. (As we’re in the midst of a P2V project where direct-attached storage is moving to our NetApps, we’re actually growing faster than that now, but that’s a temporary bump.) We already have multi-terabyte volumes – so, you do the math… the 16TB aggregate limit (of the 2020) is a real problem for sites like us.

Storage Math

It’s also worth noting that a 16TB aggregate is not a 16TB file system available to a server. 750GB SATA drives become Rightsize 621 GB drives. Then, for RAID-DP, subtract two disks out of each RAID group. Next, there’s the 10% WAFL overhead. And don’t forget to translate from marketing GB to real GB (or GB to GiB, if you will). So that maximum-size 26-disk aggregate made up of 750GB drives winds up as 11.4TB. And – of course – don’t forget your snap reserves after that.

Backups

As you mention, backups could be a challenge for large volumes; here’s how we solve it: The 2020 in question was purchased as a SnapVault secondary. Backups go from our primary 3040s to it, and then go via NDMP to tape for off-site/DR purposes. The secondary tier gives us the extended backup window we need to get the data to tape and meet our DR requirements. (I actually think this is a pretty common setup in this day and age.)

Archiving

Of course, I’m not naive enough to think we can grow by adding drive shelves indefinitely (just added another one last Friday…). My personal opinion is that we’ll ultimately move to an HSM system, especially since much of the storage is used for instrument data (mass spec, microscopy, etc.) that is often difficult for researchers to categorize immediately as to its value. The thought is to let the HSM algorithms find the appropriate tier for the data automatically.

Written by paule1s

December 10, 2009 at 4:42 pm

EMC Celerra NAS SAN Deduplication

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The EMC Celerra Deduplication is substantially different in concept, implementation and its benefits from the block-level deduplication offered by NetApp, Data Domain and others in their products. To understand the differences, let us first look at the comparison of data reduction technologies:

Data reduction technologies

Technology Typical Space Savings Resource footprint
File-level deduplication 10% Low
Fixed block deduplication   20% High
Variable-block deduplication   28% High
Compression 50% Medium

 

  • File-level deduplication provides relatively modest space savings.
  • Fixed-block deduplication provides better space savings, but consumes more CPU to calculate hashes for each block of data, and more memory to hold the indices used to determine if a given hash has been seen before.
  • Variable-block deduplication provides slightly better space savings; but the difference is not significant when applied to file system data. it is most effective when applied to data sets that contain repeated but block-misaligned data, such as backup data in backup-to-disk or virtual tape library (VTL) environments.
  • Compression is different from file-level or block-level deduplication in the granularity at which it applies. It is described as infinitely variable, bit-level, intra-object deduplication.  It offers the greatest space savings of all the techniques listed for typical NAS data, and is relatively modest in terms of its resource footprint. It is relatively CPU-intensive but requires very little memory.

The storage space savings realized by  compression is far greater than those offered by the other techniques and its resource requirements are quite modest by comparison. However, compression has a disadvantage in that there is a potential performance “penalty” associated with   decompressing the data when it is read or modified. This decompression “penalty” can work both ways. Reading a compressed file can often be quicker than reading a non-compressed file. The reduction in the size of data that you must retrieve from the disk more than offsets the additional processing required to decompress the data. 

Celerra Data Deduplication

Celerra Data Deduplication combines file-level deduplication and compression to provide maximum space savings for file system data based on

  • Frequency of file access: files that are not “new” (creation time older than a configuration parameter), or  not “hot”, i.e., in active use (access time or modification time older than a configuration parameter)
  • File size: It avoids compressing files either if the files are small and the anticipated space savings are minimal, or  if the file is large and its decompression could degrade performance and impact file  access service levels.

Deduplication is enabled at the file system level and is transparent to access protocols. Mark Twomey‘s post provides an excellent overview of Celerra Data Deduplication.

The space reduction process

Celerra Data Deduplication has a flexible policy engine that specifies data for exclusion from processing and decides whether to deduplicate specific files based on  their age. When enabled on a file system, Celerra Data Deduplication periodically scans the file system for files that match the policy criteria and then compresses them. The compressed file data is hashed to  determine if the file has been identified before. If the compressed file data has not been identified before, it is copied into a hidden portion of the file system. The space that the file data occupied in the user portion of the file system is freed and the file’s internal metadata is updated to reference an existing copy of the data. If the data associated with the file has been identified before, the space it occupies is freed and the internal file metadata is updated. Note that Celerra detects non-compressible files and stores them in their original form. However, these files can still benefit from file-level deduplication.

Celerra Data Deduplication employs SHA-1 (Secure Hash Algorithm) for its file-level deduplication. SHA1 can take a stream of data less than 2 bits in length and produce a 160-bit hash, which is designed to be unique to the original data stream. The likelihood of different files hashing the same value is so substantially low that a collision rate has been reported after 2^69  hash operations. Unlike in compression, you can disable file-level deduplication in Celerra Data Deduplication.

Designed to minimize client impact

Celerra Data Deduplication processes the bulk of the data in a file system without affecting the production workload. All deduplication processing is performed as a background asynchronous operation that acts on file data after it is written into the file system. This avoids latency in the client data path, because access to production data is sensitive to latency. By policy, deduplication is performed only for those files that are not in active use. This avoids introducing any performance penalty on the data that clients and users are using to run their business.

Written by paule1s

December 7, 2009 at 12:51 pm

Thin Provisioning – when to use, benefits and challenges

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There are excellent posts by two prominent authors that provide a lot of insight into the nuances of using thick or thin provisioning for VM’s: Thin Provisioning Part 1 – The Basics and Thin Provisioning Part 2 – Going Beyond by Vaughn Stewart of NetApp and Thin on Thin – where should you do Thin Provisioning by Chad Sakac of EMC.

Synopsis:
Escalating storage costs are stalling the deployment of virtualized data centers and it is becoming increasingly important for customers to leverage storage technology developed by VMware and its storage partners, Netapp and EMC for reducing storage costs.

vmdk formats:

vmdk formats

VMFS blocks
pre-allocated

Disk array block
pre-allocated

Disk array blocks
pre-allocated

Thin

No

No

No

Thick (Non-zeroed)

Yes

No

No

Eager zeroed thick

Yes

Yes

Yes

 

Recommendations:
Use Thin on Thin (Thin vmdk’s and Thin Provisioning on the storage array) for the best storage utilization because they allocate storage capacity from the datastore and storage array only on demand.

Stewart:

The Goal of Thin Provisioning is Datastore Oversubscription  The challenge is that datastore, and all of its components (VMFS, LUNs, etc…) are static in terms of storage capacity. While the capacity of a datastore can be increased on the fly, this process is not automated or policy driven. Should an oversubscribed datastore encounter an out of space condition, all of the running VMs will become unavailable to the end user. In these scenarios the VMs don’t ‘crash’ the ‘pause’; however, applications running inside of VMs may fail if the out of space condition isn’t addressed in a relatively short period of time. For example Oracle databases will remain active for 180 seconds, after that time has elapsed the database will fail.

Sakac:

If you DO use Thin on Thin, use VMware or 3rd party usage reports in conjunction with array-level reports, and set thresholds with notification and automated action on both the VMware layer (and the array level (if you array supports that). Why? Thin provisioning needs to carefully manage for “out of space” conditions, since you are oversubscribing an asset which has no backdoor (unlike how VMware oversubscribes guest memory which can use VM swap if needed). When you use Thin on Thin – this can be very efficient, but can “accelerate” the transition to oversubscription.

Sakac:

The eagerzeroedthick virtual disk format is required for VMware Fault Tolerant VMs on VMFS (if they are thin, conversion occurs automatically as the VMware Fault Tolerant feature is enabled). It continues to also be mandatory for Microsoft clusters (refer to KB article) and recommended in the highest I/O workload Virtual Machines, where the slight latency and additional I/O created by the “zeroing” that occurs as part and parcel of virtual machine I/O to new blocks is unacceptable.

vmdk growth:

Stewart:

VMDK grew beyond the capacity of the data which it is storing. The reason for this phenomenon is deleted data is stored in the GOS file system. When data is deleted the actual process merely removes the content from the active file system table and marks the blocks as available to be overwritten. The data still resides in the file system and thus in the virtual disk. This is why you can purchase undelete tools like WinUndelete.

Don’t run defrag within a thin provisioned VM

Stewart:

the defragmentation process results in the rewriting all of the data within a VMDK. This operation can cause a considerable expansion in the size of the virtual disk, costing you your storage savings.

How to recover storage

Stewart:

First is to zero out the ‘free’ blocks within in the GOS file system. This can be accomplished by using the ‘shrink disk’ feature within VMTools or with tools like sdelete from Microsoft. The second half, or phase in this process, is to use Storage VMotion to migrate the VMDK to a new datastore.

The second half, or phase in this process, is to use Storage VMotion to migrate the VMDK to a new datastore. You should note that this process is manual; however, Mike Laverick has posted the following guide which includes how to automate some of the components in this process. Duncan Epping has also covered automating parts of this process.

NetApp features for virtualization storage savings

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The feature set that gives customers storage savings is described in a 42 minute informative video on Hyper-V and Netapp storage – Overview. I have summarized it in a 5 minute long post below.

Enterprise System Storage Portfolio

The Enterprise product portfolio consists of the FA series, V Series storage systems. These systems have a unified storage architecture based on the Data ONTAP, OS running across all storage arrays. Data ONTAP provides a single app interface and supports protocols such as FC-SAN, FCoE-SAN, IP-SAN (iSCSI), NAS, NFS, CIFS. The V-Series controllers also offer multiple vendor array support, i.e., they can offer the same features on disk arrays manufactured by Netapp’s competitors.

Features

  • Block-level de-duplication, or de-dupe, retains exactly one instance of each unique disk block. When applied to live production systems, it can reduce data 95% for full backups, especially when there are identical VM images created from the same template, and as much as 25%-55% for most data sets.
  • Snapshot copies of a VM are lightweight because they share the same disk blocks with the parent and do not require as much space for the copy as the parent. If a disk block is updated with a snapshot, e.g., if a configuration parameter is customized for an application, or when a patch is applied, the Write Anywhere File Layout (WAFL) file system associates the updated block with the snapshot copy and writes to the disk, leaving the original block and its referrers intact. Snapshot copies therefore impose negligible storage performance impact on running VM’s.
  • Thin provisioning allows users to define storage pools (Flexvol) for which storage allocation is done dynamically from the storage array on demand. Flexvol can be enabled at any point in time while the storage system is in operation.
  • Thin replication between disks provides data protection. Differential Backups and mirroring over the IP network works at the block level copying only the changed blocks – compressed blocks are sent over the wire It enables virtual restores of full, point in time data at granular levels
  • Double parity RAID, called Raid DP, provides superior fault tolerance and provides 46% saving vs mirrored data or RAID 10. You can think of it as being a RAID 6 (RAID 5 + 1 Double Parity disk). RAID DP can lose any two disk in the raid stripe without losing any data. It offers availability equivalent to RAID 1 and allows lower cost /higher capacity SATA disks for applications. The industry standard best practice is to use RAID 1 for important data, RAID 5 for other data.
  • Virtual Clones (Flex clones). You can clone a volume / LUN or individual files. Savings = size of the original data set minus blocks subsequently changed in clone. Enables ease of dev and test cycles. Typical use cases: Build a tree of clones (clone of clones), clone a sysprep‘ed vhd, DR testing, VDI

There are several other videos on the same site that show the setup for the storage arrays. They are worth seeing to get an idea of what is involved to get all the machinery working in order to leverage the above features. It involves many steps and seems quite complex. (The hallmark of an “Enterprise-class” product? 😉 ) The SE’s have done a great job of making it seem simple. Hats off to them!

Netapp promises to reduce your virtualization storage needs by 50%

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50% Storage Savings Guarantee

NetApp‘s  Virtualization Gurantee Program promises that you will install 50% less storage for virtualization than if you buy from their competition, when you

  • Engage them for planning your virtualization storage need
  • Implement best practices recommended by them
  • Leverage features like, De duplication, Thin provisioning, RAID DP (Double Parity RAID), NetApp Snapshot copies

If you don’t use 50% less storage, you can get the required additional capacity at no additional costs

I learned about this in a 42 minute informative video on Hyper-V and Netapp storage – Overview

Written by paule1s

November 29, 2009 at 11:20 am