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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Really? Disk is Dead? From Violin?

A catchy email from one of the forums I subscribed to, caught my attention. It goes something like “…Grateful … Disk is Dead“. Here the blog from Kevin Doherty, a Senior Account Manager at Violin Memory.

Coming from Violin Memory, this is pretty obvious because they have an agenda against HDDs. They don’t use any disks at all …. in any form factor. They use VIMMs (Violin Inline Memory Modules), something no vendor in the industry use today.

violin-memory4

I recalled my blog in 2012, titled “Violin pulling the strings“. It came up here in South Asia with much fan fare, lots of razzmatazz and there was plenty of excitement. I was even invited to their product training at Ingram Micro in Singapore and met their early SE, Mike Thompson. Mike is still there I believe, but the EMC veteran in Singapore whom I mentioned in my previous blog, left almost a year later after joining. So was the ex-Sun, General Manager of Violin Memory in Singapore.

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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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Why demote archived data access?

We are all familiar with the concept of data archiving. Passive data gets archived from production storage and are migrated to a slower and often, cheaper storage medium such tapes or SATA disks. Hence the terms nearline and offline data are created. With that, IT constantly reminds users that the archived data is infrequently accessed, and therefore, they have to accept the slower access to passive, archived data.

The business conditions have certainly changed, because the need for data to be 100% online is becoming more relevant. The new competitive nature of businesses dictates that data must be at the fingertips, because speed and agility are the new competitive advantage. Often the total amount of data, production and archived data, is into hundred of TBs, even into PetaBytes!

The industries I am familiar with – Oil & Gas, and Media & Entertainment – are facing this situation. These industries have a deluge of files, and unstructured data in its archive, and much of it dormant, inactive and sitting on old tapes of a bygone era. Yet, these files and unstructured data have the most potential to be explored, mined and analyzed to realize its value to the organization. In short, the archived data and files must be democratized!

The flip side is, when the archived files and unstructured data are coupled with a slow access interface or unreliable storage infrastructure, the value of archived data is downgraded because of the aggravated interaction between access and applications and business requirements. How would organizations value archived data more if the access path to the archived data is so damn hard???!!!

An interesting solution fell upon my lap some months ago, and putting A and B together (A + B), I believe the access path to archived data can be unbelievably of high performance, simple, transparent and most importantly, remove the BLOODY PAIN of FILE AND DATA MIGRATION!  For storage administrators and engineers familiar with data migration, especially if the size of the migration is into hundreds of TBs or even PBs, you know what I mean!

I have known this solution for some time now, because I have been avidly following its development after its founders left NetApp following their Spinnaker venture to start Avere Systems.

avere_220

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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 😉

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

Convergence data strategy should not forget the branches

The word “CONVERGENCE” is boiling over as the IT industry goes gaga over darlings like Simplivity and Nutanix, and the hyper-convergence market. Yet, if we take a step back and remove our emotional attachment from the frenzy, we realize that the application and implementation of hyper-convergence technologies forgot one crucial elementThe other people and the other offices!

ROBOs (remote offices branch offices) are part of the organization, and often they are given the shorter end of the straw. ROBOs are like the family’s black sheeps. You know they are there but there is little mention of them most of the time.

Of course, through the decades, there are efforts to consolidate the organization’s circle to include ROBOs but somehow, technology was lacking. FTP used to be a popular but crude technology that binds the branch offices and the headquarter’s operations and data services. FTP is still used today, in countries where network bandwidth costs a premium. Data cloud services are beginning to appear of part of the organization’s outreaching strategy to include ROBOs but the fear of security weaknesses, data breaches and misuses is always there. Often, concerns of the weaknesses of the cloud overcome whatever bold strategies concocted and designed.

For those organizations in between, WAN acceleration/optimization techonolgy is another option. Companies like Riverbed, Silverpeak, F5 and Ipanema have addressed the ROBOs data strategy market well several years ago, but the demand for greater data consolidation and centralization, tighter and more effective data management and data control to meet the data compliance and data governance requirements, has grown much more sophisticated and advanced. 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

SMB Witness Protection Program

No, no, FBI is not in the storage business and there are no witnesses to protect.

However, SMB 3.0 has introduced a RPC-based mechanism to inform the clients of any state change in the SMB servers. Microsoft calls it Service Witness Protocol [SWP], and its objective is provide a much faster notification service allow the SMB 3.0 clients to do a failover. In previous SMB 1.0 and even in SMB 2.x, the SMB clients rely on time-out services. The time-out services, either SMB or TCP, could take up as much as 30-45 seconds, and this creates a high latency that is disruptive to enterprise applications.

SMB 3.0, as mentioned in my previous post, had a total revamp, and is now enterprise ready. In what Microsoft calls “Continuously Available” File Service, the SMB 3.0 supports clustered or scale-out file servers. The SMB shares must be shared as “Continuously Available” shares and mapped to SMB 3.0 clients. As shown in the diagram below (provided by SNIA’s webinar),

SMB 3.0 CA Shares

Client A mapping to Server 1 share (\\srv1\CAshr). Client A has a share “handle” that establishes a connection with a corresponding state of the session. The state of the session is synchronously kept consistent with a corresponding state in Server 2.

The Service Witness Protocol is not responsible for the synchronization of the states in the SMB file server cluster. Microsoft has left the HA/cluster/scale-out capability to the proprietary technology method of the NAS vendor. However, SWP regularly observes the status of all services under its watch. Continue reading