099: GreyBeards talk Folding@Home with Mike Harsch, a longtime enthusiast

Microscopic picture of Coronavirus

Mike Harsch (@harschness) is a personal friend, a computer enthusiast with a particular and enduring interest in distributed systems and GPU computing. MIke’s been a longtime user and proponent of Folding@Home, a distributed system focused on protein dynamics that anyone can download and run on their personal computer(s) or gaming devices.

We started the discussion on the history of distributed processing using home computers. Mike apparently first ran accross these systems in college and was using one in his college dorm room, back in 1997. At the time there was a system called, distributed.net, which was attempting to crack the (RC5-56[bit]) encryption keys used for computer security and offered a $10K prize for solving it. That was solved in 250 days (source: wikipedia article on distributed.net). Distributed.net is still up and working but since then they have moved to ever larger keys.

Next came Seti@Home which was a 2nd gen distributed system. SETI @Home sent out slices of recorded radio telescope spectrum and tasked people’s computers (during screen saving) to analyze that spectrum for alien signals. Seti@Home painted a nice image of the analysis. Seti@Home also used some gamification, where users gained points for analyzing spectrum. Over time they had something like a leader board tracking the top users. Recently, Seti@Home shut down their distributed system and changed their focus to analyze all the results they received from their users. I was a SETI@Home user for a while.

Folding@Home

Folding@Home is 3rd generation distributed computing solution built along the same lines but rather than searching for aliens, with Folding@Home you are running a simulation of what a protein molecule does over time. Mike mentioned that a typical Folding@Home work unit is to simulate a few nanoseconds in the life of a protein and this could take an hour or more on a x86 class multi-core CPU (with less time on GPUs).

Mike mentioned that there was a recent Ask Me Anything (AMA) event on Reddit with the team on Folding@Home answering questions. And on March 15th, the team at Folding@Home clarified how they are helping to solve the COVID-19 pandemic.

Keith has used Folding@Home in the past. And my son was an early user as well.

What Folding@Home does

Fold@Home uses idle CPU or GPU time on home gaming platforms/computers/servers or data center servers. Initially, in October of 2000, it was used to understand protein folding. But nowadays it’s gone beyond just folding, to simulate the life of a protein.

Prior to their turn to concentrate on COVID-19, they usually had ~30K active users, supplying ~100PFlops (100 quintillian x86 double precision floating point operations per second) of compute power. 

You get points for doing Folding@Home work. When Folding@Home was launched it was designed to use a single CPU/single core. Sometime in 2006, they released a SMP version of the code ,which could use multi-cores. Later they released a multi-threaded version which worked better on multi-core CPUs. And within the last few years, they have released a GPU support that could take advantage of the massive numbers of GPU cores available today.

Mike said that Folding@Home work unit GPU is generally 10 to 100X faster than what can be done with multi-core/multi-threaded CPU systems. 

Around Feb 27, Folding@Home announced they were going to focus all their efforts on understanding how to combat the COVID-19 coronavirus. After the announcement, their user count went through the roof, to now ~400K active users/day. This led to throttling requests for work and delays in handling responses. Over the ensuing weeks, (as of 3/18), they seem to have added enough resources to support their current levels of users.

The architecture of the old Folding@Home system was 2 tiered, they had a set of Folding@Home front-end servers that handled web traffic and distributed the work requests/responses to a set of backend servers that supplied work requests to users and combined work results. In their latest rush they seemed to have had to add servers, networking and storage to both tiers.

Sometime around March 25th, Folding@Home became the firsth and only ExaFlop supercomputer, achieving 1.56 (x86) ExaFlops (10**18 FLOPS, source: wikipedia article on Folding@Home) and have over 1 million active computing devices (GPUs & CPUs) in their network (see: Greg Bowwan’s status tweet).

Deploying Folding@Home on your systems

Folding@Home operates on any number of endpoint devices OSs and gaming console -systems. It comes in two software packages, one is the software that logs into the Folding@Home server to gather the next slice of work unit to perform and the other is the one that does the simulation work. They have an option to paint a picture of what is happening but most disable this feature to devote 100% of any idle CPU/GPU resources to the simulation. They also have a support forum, if you have any questions or need assistance in deploying their software.

Keith mentioned that some gal at VMware asked VMware users to devote their home server CPUs/GPUs to the project. I checked their website and they have a vSphere appliance (FLING) that will run Folding@Home and will register itself as joining the VMware team. Mike mentioned that GitHub (announced on Twitter) was going to supply up to 60K CPU core hours a day to the project. They recently reported that they are shifting work units from understanding COVID-19 to screening compounds for therapeutic potential against the coronavirus.

The world needs you to help solve the COVID-19 pandemic. So join up with Folding@Home to do your part. Downloading the software and installing it on a Mac was easy. Just don’t forget to reboot afterwards and then run FAHcontrol and FAHviewer in “Applications/Folding@home” folder to see what’s going on.

The podcast runs a little under 40 minutes. Mike was very knowledgeable about the IT side of Folding@Home, but was less knowledgeable about the biological side of what they are doing.  Listen to the podcast to learn more.

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Mike Harsch, a computer

Mike is a long time computer enthusiast with particular interests in distributed systems and GPU computing.  He lives in CO and has a basement full of (GPUs &) computers.

Mike and I have co-coached a local high school, FTC robotics team for the last 4 years. And Mike has been involved with FTC robotics for much longer than that.

098: GreyBeards talk data protection & visualization for massive unstructured data repositories with Christian Smith, VP Product at Igneous

Sponsored By:

Even before COVID-19 there was a lot of file data being created and mined, but with the advent of the pandemic, this has accelerated considerably. As such, it seemed an appropriate time to talk with Christian Smith, VP of Product at Igneous, (@IgneousIO) a company that targets the protection and visibility of massive quantities of unstructured data, on premise, in the cloud, or just about anywhere else it may live.

Let me state at the outset, that my belief had always been, that you don’t backup 10PB of data, rather you bite the (big expense) bullet to replicate it and hope for the best. After talking with Christian and Igneous I am going to have to modify that belief by a couple of more orders of magnitude.

All this data is coming from: LIDAR, RADAR, audio, video, pictures, medical film, MRI/CAT Scans, etc., and as noted above, it’s exploding. Christian talked about one customer of theirs that supplies aerial photography/LIDAR/RADAR scans of areas on request. This can used to better understand crop, forest, wildlife, land health and use. One surprise Igneous found with this customer is that the data is typically archived after first use, but within a month or so it’s moved back online for some other purpose.

Igneous heritage

Many of the people who started up and currently work at Igneous have been around file storage for some time having, primarily coming from (Dell EMC) Isilon, NetApp, Qumulo and other industry heavyweights. When they started Igneous, they realized the world didn’t need another NAS box or file system. Rather, with the advent of 10-100PB unstructured data farms, what was needed was an effective way to protect and understand that data.

When they considered how to protect and visualize 100PB of unstructured data, the only they found to do this was to build a scale-out solution that used on premise and cloud infrastructure and was offered as a service.

Igneous DataProtect solution

With 10PB or 100PB of files, located across a gaggle of heterogeneous file servers, with billions of files across ~100s of servers, each of with has ~1K or more file shares, just scanning all the file servers would take weeks, if not longer and then you need to move the data someplace to protect it. Seems like an impossible task.

Igneous immediately figured out the first thing they needed was a radically new, scale out architecture to rapidly scan of the file servers. Thus was born ActiveScan. Christian said it was designed to scan a trillion files and they have customers with a billion files using their service today. ActiveScan doesn’t use NFS/SMB/Object (S3) access protocols to talk with file servers rather it uses internal APIs to access file metadata. DataProtect currently supports APIs for NetApp, Dell EMC Isilon, Pure FlashBlade, Qumulo, Gluster, Lustre, & GPFS (IBM Spectrum Scale) file systems. They use ActiveScan to build a file index database.

Their other major concern was hot to move PBs of data rapidly across to the cloud and other locations. Again they created a scale out, multi-threaded service to do this and also made use of internal APIs rather than standard file or object protocols. This became IntelliMove. That same customer above with billions of files, has 6PB of file data to protect.

Normal data movement is fine for largish, files but bogs down with lots of small files or extremely large files to back up. DataProtect gathers together small files into a large chunks and splits up extremely large files into smaller chunks and moves these chunks to secondary storage.

Data expiration is another problem, especially when you chunk files together. Here they came up with an intelligent garbage collection algorithm which only collects free space when it makes the most sense but deletes data access at the time of expiration.

DataProtect uses a cloud based, SaaS control plane that manages and coordinates its activities across data centers, sites and cloud instances. It also has a client VM (OVA, with 8 core CPU, 32GB DRAM, ~100MB) that runs in the customers infrastructure, on site, in CoLo’s or in the cloud that is used to scan-move-protect customer unstructured data. If more scan and data movement performance is needed, the VM can spawn additional threads automatically and more VMs can be added to provide even more throughput.

DataDiscover solution

The other service that Igneous offers is DataDiscover a data visualization tool. DataDiscover uses ActiveScan and its database to provide customers a way to understand the file data that resides in their massive unstructured data farms across the data center, cloud or wherever else it resides.

We didn’t discuss this solution as much but having a way to better understand the files in a 10-100PB unstructured data farm could be very useful and a great way to keep that 100PB from growing to 1EB faster than it has too.

As part of their outreach to the world, Igneous is giving away free DataProtect services to organizations that are focused on COVID-19 research. Check out their offer here

The podcast ran ~24 minutes. Christian was extremely knowledgeable about the problems that happen with very large unstructured data farms and how Igneous solutions can provide a better way to protect and visualize that data. Matt and I had a fun time discussing Igneous’s approach with Christian. Listen to the podcast to learn more.

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Christian Smith, VP Product at Igneous

Christian is VP of Product, responsible for product management, solutions, and customer success. Prior to Igneous, Christian spent 15 years running field engineering organizations at EMC, Isilon Systems, NetApp and Silicon Graphics.

Christian has been working with organizations that work with file data since working at Silicon Graphics. Before that Christian was co-founder of a small management consulting company associated with Y2K and deregulation.

Christian received dual bachelor’s degrees in Chemistry and Computer Science from the University of Missouri-Columbia. Christian is an avid camper, skier and traveler and has long since traveled through all of the continental 48 states.

097: GreyBeards talk open source S3 object store with AB Periasamy, CEO MinIO

Ray was at SFD19 a few weeks ago and the last session of the week (usually dead) was with MinIO and they just blew us away (see videos of MinIO’s session here). Ray thought Anand Babu (AB) Periasamy (@ABPeriasamy), CEO MinIO, who was the main presenter at the session, would be a great invite for our GreyBeards podcast. Keith and I had a ball talking with AB.

Why object store

There’s something afoot in object storage space over the last year or so. It seems everybody is looking to deploy object store whether that be on prem, in CoLo facilities and in the cloud. It could be just the mass of data coming online but that trend has remained the same for years no. No it’s something else.

It all starts with AWS and S3. Over the last couple of years AWS has been rolling out new functionality that only works with S3 and this has been driving even more adoption of S3 as well as other object storage solutions.

S3 compatible object stores are available in just about every cloud service, available from major (and minor) storage vendors and in open source from MinIO.

Why S3 is so popular

Because object store is accessed via RestFUL interfaces, traditionally most implementations used their own API to access it. But when AWS created S3 (simple storage service) with their own API/SDK to access it, it somehow became the de-facto standard interface for all other object stores. S3 compatibility became a significant feature that all object stores had to support.

Sometime after that MinIO came into existence. MinIO provides a 100% open source, fully AWS S3 compatible object store that you can run anywhere on prem, in CoLo facilities and indeed in the cloud. In fact, there exist customers that run MinIO in AWS AB says this is probably just customers using a packaged software solution which happens to include MinIO but it’s nonetheless more expensive than AWS S3 as it uses EC2 instances and EBS storage to create an object store

Customers can access MinIO object stores with the AWS S3 SDK or the MinIO SDK. and you can access AWS S3 storage with AWS S3 SDK or use MinIO SDK. Occosionally, AWS S3 updates have broken MinIO’s SDK but these have been later fixed by AWS. It seems AWS and MinIO are on good terms.

AB mentioned that as customers get up to a few PBs of AWS S3 storage they often find the costs to be too high. It’s at this point that they start looking at other object storage solutions. But because MinIO is 100% S3 compatible and it’s open source many of these customers deploy it in their own data center facilities or in colo environments.

For those customers that want it, MinIO also offers an S3 gateway. With the gateway on prem customers can use S3 or standard file services to access S3 object storage located in the cloud. The gateway also works in the public cloud and can support both AWS s3 as well as Microsoft Blob storage as a backend.

MinIO matches AWS S3 features

AWS S3 has a number of great features and MinIO has matched or exceeded them all, step by step. AWS S3 has cross region replication options where customers can replicate S3 data from one region to another. MinIO supports both asynchronous replication of S3 data and synchronous replication (using RADIO).

But MinIO adds support for erasure coding within a fault domain. Default is Nx2 erasure coding which duplicates all your data so as long as 1/2 of your servers and storage are available you continue to have access to all your data. But this can be configured down like 12+4 where data is split accross 16 servers any four of which can fail and you can still access data.

AWS customers can use a Snowball (standalone storage device) to transfer data to or from S3 storage. AWS Snowball implements a subset of S3 API and requires a NAS staging area of equivalent size to migrate data out of S3. MinIO has support for Snowball’s limited S3 API and as such, Snowball’s can be used to migrate data into or out of MinIO. MinIO has a blog post which describes their support for AWS Snowball.

AWS also offers S3 Lambda services or server less computing services where compute services can be invoked when data is loaded in a bucket and then turned off when no longer needed. AWS Lambda depends on AWS messaging and other services to work properly. But MinIO supports Lambda like functionality using other open source services. AB mentions MQTT and Kafka services. MinIO has another blog post discussing their Lambda like services based on Kafka.

AWS recently implemented Snowflake a SQL database server for unstructured data that uses S3 storage to hold data. Ray and Keith almost choked on that statement as unstructured data and databases never used to be uttered in the same breath. But what AWS has shown was that you can use object store for database data as long as you are willing to load the table into memory and process it there and then unload any modified table data back into the object store. Indexing of the object data seems to be done as the data is being loaded and is also being done in a (random IO) cache or in memory and once done can also be unloaded into the object store.

Now Snowflake uses S3 but it’s not available on prem. MinIO has a number of data base partners that make use of their object store as a backend to host a Snowflake like service onprem. AB mentioned Spark and Splunk but there are others as well.

We ended up the discussion with what does it mean to have 20K stars on GitHub. AB said if you did a java script getting 20K stars would be easy but you just don’t see this sort of open source popularity for storage systems. He said the number is interesting but the growth rate is even more interesting.

The podcast runs ~47 minutes. AB was a great to talk tech with. Keith and I could have talked all afternoon with AB. It was very hard to stop the recording as we could have talked with him for another hour or more. AB said he doesn’t like to do podcasts or videos but he had no problem with us firing away questions. Listen to the podcast to learn more.

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Anand Babu Periasamy, CEO MinIO

AB Periasamy is the CEO and co-founder of MinIO. One of the leading thinkers and technologists in the open source software movement, AB was a co-founder and CTO of GlusterFS which was acquired by RedHat in 2011. Following the acquisition, he served in the office of the CTO at RedHat prior to founding MinIO in late 2015. AB is an active angel investor and serves on the board of H2O.ai and the Free Software Foundation of India.

He earned his BE in Computer Science and Engineering from Annamalai University.

096: GreyBeards YE2019 IT Industry Trends podcast

In this, our yearend industry wrap up episode, the GreyBeards discuss trends and technologdies impacting the IT industry in 2019 and what’s ahead for 2020. This year we have Matt and Keith on the podcast along with Ray. Just like last year, we start off with NVMeoF.

NVMeoF unleashed

This year just about every major storage vendor announced new systems that either have support for NVMeoF or currently offer NVMeoF on their storage systems. Most offer FC based NVMeoF but a few offer NVMeoF/Ethernet, fewer still offer both.

All of the NVMeoF/Ethernet seem to be using RoCE or iWARP. Unclear if one is more often used that the other, so for now both continue to be used in the market. Some storage vendors are offering NVMeoF as an internal fabric to access storage while still using iSCSI or FC/SCSI to access the data. This works better than SAS but won’t provide all the performance you can get from end-to-end NVMeoF.

NVMeoF is all about increasing IOPS and reducing response times. That and getting ready for SCM SSDs. In the mean time the SSD industry has introduced some very attractive NVMe (NAND) SSDs that in NVMeoF storage system can increase IOPS and reduce latencies.

We talked last year about NVMeoF standards finally stabilizing and this year the rollout across enterprise storage systems is testament to that.

SCM hits the enterprise

Most of us attended an Intel Data Center Event earlier this past yea,r where Optane DC PM was introduced. Optane DC PM is the memory version of Optane SCM (3DX Crosspoint) technology. Intel offers two distinct modes of accessing Optane DC PM as memory: 1) App Direct mode, where data in Optane DC PM persists across power cycles but requires one to use a special AP; and 2) Memory mode where Optane DC PM is cleared during a power cycle, (see our RayOnStorage post Need memory, Intel’s Optane DC PM…).

Vendors seem to be using Optane both memory and SCM technology differently. Pure is using Optane SSDs plugged into their FlashArray as sort of a read cache for customer IO. They suggest for well behaved applications this can reduce IO response times considerably.

Dell EMC introduced SCM as a storage tier and are using their automated storage tiering to move the hottest data to SCM. Oracle’s latest Exadata appliance uses Optane DC PM as both a read and write caching layer.

It won’t be long before every enterprise vendor offers SCM drives in their storage systems with a few offering Optane DC PM as in memory caching technology.

Of course, the big news for Optane DC PM is its use in memory databases, specifically SAP HANA. HANA can take advantage of the (6) TB of memory to to handle larger databases. Keith mentioned that even Microsoft SQL server can take advantage of the additional memory to provide faster responses to queries.

Keith also mentioned that there are some systems out there that can be configured to share Optane memory (or storage). When SAP or other databases use this solution they are able to amortize the cost of the technology over more use cases.

Of course, Optane DC PM are only available on the lastest generation Intel processors. None of us have heard anything from AMD (or Micron) on providing a second source for support of Optane DC PM (or the memory technology itself). Presumably most customers would want a second source for Optane DC PM processor support (as well as the technology)

Cloud enterprise storage hits mainstream

The other thing we saw more of this year is enterprise vendors offering versions of storage in public cloud environments. NetApp was an early proponent of doing this.

We saw at Pure that they have a new Cloud Block Store witch is a re-architected version of FlashArray//X storage using AWS hardware and networking services. We were very impressed with what they have accomplished and it was the subject of more than one late night discussion. Listen to the Keith & Ray show at Pure//Accelerate2019 podcast to learn more.

Matt mentioned Nimble’s cloud volume storage which is cloud adjacent. Most enterprise vendors offer something similar today. They differentiate on how easy it is to configure, use and where (which regions) it’s available in.

NetApp has arguably been at this the longest and has the deepest offerings available from cloud adjacent file and block storage, to offering native enterprise file services for all public cloud environments, to supplying a suite of dedicated data services to surround all of their storage technology operating in public clouds and on premises.

While Dell EMC may have missed the turn to the cloud, they are quickly trying to catch up. Keith mentioned Faction, a Dell partner that offers cloud storage services using VMware with VMC. With Faction and vSAN customers have access to software defined storage that uses cloud hardware to support data services.

What’s driving data growth

There seems to be no end for the need for storage to store data. The GreyBeards point to three trends driving data growth today.

  1. IoT seems to have no bounds. A recent RayOnStorage post Internet of Tires discussed how tire companies were tying their tires to the internet. And that’s just the start, pretty soon every artifact, every device, every manufactured item will have a number of sensors attached all of which will be creating massive amounts of data.
  2. AI ML DL has an insatiable appetite for data. IoT is being used largely to optimize products and services. But it’s DL, with a large dollop of data, that is behind much of that optmization.
  3. SaaS applications is a relatively new application approach that’s being rolled out to more arenas and as it’s online and user oriented, seems to generate lots of data.

Containers storage debate

We closed the podcast with a heavy debate on whether container applications have need for storage. Keith was adamant that containers by their very nature are stateless and that Kubernetes ability to stop and start container applications at will almost requires stateless operations.

Ray was a bit more theoretical on the topic and believed that most container applications today take advantage of some sort of database or other services to store state and that state is just another word for storage.

Keith mentioned encoding as a typical container app. Encoding containers can be fired up and taken down at will without hurting anything but throughput. Yes, but those encoder container apps must access some database or other state information to find out what work is left to do and as they complete their work they update this data as well as store their newly encoded segments. This all involves the use of state information.

In the end, I think we were talking about the same thing but using different terminology. Keith believes that persistent state information is needed and Ray says that this is just another word for (containers) storage. Matt said we probably need Nigel (@NigelPoulton) on the podcast to straighten us both out.

The podcast ran a bit long and could have run longer. Keith and Matt bring systems level perspective to what’s happening in the storage market. But they come at it from different sides. Ray seems to frame everything from a storage perspective. Diverse perspectives lead to a more fuller and interesting discussion. Listen to the podcast to learn more.


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Ray Lucchesi ( @RayLucchesi) is the host of GreyBeardsOnStorage and is President/Founder of Silverton Consulting, and a prominent blogger at RayOnStorage.com.

Keith Townsend (@CTOAdvisor) is a IT thought leader who has written articles for many industry publications, interviewed many industry heavyweights, worked with Silicon Valley startups, and engineered cloud infrastructure for large government organizations. Keith is the co-founder of The CTO Advisor, blogs at Virtualized Geek

Matt Leib (@MBLeib), one of our co-hosts, has been blogging in the storage space for over 10 years, with work experience both on the engineering and presales/product marketing. His blog is at Virtually Tied to My Desktop.


095: GreyBeards talk file sync&share with S. Azam Ali, VP Customer Success at CentreStack

We haven’t talked with a file synch and share vendor in a while now and Matt was interested in the technology. He had been talking with CentreStack, and found that they had been making some inroads in the enterprise. So we contacted S. Azam Ali, VP of Customer Success at CentreStack and asked if he wanted to talk about their product on our podcast.

File synch and share, is part collaboration tool, part productivity tool. With file synch & share many users share the same files, across many different environments and end point devices. It’s especially popular with road warriors that need access to the same files on the road that reside in corporate data centers. With this technology, files updated anywhere would be available to all.

Most file synch&share systems require you to use their storage. But CentreStack just provides synch and share access to NFS and SMB storage that’s already in the data center.

CentreStack doesn’t use VPNs to access data, many other vendor do. But with CentreStack, one just log’s into a website (with AD credentials) and they have immediate browser access to files.

CentreStack uses a gateway VM, that runs in the corporate data center, configured to share files/file directories/shares. We asked whether they were in the data path and Azam said no. However, the gateway does register for file system notifications (e.g. when files are updated, outside CentreStack, they get notified).

CentreStack does maintain meta-data on the files, directories, shares that are under it’s control. Presumably, once an admin sets it up, it goes out and access the file systems that have shared files and populates their meta-data for those files.

CentreStack works with any NFS and SMB file system as well as NAS servers that support these two. It’s unclear whether customers can have more than one gateway server in their data center supporting synch and share but Azam did say that it wasn’t unusual for customers with multi-data centers to have a gateway in each, to support synch&share requirements for each data center.

They use client software on end point devices, which presents the shared files as an external drive (to Mac), presumably a cloud drive for Windows PCs and similar services (in an App) for other systems (IOS, Android phones, iPad, etc.). We believe Azam said Linux was coming soon.

The client software can be configured in cache mode or offline mode:

  • Cache mode – the admin can configure how much space to use on the endpoint device and the software will cache the most recently used files in that space for faster access
  • Offline mode – the software moves all files that the endpoint login can access, to the device.

In cache mode, when users open a file (not in the most recently used cache), there will be some delay as the system retrieves data from the internet and copies it to the endpoint device. Unclear what the delay might be but it’s probably a function of internet speed and load on the gateway, with possibly some overhead for the NFS/SMB/NAS system to supply the data. If there’s not enough space to hold the file, the oldest non-open file is erased from the cache.

In both modes, Centrestack supports cross domain locking. That is, if one client has a file open (for update), all other systems/endpoints may only access the file in read-only mode. After the file is closed. the file can then be opened for update by other users.

When CentreStack clients are used to update files, the data is stored back in the original file systems with versioning. This way if the data is corrupted, admins can easily return back to a known good copy version.

CentreStack also offers a cloud backup and DR service. Gateway admins can request that synch&share files be backed up to cloud storage (AWS S3, Azure Blob and Wasabi). When CentreStack backups file data to the cloud, it also includes metadata information about the files so they can be re-constituted anywhere.

A CentreStack cloud gateway VM can be activated in the cloud to supply access to backed up files. Unclear whether the CentreStack cloud backup has to be restored to block or file storage first or if it just accesses the data on cloud storage directly. But one customers using CentreStack cloud DR would need to run client software in their applications accessing these files.

Wasabi seemed an odd solution to have on their list of supported cloud storage providers, but Azam said for their market, the economics of Wasabi storage were hard to ignore. See our previous podcast with David Friend, Co-Founder& CEO, Wasabi, to learn more about Wasabi.

CentreStack is licensed on a per user basis, not storage capacity bucking industry trends. But they don’t actually own the storage so it makes sense. For CentreStack cloud backup, customers also have to supply the cloud storage.

They also offer a 30 day free trial on their website with unlimited users. We assume this uses CentreStacks cloud gateway and customers bring their own cloud storage to support it.

The podcast runs about 35 minutes. Azam was a bit more marketing than we are used to, but he warmed up once we started asking questions. Listen to the podcast to learn more.

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S. Azam Ali, VP of Customer Success, CentreStack

S. Azam Ali, is VP of Customer Success at CentreStack and is an executive with extensive experience in managing global teams including sales, support and consulting services.

Azam’s channel experience includes on-boarding new partners including creation of marketing and training collateral for the partners. Azam is an executive with a passion for customer success and establishing long term relationships and partnerships.

Azam is also an advisor to startups as well as established technology companies.