Category Archives: Data Management

Did Cloud Kill LTFS?

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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 →

Scaling new HPC with Composable Architecture

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[Disclosure: I was invited by Dell Technologies as a delegate to their Dell Technologies World 2019 Conference from Apr 29-May 1, 2019 in the Las Vegas USA. Tech Field Day Extra was an included activity as part of the Dell Technologies World. My expenses, travel, accommodation and conference fees were covered by Dell Technologies, the organizer and I was not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

Deep Learning, Neural Networks, Machine Learning and subsequently Artificial Intelligence (AI) are the new generation of applications and workloads to the commercial HPC systems. Different from the traditional, more scientific and engineering HPC workloads, I have written about the new dawn of supercomputing and the attractive posture of commercial HPC.

Don’t be idle

From the business perspective, the investment of HPC systems is high most of the time, and justifying it to the executives and the investors is not easy. Therefore, it is critical to keep feeding the HPC systems and significantly minimize the idle times for compute, GPUs, network and storage.

However, almost all HPC systems today are inflexible. Once assigned to a project, the resources pretty much stay with the project, even when the workload processing of the project is idle and waiting. Of course, we have to bear in mind that not all resources are fully abstracted, virtualized and software-defined whereby you can carve out pieces of the hardware and deliver a percentage of that resource. Case in point is the CPU, where you cannot assign certain clock cycles of CPU to one project and another half to the other. The technology isn’t there yet. Certain resources like GPU is going down the path of Virtual GPU, and into the realm of resource disaggregation. Eventually, all resources of the HPC systems – CPU, memory, FPGA, GPU, PCIe channels, NVMe paths, IOPS, bandwidth, burst buffers etc – should be disaggregated and pooled for disparate applications and workloads based on demands of usage, time and performance.

Hence we are beginning to see the disaggregated HPC systems resources composed and built up the meet the diverse mix and needs of HPC applications and workloads. This is even more acute when a AI project might grow cold, but the training of AL/ML/DL workloads continues to stay hot

Liqid the early leader in Composable Architecture

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Connecting ideas and people with Dell Influencers

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[Disclosure: I was invited by Dell Technologies as a delegate to their Dell Technologies World 2019 Conference from Apr 29-May 1, 2019 in the Las Vegas USA. My expenses, travel, accommodation and conference fees were covered by Dell Technologies, the organizer and I was not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

I just got home from Vegas yesterday after attending my 2nd Dell Technologies World as one of the Dell Luminaries. The conference was definitely a bigger one than the one last year, with more than 15,000 attendees. And there was a frenzy of announcements, from Dell Technologies Cloud to new infrastructure solutions, and more. The big one for me, obviously was Azure VMware Solutions officiated by Microsoft CEO Satya Nadella and VMware CEO Pat Gelsinger, with Michael Dell bringing together the union. I blogged about Dell jumping into the cloud in a big way.

AI Tweetup

In the razzmatazz, the most memorable moments were one of the Tweetups organized by Dr. Konstanze Alex (Konnie) and her team, and Tech Field Day Extra.

Tweetup was alien to me. I didn’t know how the concept work and I did google tweetup before that. There were a few tweetups on the topics of data protection and 5G, but the one that stood out for me was the AI tweetup.

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Dell go big with Cloud

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[Disclaimer: I have been invited by Dell Technologies as a delegate to their Dell Technologies World 2019 Conference from Apr 29-May 1, 2019 in the Las Vegas USA. My expenses, travel and accommodation are covered by Dell Technologies, the organizer and I am not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

Talk about big. Dell Technologies just went big with the Cloud.

The Microsoft Factor

Day 1 of Dell Technologies World 2019 (DTW19) started with a big surprise to many, including yours truly when Michael Dell, together with Pat Gelsinger invited Microsoft CEO, Satya Nadella on stage.

There was nothing new about Microsoft working with Dell Technologies. Both have been great partners since the PC days, but when they announced Azure VMware Solutions to the 15,000+ attendees of the conference, there was a second of disbelief, followed by an ovation of euphoria.

VMware solutions will run native on Microsoft Azure Cloud. The spread of vSphere, VSAN, vCenter, NSX-T and VMware tools and environment will run on Azure Bare Metal Infrastructure at multiple Azure locations. How big is that. Continue reading →

Lift and Shift Begone!

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I am excited. New technologies are bringing the data (and storage) closer to processing and compute than ever before. I believe the “Lift and Shift” way would be a thing of the past … soon.

Data is heavy

Moving data across the network is painful. Moving data across distributed networks is even more painful. To compile the recent first image of a black hole, an amount of 5PB or more had to shipped for central processing. If this was moved over a 10 Gigabit network, it would have taken weeks.

Furthermore, data has dependencies. Snapshots, clones, and other data relationships with applications and processes render data inert, weighing it down like an anchor of a ship.

When I first started in the industry more than 25 years ago, Direct Attached Storage (DAS) was the dominating storage platform. I had a bulky Sun MultiDisk Pack connected via Fast SCSI to my SPARCstation 2 (diagram below):

Then I was assigned as the implementation engineer for Hock Hua Bank (now defunct) retail banking project in their Sibu HQ in East Malaysia. It was the first Sun SPARCstorage 1000 (photo below), running a direct attached Fibre Channel 0.25 Gbps FCAL (Fibre Channel Arbitrated Loop). It was the cusp of the birth of SAN (Storage Area Network).

Photo from https://www.cca.org/dave/tech/sys5/

The proliferation of SAN over the next 2 decades pushed DAS into obscurity, until SAS (Serial Attached SCSI) came about. Added to the mix was the prominence of Cloud Storage. But on-premises storage and Cloud Storage didn’t always come together. There was always a valley between the 2, until the public clouds gained a stronger foothold in the minds of IT and businesses. Today, both on-premises storage and cloud storage are slowly cosying as one Data Singularity, thanks to vision and conceptualization of data fabrics. NetApp was an early proponent of the Data Fabric concept 4 years ago. Continue reading →

Is AI my friend?

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I am sorry, Dave …

Let’s start this story with 2 supposed friends – Dave and Hal.

How do we become friends?

We have friends and we have enemies. We become friends when trust is established. Trust is established when there is an unsaid pact, a silent agreement that I can rely on you to keep my secrets private. I will know full well that you will protect my personal details with a strong conviction. Your decisions and your actions towards me are in my best interest, unbiased and would benefit both me and you.

I feel secure with you.

AI is my friend

When the walls of uncertainty and falsehood are broken down, we trust our friends more and more. We share deeper secrets with our friends when we believe that our privacy and safety are safeguarded and protected. We know well that we can rely on them and their decisions and actions on us are reliable and unbiased.

AI, can I count on you to protect my privacy and give me security that my personal data is not abused in the hands of the privileged few?

AI, can I rely on you to be ethical, unbiased and give me the confidence that your decisions and actions are for the benefit and the good of me, myself and I?

My AI friends (maybe)

As I have said before, I am not a skeptic. When there is plenty of relevant, unbiased data fed into the algorithms of AI, the decisions are fair. People accept these AI decisions when the degree of accuracy is very close to the Truth. The higher the accuracy, the greater the Truth. The greater the Truth, the more confident people are towards the AI system.

Here are some AI “friends” in the news:

But we have to careful here as well. Accuracy can be subjective, paradoxical and enigmatic. When ethics are violated, we terminate the friendship and we reject the “friend”. We categorically label him or her as an enemy. We constantly have to check, just like we might, once in a while, investigate on our friends too.

In Conclusion

AI, can we be friends now?

[Apology: sorry about the Cyberdyne link 😉 ]

[This blog was posted in LinkedIn on Apr 19th 2019]

Figuring out storage for Kubernetes and containers

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Oops! I forgot about you!

To me, containers and container orchestration (CO) engines such as Kubernetes, Mesos, Docker Swarm are fantastic. They scale effortlessly and are truly designed for cloud native applications (CNA).

But one thing irks me. Storage management for containers and COs. It was as if when they designed and constructed containers and the containers orchestration (CO) engines, they forgot about the considerations of storage and storage management. At least the persistent part of storage.

Over a year ago, I was in two minds about persistent storage, especially when it comes to the transient nature of microservices which was so prevalent and were inundating the cloud native applications landscape. I was searching for answers in my blog. The decentralization of microservices in containers means mass deployment at the edge, but to have the pre-processed and post-processed data stick to the persistent storage at the edge device is a challenge. The operative word here is “STICK”.

Two different worlds

Containers were initially designed and built for lightweight applications such as microservices. The runtime, libraries, configuration files and dependencies are all in one package. They were meant to do simple tasks quickly and scales to thousands easily. They could be brought up and brought down in little time and did not have to bother about the persistent data stored by the host. The state of the containers were also not important to the application tasks at hand.

Today containers like Docker have matured to run enterprise applications and the state of the container is important. The applications must know the state and the health of the container. The container could be in online mode, online but not accepting data mode, suspended mode, paused mode, interrupted mode, quiesced mode or halted mode. Each mode or state of the container is important to the running applications and the container can easily brought up or down in an instance of a command. The stateful nature of the containers and applications is critical for the business. The same situation applies to container orchestration engines such as Kubernetes.

Container and Kubernetes Storage

Docker provides 3 methods to local storage. In the diagram below, it describes:

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Malaysia, when will you take data privacy seriously?

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It is sad. I get about 5-10 silly calls a week and a bunch of nonsense messages in my WhatsApp text and SMS. They waste my time, and it has been going on for years. Even worse is that my private details are out there, exposed and likely be abused too.

Once I got a call from a municipal attorney in the state of Kelantan that I have unpaid summons of several thousand ringgit. They have phone number, my IC number and they threatened to send me a note to arrest me if I didn’t pay up. The thing is, I have never been to Kelantan and I challenged them to send the attorney letter to my home address. The guy on the phone hung up.

In this age where digital information is there at our finger tips, the private details of victims are out there, easily used for unsavoury gains. And we as Malaysians should not shrug our shoulders and not assume that everything is like that, as if it is a Malaysia way of life. That apathy, our state of indifference, should be wiped out from our attitude. We should question the government, the agencies of why is our privacy not protected?

We have the Personal Data Protection Act, ratified in 2010. I don’t know the details of the act, but in its most basic form, don’t you think our private details should at least be protected from the telemarketers calling us selling their personal loans, time share travel suites, private massage (with benefits?) and other silly stuff? How can an act, as a law, be so toothless? Why bother drafting the act, and going through multiple iterations, I would suppose, and making it a law, and yet remain so unworthy to be called a act? Continue reading →

The full force of Western Digital

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[Preamble: I have been invited by GestaltIT as a delegate to their Tech Field Day for Storage Field Day 18 from Feb 27-Mar 1, 2019 in the Silicon Valley USA. My expenses, travel and accommodation were covered by GestaltIT, the organizer and I was not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

3 weeks after Storage Field Day 18, I was still trying to wrap my head around the 3-hour session we had with Western Digital. I was like a kid in a candy store for a while, because there were too much to chew and I couldn’t munch them all.

From “Silicon to System”

Not many storage companies in the world can claim that mantra – “From Silicon to Systems“. Western Digital is probably one of 3 companies (the other 2 being Intel and nVidia) I know of at present, which develops vertical innovation and integration, end to end, from components, to platforms and to systems.

For a long time, we have always known Western Digital to be a hard disk company. It owns HGST, SanDisk, providing the drives, the Flash and the Compact Flash for both the consumer and the enterprise markets. However, in recent years, through 2 eyebrow raising acquisitions, Western Digital was moving itself up the infrastructure stack. In 2015, it acquired Amplidata. 2 years later, it acquired Tegile Systems. At that time, I was wondering why a hard disk manufacturer was buying storage technology companies that were not its usual bread and butter business.

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Bridges to the clouds and more – NetApp NDAS

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[Preamble: I have been invited by GestaltIT as a delegate to their Tech Field Day for Storage Field Day 18 from Feb 27-Mar 1, 2019 in the Silicon Valley USA. My expenses, travel and accommodation were covered by GestaltIT, the organizer and I was not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

The NetApp Data Fabric Vision

The NetApp Data Fabric vision has always been clear to me. Maybe it was because of my 2 stints with them, and I got well soaked in their culture. 3 simple points define the vision.

  • The Data Fabric is THE data singularity. Data can be anywhere – on-premises, the clouds, and more.
  • Have bridges, paths and workflows management to the Data, to move the data to wherever the data may be.
  • Work with technology partners to build tools and data systems to elevate the value of the data

That is how I see it. I wrote about the Transcendence of the Data Fabric vision 3+ years ago, and I emphasized the importance of the Data Pipeline in another NetApp blog almost a year ago. The introduction of NetApp Data Availability Services (NDAS) in the recently concluded Storage Field Day 18 was no different as NetApp constructs data bridges and paths to the AWS Cloud.

NetApp Data Availability Services

The NDAS feature is only available with ONTAP 9.5. With less than 5 clicks, data from ONTAP primary systems can be backed up to the secondary ONTAP target (running the NDAS proxy and the Copy to Cloud API), and then to AWS S3 buckets in the cloud.

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