Category Archives: Cloud

Backup – Lest we forget

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World Backup Day – March 31st

Last week was World Backup Day. It is on March 31st every year so that you don’t lose your data and become an April’s Fool the next day.

Amidst the growing awareness of the importance of backup, no thanks to the ever growing destructive nature of ransomware, it is important to look into other aspects of data protection – both a data backup/recovery and a data security –  point of view as well.

3-2-1 Rule, A-B-C and Air Gaps

I highlighted the basic 3-2-1 rule before. This must always be paired with a set of practised processes and policies to cultivate all stakeholders (aka the people) in the organization to understand the importance of protecting the data and ensuring data recoverability.

The A-B-C is to look at the production dataset and decide if the data should be stored in the Tier 1 storage. In most cases, the data becomes less active and these datasets may be good candidates to be archived. Once archived, the production dataset is smaller and data backup operations become lighter, faster and have positive causation as well.

Air gaps have returned to prominence since the heightened threats on data in recent years. The threats have pushed organizations to consider doing data offsite and offline with air gaps. Cost considerations and speed of recovery can be of concerns, and logical air gaps are also gaining style as an acceptable extra layer of data. protection.

Backup is not total Data Protection cyberdefence

If we view data protection more holistically and comprehensively, backup (and recovery) is not the total data protection solution. We must ignore the fancy rhetorics of the technology marketers that backup is the solution to ensure data protection because there is much more than that.

The well respected NIST (National Institute of Standards and Technology) Cybersecurity Framework places Recovery (along with backup) as the last pillar of its framework.

NIST Cybersecurity Framework

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Nakivo Backup Replication architecture and installation on TrueNAS – Part 1

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Backup and Replication software have received strong mandates in organizations with enterprise mindsets and vision. But lower down the rung, small medium organizations are less invested in backup and replication software. These organizations know full well that they must backup, replicate and protect their servers, physical and virtual, and also new workloads in the clouds, given the threat of security breaches and ransomware is looming larger and larger all the time. But many are often put off by the cost of implementing and deploying a Backup and Replication software.

So I explored one of the lesser known backup and recovery software called Nakivo® Backup and Replication (NBR) and took the opportunity to build a backup and replication appliance in my homelab with TrueNAS®. My objective was to create a cost effective option for small medium organizations to enjoy enterprise-grade protection and recovery without the hefty price tag.

This blog, Part 1, writes about the architecture overview of Nakivo® and the installation of the NBR software in TrueNAS® to bake in and create the concept of a backup and replication appliance. Part 2, in a future blog post, will cover the administrative and operations usage of NBR.

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Computational Storage embodies Data Velocity and Locality

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I have been earnestly observing the growth of Computational Storage for a number of years now.  It was known by several previous names, with the name “in-situ data processing” stuck with me the most. The Computational Storage nomenclature became more cohesive when SNIA® put together the CMSI (Compute Memory Storage Initiative) some time back. This initiative is where several standards bodies, the major technology players and several SIGs (special interest groups) in SNIA® collaborated to advance Computational Storage segment in the storage technology industry we know of today.

The use cases for Computational Storage are burgeoning, and the functional implementations of Computational Storage are becoming vital to tackle the explosive data tsunami. In 2018 IDC, in its Worldwide Global Datasphere Forecast 2021-2025 report, predicted that the world will have 175 ZB (zettabytes) of data. That number, according to hearsay, has been revised to a heady figure of 250ZB, given the superlative rate data is being originated, spawned and more.

Computational Storage driving factors

If we take the Computer Science definition of in-situ processing, Computational Storage can be distilled as processing data where it resides. In a nutshell, “Bring Compute closer to Storage“. This means that there is a processing unit within the storage subsystem which does not require the host CPU to perform processing. In a very simplistic manner, a RAID card in a storage array can be considered a Computational Storage device because it performs the RAID functions instead of the host CPU. But this new generation of Computational Storage has much more prowess than just the RAID function in a RAID card.

There are many factors in Computational Storage that make a lot sense. Here are a few:

  1. Voluminous data inundate the centralized architecture of the cloud platforms and the enterprise systems today. Much of the data come from end point devices – mobile devices, sensors, IoT, point-of-sales, video cameras, et.al. Pre-processing the data at the origin data points can help filter the data, reduce the size to be processed centrally, and secure the data before they are ingested into the central data processing systems
  2. Real-time processing of the data at the moment the data is received gives the opportunity to create the Velocity of Data Analytics. Much of the data do not need to move to a central data processing system for analysis. Often in use cases like autonomous vehicles, fraud detection, recommendation systems, disaster alerts etc require near instantaneous responses. Performing early data analytics at the data origin point has tremendous advantages.
  3. Moore’s Law is waning. The CPU (central processing unit) is no longer the center of the universe. We are beginning to see CPU offloading technologies to augment the CPU’s duties such as compression, encryption, transcoding and more. SmartNICs, DPUs (data processing units), VPUs (visual processing units), GPUs (graphics processing units), etc have come forth to formulate a new computing paradigm.
  4. Freeing up central resources with Computational Storage also accelerates the overall distributed data processing in the whole data architecture. The CPU and the adjoining memory subsystem are less required to perform context switching caused by I/O interrupts as in most of the compute/storage architecture today. The total effect relieves the CPU and giving back more CPU cycles to perform higher processing tasks, resulting in faster performance overall.
  5. The rise of memory interconnects is enabling a more distributed computing fabric of data processing subsystems. The rising CXL (Compute Express Link™) interconnect protocol, especially after the Gen-Z annex, has emerged a force to be reckoned with. This rise of memory interconnects will likely strengthen the testimony of Computational Storage in the fast approaching future.

Computational Storage Deployment Models

SNIA Computational Storage Universe in 2019

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IT Data practices and policies still wanting

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There is an apt and honest editorial cartoon about Change.

From https://commons.wikimedia.org/wiki/File:Who-Wants-Change-Crowd-Change-Management-Yellow.png

I was a guest of Channel Asia Executive Roundtable last week. I joined several luminaries in South East Asia to discuss about the topic of “How Partners can bring value to the businesses to manage their remote workforce“.

Covid-19 decimated what we knew as work in general. The world had to pivot and now, 2+ years later, a hybrid workforce has emerged. The mixture of remote work, work-from-home (WFH), physical office and everywhere else has brought up a new mindset and new attitudes with both the employers and their staff alike. Without a doubt, the remote way of working is here to stay.

People won but did the process lose?

The knee jerk reactions when the lockdowns of Covid hit were to switch work to remote access to applications on premises or in the clouds. Many companies have already moved to the software-as-a-service (SaaS) way of working but not all have made the jump, just like not all the companies’ applications were SaaS based. Of course, the first thing these stranded companies do was to look for the technologies to solve this unforeseen disorder.

People Process Technology.
Picture from https://iconstruct.com/blog/people-process-technology/

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Please cultivate 3-2-1 and A-B-C of Data Management

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My Sunday morning was muddled 2 weeks ago. There was a frenetic call from someone whom I knew a while back and he needed some advice. Turned out that his company’s files were encrypted and the “backups” (more on this later) were gone. With some detective work, I found that their files were stored in a Synology® NAS, often accessed via QuickConnect remotely, and “backed up” to Microsoft® Azure. I put “Backup” in inverted commas because their definition of “backup” was using Synology®’s Cloud Sync to Azure. It is not a true backup but a file synchronization service that often mislabeled as a data protection backup service.

All of his company’s projects files were encrypted and there were no backups to recover from. It was a typical ransomware cluster F crime scene.

I would have gloated because many of small medium businesses like his take a very poor and lackadaisical attitude towards good data management practices. No use crying over spilled milk when prevention is better than cure. But instead of investing early in the prevention, the cure would likely be 3x more expensive. And in this case, he wanted to use Deloitte® recovery services, which I did not know existed. Good luck with the recovery was all I said to him after my Sunday morning was made topsy turvy of sorts.

NAS is the ransomware goldmine

I have said it before and I am saying it again. NAS devices, especially the consumer and prosumer brands, are easy pickings because there was little attention paid to implement a good data management practice either by the respective vendor or the end users themselves. 2 years ago I was already seeing a consistent pattern of the heightened ransomware attacks on NAS devices, especially the NAS devices that proliferated the small medium businesses market segment.

The WFH (work from home) practice trigged by the Covid-19 pandemic has made NAS devices essential for businesses. NAS are the workhorses of many businesses after all.  The ease of connecting from anywhere with features similar to the Synology® QuickConnect I mentioned earlier, or through VPNs (virtual private networks), or a self created port forwarding (for those who wants to save a quick buck [ sarcasm ]), opened the doors to bad actors and easy ransomware incursions. Good data management practices are often sidestepped or ignored in exchange for simplicity, convenience, and trying to save foolish dollars. Until ….

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Time to Conflate Storage with Data Services

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Around the year 2016, I started to put together a better structure to explain storage infrastructure. I started using the word Data Services Platform before what it is today. And I formed a pictorial scaffold to depict what I wanted to share. This was what I made at that time.

Data Services Platform (circa 2016)- Copyright Heoh Chin Fah

One of the reasons I am bringing this up again is many of the end users and resellers still look at storage from the perspective of capacity, performance and price. And as if two plus two equals five, many storage pre-sales and architects reciprocate with the same type of responses that led to the deteriorated views of the storage technology infrastructure industry as a whole. This situation irks me. A lot.

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Celebrating MinIO

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“Essentially MinIO is a web server …“

I vaguely recalled Anand Babu Periasamy (AB as he is known), the CEO of MinIO saying that when I first met him in 2017. I was fresh “playing around” with MinIO and instantly I fell in love with software technology. Wait a minute. Object storage wasn’t supposed to be so easy. It was not supposed to be that simple to set up and use, but MinIO burst into my storage universe like the birth of the Infinity Stones. There was a eureka moment. And I was attending one of the Storage Field Days in the US shortly after my MinIO discovery in late 2017. What an opportunity!

I could not recall how I made the appointment to meeting MinIO, but I recalled myself taking an Uber to their cosy office on University Avenue in Palo Alto to meet. Through Andy Watson (one of the CTOs then), I was introduced to AB, Garima Kapoor, MinIO’s COO and his wife, Frank Wessels, Zamin (one of the business people who is no longer there) and Ugur Tigli (East Coast CTO) who was on the Polycom. I was awe struck.

Last week, MinIO scored a major Series B round funding of USD103 million. It was delayed by the pandemic because I recalled Garima telling me that the funding was happening in 2020. But I think the delay made it better, because the world now is even more ready for MinIO than ever before.

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The Currency to grow Decentralized Storage

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Unless you have been living under a rock in the past months, the fervent and loud, but vague debates of web3.0 have been causing quite a scene on the Internet. Those tiny murmurs a few months ago have turned into an avalanche of blares and booms, with both believers and detractors crying out their facts and hyperboles.

Within the web3.0, decentralized storage technologies have been rising to a crescendo. So many new names have come forth into the decentralized storage space, most backed by blockchain and incentivized by cryptocurrencies and is putting the 19th century California Gold Rush to shame.

At present, the decentralized storage market segment is fluid, very vibrant and very volatile. Being the perennial storage guy that I am, I would very much like the decentralized storage to be sustainably successful but first, it has to make sense. Logic must prevail before confidence follows.

Classic “Crossing the Chasm”

To understand this decentralization storage chaos, we must understand where it is now, and where it is going. History never forgets to teach us of the past to be intelligible in the fast approaching future.

I look to this situation as a classic crossing the chasm case. This Crossing the Chasm concept was depicted in Geoffrey Moore’s 1991 book of the same name. In his book, he spoke well about the Technology Adoption Cycle that classifies and demonstrates the different demographics and psychological progression (and regression) of how a technology is taken to mainstream.

Geoffrey Moore’s Crossing the Chasm Technology (Disruption) Adoption Cycle

As a new technology enters the market, the adoption is often fueled by the innovators, the testers, the crazy ones. It progresses and the early adopters set in. Here we get the believers, the fanatics, the cults that push the envelope a bit further, going against the institutions and the conventions. This, which is obvious, describes the early adopter stage of the decentralized storage today.

Like all technologies, it has to go mainstream to be profitable and to get there, its value to the masses must be well defined to be accepted. This is the market segment that decentralized storage must move to, to the early majority stage. But there is a gap, rightly pointed out and well defined by Geoffrey Moore. The “Chasm“. [ Note: To read about why the chasm, read this article ].

So how will decentralized storage cross the chasm to the majority of the market?

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A conceptual distributed enterprise HCI with open source software

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Cloud computing has changed everything, at least at the infrastructure level. Kubernetes is changing everything as well, at the application level. Enterprises are attracted by tenets of cloud computing and thus, cloud adoption has escalated. But it does not have to be a zero-sum game. Hybrid computing can give enterprises a balanced choice, and they can take advantage of the best of both worlds.

Open Source has changed everything too because organizations now has a choice to balance their costs and expenditures with top enterprise-grade software. The challenge is what can organizations do to put these pieces together using open source software? Integration of open source infrastructure software and applications can be complex and costly.

The next version of HCI

Hyperconverged Infrastructure (HCI) also changed the game. Integration of compute, network and storage became easier, more seamless and less costly when HCI entered the market. Wrapped with a single control plane, the HCI management component can orchestrate VM (virtual machine) resources without much friction. That was HCI 1.0.

But HCI 1.0 was challenged, because several key components of its architecture were based on DAS (direct attached) storage. Scaling storage from a capacity point of view was limited by storage components attached to the HCI architecture. Some storage vendors decided to be creative and created dHCI (disaggregated HCI). If you break down the components one by one, in my opinion, dHCI is just a SAN (storage area network) to HCI. Maybe this should be HCI 1.5.

A new version of an HCI architecture is swimming in as Angelfish

Kubernetes came into the HCI picture in recent years. Without the weights and dependencies of VMs and DAS at the HCI server layer, lightweight containers orchestrated, mostly by, Kubernetes, made distribution of compute easier. From on-premises to cloud and in between, compute resources can easily spun up or down anywhere.

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How well do you know your data and the storage platform that processes the data

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Last week was consumed by many conversations on this topic. I was quite jaded, really. Unfortunately many still take a very simplistic view of all the storage technology, or should I say over-marketing of the storage technology. So much so that the end users make incredible assumptions of the benefits of a storage array or software defined storage platform or even cloud storage. And too often caveats of turning on a feature and tuning a configuration to the max are discarded or neglected. Regards for good storage and data management best practices? What’s that?

I share some of my thoughts handling conversations like these and try to set the right expectations rather than overhype a feature or a function in the data storage services.

Complex data networks and the storage services that serve it

I/O Characteristics

Applications and workloads (A&W) read and write from the data storage services platforms. These could be local DAS (direct access storage), network storage arrays in SAN and NAS, and now objects, or from cloud storage services. Regardless of structured or unstructured data, different A&Ws have different behavioural I/O patterns in accessing data from storage. Therefore storage has to be configured at best to match these patterns, so that it can perform optimally for these A&Ws. Without going into deep details, here are a few to think about:

  • Random and Sequential patterns
  • Block sizes of these A&Ws ranging from typically 4K to 1024K.
  • Causal effects of synchronous and asynchronous I/Os to and from the storage

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