141: GreyBeards annual 2022 wrap-up podcast

Well it has been another year and time for our annual year end wrap up. Since Covid hit, every year has certainly been interesting. This year we have seen the start of back in person conferences which was a welcome change from the covid lockdown. We are very glad to start seeing everybody again.

From the tech standpoint, the big news this year was CXL. As everyone should recall, CXL is a new-ish PCIe hardware and protocol that supports larger memory sitting out on a PCIe bus and in the future shared memory between servers. All this is to enable a new wave of memory based computing. We spent probably half our time discussing CXL and it’s impact on IT.

The other major topic was the Cloud Native ecosystem. In the past all we talked about was K8s but nowadays the ecosystem that surrounds it is almost as important as K8s itself. The final topic was a bit of a shock earlier this year and yes it was the Broadcom’s acquisition of VMware. Jason and I spend our Explore podcast talking about it (see our 137: VMware Explore wrap-up). Keith has high hopes that the EU will shut it down but the jury’s still out on that one. Listen to the podcast to learn more.

As for CXL, it turns out that AMD have just released full support for CXL hardware and protocols with their latest round of CPU chips. But the new AMD CPUs only support DDR5 memory, (something about there’s only so much logic one can fit on a chip…) which means all those DDR4 DIMs out in the wild need somewhere to land. CXL could supply a new lease on life for DDR4 DIMs.

And it’s not just about shared memory or increased memory sizes, CXL can also provide a tiered memory hierarchy, with gobs of flash behind memory DIMs (see: 136: FMS2022 wrap up …) So, now its no longer a TB or ten of server memory but potentially 100s of TBs. What this means for SAP HANNA, AWS Aurora and other heavy-memory solutions has yet to play out.

Cloud Native won. We see this in the increasing adoption of containers and K8s in the enterprise, cloud and just about anywhere IT happens these days. But the ecosystem surrounding K8s is chaos.

Over time, many of these ecosystem solutions will die off, be purchased, or consolidated but in the mean time, it’s entirely too confusing. Red Hat’s OpenShift is one answer and VMware’s Tanzu is another. And of course all the clouds have their own K8s packaged solution. But just to cover their bets, everyone also supports native K8s and just about every software package that works with it. So, K8s’s ecosystem is in a state of flux and may take time to become a stable set of tools useable by the enterprise IT.

Finally, Broadcom’s acquisition of VMware has everyone up in arms. Customers are concerned the R&D juggernaut that VMware has been, since its very beginning, will be jettisoned in favor of profits. And HCI vendors that always felt Dell EMC had an unfair advantage will all look at Broadcom in a similar light.

Keith says there’s a major difference in how USA regulators view an acquisition and how EU regulators view one. According to Keith, EU views acquisitions in how they help or hurt the customer. USA regulators view acquisitions on show they help or hurt the competition. Will have to wait and see how this all plays for Broadcom-VMware.

On the other hand, speaking of competition, Nutanix seems to be feeling the heat as well. Rumors are it’s up for sale. Who will want it and how the regulators view both of these acquisitions may be as interesting story for 2023

2023 looks to be another year of transition for enterprise IT. The cloud players all seem to be coming around to the view that they can’t be all things to all (IT) people. And the enterprise vendors are finally seeing some modicum of staying power in the face of a relentless push to the cloud. How this plays out over the next few years will be of major interest to everybody.

Happy New Year from the GreyBeards!

Keith Townsend, The CTO Advisor

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, and can be found on LinkedIN.

Jason Collier, Principal Member of Technical Staff, AMD

Jason Collier (@bocanuts) is a long time friend, technical guru and innovator who has over 25 years of experience as a serial entrepreneur in technology. He was founder and CTO of Scale Computing and has been an innovator in the field of hyperconvergence and an expert in virtualization, data storage, networking, cloud computing, data centers, and edge computing for years. He’s on LinkedIN.

140: Greybeards talk data orchestration with Matt Leib, Product Marketing Manager for IBM Spectrum Fusion

As our listeners should know, Matt Leib (@MBleib) was a GreyBeards co-host But since then, Matt has joined IBM to become Product Marketing Manager on IBM Spectrum Fusion, a data orchestration solution for Red Hat OpenShift environments. Matt’s been in and around the storage and data management industry for many years which is why we tapped him for GreyBeards co-host duties.

IBM Fusion, in its previous incarnation, came as an OpenShift software defined storage or as an OpenShift (H)CI solution. But recently, Fusion has taken on more of a data orchestration role for OpenShift stateful containerized applications. Listen to the podcast to learn more.

Fusion can run in any OpenShift deployment whether (currently AWS, Azure, & IBM) clouds, under VMware (wherever it runs), or on (x86 or IBM Z) bare metal. It supplies NFS file or S3 compatible object storage for container applications running under OpenShift. But it does more than just storage.

Beyond storage, Fusion includes backup/recovery, site to site DR and global (file & object) data access. It’s almost like someone opened up the IBM Spectrum software pantry and took out the best available functionality and cooked it up in to an OpenShift solution. IBM’s Spectrum Fusion current website (linked to above (Dec.’22)) still refers only to the software defined storage and (H)CI solution, but today’s Fusion includes all of the functions identified above.

All Fusion facilities run as containers under OpenShift. Customers can elect to run all Fusion services or pick and chose which ones they want for their environment. IBM Fusion supports an API, an API backed GUI, and CLI for its storage & data management as well as REST access. Fusion is fully compatible with Red Hat Ansible.

IBM Fusion is intended to be storage agnostic. Which means it can support its data management services for any NFS file storage as well as anyone’s S3 compatible, object storage.

Now that Red Hat software defined CEPH and ODF are under IBM product management, CEPH and ODF options will become available under Fusion. And CEPH offers block as well as file and object. We’ve talked about CEPH before, packaged in a hardware appliance, see our SoftIron podcast.

One intriguing part of the Fusion solution is its global data access. With global access, any OpenShift application can access data from any Fusion data store, across clouds, across on prem installations, or just about anywhere OpenShift is running. Matt mentioned that compute could be on AWS OpenShift, Fusion’s data control plane could be running on prem OpenShift and the data storage could be running on Azure OpenShift. All this would be glued together by Fusion global access, so that AWS compute had access to data on Azure.

There’s some sophisticated caching magic to make global access happen seamlessly and with decent levels of performance, but customers no longer have to copy whole file systems over from one cloud to another in order to move compute or data. IBM Fusion would need to run in all those locations for global access.

Keith asked if it was directly available in the AWS marketplace. Matt said not yet but you can deploy OpenShift out of the marketplace and then deploy IBM Fusion onto that.

It took us sometime to get our heads wrapped around what Fusion has to offer and throughout it all, Keith and I had a bit of fun with Matt.

Matthew Leib, Product Marketing Manager, IBM Spectrum Fusion

Matt has spent years in IT, from Engineering, to Architecture, from PreSales to analyst work, and finally to Product Marketing at IBM.

He’s spent years trying to achieve both credibility in the space, as a podcaster, blogger, and community member.

In his spare time, he’s a dad, dog owner, and amateur guitar player..

139: GreyBeards talk HPC file systems with Marc-André Vef and Alberto Miranda of GekkoFS

In honor of SC22 conference this month in Dallas, we thought it time to check in with our HPC brethren to find out what’s new in storage for their world. We happened to see that IO500 had some recent (ISC22) results using a relative new comer, GekkoFS (@GekkoFS). So we reached out to the team to find out how they managed to crack into the top 10. We contacted Marc-André Vef (@MarcVef), a Ph.D. student at Johannes Guttenberg University Mainz and Alberto Miranda (@amiranda_hpc) Ph.D. of Barcelona Supercomputing Center two of the authors on the GekkoFS paper.

GekkoFS is a new burst file system that is tailor made to create, process and tear down scratch data sets for HPC workloads. It turns out that HPC does lots of work using scratch files as working data sets. Burst file systems typically use another parallel file systems to (stage) read (permanent) data into the scratch files and write (permanent) result data out. But during processing, the burst file system handles all scratch data access. Listen to the podcast to learn more

We had never heard of a burst file system before but it’s been around for a while now in HPC. For example, BeeGFS provides one (check out our GreyBeards podcast on BeeGFS). BeeGFS supports both a PFS and a burst file system. GekkoFS only offers a burst file system.

GekkoFS is a distributed burst file systems which operates across nodes to stitch together a single global file system. GekkoFS is strictly open source at the moment and can be downloaded (see: GekkoFS Gitlab) and used by anyone.

They are considering in the future of supplying professional support but at the moment if you have an issue, Marc and Alberto suggest you use the GekkoFS GitLab incident tracking system to tell them about it.

Turns out Lustre, IBM Spectrum Scale, DAOS and other HPC file systems take gobs of overhead to create scratch files. And even though it takes a lot of IO to load scratch file data and write out results, there’s a whole lot more IO that gets done to scratch files during HPC jobs.

This sort of IO also occurs for AI/ML/DLL where training data is staged into a sort of scratch area (typically in memory, depending on size) and then repeatedly (re-)processed there. GekkoFS can offer significant advantages to AI/ML/DL work when training data is very large. Normally without a burst file system, one would need to shard this data across nodes and then deal with the partial training that results. But with GekkoFS, all you need do is stage it into the burst file system and read it from there.

GekkoFS is partially posix compliant. They install a client-side interposer library that intercepts those posix requests destined for GekkoFS files.

GekkoFS has no central metadata server, which means that all nodes in the GekkoFS cluster support metadata services. Filenames are hashed to tell GekkoFS which node has its (metadata &) data.

GekkoFS stores their data and metadata on local disks, SSDs or in memory (tempfs) storage. All local node storage in the cluster is stitched together into a single global file system.

GekkoFS supports strict consistency for IO and file creation/deletion within nodes. They use an internal transaction database to enforce this strict consistency.

Across nodes they support eventual consistency. Which means files created on one node may not be immediately viewable/accessible by other nodes in the cluster for a short period of time while (meta) data updates are propagated across the cluster.

As part of their consistency paradigm, GekkoFS doesn’t support directory locking. Jason mentioned that HPC “LS” (directory listings) commands can sometimes take forever due to directory locking No directory locking makes LS commands happen faster but may show inconsistent results (due to eventual consistency).

We had some discussion on this lack of directory locking and eventual consistency in file systems, but we agreed to disagree. They did say that for the HPC workloads (and probably AI/ML/DLL) workloads, their approach seems appropriate as they are way more read intensive than write intensive.

In any case, they must be doing something right as they have a screaming scratch file system for HPC work.

Marc will be attending SC22 in Dallas this month, so if your attending please look him up and say hello from us.

Marc-André Vef, Ph.D. student

Marc-André Vef is a Ph.D. candidate at the Johannes Gutenberg University Mainz. He started his Ph.D. in 2016 after receiving his B.Sc. and M.Sc. degrees in computer science from the Johannes Gutenberg University Mainz. His master’s thesis was in cooperation with IBM Research about analyzing file create performance in the IBM Spectrum Scale parallel file system (formerly GPFS).

During his Ph.D., he has worked on several projects focusing on file system tracing (in collaboration with IBM Research) and distributed file systems, among others. Most notably, he designed two ad-hoc distributed file systems: DelveFS (in collaboration with OpenIO), which won the Best Paper in its category, and GekkoFS (in collaboration with the Barcelona Supercomputing Center). GekkoFS placed fourth in its first entry in the 10-node challenge of the IO500 benchmark. The file system is actively developed in the scope of the EuroHPC ADMIRE project.

His research interests focus on file systems and system analytics.

Alberto Miranda, Ph.D., Senior Researcher, Barcelona Supercomputing Center

Dr. Eng. Alberto Miranda is a Senior Researcher in
advanced storage systems in the Computer Science Department of the Barcelona Supercomputing Center (BSC) and co-leader of the Storage Systems Research Group since 2019. Dr. Eng. Miranda received a diploma in Computer Engineering (2004), a M.Sc. degree in Computer Science (2006) and a M.Sc. degree in Computer Architectures, Networks and Systems (2008) from the Technical University of Catalonia (UPC-BarcelonaTech). He later received a Ph.D. degree Cum Laude in Computer Science from the Technical University of Catalonia in 2014 with his thesis “Scalability in Extensible and Heterogeneous Storage Systems”.

His current research interests include efficient file and storage systems, operating systems, distributed system architectures, as well as information retrieval systems. Since he started his work at BSC in 2007, he has published 14 papers in international conferences and journals, as well as 5 white papers and technical reports and 1 book chapter. Dr. Eng. Miranda is currently involved in several European and national research projects and has participated in competitively funded EU projects XtreemOS, IOLanes, Prace2IP, IOStack, Mont-Blanc 2, EUDAT2020, Mont-Blanc 3, and NEXTGenIO.

138: GreyBeards talk big data orchestration with Adit Madan, Dir. of Product, Alluxio

We have never talked with Alluxio before but after coming back last week from Cloud Field Day 15 (CFD15) it seemed a good time to talk with other solution providers attempting to make hybrid cloud easier to use. Adit Madan (@madanadit) , Director of Product Management, Alluxio, which is a data orchestration solution that’s available in both a free to download/use, open source, community edition (apparently, Meta is a customer ) or a licensed, closed source, enterprise edition.

Alluxio data orchestration is all about suppling local like, IO access to data that resides elsewhere for BI, AI/ML/DL, and just about any other application needing to process data residing elsewhere. Listen to the podcast to learn more

Alluxio started out at UC Berkeley’s AMPlab, which is focused on big data problems and was designed to provide local access to massive amounts of distributed data. Alluxio ends up constructing a locally accessible, federation of data sources for compute apps running elsewhere,

Alluxio software installs near where compute apps run that need access to remote data. We asked about a typical cloud bursting case where S3 object data needed by an app are sitting on prem, but the apps need to run in a cloud, e.g., AWS.

He said Alluxio software would be deployed in AWS, close to app compute and that’s all there is. There’s no Alluxio software running on prem, as Alluxio just uses normal (remote access) S3 APIs to supply data to the compute apps running in AWS.

Adit mentioned that BI was one of the main applications to take advantage of Alluxio, but AI/ML/DL learning is another that could use data orchestration. It turns out that AI/ ML/DL training’s consumption of data is repetitive and highly sequential, so caching, sequential pre-fetch and other Alluxio techniques can work well there to provide local-like access to remote data.

Adit said that enterprises are increasingly looking to avoid vendor lock-in and this applies equally well to the cloud. By supporting data access in one location, say GC,P and accessing that data from another, say Azure, data gravity need no longer limit where work is done.

Adit said what makes their solution so valuable is that instead of duplicating all data from one place to another all that Alluxio moves is just the data required/requested by the apps running there.

Keith asked whether Adit considered Alluxio a data mesh or data fabric. Keith had to explain the terms to me and said data fabrics are pipes and physical infrastructure/functionality that moves data around and data mesh is what gives clients/apps/users access to that data. From that perspective Alluxio is a data mesh.

Alluxio Caching

Adit said that caching is one of the keys to making Alluxio work. Much of the success of their solution depends on applications having a well behaved working set. He also mentioned they use pre-fetching and other techniques to minimize access latency and maximize throughput. However, the first byte of data being accessed may take some time to get to where compute executes.

Adit said it’s not unusual for them to have a 1/2PB of cache (storage) for an application with multiPBs of source data.

Keith asked how Alluxio’s performance can be managed. Adit said they (we assume enterprise edition) have a solution called Cache Insights which uses Alluxio’s extensive access pattern history to predict application IO performance with larger cache (storage), higher speed networking, higher performing/more compute cores, etc. In this way, customers can see what can be done to improve application IO performance and what it would cost.

Keith asked if Alluxio were available as a SaaS solution. Adit said, although it could be deployed in that fashion, it’s not currently a SaaS solution. When asked how Alluxio (enterprise) was priced, Adit said it’s a function of the total resources consumed by their service, i.e, storage (cache), cores, networking that runs Alluxio software etc.

As for deployment options, it turns out for Spark, Alluxio is just another lib package installed inside Spark. For K8s, Alluxio is installed as a CSI drivers and a set of containers and can be deployed as containers within a cluster that needs access to data or in an external, standalone K8s cluster, servicing IO from other clusters. Alluxio HA is supplied by using multiple nodes to provide IO access.

Alluxio also supports access to multiple data locations. In this case, the applications would just access different mount points.

Data reads are easy, writes can be harder due to data integrity issues. As such, trying to supply IO performance becomes a trade off for data integrity when data updates are supported. Adit said Alluxio offers a couple of different configuration options for write concurrency (data integrity) that customers can select from. We assume this includes write through, write back and perhaps other write consistency options.

Alluxio supports AWS, Azure and GCP cloud compute accessing HDFS, S3 and Posix protocol access to data residing at remote sites. At remote sites, they currently support MinIO, Cloudian and any other S3 compatible storage solutions as well as NetApp (ONTAP) and Dell (ECS) storage as data sources.

Adit Madan, Director of Product, Alluxio

Adit Madan is the Director of Product Management at Alluxio. Adit has extensive experience in distributed systems, storage systems, and large-scale data analytics.

Adit holds an MS from Carnegie Mellon University and a BS from the Indian Institute of Technology – Delhi.

Adit is the Director of Product Management at Alluxio and is also a core maintainer and Project Management Committee (PMC) member of the Alluxio Open Source project.

137: GreyBeards talk VMware Explore 2022 Wrap-up

Jason Collier Principle Member of Technical Staff, AMD (@bocanuts), a current GreyBeardsOnStorage co-host and I both attended VMware Explore 2022 this past week and we recorded a podcast discussing VMware’s announcements on the show floor. It turns out that Keith Townsend, TheCTOAdvisor (@thectoadvisor) had brought his Airstream &studio and was exhibiting on the show floor. Keith kindly offered the use of his studio to record the podcast.

This one is a video. Let us know what you think. I clearly need a cowboy hat and Jason said (off camera) that I’m showing more grey in my beard than before. I take that as a compliment here.

Here’s the news as we saw it:

  • vSphere 8 – has a number of new features but the ones we thought important were the GA of Project Monterey. This supports new DPUs that now run ESXi out board from the CPU. They are able to offload lot’s of the CPU networking cycles to the DPU freeing up these for other (more important) work. vSphere 8 supports 2 DPUs now, the NVIDIA (Mellanox) BlueField(-2?) DPU and the AMD (Pensando) DPU. AMD recently purchased Pensando and Jason seemed to know an awful lot about this tech. VMware also announced support for concurrent ESXi upgrades which can now allow upgrading ESXi running in DPUs while hosts and clusters continue to operate. Finally, the other item of interest was vSphere is now more API driven. I guess it’s only a matter of time before all VMware functionality is API driven to make it even more cloud-like
  • vSAN 8 – also has a number of new features. The first we discussed was is a faster data path. This means more IOPS, more bandwidth and lower latency for IOs. Next, vSAN 8 now supports single tier storage pools . These will no longer require a caching layer. This should also speed up IO operations (as long as the single tier is at least as fast as the old caching layer). They also announced faster snapshots. Apparently this has been a problem in the past and they’ve done the work to speed this up considerably. Jason mentioned an AMD open source VM migration tool (from somebody else’s X86 CPUs to AMDs) that depends a lot on vSAN snapshots.
  • Cloud Flex Storage – mentioned at the show but not well explained, Jason and I speculated that this was an internal storage service available on for Cloud Foundation users on AWS where customers could subscribe to storage as-a-service in much lower increments (maybe even GB/month) than standing up more vSAN hosts to increase storage.
  • NetApp FsX (ONTAP) storage – along the same line, VMware announced support for NetApp’s FsX as yet another storage option for Cloud Foundation users on AWS. Supplying yet another storage-as-a-service option for this environment.
  • Cloud Flex Compute – also mentioned at the show was their new Compute-As-A-Service for Cloud Foundation users on AWS. This way users could subscribe to more or less compute, on an as needed basis rather than having to spin up new ESXi hosts. I later found out this allows users to run a single VM and pay for it on a subscription basis.
  • Tanzu Application Platform (TAP) – is a new VMware supplied (and supported) “development experience” for K8s on vSphere. Note, it doesn’t include any advanced Tanzu services such as Tanzu K8s Grid (TKG) so it’s a true DevOps bare bones environment.
  • Tanzu K8S Operations (TKO) – another new Tanzu based service which offers operations complete control over the Tanzu services running on vSphere. Note Tanzu Mission Control (TMC) is not part of TKO.
  • Aria management – VMware rebranded vRealize and CloudHealth, which now comes in 3 bundles, Aria Cost (CloudHealth+), Aria Operations and Aria Automation. Which are all built onto of Aria Graph that graphs all the nodes in your VMware clusters with all their connections so that Aria management can traverse this graph to find out what’s where. On top of Aria Graph are Aria Hub, Aria Insights, and Aria Guardrails (sort of like providing boundary’s where services can be deployed).

They also announced Ransomware Recovery [changed 7Sep22, the Eds] as a Service which builds on VMware’s DR-aaS announced last year and Tanzu now works with Red Hat OpenShift

We also discussed the show. I heard somewhere there were 10K people there, Jason heard somewhere between 6K and 9K. In any case much smaller than VMworlds prior to Covid (25kish). And of course the rebranding of the show seemed counter-intuitive at best.

The show floor was much smaller than usual, (not withstanding Keith’s Airstream RV exhibit). And there were a number of storage vendors not at the show?? There was less hardware on the show floor, this could be a Covid thing but there were just as many mini-white boards/class rooms per large exhibiter, so don’t think it was because of Covid.

But the elephant in the room was Broadcom’s acquisition of VMware. At one of the analyst briefings I asked an exec about attrition. He made a couple of comments but in the end said VMware has been bought and sold before and has always come out of it in better shape. This will be no different.

That’s about all from the show.

And Thanks again to Keith and his crew, for lending us his studio to record the show. It’s been a while since I’ve seen an RV on a show floor. Keith seemed to have a ball with it

Tell us how you like our video. If everyone is for it we could do something like this with a Zoom (in this case Zencastr) recording, Or just try this at the next joint conference. .

Jason Collier, Principle Member of Technical Staff at AMD

Jason Collier (@bocanuts) is a long time friend, technical guru and innovator who has over 25 years of experience as a serial entrepreneur in technology.

He was founder and CTO of Scale Computing and has been an innovator in the field of hyperconvergence and an expert in virtualization, data storage, networking, cloud computing, data centers, and edge computing for years.

He’s on LinkedIN. He’s currently working with AMD on new technology and he has been a GreyBeards on Storage co-host since the beginning of 2022