A conceptual distributed enterprise HCI with open source software

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

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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OpenZFS with Object Storage

At AWS re:Invent last week, Amazon Web Services announced Amazon FSx for OpenZFS. This is the 4th managed service under the Amazon FSx umbrella, joining NetApp® ONTAP™, Lustre and Windows File Server. The highly scalable OpenZFS filesystem can provide high throughput and IOPS bandwidth to Amazon EC2, ECS, EKS and VMware® Cloud on AWS.

I am assuming the AWS OpenZFS uses EBS as the block storage backend, given the announcement that it can deliver 4GB/sec of throughput and 160,000 IOPS from the “drives” without caching. How the OpenZFS is provisioned to the AWS clients is well documented in this blog here. It is an absolutely joy (for me) to see the open source OpenZFS filesystem getting the validation and recognization from AWS. This is one hell of a filesystem.

But this blog isn’t about AWS FSx for OpenZFS with block storage. It is about what is coming, and eventually AWS FSx for OpenZFS could expand into AWS’s proficient S3 storage as well.  Can OpenZFS integrate with an S3 object storage backend? This blog looks into the burning question.

In the recently concluded OpenZFS Developer Summit 2021, one of the topics was “ZFS on Object Storage“, and the short answer is a resounding YES!

OpenZFS Developer Summit 2021

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Storage Elephant Compute Birds

Data movement is expensive. Not just costs, but also latency and resources as well. Thus there were many narratives to move compute closer to where the data is stored because moving compute is definitely more economical than moving data. I borrowed the analogy of the 2 animals from some old NetApp® slides which depicted storage as the elephant, and compute as birds. It was the perfect analogy, because the storage is heavy and compute is light.

“Close up of a white Great Egret perching on top of an African Elephant aa Amboseli national park, Kenya”

Before the animals representation came about I used to use the term “Data locality, Data Mobility“, because of past work on storage technology in the Oil & Gas subsurface data management pipeline.

Take stock of your data movement

I had recent conversations with an end user who has been paying a lot of dollars keeping their “backup” and “archive” in AWS Glacier. The S3 storage is cheap enough to hold several petabytes of data for years, because the IT folks said that the data in AWS Glacier are for “backup” and “archive”. I put both words in quotes because they were termed as “backup” and “archive” because of their enterprise practice. However, the face of their business is changing. They are in manufacturing, oil and gas downstream, and the definitions of “backup” and “archive” data has changed.

For one, there is a strong demand for reusing the past data for various reasons and these datasets have to be recalled from their cloud storage. Secondly, their data movement activities still mimicked what they did in the past during their enterprise storage days. It was a classic lift-and-shift when they moved to the cloud, and not taking stock of  their data movements and the operations they ran on these datasets. Still ongoing, their monthly AWS cost a bomb.

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Open Source Storage Technology Crafters

The conversation often starts with a challenge. “What’s so great about open source storage technology?

For the casual end users of storage systems, regardless of SAN (definitely not Fibre Channel) or NAS on-premises, or getting “files” from the personal cloud storage like Dropbox, OneDrive et al., there is a strong presumption that open source storage technology is cheap and flaky. This is not helped with the diet of consumer brands of NAS in the market, where the price is cheap, but the storage offering with capabilities, reliability and performance are found to be wanting. Thus this notion floats its way to the business and enterprise users, and often ended up with a negative perception of open source storage technology.

Highway Signpost with Open Source wording

Storage Assemblers

Anybody can “build” a storage system with open source storage software. Put the software together with any commodity x86 server, and it can function with the basic storage services. Most open source storage software can do the job pretty well. However, once the completed storage technology is put together, can it do the job well enough to serve a business critical end user? I have plenty of sob stories from end users I have spoken to in these many years in the industry related to so-called “enterprise” storage vendors. I wrote a few blogs in the past that related to these sad situations:

We have such storage offerings rigged with cybersecurity risks and holes too. In a recent Unit 42 report, 250,000 NAS devices are vulnerable and exposed to the public Internet. The brands in question are mentioned in the report.

I would categorize these as storage assemblers.

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SSOT of Files

[ This is part two of “Where are your files living now?”. You can read Part One here ]

Data locality, Data mobility“. It was a term I like to use a lot when describing about data consolidation, leading to my mention about files and folders, and where they live in my previous blog. The thinking of where the files and folders are now as in everywhere as they can be in a plethora of premises stretches the premise of SSOT (Single Source of Truth). And this expatriation of files with minimal checks and balances disturbs me.

A year ago, just before I joined iXsystems, I was given Google® embargoed news, probably a week before they announced BigQuery Omni. Then I was interviewed by Enterprise IT News, a local Malaysian technology news portal to provide an opinion quote. This was what I quoted:

“’The data warehouse in the cloud’ managed services of Big Query is underpinned by Google® Anthos, its hybrid cloud infra and service management platform based on GKE (Google® Kubernetes Engine). The containerised applications, both on-prem and in the multi-clouds, would allow Anthos to secure and orchestrate infra, services and policy management under one roof.”

I further quoted ” The data repositories remain in each cloud is good to address data sovereignty, data security concerns but it did not mention how it addresses “single source of truth” across multi-clouds.

Single Source of Truth – regardless of repositories

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First looks into Interplanetary File System

The cryptocurrency craze has elevated another strong candidate in recent months. Filecoin, is leading the voice of a decentralized Internet, the next generation Web 3.0. In this blog, I am not going to write much about the Filecoin frenzy but the underlying distributed file system that powers this phenomenon – The Interplanetary File System.

[ Note: This is still a very new area for me, and the rest of the content of this blog is still nascent and developing ]

Interplanetary File System

Tremulous Client-Server web architecture

The entire Internet architecture is almost client and server. Your clients like browsers, apps, connect to Web services served from a collection of servers. As Web 3.0 approaches (some say it is already here), the client-server model is no longer perceived as the Internet architecture of choice. Billions, and billions of users, applications, devices relying solely on a centralized service would lead to many impactful consequences, and the reasons for decentralization, away from the client-server architecture models of the Internet are cogent.

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Layers in Storage – For better or worse

Storage arrays and storage services are built upon by layers and layers beneath its architecture. The physical components of hard disk drives and solid states are abstracted into RAID volumes, virtualized into other storage constructs before they are exposed as shares/exports, LUNs or objects to the network.

Everyone in the storage networking industry, is cognizant of the layers and it is the foundation of knowledge and experience. The public cloud storage services side is the same, albeit more opaque. Nevertheless, both have layers.

In the early 2000s, SNIA® Technical Council outlined a blueprint of the SNIA® Shared Storage Model, a framework describing layers and properties of a storage system and its services. It was similar to the OSI 7-layer model for networking. The framework helped many industry professionals and practitioners shaped their understanding and the development of knowledge in their respective fields. The layering scheme of the SNIA® Shared Storage Model is shown below:

SNIA Shared Storage Model – The layering scheme

Storage vendors layering scheme

While SNIA® storage layers were generic and open, each storage vendor had their own proprietary implementation of storage layers. Some of these architectures are simple, but some, I find a bit too complex and convoluted.

Here is an example of the layers of the Automated Volume Management (AVM) architecture of the EMC® Celerra®.

EMC Celerra AVM Layering Scheme

I would often scratch my head about AVM. Disks were grouped into RAID groups, which are LUNs (Logical Unit Numbers). Then they were defined as Celerra® dvols (disk volumes), and stripes of the dvols were consolidated into a storage pool.

From the pool, a piece of a storage capacity construct, called a slice volume, were combined with other slice volumes into a metavolume which eventually was presented as a file system to the network and their respective NAS clients. Explaining this took an effort because I was the IP Storage product manager for EMC® between 2007 – 2009. It was a far cry from the simplicity of NetApp® ONTAP 7 architecture of RAID groups and volumes, and the WAFL® (Write Anywhere File Layout) filesystem.

Another complicated layered framework I often gripe about is Ceph. Here is a look of how the layers of CephFS is constructed.

Ceph Storage Layered Framework

I work with the OpenZFS filesystem a lot. It is something I am rather familiar with, and the layered structure of the ZFS filesystem is essentially simpler.

Storage architecture mixology

Engineers are bizarre when they get too creative. They have a can do attitude that transcends the boundaries of practicality sometimes, and boggles many minds. This is what happens when they have their own mixology ideas.

Recently I spoke to two magnanimous persons who had the idea of providing Ceph iSCSI LUNs to the ZFS filesystem in order to use the simplicity of NAS file sharing capabilities in TrueNAS® CORE. From their own words, Ceph NAS capabilities sucked. I had to draw their whole idea out in a Powerpoint and this is the architecture I got from the conversation.

There are 3 different storage subsystems here just to provide NAS. As if Ceph layers aren’t complicated enough, the iSCSI LUNs from Ceph are presented as Cinder volumes to the KVM hypervisor (or VMware® ESXi) through the Cinder driver. Cinder is the persistent storage volume subsystem of the Openstack® project. The Cinder volumes/hypervisor datastore are virtualized as vdisks to the respective VMs installed with TrueNAS® CORE and OpenZFS filesystem. From the TrueNAS® CORE, shares and exports are provisioned via the SMB and NFS protocols to Windows and Linux respectively.

It works! As I was told, it worked!

A.P.P.A.R.M.S.C. considerations

Continuing from the layered framework described above for NAS, other aspects beside the technical work have to be considered, even when it can work technically.

I often use a set of diligent data storage focal points when considering a good storage design and implementation. This is the A.P.P.A.R.M.S.C. Take for instance Protection as one of the points and snapshot is the technology to use.

Snapshots can be executed at the ZFS level on the TrueNAS® CORE subsystem. Snapshots can be trigged at the volume level in Openstack® subsystem and likewise, rbd snapshots at the Ceph subsystem. The question is, which snapshot at which storage subsystem is the most valuable to the operations and business? Do you run all 3 snapshots? How do you execute them in succession in a scheduled policy?

In terms of performance, can it truly maximize its potential? Can it churn out the best IOPS, and deliver at wire speed? What is the latency we can expect with so many layers from 3 different storage subsystems?

And supporting this said architecture would be a nightmare. Where do you even start the troubleshooting?

Those are just a few considerations and questions to think about when such a layered storage architecture along. IMHO, such a design was over-engineered. I was tempted to say “Just because you can, doesn’t mean you should

Elegance in Simplicity

Einstein (I think) quoted:

Einstein’s quote on simplicity and complexity

I am not saying that having too many layers is wrong. Having a heavily layered architecture works for many storage solutions out there, where they are often masked with a simple and intuitive UI. But in yours truly point of view, as a storage architecture enthusiast and connoisseur, there is beauty and elegance in simple designs.

The purpose here is to promote better understanding of the storage layers, and how they integrate and interact with each other to deliver the data services to the network. In the end, that is how most storage architectures are built.


Fueling the Flywheel of AWS Storage

It was bound to happen. It happened. AWS Storage is the Number 1 Storage Company.

The tell tale signs were there when Silicon Angle reported that AWS Storage revenue was around USD$6.5-7.0 billion last year and will reach USD$10 billion at the end of 2021. That news was just a month ago. Last week, IT Brand Pulse went a step further declaring AWS Storage the Number 1 in terms of revenue. Both have the numbers to back it up.

AWS Logo

How did it become that way? How did AWS Storage became numero uno?

Flywheel juggernaut

I became interested in the Flywheel concept some years back. It was conceived in Jim Collins’ book, “Good to Great” almost 20 years ago, and since then, Amazon.com has become the real life enactment of the Flywheel concept.

Amazon.com Flywheel – How each turn becomes sturdier, brawnier.

Every turn of the flywheel requires the same amount of effort although in the beginning, the noticeable effect is minuscule. But as every turn gains momentum, the returns of each turn scales greater and greater to the fixed efforts of operating a single turn.

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