The waning light of OpenStack Swift

I was at the 9th Openstack Malaysia anniversary this morning, celebrating the inception of the OpenInfra brand. The OpenInfra branding, announced almost a year ago, represented a change of the maturing phase of the OpenStack project but many have been questioning its growing irrelevance. The foundational infrastructure components – Compute (Nova), Image (Glance), Object Storage (Swift) – are being shelved further into the back closet as the landscape evolved in recent years.

The writing is on the wall

Through the storage lens, I already griped about the conundrum of OpenStack storage in Malaysia in last year’s 8th anniversary. And at the thick of this conundrum is OpenStack Swift. The granddaddy of OpenStack storage has not gotten much attention from technology vendors and service providers alike. For one, storage vendors have their own object storage offering, and has little incentive to place OpenStack Swift into their technology development. Continue reading

Intel IoT Revolution for Malaysia Industry 4.0

Intel rocks!

I have been following Intel for a few years now, a big part was for their push of the 3D Xpoint technology. Under the Optane brand, Intel has several forms of media types, addressing persistent memory to storage class and solid state storage. Intel, in recent years, has been more forefront with their larger technology portfolio and it is not just about their processors anymore. One of the bright areas I am seeing myself getting more engrossed in (and involved into) is their IoT (Internet of Things) portfolio, and it has been very exciting so far.

Intel IoT and Deep Learning Frameworks

The efforts of the Intel IoTG (Internet of Things Group) in Asia Pacific are recognized rapidly. The drive of the Industry 4.0 revolution is strong. And I saw the brightest spark of the Intel folks pushing the Industry 4.0 message on homeground Malaysia.

After the large showing by Intel at the Semicon event 2 months ago, they turned up a notch in Penang at their own Intel IoT Summit 2019, which concluded last week.

At the event, Intel brought out their solid engineering geeks. There were plenty of talks and workshops on Deep Learning, AI, Neural Networks, with chatters on Nervana, Nauta and Saffron. Despite all the technology and engineering prowess of Intel was showcasing, there was a worrying gap.

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Secure Private Sync and Share with EasiShare

Shadow IT /sh-A-doE  Eye-Tee/

noun: An IT project outside the organization IT department’s domain and often unapproved. A dark area.

verb: A defiant user-level practice to perform IT activities where the organization’s IT department has little or no control.

Shadow IT or Stealth IT

There was a BYOD (bring your own device) craze about a decade ago. The darling of the BYOD craze, Dropbox was on every vendor’s lips and many look-a-likes sprouted like mushrooms. Microsoft OneDrive (previously known as SkyDrive), Google Drive, and of course, Dropbox and many others are still serving a growing customer base, together with many others. But most of them have taken a different, more mature form, a market where Gartner has defined as Enterprise File Sync and Share several years ago. And today, that market is shifting again, and soon to be known as Content Collaboration Platform.

But Shadow IT remains where many users are facing challenges with their IT department. Rigid, archaic, and difficult have forced end users to take matters into their own hands to share files, away from the controls and structures. And those free GBs from those cloud storage providers looked so tempting …

The picture above is someone unlocking a safe. I have literally seen an IT department keeping their files on disks and then keep them in a safe! When they want to share it, they have to run the safe combinations to bring out the disks, and they did it in front of me. It was funny then but the paranoia is real! Some IT departments are really that pain-in-the-a$$.

A business risk

Shadow IT is a risk. Security is often the touted risk, but the issue goes beyond just security. Often, the compromised issue represents a degradation of the company’s brand, image and customer confidence, and could lead to negative reverberation of the company’s business.

Time to regain control and secure file access

EasiShare, a private military-grade, enterprise file sync and share platform is a solution I am exploring. It is similar to the Dropbox concept many are familiar with, but without the security concerns and heavy applications of Dropbox, OneDrive or Google Drive.

Many organizations in Malaysia have expressed concerns about data privacy and security. And this is a great opportunity for Malaysian companies to consider data privacy and security seriously, especially with Shadow IT looming to comprise the control of the IT departments.

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Hybrid is the new Black

It is hard for enterprise to let IT go, isn’t it?

For years, we have seen the cloud computing juggernaut unrelenting in getting enterprises to put their IT into public clouds. Some of the biggest banks have put their faith into public cloud service providers. Close to home, Singapore United Overseas Bank (UOB) is one that has jumped into the bandwagon, signing up for VMware Cloud on AWS. But none will come bigger than the US government Joint Enterprise Defense Infrastructure (JEDI) project, where AWS and Azure are the last 2 bidders for the USD10 billion contract.

Confidence or lack of it

Those 2 cited examples should be big enough to usher enterprises to confidently embrace public cloud services, but many enterprises have been holding back. What gives?

In the past, it was a matter of confidence and the FUDs (fears, uncertainties, doubts). News about security breaches, massive blackouts have been widely spread and amplified to sensationalize the effects and consequences of cloud services. But then again, we get the same thing in poorly managed data centers in enterprises and government agencies, often with much less fanfare. We shrug our shoulder and say “Oh well!“.

The lack of confidence factor, I think, has been overthrown. The “Cloud First” strategy in enterprises in recent years speaks volume of the growing and maturing confidence in cloud services. The poor performance and high latency reasons, which were once an Achilles heel of cloud services, are diminishing. HPC-as-a-Service is becoming real.

The confidence in cloud services is strong. Then why is on-premises IT suddenly is a cool thing again? Why is hybrid cloud getting all the attention now?

Hybrid is coming back

Even AWS wants on-premises IT. Its Outposts offering outlines its ambition. A couple of years earlier, the Azure Stack was already made beachhead on-premises in its partnership with many server vendors. VMware, is in both on-premises and the public clouds. It has strong business and technology integration with AWS and Azure. IBM Cloud, Big Blue is thinking hybrid as well. 2 months ago, Dell jumped too, announcing Dell Technologies Cloud with plenty of a razzmatazz, using all the right moves with its strong on-premises infrastructure portfolio and its crown jewel of the federation, VMware. Continue reading

Storage Performance Considerations for AI Data Paths

The hype of Deep Learning (DL), Machine Learning (ML) and Artificial Intelligence (AI) has reached an unprecedented frenzy. Every infrastructure vendor from servers, to networking, to storage has a word to say or play about DL/ML/AI. This prompted me to explore this hyped ecosystem from a storage perspective, notably from a storage performance requirement point-of-view.

One question on my mind

There are plenty of questions on my mind. One stood out and that is related to storage performance requirements.

Reading and learning from one storage technology vendor to another, the context of everyone’s play against their competitors seems to be  “They are archaic, they are legacy. Our architecture is built from ground up, modern, NVMe-enabled“. And there are more juxtaposing, but you get the picture – “We are better, no doubt“.

Are the data patterns and behaviours of AI different? How do they affect the storage design as the data moves through the workflow, the data paths and the lifecycle of the AI ecosystem?

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The Heart of Digital Transformation is …

Businesses have taken up Digital Transformation in different ways and at different pace. In Malaysia, company boardrooms are accepting Digital Transformation as a core strategic initiative, crucial to develop competitive advantage in their respective industries. Time and time again, we are reminded that Data is the lifeblood and Data fuels the Digital Transformation initiatives.

The rise of CDOs

In line with the rise of the Digital Transformation buzzword, I have seen several unique job titles coming up since a few years ago. Among those titles, “Chief Digital Officer“, “Chief Data Officer“, “Chief Experience Officer” are some eye-catching ones. I have met a few of them, and so far, those I met were outward facing, customer facing. In most of my conversations with them respectively, they projected a front that their organization, their business and operations have been digital transformed. They are ready to help their customers to transform. Are they?

Tech vendors add more fuel

The technology vendors have an agenda to sell their solutions and their services. They paint aesthetically pleasing stories of how their solutions and wares can digitally transform any organizations, and customers latch on to these ‘shiny’ tech. End users get too fixated that technology is the core of Digital Transformation. They are wrong.

Missing the Forest

As I gather more insights through observations, and more conversations and more experiences, I think most of the “digital transformation ready” organizations are not adopting the right approach to Digital Transformation.

Digital Transformation is not tactical. It is not a one-time, big bang action that shifts from not-digitally-transformed to digitally-transformed in a moment. It is not a sprint. It is a marathon. It is a journey that will take time to mature. IDC and its Digital Transformation MaturityScape Framework is spot-on when they first released the framework years ago.

IDC Digital Transformation Maturityscape

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Scaling new HPC with Composable Architecture

[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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Dell go big with Cloud

[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

Figuring out storage for Kubernetes and containers

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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Data Privacy First before AI Framework

A few days ago, I discovered that Malaysia already had plans for a National Artificial Intelligence (AI) Framework. It is led by Malaysia Digital Economy Corporation (MDEC) and it will be ready by the end of 2019. A Google search revealed a lot news and announcements, with a few dating back to 2017, but little information of the framework itself. Then again, Malaysia likes to take the “father knows best” approach, and assumes that what it is doing shouldn’t be questioned (much). I will leave this part as it is, because perhaps the details of the framework is under the OSA (Official Secrets Act).

Are we AI responsible or are we responsible for AI?

But I would like to highlight the data privacy part that is likely to figure strongly in the AI Framework, because the ethical use of AI is paramount. It will have economical, social and political impact on Malaysians, and everybody else too. I have written a few articles on LinkedIn about ethics, data privacy, data responsibility, impact of AI. You can read about them in the links below:

I may sound like a skeptic of AI. I am not. I believe AI will benefit mankind, and bring far reaching developments to the society as a whole. But we have to careful and this is my MAIN concern when I voice about AI. I continue to question the human ethics and the human biases that go into the algorithms that define AI. This has always been the crux of my gripes, my concerns, my skepticism of everything we call AI. I am not against AI but I am against the human flaws that shape the algorithms of AI.

Everything is a Sheep (or a Giraffe)

A funny story was shared with me last year. It was about Microsoft Azure computer vision algorithm in recognizing visuals in photos. Apparently the algorithm of the Microsoft Azure’s neural network was fed with some overzealous data of sheep (or giraffes), and the AI system started to point out that every spot that it “saw” was either a sheep, or any vertical long ones was a giraffe.

In the photo below, there were a bunch of sheep on a tree. Check out the tags/comments in the red rectangle published by the AI neural network software below and see how both Microsoft Azure and NeutralTalk2 “saw” in the photo. You can read more about the funny story here.

This proves my point that if you feed the learning system and the AI behind it with biased and flawed information, the result can be funny (in this case here) or disastrous. Continue reading