Don’t get too drunk on Hyper Converged

I hate the fact that I am bursting the big bubble brewing about Hyper Convergence (HC). I urge all to look past the hot air and hype frenzy that are going on, because in the end, the HC platforms have to be aligned and congruent to the organization’s data architecture and business plans.

The announcement of Gartner’s latest Magic Quadrant on Integrated Systems (read hyper convergence) has put Nutanix as the leader of the pack as of August 2015. Clearly, many of us get caught up because it is the “greatest feeling in the world”. However, this faux feeling is not reality because there are many factors that made the pack leaders in the Magic Quadrant (MQ).

Gartner MQ Integrated Systems Aug 2015

First of all, the MQ is about market perception. There is no doubt that the pack leaders in the Leaders Quadrant have earned their right to be there. Each company’s revenue, market share, gross margin, company’s profitability have helped put each as leaders in the pack. However, it is also measured by branding, marketing, market perception and acceptance and other intangible factors.

Secondly, VMware EVO: Rail has split the market when EMC has 3 HC solutions in VCE, ScaleIO and EVO: Rail. Cisco wanted to do their own HC piece in Whiptail (between the 2014 MQ and 2015 MQ reports), and closed down Whiptail when their new CEO came on board. NetApp chose EVO: Rail and also has the ever popular FlexPod. That is why you see that in this latest MQ report, NetApp and Cisco are interpreted independently whereas in last year’s report, it was Cisco/NetApp. Market forces changed, and perception changed.  Continue reading

The transcendence of Data Fabric

The Register wrote a damning piece about NetApp a few days ago. I felt it was irresponsible because this is akin to kicking a man when he’s down. It is easy to do that. The writer is clearly missing the forest for the trees. He was targeting NetApp’s Clustered Data ONTAP (cDOT) and missing the entire philosophy of NetApp’s mission and vision in Data Fabric.

I have always been a strong believer that you must treat Data like water. Just like what Jeff Goldblum famously quoted in Jurassic Park, “Life finds a way“, data as it moves through its lifecycle, will find its way into the cloud and back.

And every storage vendor today has a cloud story to tell. It is exciting to listen to everyone sharing their cloud story. Cloud makes sense when it addresses different workloads such as the sharing of folders across multiple devices, backup and archiving data to the cloud, tiering to the cloud, and the different cloud service models of IaaS, PaaS, SaaS and XaaS.

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Oops, excuse me but your silo is showing

It is the morning that the SNIA Global Steering Committee reporting session is starting soon. I am in the office extremely early waiting for my turn to share the happenings in SNIA Malaysia.

And of late, I have been getting a lot of calls to catch up on hot technologies, notably All Flash Storage arrays and hyper-converged infrastructure. Even though I am now working for Interica, a company that focuses on Oil & Gas exploration and production software, my free coffee sessions with folks from the IT side have not diminished. And I recalled a week back in mid-March where I had coffee overdose!

Flash storage and hyperconvergence are HOT! Despite the hypes and frenzies of both flash storage and hyperconvergence, I still believe that integrating either or, or both, still have an effect that many IT managers overlook. The effect is a data silo.

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The reverse wars – DAS vs NAS vs SAN

It has been quite an interesting 2 decades.

In the beginning (starting in the early to mid-90s), SAN (Storage Area Network) was the dominant architecture. DAS (Direct Attached Storage) was on the wane as the channel-like throughput of Fibre Channel protocol coupled by the million-device addressing of FC obliterated parallel SCSI, which was only able to handle 16 devices and throughput up to 80 (later on 160 and 320) MB/sec.

NAS, defined by CIFS/SMB and NFS protocols – was happily chugging along the 100 Mbit/sec network, and occasionally getting sucked into the arguments about why SAN was better than NAS. I was already heavily dipped into NFS, because I was pretty much a SunOS/Solaris bigot back then.

When I joined NetApp in Malaysia in 2000, that NAS-SAN wars were going on, waiting for me. NetApp (or Network Appliance as it was known then) was trying to grow beyond its dot-com roots, into the enterprise space and guys like EMC and HDS were frequently trying to put NetApp down.

It’s a toy”  was the most common jibe I got in regular engagements until EMC suddenly decided to attack Network Appliance directly with their EMC CLARiiON IP4700. EMC guys would fondly remember this as the “NetApp killer“. Continue reading

Hail Hydra!

The last of the Storage Field Day 6 on November 7th took me and the other delegates to NEC. There was an obvious, yet eerie silence among everyone about this visit. NEC? Are you kidding me?

NEC isn’t exactly THE exciting storage company in the Silicon Valley, yet I was pleasantly surprised with their HydraStorprowess. It is indeed quite a beast, with published numbers of backup throughput of 4PB/hour, and scales to 100PB of capacity. Most impressive indeed, and HydraStor deserves this blogger’s honourable architectural dissection.

HydraStor is NEC’s grid-based, scale-out storage platform with an object storage backend. The technology, powered by the DynamicStor ™ software, a distributed file system laid over the HydraStor grid architecture. At the same time, it has the DataRedux™ technology that provides the global in-line deduplication as the HydraStor ingests data for data protection, replication, archiving and WORM purposes. It is a massive data consolidation platform, storing gazillion loads of data (100PB you say?) for short-term and long-term retention and recovery.

The architecture is indeed solid, and its data availability goes beyond traditional RAID-level resiliency. HydraStor employs their proprietary erasure coding, called Distributed Resilient Data™. The resiliency knob can be configured to withstand 6 concurrent disks or nodes failure, but by default configured with a resiliency level of 3.

We can quickly deduce that DynamicStor™, DataRedux™ and Distributed Resilient Data™ are the technology pillars of HydraStor. How do they work, and how do they work together?

Let’s look a bit deeper into the HydraStor architecture.

HydraStor is made up of 2 types of nodes:

  • Accelerator Nodes
  • Storage Nodes

The Accelerator Nodes (AN) are the access nodes. They interface with the HydraStor front end, which could be CIFS, NFS or OST (Open Storage Technology). The AN nodes chunks the in-coming data and performs in-line deduplication at a very high speed. It can reach speed of 300TB/hour, which is blazingly fast!

The AN nodes also runs DynamicStor™, handling the performance heavy-lifting portion of HydraStor. The chunked data from the AN nodes are then passed on to the Storage Nodes (SN), where they are further “deduped in-line” to determined if the chunks are unique or not. It is a two-step inline deduplication process. Below is a diagram showing the ANs built above the SNs in the HydraStor grid architecture.

NEC AN & SN grid architecture

 

The HydraStor grid architecture is also a very scalable architecture, allow the dynamic scale-in and scale-out of both ANs and SNs. AN nodes and SN nodes can be added or removed into the system, auto-configuring and auto-optimizing while everything stays online. This capability further strengthens the reliability and the resiliency of the HydraStor.

NEC Hydrastor dynamic topology

Moving on to DataRedux™. DataRedux™ is HydraStor’s global in-line data deduplication technology. It performs dedupe at the sub-file level, with variable length window. This is performed at the AN nodes and the SN nodes level,chunking and creating unique hash values. All unique chunks are further compressed with a modified LZ compression algorithm, shrinking the data to its optimized footprint on the disk storage. To maintain the global in-line deduplication, the hash table is available across the HydraStor cluster.

NEC Deduplication & Compression

The unique data chunk resulting from deduplication and compression are then written to disks using the configured Distributed Resilient Data™ (DRD) algorithm, at its set resiliency level.

At the junction of DRD, with erasure coding parity, the data is broken up into multiples of fragments and assigned a parity to a grouping of fragments. If the resiliency level is set to 3 (the default), the data is broken into 12 pieces, 9 data fragments + 3 parity fragments. The 3 parity fragments corresponds to the resiliency level of 3. See diagram below of the 12 fragments spread across a group of selected disks in the storage pool of the Storage Nodes.

NEC DRD erasure coding on Storage Nodes

 

If the HydraStor experiences a failure in the disks or nodes, and has resulted in the loss of a fragment or fragments, the DRD self-healing function will auto-rebuild and auto-reconfigure the recovered fragments in another set of disks, maintaining the level of 3 parities.

The resiliency level, as mentioned earlier, can be set up to 6, boosting the HydraStor survival factor of 6 disks or nodes failure in the grid. See below of how the autonomous DRD recovery works:

NEC Autonomous Data recovery

Despite lacking the razzle dazzle of most Silicon Valley storage startups and upstarts, credit be given where credit is due. NEC HydraStor is indeed a strong show stopper.

However, in a market that is as fickle as storage, deduplication solutions such as HydraStor, EMC Data Domain, and HP StoreOnce, are being superceded by Copy Data Management technology, touted by Actifio. It was rumoured that EMC restructured their entire BURA (Backup Recovery Archive) division to DPAD (Data Protection and Availability Division) to go after the burgeoning copy data management market.

It would be good if NEC can take notice and turn their HydraStor “supertanker” towards the Copy Data Management market. That would be something special to savour.

P/S: NEC. Sorry about the title. I just couldn’t resist it 😉

Praying to the hypervisor God

I was reading a great article by Frank Denneman about storage intelligence moving up the stack. It was pretty much in line with what I have been observing in the past 18 months or so, about the storage pendulum having swung back to DAS (direct attached storage). To be more precise, the DAS form factor I am referring to are physical server hardware that houses many disk drives.

Like it or not, the hypervisor has become the center of the universe in the IT space. VMware has become the indomitable force in the hypervisor technology, with Microsoft Hyper-V playing catch-up. The seismic shift of these 2 hypervisor technologies are leading storage vendors to place them on to the altar and revering them as deities. The others, with the likes of Xen and KVM, and to lesser extent Solaris Containers aren’t really worth mentioning.

This shift, as the pendulum swings from networked storage back to internal “direct-attached” storage are dictated by 4 main technology factors:

  • The x86 server architecture
  • Software-defined
  • Scale-out architecture
  • Flash-based storage technology

Anyone remember Thumper? Not the Disney character from the Bambi movie!

thumper-bambi-cartoon-character

When the SunFire X4500 (aka Thumper) was first released in (intermission: checking Wiki for the right year) in 2006, I felt that significant wound inflicted in the networked storage industry. Instead of the usual 4-8 hard disk drives in the all the industry servers at the time, the X4500 4U chassis housed 48 hard disk drives. The design and architecture were so astounding to me, I even went and bought a 1U SunFire X4150 for my personal server collection. Such was my adoration for Sun’s technology at the time.

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Technology prowess of Riverbed SteelFusion

The Riverbed SteelFusion (aka Granite) impressed me the moment it was introduced to me 2 years ago. I remembered that genius light bulb moment well, in December 2012 to be exact, and it had left its mark on me. Like I said last week in my previous blog, the SteelFusion technology is unique in the industry so far and has differentiated itself from its WAN optimization competitors.

To further understand the ability of Riverbed SteelFusion, a deeper inspection of the technology is essential. I am fortunate to be given the opportunity to learn more about SteelFusion’s technology and here I am, sharing what I have learned.

What does the technology of SteelFusion do?

Riverbed SteelFusion takes SAN volumes from supported storage vendors in the central datacenter and projects the storage volumes (aka LUNs)to applications and hosts at the remote branches. The technology requires a paired relationship between SteelFusion Core (in the centralized datacenter) and SteelFusion Edge (at the branch). Both SteelFusion Core and Edge are fronted respectively by the Riverbed SteelHead WAN optimization device, to deliver the performance required.

The diagram below gives an overview of how the entire SteelFusion network architecture is like:

Riverbed SteelFusion Overall Solution 2 Continue reading

No Flash in the pan

The storage networking market now is teeming with flash solutions. Consumers are probably sick to their stomach getting a better insight which flash solution they should be considering. There are so much hype, fuzz and buzz and like a swarm of bees, in the chaos of the moment, there is actually a calm and discerning pattern slowly, but surely, emerging. Storage networking guys would probably know this thing well, but for the benefit of the other readers, how we view flash (and other solid state storage) becomes clear with the picture below: Flash performance gap

(picture courtesy of  http://electronicdesign.com/memory/evolution-solid-state-storage-enterprise-servers)

Right at the top, we have the CPU/Memory complex (labelled as Processor). Our applications, albeit bytes and pieces of them, run in this CPU/Memory complex.

Therefore, we can see Pattern #1 showing up. Continue reading

The Prophet has arrived

Early last week, I had a catch up with my friend. He was excited to share with me the new company he just joined. It was ProphetStor. It was a catchy name and after our conversation, I have decided to spend a bit of my weekend afternoon finding out more about the company and its technology.

From another friend at FalconStor, I knew of this company several months ago. Ex-FalconStor executives have ventured to found ProphetStor as the next generation of storage resource orchestration engine. And it has found a very interesting tack to differentiate from the many would-bes of so-called “software-defined storage” leaders. ProphetStor made their early appearance at the OpenStack Summit in Hong Kong back in November last year, positioning several key technologies including OpenStack Cinder, SNIA CDMI (Cloud Data Management Interface) and SMI-S (Storage Management Initiative Specification) to provide federation of storage resources discovery, provisioning and automation. 

The federation of storage resources and services solution is aptly called ProphetStor Federator. The diagram I picked up from the El Reg article presents the Federator working with different OpenStack initiatives quite nicely below:  There are 3 things that attracted me to the uniqueness of ProphetStor.

1. The underlying storage resources, be it files, objects, or blocks, can be presented and exposed as Cinder-style volumes.

2. The ability to define the different performance capabilities and SLAs (IOPS, throughput and latency) from the underlying storage resources and matching them to the right application requirements.

3. The use of SNIA of SMI-S and CDMI Needless to say that the Federator software will abstract the physical and logical structures of any storage brands or storage architectures, giving it a very strong validation of the “software-defined storage (SDS)” concept.

While the SDS definition is still being moulded in the marketplace (and I know that SNIA already has a draft SDS paper out), the ProphetStor SDS concept does indeed look similar to the route taken by EMC ViPR. The use of the control plane (ProphetStor Federator) and the data plane (underlying physical and logical storage resource) is obvious.

I wrote about ViPR many moons ago in my blog and I see ProphetStor as another hat in the SDS ring. I grabbed the screenshot (below) from the ProphetStor website which I thought did beautifully explained what ProphetStor is from 10,000 feet view.

ProphetStor How it works

The Cinder-style volume is a class move. It preserves the sanctity of many enterprise applications which still need block storage volumes but now it comes with a twist. These block storage volumes now will have different capability and performance profiles, tagged with the relevant classifications and SLAs.

And this is where SNIA SMI-S discovery component is critical because SMI-S mines these storage characteristics and presents them to the ProphetStor Federator for storage resource classification. For storage vendors that do not have SMI-S support, ProphetStor can customize the relevant interfaces to the proprietary API to discover the storage characteristics.

On the north-end, SNIA CDMI works with the ProphetStor Federator’s Offer & Provisioning functions to bundle wrap various storage resources for the cloud and other traditional storage network architectures.

I have asked my friend for more technology deep-dive materials (he has yet to reply me) of ProphetStor to ascertain what I have just wrote. (Simon, you have to respond to me!)

This is indeed very exciting times knowing ProphetStor as one of the early leaders in the SDS space. And I like to see ProphetStor go far with this.

Now let us pray … because the prophet has arrived.