Layers in Storage – For better or worse

Storage arrays and storage services are built upon by layers and layers beneath its architecture. The physical components of hard disk drives and solid states are abstracted into RAID volumes, virtualized into other storage constructs before they are exposed as shares/exports, LUNs or objects to the network.

Everyone in the storage networking industry, is cognizant of the layers and it is the foundation of knowledge and experience. The public cloud storage services side is the same, albeit more opaque. Nevertheless, both have layers.

In the early 2000s, SNIA® Technical Council outlined a blueprint of the SNIA® Shared Storage Model, a framework describing layers and properties of a storage system and its services. It was similar to the OSI 7-layer model for networking. The framework helped many industry professionals and practitioners shaped their understanding and the development of knowledge in their respective fields. The layering scheme of the SNIA® Shared Storage Model is shown below:

SNIA Shared Storage Model – The layering scheme

Storage vendors layering scheme

While SNIA® storage layers were generic and open, each storage vendor had their own proprietary implementation of storage layers. Some of these architectures are simple, but some, I find a bit too complex and convoluted.

Here is an example of the layers of the Automated Volume Management (AVM) architecture of the EMC® Celerra®.

EMC Celerra AVM Layering Scheme

I would often scratch my head about AVM. Disks were grouped into RAID groups, which are LUNs (Logical Unit Numbers). Then they were defined as Celerra® dvols (disk volumes), and stripes of the dvols were consolidated into a storage pool.

From the pool, a piece of a storage capacity construct, called a slice volume, were combined with other slice volumes into a metavolume which eventually was presented as a file system to the network and their respective NAS clients. Explaining this took an effort because I was the IP Storage product manager for EMC® between 2007 – 2009. It was a far cry from the simplicity of NetApp® ONTAP 7 architecture of RAID groups and volumes, and the WAFL® (Write Anywhere File Layout) filesystem.

Another complicated layered framework I often gripe about is Ceph. Here is a look of how the layers of CephFS is constructed.

Ceph Storage Layered Framework

I work with the OpenZFS filesystem a lot. It is something I am rather familiar with, and the layered structure of the ZFS filesystem is essentially simpler.

Storage architecture mixology

Engineers are bizarre when they get too creative. They have a can do attitude that transcends the boundaries of practicality sometimes, and boggles many minds. This is what happens when they have their own mixology ideas.

Recently I spoke to two magnanimous persons who had the idea of providing Ceph iSCSI LUNs to the ZFS filesystem in order to use the simplicity of NAS file sharing capabilities in TrueNAS® CORE. From their own words, Ceph NAS capabilities sucked. I had to draw their whole idea out in a Powerpoint and this is the architecture I got from the conversation.

There are 3 different storage subsystems here just to provide NAS. As if Ceph layers aren’t complicated enough, the iSCSI LUNs from Ceph are presented as Cinder volumes to the KVM hypervisor (or VMware® ESXi) through the Cinder driver. Cinder is the persistent storage volume subsystem of the Openstack® project. The Cinder volumes/hypervisor datastore are virtualized as vdisks to the respective VMs installed with TrueNAS® CORE and OpenZFS filesystem. From the TrueNAS® CORE, shares and exports are provisioned via the SMB and NFS protocols to Windows and Linux respectively.

It works! As I was told, it worked!

A.P.P.A.R.M.S.C. considerations

Continuing from the layered framework described above for NAS, other aspects beside the technical work have to be considered, even when it can work technically.

I often use a set of diligent data storage focal points when considering a good storage design and implementation. This is the A.P.P.A.R.M.S.C. Take for instance Protection as one of the points and snapshot is the technology to use.

Snapshots can be executed at the ZFS level on the TrueNAS® CORE subsystem. Snapshots can be trigged at the volume level in Openstack® subsystem and likewise, rbd snapshots at the Ceph subsystem. The question is, which snapshot at which storage subsystem is the most valuable to the operations and business? Do you run all 3 snapshots? How do you execute them in succession in a scheduled policy?

In terms of performance, can it truly maximize its potential? Can it churn out the best IOPS, and deliver at wire speed? What is the latency we can expect with so many layers from 3 different storage subsystems?

And supporting this said architecture would be a nightmare. Where do you even start the troubleshooting?

Those are just a few considerations and questions to think about when such a layered storage architecture along. IMHO, such a design was over-engineered. I was tempted to say “Just because you can, doesn’t mean you should

Elegance in Simplicity

Einstein (I think) quoted:

Einstein’s quote on simplicity and complexity

I am not saying that having too many layers is wrong. Having a heavily layered architecture works for many storage solutions out there, where they are often masked with a simple and intuitive UI. But in yours truly point of view, as a storage architecture enthusiast and connoisseur, there is beauty and elegance in simple designs.

The purpose here is to promote better understanding of the storage layers, and how they integrate and interact with each other to deliver the data services to the network. In the end, that is how most storage architectures are built.

 

Do we still need FAST (and its cohorts)?

In a recent conversation with an iXsystems™ reseller in Hong Kong, the topic of Storage Tiering was brought up. We went about our banter and I brought up the inter-array tiering and the intra-array tiering piece.

After that conversation, I started thinking a lot about intra-array tiering, where data blocks within the storage array were moved between fast and slow storage media. The general policy was simple. Find all the least frequently access blocks and move them from a fast tier like the SSD tier, to a slower tier like the spinning drives with different RPM speeds. And then promote the data blocks to the faster media when accessed frequently. Of course, there were other variables in the mix besides storage media and speeds.

My mind raced back 10 years or more to my first encounter with Compellent and 3PAR. Both were still independent companies then, and I had my first taste of intra-array tiering

The original Compellent and 3PAR logos

I couldn’t recall which encounter I had first, but I remembered the time of both events were close. I was at Impact Business Solutions in their office listening to their Compellent pitch. The Kuching boys (thank you Chyr and Winston!) were very passionate in evangelizing the Compellent Data Progression technology.

At about the same time, I was invited by PTC Singapore GM at the time, Ken Chua to grace their new Malaysian office and listen to their latest storage vendor partnership, 3PAR. I have known Ken through my NetApp® days, and he linked me up Nathan Boeger, 3PAR’s pre-sales consultant. 3PAR had their Adaptive Optimization (AO) disk tiering and Dynamic Optimization (DO) technology.

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Dell EMC Isilon is an Emmy winner!

[ Disclosure: I was invited by GestaltIT as a delegate to their Storage Field Day 19 event from Jan 22-24, 2020 in the Silicon Valley USA. My expenses, travel, accommodation and conference fees were covered by GestaltIT, the organizer and I was not obligated to blog or promote the vendors’ technologies presented at this event. The content of this blog is of my own opinions and views ]

And the Emmy® goes to …

Yes, the Emmy® goes to Dell EMC Isilon! It was indeed a well deserved accolade and an honour!

Dell EMC Isilon had just won the Technology & Engineering Emmy® Awards a week before Storage Field Day 19, for their outstanding pioneering work on the NAS platform tiering technology of media and broadcasting content according to business value.

A lasting true clustered NAS

This is not a blog to praise Isilon but one that instill respect to a real true clustered, scale-out file system. I have known of OneFS for a long time, but never really took the opportunity to really put my hands on it since 2006 (there is a story). So here is a look at history …

Back in early to mid-2000, there was a lot of talks about large scale NAS. There were several players in the nascent scaling NAS market. NetApp was the filer king, with several competitors such as Polyserve, Ibrix, Spinnaker, Panasas and the young upstart Isilon. There were also Procom, BlueArc and NetApp’s predecessor Auspex. By the second half of the 2000 decade, the market consolidated and most of these NAS players were acquired.

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DellEMC Project Nautilus Re-imagine Storage for Streams

[ Disclosure: I was invited by GestaltIT as a delegate to their Storage Field Day 19 event from Jan 22-24, 2020 in the Silicon Valley USA. My expenses, travel, accommodation and conference fees were covered by GestaltIT, the organizer and I was not obligated to blog or promote the vendors’ technologies presented at this event. The content of this blog is of my own opinions and views ]

Cloud computing will have challenges processing data at the outer reach of its tentacles. Edge Computing, as it melds with the Internet of Things (IoT), needs a different approach to data processing and data storage. Data generated at source has to be processed at source, to respond to the event or events which have happened. Cloud Computing, even with 5G networks, has latency that is not sufficient to how an autonomous vehicle react to pedestrians on the road at speed or how a sprinkler system is activated in a fire, or even a fraud detection system to signal money laundering activities as they occur.

Furthermore, not all sensors, devices, and IoT end-points are connected to the cloud at all times. To understand this new way of data processing and data storage, have a look at this video by Jay Kreps, CEO of Confluent for Kafka® to view this new perspective.

Data is continuously and infinitely generated at source, and this data has to be compiled, controlled and consolidated with nanosecond precision. At Storage Field Day 19, an interesting open source project, Pravega, was introduced to the delegates by DellEMC. Pravega is an open source storage framework for streaming data and is part of Project Nautilus.

Rise of  streaming time series Data

Processing data at source has a lot of advantages and this has popularized Time Series analytics. Many time series and streams-based databases such as InfluxDB, TimescaleDB, OpenTSDB have sprouted over the years, along with open source projects such as Apache Kafka®, Apache Flink and Apache Druid.

The data generated at source (end-points, sensors, devices) is serialized, timestamped (as event occurs), continuous and infinite. These are the properties of a time series data stream, and to make sense of the streaming data, new data formats such as Avro, Parquet, Orc pepper the landscape along with the more mature JSON and XML, each with its own strengths and weaknesses.

You can learn more about these data formats in the 2 links below:

DIY is difficult

Many time series projects started as DIY projects in many organizations. And many of them are still DIY projects in production systems as well. They depend on tribal knowledge, and these databases are tied to an unmanaged storage which is not congruent to the properties of streaming data.

At the storage end, the technologies today still rely on the SAN and NAS protocols, and in recent years, S3, with object storage. Block, file and object storage introduce layers of abstraction which may not be a good fit for streaming data.

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Komprise is a Winner

[Disclosure: I was invited by GestaltIT as a delegate to their Storage Field Day 19 event from Jan 22-24, 2020 in the Silicon Valley USA. My expenses, travel, accommodation and conference fees were covered by GestaltIT, the organizer and I was not obligated to blog or promote the vendors’ technologies to be presented at this event. The content of this blog is of my own opinions and views]

I, for one perhaps have seen far too many “file lifecycle and data management” software solutions that involved tiering, hierarchical storage management, ILM or whatever you call them these days. If I do a count, I would have managed or implemented at least 5 to 6 products, including a home grown one.

The whole thing is a very crowded market and I have seen many which have come and gone, and so when the opportunity to have a session with Komprise came at Storage Field Day 19, I did not carry a lot of enthusiasm.

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Did Cloud Kill LTFS?

I like LTFS (Linear Tape File System). I was hoping it would take off but it has not. And looking at its future, its significance is becoming less and less relevant. I look if Cloud has been a factor in the possible demise of LTFS in the next few years.

What is LTFS?

In a nutshell, Linear Tape File System makes LTO tapes look like a disk with a file system. It takes a tape and divides it into 2 partitions:

  • Index Partition (XML Index Schema with file names, metadata and attributes details)
  • Data Partition (where the data resides)

Diagram from https://www.snia.org/sites/default/orig/SDC2011/presentations/tuesday/DavidPease_LinearTape_File_System.pdf

It has a File System module which is implemented in supported OS of Unix/Linux, MacOS and Windows. And the mounted file system “tape partition” shows up as a drive or device.

Assassination attempts

There were many attempts to kill off tapes and so far, none has been successful.

Among the “tape-killer” technologies, I think the most prominent one is the VTL (Virtual Tape Library). There were many VTLs I encountered during my days in mid-2000s. NetApp had Alacritus and EMC had Clariion Disk Libraries. There were also IBM ProtecTIER, FalconStor VTL (which is still selling today) among others and Sepaton (read in reverse is “No Tapes’). Sepaton was acquired by Hitachi Data Systems several years back. Continue reading

Sexy HPC storage is all the rage

HPC is sexy

There is no denying it. HPC is sexy. HPC Storage is just as sexy.

Looking at the latest buzz from Super Computing Conference 2018 which happened in Dallas 2 weeks ago, the number of storage related vendors participating was staggering. Panasas, Weka.io, Excelero, BeeGFS, are the ones that I know because I got friends posting their highlights. Then there are the perennial vendors like IBM, Dell, HPE, NetApp, Huawei, Supermicro, and so many more. A quick check on the SC18 website showed that there were 391 exhibitors on the floor.

And this is driven by the unrelentless demand for higher and higher performance of computing, and along with it, the demands for faster and faster storage performance. Commercialization of Artificial Intelligence (AI), Deep Learning (DL) and newer applications and workloads together with the traditional HPC workloads are driving these ever increasing requirements. However, most enterprise storage platforms were not designed to meet the demands of these new generation of applications and workloads, as many have been led to believe. Why so?

I had a couple of conversations with a few well known vendors around the topic of HPC Storage. And several responses thrown back were to put Flash and NVMe to solve the high demands of HPC storage performance. In my mind, these responses were too trivial, too irresponsible. So I wanted to write this blog to share my views on HPC storage, and not just about its performance.

The HPC lines are blurring

I picked up this video (below) a few days ago. It was insideHPC Rich Brueckner interview with Dr. Goh Eng Lim, HPE CTO and renowned HPC expert about the convergence of both traditional and commercial HPC applications and workloads.

I liked the conversation in the video because it addressed the 2 different approaches. And I welcomed Dr. Goh’s invitation to the Commercial HPC community to work with the Traditional HPC vendors to help push the envelope towards Exascale SuperComputing.

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Is Pure Play Storage good?

I post storage and cloud related articles to my unofficial SNIA Malaysia Facebook community (you are welcomed to join) every day. It is a community I started over 9 years ago, and there are active live banters of the posts of the day. Casual, personal were the original reasons why I started the community on Facebook rather than on LinkedIn, and I have been curating it religiously for the longest time.

The Big 5 of Storage (it was Big 6 before this)

Looking back 8-9 years ago, the storage vendor landscape of today has not changed much. The Big 5 hegemony is still there, still dominating the Gartner Magic Quadrant for Enterprise and Mid-end Arrays, and is still there in the All-Flash quadrant as well, albeit the presence of Pure Storage in that market.

The Big 5 of today – Dell EMC, NetApp, HPE, IBM and Hitachi Vantara – were the Big 6 of 2009-2010, consisting of EMC, NetApp, Dell, HP, IBM and Hitachi Data Systems. The All-Flash, or Gartner calls it Solid State Arrays (SSA) market was still an afterthought, and Pure Storage was just founded. Pure Storage did not appear in my radar until 2 years later when I blogged about Pure Storage’s presence in the market.

Here’s a look at the Gartner Magic Quadrant for 2010:

We see Pure Play Storage vendors in the likes of EMC, NetApp, Hitachi Data Systems (before they adopted the UCP into their foray), 3PAR, Compellent, Pillar Data Systems, BlueArc, Xiotech, Nexsan, DDN and Infortrend. And when we compare that to the 2017 Magic Quadrant (I have not seen the 2018 one yet) below:

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DellEMC SC progressing well

[Preamble: I was a delegate of Storage Field Day 14. My expenses, travel and accommodation were paid for by GestaltIT, the organizer and I was not obligated to blog or promote the technologies presented at this event. The content of this blog is of my own opinions and views]

I haven’t had a preview of the Compellent technology for a long time. My buddies at Impact Business Solutions were the first to introduce the Compellent technology called Data Progression to the local Malaysian market and I was invited to a preview back then. Around the same time, I also recalled another rather similar preview invitation by PTC Singapore for the 3PAR technology called Adaptive Provisioning (it is called Adaptive Optimization now).

Storage tiering was on the rise in the 2009-2010 years. Both Compellent and 3PAR were neck and neck leading the conversation and mind share of storage tiering, and IBM easyTIER and EMC FAST (Fully Automated Storage Tiering) were nowhere to be seen or heard. Vividly, the Compellent Data Progression technology was much more elegant compared to the 3PAR technology. While both intelligent storage tiering technologies were equally good, I took that the 3PAR founders were ex-Sun Microsystems folks, and Unix folks sucked at UX. In this case, Compellent’s Data Progression was a definitely a leg up better than 3PAR.

History aside, this week I have the chance to get a new preview of the Compellent technology again. Compellent was now rebranded as the SC series and was positioned as the mid-range storage arrays of DellEMC. And together with the other Storage Field Day 14 delegates, I have the pleasure to experience the latest SC Data Progression technology update, as well their latest SC All-Flash.

In Data Progression, one interesting feature which caught my attention was the RAID Tiering. This was a dynamic auto expand and auto contract set of RAID tiersRAID 10 and RAID 5/6 in the Fast Tier and RAID 5/6 in the Lower Tier. RAID 10, RAID 5 and RAID 6 on the same set of drives (including SSDs), and depending on the “hotness” of the data, the location of the data blocks switched between the several RAID tiers in the Fast Tier. Over a longer period, the data blocks would relocate transparently to the Capacity Tier from the Fast Tier.

The Data Progression technology is extremely efficient. The movement of the data between the RAID Tiers and between the Performance/Capacity Tiers are in pages instead of blocks, making the write penalty and bandwidth to a negligible minimum.

The Storage Field Day 14 delegates were also privileged to be the first to get into the deep dive of the new All-Flash SC, just days of the announcement of the All-Flash SC. The All Flash SC redefines and refines the Data Progression to the next level. Among the new optimization, NAND Flash in the SC (both SLCs and MLCs, read-intensive and write-intensive) set the Data Progression default page size from 2MB to 512KB. These smaller 512KB pages enabled reduced bandwidth for tiering between the write-intensive and the read-intensive tier.

I didn’t get the latest SC family photos yet, but I managed to grab a screenshot of the announcement from The Register of the new DellEMC SC Series.

I was very encouraged with the DellEMC Midrange Storage presentation. Besides giving us a fantastic deep dive about the DellEMC SC All-Flash Storage, I was also very impressed by the candid and straightforward attitude of the team, led by their VP of Product Management, Pierluca Chiodelli. An EMC veteran, he was taking up the hard questions onslaught by the SFD14 delegate like a pro. His team’s demeanour was critical in instilling confidence and trust in how the bloggers and the analysts viewed Dell EMC merger, and how the SC and the Unity series would pan out in the technology roadmap.

Unlike the fiasco I went through with the DellEMC Forum 2017 in Malaysia, where I was disturbed with 3 calls in 3 consecutive days by DellEMC Malaysia, I was left with a profound respect for this DellEMC Storage team. They strongly supported their position within the DellEMC storage universe, and imparted their confidence in their technology solution in the marketplace.

Without a doubt, in my point of view, this DellEMC Mid-Range Storage team was the best I have enjoyed in Storage Field Day 14. Thank you.