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Arcserve StorageCraft platform woes continue

Nasuni Skid crash
Skid crash

The Arcserve StorageCraft data protection platform is going through an extended episode of “cloud degradation” with no end in sight.

According to Arcserve’s status page, since March 12, StorageCraft Cloud Services (DRaaS) in Australia; Ireland; and Utah, US have been unavailable. The page isn’t entirely clear but the outage may go as far back as March 9. Meanwhile, one part of the status page shows DRaaS has suffered degradation in Canada since roughly March 7.

Yes, DRaaS, as in, disaster recovery as a service. This means cloud backups stored by Arcserve may be unavailable.

The latest explanation to customers reads: “Our engineers continue to actively work on the resolution for this issue. If you have questions or concerns, please refer to the email that was sent to all affected customers. ShadowXafe customers were sent a separate email with specific instructions. Please refer to that email for more details.”

A Redditor going by the handle dartdou claims to have been a customer and said they got a call from a StorageCraft representative saying their “cloud data is permanently lost.”

The netizen went on to quote an email they claim they received from Arcserve: “During a recent planned maintenance window, a redundant array of servers containing critical metadata was decommissioned prematurely. As a result, some metadata was compromised, and critical links between the storage environment and our DRaaS cloud (Cloud Services) were disconnected.

“Engineers could not re-establish the required links between the metadata and the storage system, rendering the data unusable. This means partners cannot replicate or failover machines in our datacenter.”

The Redditor continued that they had a conversation with an Arcserve representative, who, it is claimed, said the business had hoped to recover the data but stopped when it became clear that would not be possible. The netizen also said there was no offer of compensation; instead, customers must ask to be put on a list to be considered for a rebate, it was claimed.

Private equity-owned Arcserve merged with StorageCraft in March last year. The intention was to create a single entity providing workload protection and supporting SaaS applications. Arcserve brought its UDP appliances that integrate backup, disaster recovery, and backend cloud storage to the table plus its partnership with Sophos for security, cloud, and SaaS offerings. The smaller StorageCraft has backup for Office365, G Suite, and other SaaS options as well as OneXafe, a converged, scale-out storage product. StorageCraft bought SMB object storage startup Exablox in January 2017.

We have asked Arcserve to comment on the outage and compensation options available to users. We’ll update this story if we receive a reply.

VergeIO – an HCI curiosity shop survivor

Verge.io’s technology creates nested, multi-tenant, virtual datacenters for enterprises and MSPs from hyperconverged infrastructure servers. Back in the HCI glory days before 2018, it was known as Yottabyte and lauded as a potential giant killer. Now this minnow survives as Verge.io, still fighting the software-defined datacenter (SDDC) fight but making less of a wave.

Greg Campbell, Verge.io
Greg Campbell

Yottabyte was founded in 2010 by VP of technology Greg Campbell, along with President and CEO Paul E Hodges III, and principal Duane Tursi, all based in Michigan. Campbell drove the vCenter software research and development to create an SDDC layer that sat atop commodity servers fitted out for compute, storage, and software-defined networking, formed them into resource pools, and composed them into an SDDC. The systems could scale out, with storage scaling up as well for archive capacity. 

Around that time commentators became enthused about its prospects, not least Trevor Pott in The Register who talked about Infrastructure Endgame machines in 2015, saying: “The end of IT as we know it is upon us. Decades of hype, incremental evolution and bitter disappointment are about to come to an end as the provisioning of IT infrastructure is finally commoditised.”

Pott said of Yottabyte: “The result of years of this R&D is yCenter. It can be a public cloud, a private cloud or a hybrid cloud. Yottabyte has gone through and solved almost all the tough problems first – erasure coded storage, built-in layer 2 and 3 networking extensibility, management UI that doesn’t suck – and is only now thinking about solving the easy ones.”

Alas, HCI didn’t kill off conventional IT infrastructure and its proponents were bought by larger suppliers such as HPE, or crashed and burned. Just Dell, Nutanix, and VMware dominate the field these days, followed at some distance by HPE, Cisco and Scale Computing.

Yottabyte formed a partnership with the University of Michigan in late 2016 to build the Yottabyte Research Cloud for use by academic researchers.

A video talks about the university’s Advanced Computing Research Division using Yottabyte “to provide thousands of secure, compliant virtual data centers that house some of the world’s most advanced medical research data.” 

University of Michigan and Verge.io

The beauty of the SDDC software was that entire datacenters could be moved with button clicks from one platform to another, or cloned for another bunch of tenants or a department or an MSP customer.

Exec churn and name change

Yann Ness, Verge.io
Yan Ness

A period of exec churn ensued. Between 2017-2018, Yottabyte was renamed Verge.io with Campbell becoming CTO. Tursi changed from a principal to an advisor in February 2018 and quit that role in March 2019. Michael Aloe was SVP sales and operations from April 2017, becoming COO in 2018, but he’d left by October 2019. Matt Wenzler became CEO in January 2020 but exited September 2021, when Yan Ness took over. Kathy Fox became CFO in July last year and Chris Lehman SVP of sales in December 2021.

Verge.io does not have a history of VC funding. This means it has not had that fierce boardroom presence of investors driving it to refine its product and market positioning. And now the HCI market boom is over. The SDDC concept is dominated by VMware, also Nutanix, and is less x86 centric with GPU servers for AI and large-scale analytics, specific AI processor developments, and DPU/SmartNIC technology for composability.

Verge.io software

Against this background, Verge.io now has software that is clean and elegant and easy to use but possibly too capable, in its niche, and too restricted to be of widespread use.

The Verge.io software is a datacenter OS based on a QEMU/KVM hypervisor which can have Linux and Windows guests. This OS can create multiple virtual datacenters, with multi-tenancy, which can be nested, using compute, storage, and network servers from the resource pools. The storage is a vSAN (block) and a NAS (files) using blockchain technology. It is encrypted, supports global deduplication, and has built in snapshotting and recovery.

The storage software supports silent data corruption detection and recovery, multi-site synchronisation, and disaster recovery through site failover. It supports storage tiers for different workloads – NVMe SSDs, SAS and SATA SSDs, and hard disk drives. System management has ML and AI features, and it’s claimed a sysadmin generalist can manage Verge.io virtual datacenters.

The pitch is that “Verge.io is one single, powerful, thin piece of software that replaces many disparate vendors and orchestration challenges. It’s just one SKU, one bill, one dashboard, one API managed by generalist IT staff.” It has pay-per-use consumption billing.

Customers get much reduced capex, more operational efficiencies, and rapid scalability.

Datto looking for takeover or private equity sale

Datto banner
Datto banner

Datto could be going private again with equity investors understood to be circling the cloud backup and security supplier just 18 months after it listed on the NYSE, with its share price currently languishing below the IPO offer price.

The company sells its cloud services to small and medium businesses through more than 18,500 managed service providers (MSPs). Revenues, including subscription, have grown steadily and Datto has been profitable for all but two quarters since its IPO in October 2020.

Bloomberge reported that Datto has received a takeover approach and was examining strategic options.

William Blair financial analyst Jason Ader told subscribers: “Takeover interest from potential buyers only provides us with greater confidence that the company is positioned as one of the clear leaders in the MSP software space – which is being driven by accelerating secular tailwinds (e.g. shift to remote work, digital transformation, labor shortages).”

A look at Datto’s history shows that it was bought by Vista Equity Partners in October 2020 and that firm still holds 69 per cent of the shares. In calendar Q4 2020, Datto brought in $164.3m and made a $5.7m profit. It has a stated goal of reaching a $1bn run rate in 2024, up from its current $657m, and expects to grow 20 percent annually. 

Datto revenue history

Despite this consistent revenue growth and its profitability, the stock market has not valued the company highly, as its share price history shows:

Datto share price history

Datto’s competitors include ConnectWise, Kaseya, N-able, Barracuda, Acronis, and Veeam. Ader suggested that a risk for Datto is that the prolonged COVID-19 pandemic could restrict Datto’s SMB end customers, while another is that the MSP market could consolidate. 

Ader reckoned: ”Ultimately, given our view that the MSP market is entering a golden age, we see scarcity value in the story as the top publicly traded, pure-play software vendor leveraged to MSP market trends.”

Datto has a wide, growing, and profitable cloud backup and security services MSP channel to SMB customers. A larger on-premises supplier could see an opportunity here to make a move into the cloud market and add its own services on top of Datto’s to grow its business. Hypothetically, Carlyle-owned Veritas could fit this notion.

Datto history

  • 2007 – Started up
  • 2013 – $25m A-round
  • 2014 – Bought Backupify
  • 2015 – $75m B-round
  • 2017 – Bought Open Mesh
  • Oct 2017 – Bought by Vista private equity and merged with Autotask
  • 2020 – Bought Gluh and its real-time procurement platform
  • Oct 2020 – IPO at $27.50/share
  • March 2021 – Bought BitDam for cyber threat protection tech
  • January 2022 – Bought cybersecurity supplier Infocyte
  • 21 March 2022 – stock price $25.76 below IPO price

Datto has more than 1,600 employees.

SOCAMM

SOCAMM – Small Outline Compression Attached Memory Module. SOCAMMs provide over 2.5 times higher bandwidth at the same capacity when compared to RDIMMs, according to Micron. The SOCAMM form factor of 14x90mm occupies one-third of the size of the industry-standard RDIMM form factor. When using LPDDR5X memory, SOCAMM products consume one-third the power compared to standard DDR5 RDIMMs. SOCAMM implementations use four placements of 16-die stacks of LPDDR5X memory to enable a 128GB memory module.

We understand that SOCAMM is an AI server-specific memory and Nvidia is pushing for its standardization. Nvidia may use SOCAMM in its Grace Blackwell Ultra (GB300) GPU, which could increase demand markedly.

Facebook investigates silent data corruption

Facebook
Facebook

Facebook has flagged up the problem of silent data corruption by CPUs, which can cause application failures and undermine data management systems in its super dense datacenters.

An academic paper detailing the research says: “It is our observation that computations are not always accurate. In some cases, the CPU can perform computations incorrectly.” Unlike soft errors due to radiation or other interference, “silent data corruptions can occur due to device characteristics and are repeatable at scale. We observe that these failures are reproducible and not transient.”

Moreover, the researchers say: “CPU SDCs are evaluated to be a one in a million occurrence within fault injection studies.” Scale that up by the number of processors and computations Facebook’s infrastructure accommodates and the implications are obvious. The researchers state it “can result in data loss and can require months of debug engineering time.”

The researchers wrote: “It is our observation that increased density and wider datapaths increase the probability of silent errors. This is not limited to CPUs and is applicable to special function accelerators and other devices with wide datapaths.”

This can have effects at application level, including on Facebook’s data compression technology to reduce the footprint of its datastores, leading to the possibility of files being missed from databases, ultimately leading to application failure. “Eventually the querying infrastructure reports critical data loss after decompression,” said the researchers.

The answer is testing to uncover errors. But servers are tested for a few hours by the vendor then by an integrator for at best a couple of days.  After they go into production, Facebook said “it becomes really challenging to implement testing at scale.”

Testing times

The giant’s answer is to combine two types of testing regimes. Opportunistic testing means piggy backing on other routine maintenance events such as reboots, kernel or firmware upgrades, or device reimages, host provisioning, and namespace reprovisioning. “We implement this mechanism using Fleetscanner, an internal facilitator tool for testing SDCs opportunistically,” said Facebook.

But this alone is not enough. Facebook also committed to ripple testing, essentially running SDC detection in conjunction with production workloads. “Ripple tests are typically in the order of hundreds of milliseconds within the fleet,” the researchers wrote. This results in “a footprint tax” but this is negligible in comparison with other management activities.

The result is faster time to detecting errors that “can have a cascading effect on applications… As a result, detecting these at scale as quickly as possible is an infrastructure priority toward enabling a safer, reliable fleet.”

Could Russia plug the cloud gap with abandoned Western tech?

Digital Russia
Digital Russia

What happens to a country when it runs out of cloud? We might just be about to find out as Russia has apparently realized it’ll be out of compute capacity in less than three months and is planning a grab for resources left by Western companies who have exited the country after Vladimir Putin’s invasion of Ukraine.

A report in Russian newspaper Kommersant says the Kremlin is “preparing for a shortage of computing power, which in the coming months may lead to problems in the operation of state information systems.” Initial translations of the report referred to a shortage of storage.

The Russian Ministry of Digital Transformation reportedly called in local operators earlier this month to discuss the possibility of buying up commercial capacity, scaling back gaming and streaming services, and taking control “of the IT resources of companies that have announced their withdrawal from the Russian Federation.”

Apparently, authorities are “conducting an inventory of datacenter computing equipment that ensures the uninterrupted operation of systems critical to the authorities.” The ministry told the paper it did not envisage critical shortages, but was looking at “mechanisms aimed at improving efficiency.”

The report cited rising public-sector demand for computing services of 20 percent. It added that one major impact is from the use of “smart cities” and surveillance systems. Apparently, its source “explains that due to the departure of foreign cloud services, which were also used by some departments, the demand for server capacities instantly increased.”

Meanwhile, the report continues, Russia’s datacenter operators are struggling, swept up in sanctions, economic turmoil and facing the challenge of sourcing kit when the ruble is collapsing. And they are effectively left with just one key supplier – China.

Russia stretched thin

It’s not like Russia was awash with datacenter and cloud capacity in the first place. According to Cloudscene, there are 170 datacenters, eight network fabrics, and 267 providers in Russia, which has a population of 144 million.

Neither AWS, Google nor Azure maintain datacenters in Russia, and while there may be some question as to what services they provide to existing customers, it seems unlikely they’ll be offering signups to the Russian authorities. AliBaba cloud doesn’t apparently have any datacenters in Russia either.

By comparison, the UK, with 68 million citizens, has 458 data centers, 27 network fabrics, and 906 service providers, while the US’s 333 million citizens enjoy 2,762 datacenters, 80 network fabrics, and 2,534 providers.

It’s also debatable how much raw tin there is available in the territory. In the fourth quarter, external storage systems shipped in Russia totaled $211.5m, up 34.2 percent. Volumes slipped 12.3 percent on the third quarter, while in the fourth quarter 50,199 servers were delivered, up 4.1 percent, though total value was up 28.8 percent at $530.29m.

Server sales were dominated by Dell and HP. Storage sales were dominated by Huawei at 39.5 percent, with Russian vendor YADRO on 14.5 percent, and Dell on 11.2 percent by value, though YADRO dominated on capacity.

Now, presumably, Dell and HP kit will not be available. Neither will kit from Fujitsu, Apple, Nokia or Ericsson, and cloud services from AWS, Google or Azure.

Could China step in?

Chinese brands might be an option, but they’ll still want to be paid, and the ruble doesn’t go very far these days. Chinese suppliers will have to weigh the prospect of doing business in Russia against the possibility of becoming persona non grata in far more lucrative markets like Europe, and perhaps more scarily being cut off from US-controlled components. Kommersant reported that Chinese suppliers have put deliveries on hold, in part because of sanctions.

So there are plenty of reasons for Russia to eke out its cloud compute and storage capacity. According to the Kommersant: “The idea was discussed at the meeting to take control of the server capacities of companies that announced their withdrawal from the Russian market.”

Could this fill the gap? One datacenter analyst told us that, in terms of feasibility, two to three months is doable as “what normally holds up delivery of services is permits and government red tape, construction. If they are taking over existing datacenter space with connectivity and everything in place, they could stand up services pretty fast.”

But it really depends on the nature of the infrastructure being left behind. This is not a question of annexing Western hyperscalers’ estates, given they are not operating there. Which presumably leaves corporate infrastructure as the most likely target.

Andrew Sinclair, head of product at UK service provider iomart, said co-opting dedicated capacity that’s already within a managed service provider or cloud provider might be fairly straightforward.

Things would be far more complicated when it came to “leveraging dedicated private cloud infrastructure that’s been aligned to these companies that are exiting. These are well-recognized Fortune 500 businesses we’ve seen exiting. These businesses have really competent IT leaders. They’re not just going to be leaving these assets in a state that people are going to be be able to pick them up and repurpose them.”

‘Extreme challenge’

From the Russian authorities’ point of view, “they would be going out and taking those servers, and then reintegrating them into some of these larger cloud service providers more than likely. Even from a security perspective, a supply chain perspective, from Russia’s perspective, would that be a sensible idea? I don’t know,” Sinclair added.

The exiting companies would presumably have focused on making sure their data was safe, he said, which would have meant eradicating all the data and zeroing all the SAN infrastructure.

“Following that, there’s a question about whether they just actually brick all the devices that are left, whether they do that themselves, or whether the vendors are supporting them to release patches to brick them.

“Connecting Fiber Channel storage arrays that have been left behind to a Fiber Channel network? Reasonable. But to be able to do that in two to three months, and to be able to validate that the infrastructures are free of security exploits, all the drives have been zeroed, and it’s all nice and safe? I think that’s an extreme challenge.”

But he added: “When you’re backed into a corner, and there’s not many choices available…”

Of course, it’s unwise to discount raw ingenuity, or the persuasive powers the Kremlin can bring to bear. It’s hard not to recall the story of how NASA spent a million dollars developing a pen that could write in space, while the Soviets opted to give its cosmonauts pencils. Except that this is largely a myth. The Fisher Space Pen was developed privately. And Russia used it too.

Tokens

Tokens – In the context of artificial intelligence (AI), particularly in natural language processing (NLP) tokens are an intermediate representation of words, images, audio and video between the origini=al item and vectors. A token is typically a unit of text—like a word, subword, or character—that an AI Large Language Model (LLM) processes. For example, in the sentence “I love AI,” the tokens might be “I,” “love,” and “AI.” These tokens start as raw text or symbols.

To work with tokens mathematically, AI models (like those based on transformers) convert them into vectors—numerical representations in a high-dimensional space. This conversion happens through a process called embedding. Each token is mapped to a vector using an embedding layer, which is trained to capture semantic meaning, context, or relationships between tokens. For instance, “love” might become something like [0.23, -1.54, 0.89, …], a list of numbers that encodes its meaning relative to other words.

Therefore:

  • Tokens are the discrete units (e.g., words or subwords).
  • Vectors are the numerical representations of those tokens after embedding.

In practice, when people talk about “tokens” in AI models, they often mean these vector representations implicitly, especially when discussing how the model processes input.

Note that a token is the smallest unit of text that the LLM processes at a time. It’s typically a word, part of a word, or a punctuation mark, depending on the tokenization method used by the model. Tokenization is the process of breaking down input text into these smaller units so the model can understand and generate text.

However, a chunk is a larger segment of text, typically consisting of multiple tokens, that is grouped together for processing or analysis. Chunking often happens when dealing with long documents or inputs that exceed the model’s token limit, requiring the text to be split into manageable pieces.

Snowflake unveils medical data cloud

Snowflake
Snowflake

Snowflake has lifted the sheets on a dedicated Healthcare and Life Sciences Data Cloud that it claims will fast track the exchange of critical and sensitive health data and speed research.

Few data types spark as much concern around privacy and confidentiality as health data, while health organizations, pharma and medical companies, and public bodies, are all desperate to get their hands on this information for research or planning.

Snowflake says its Healthcare and Life Sciences Data Cloud gives all these stakeholders a single, integrated cross cloud data platform, that will allow them to share and analyze data more productively, while ensuring compliance.

Todd Crosslin, global head of healthcare and life sciences at Snowflake, said one of the biggest challenges when it came to healthcare and life sciences is siloed data – both within and between organizations.

For example, a pharma organization might have teams working on sensitive clinical trial data who need to collaborate with colleagues across the world. Relying on traditional tools like FTP to copy data is unwieldy and problematic when live data is being constantly updated, but also starts raising compliance flags.

“We can really accelerate these things,” said Crosslin. “We know everyone bent over backwards for Operation Warp Speed here in the US and, and around the world for COVID vaccines, but they almost killed themselves doing that.”

Snowflake’s Healthcare and Life Sciences Data Cloud allows the data to be processed into a single set of tables, which researchers can then analyze or share with partners – subject to data residency and other regulations.

Life sciences research is covered by the GxP standards that cover sensitive industries like pharmaceuticals and food. “We cannot be GXP compliant,” said Crosslin. “We can only be compatible. And it’s the global pharma firm that has to say, we have done X, Y and Z, right on Snowflake to be compliant.”

Crosslin was at pains to say Snowflake’s platform wouldn’t replace the high-end storage systems used in, for example, gene sequencing or for AI work. “What we typically find is, then you take the results of that, and you put that back in Snowflake, right to analyze… we appreciate our cousins in different areas that use those types of super high performance storage solutions”

Liqid pours GPUs, NVMe storage into ThinkTank

Liqid
Liqid

Liqid has announced general availability of its ThinkTank composable disaggregated infrastructure platform, which it says will restore the balance between expensive storage and even more expensive GPUs.

The company’s take is that while GPUs are essential to AI and other challenging modern workloads, GPU-packed servers aren’t always the most flexible of beasts. Operators can find they quickly hit a limit on how far they can scale up the number of GPUs in a system, while storage and IO shortcomings can leave the GPUs they do have underutilized.

The ThinkTank boxes feature Liqid’s Matrix CDI software which enables users to “quickly compose precise resource amounts into host servers, and move underutilized resources to other servers to satisfy changing workload needs.”

They also include the vendor’s own PCIe fabric technology, and its Honey Badger storage cards, which offer up to 4 million IOPs and 24GB/s throughput, along with compute nodes and Ethernet and InfiniBand support.

The boxes range from the X1, which packs four GPUs, through eight and 16-GPU versions, to the 32-GPU Thinktank XR. A single unit ThinkTank Flex is billed as a “bring your own hardware” option. The boxes support Nvidia or AMD GPUs, with a maximum of 20 per compute node, as well as those vendors’ respective AI stacks and IOs. The X4 carries 15 of Liqid NVMe flash storage drives, with the XR topping out 120TB.

Existing architectures can leave GPUs with utilization rates of 12-15 percent, director of product marketing George Wagner claimed, leaving AI and HPC workloads hamstrung.  Liqid’s composability approach means the customer can say, “I want this host with this many CPUs, I need this many GPUs, I need this much storage, go make it happen.”

Ben Bolles, exec director of product management, added: “By hosting the high performance storage on the PCI fabric, you then can solve the challenge that customers run into when they’re trying to feed all the data, to the GPUs and the accelerators.”

The system includes Liqid’s ioDirect technology, which Wagner said “allows the GPU to talk to GPU and bypass the CPU all together, as well as allow our storage to communicate with GPU and bypass the CPU as well.” It also includes support for Nvidia’s NVLink bridge and AMD’s Infinity fabric.

The announcement comes a few months since Liqid snagged $100m of fourth round funding, apparently pitching it into unicorn territory. Customers to date include the US Army Corps of Engineers and University of Illinois’ Electronic Visualization Lab.

Kubernetes data protection is being neglected

Kubernetes logo
Kubernetes logo

Businesses may be getting to grips with Kubernetes, but they are leaving themselves open to cyberattacks including ransomware because they haven’t thought to coordinate their cloud-native and data protection strategies.

Research conducted for Veritas showed that Kubernetes is increasingly being deployed for mission-critical applications, with a third of respondents already using it, and 86 percent of organizations claiming they would be using it within the next two years.

But while companies are clearly convinced of the case for the container orchestration platform and apparently desperate to deploy it, just a third of current users have matched their Kubernetes installations with appropriate data protection tools.

What is even more inexplicable is that 89 percent said that ransomware attacks on their Kubernetes environments are already an issue. Almost half of organizations who had deployed the container orchestration platform said they’d already experienced an attack on their Kubernetes environments.

This prompted Veritas to dub Kubernetes the “Achilles’ heel” in ransomware defense strategies. Though perhaps a more apt description would be the “great big rainbow pointing to a crock of data gold.”

This is in large part because just 40 percent of organizations have extended their legacy data protection tooling to their Kubernetes environments, with the rest either relying on standalone products, or presumably nothing at all. Which not only increases complexity while leaving potential gaps in protection, but is also likely to complicate restores.

Of those organizations that had implemented some form of data protection for Kubernetes deployments, 47 per cent plumped for a standalone version, while 40 per cent had extended existing data protection tooling. The remainder were using a mix of standalone and existing tools.

The research also suggests a certain degree of magical thinking about how things are likely to develop in the future. First of all, tech leaders are piling into Kubernetes without ensuring they have protection in place. At the same time, 29 percent believe ransomware will not be an issue five years from now, as they ramp up their spending on data protection for containerized data by 49 percent.

Which is great, as long as ransomware developers play ball and refrain from attacking unprotected organisations now and don’t work to develop new variants to take advantage of this gradualist approach to protection in the longer term.

NetApp and Cisco reshape FlexPod for hybrid cloud

NetApp logo
NetApp logo

Cisco and NetApp have mapped out a hybrid cloud future for their decade old FlexPod platform.

The firms are pitching the FlexPod XCS as a single “automated platform for modern applications, data and hybrid cloud services”.

In practice, this means adding hybrid cloud connectivity to on-prem FlexPods, together with increased automation, and unified visibility of FlexPod components and including an “as a service” procurement and consumption model. XCS is described as “natively integrated across all three major public cloud providers”.

The key to all of this is the full integration of the FlexPod line with Cisco’s Intersight hybrid cloud ops platform.

Siva Sivakumar, vice president for Cisco Computing and Solutions product management, explained that Intersight already provides access to the storage elements of the FlexPod platform.

“What we are bringing on top of that integration is bringing FlexPod as a first class citizen within Intersight through automation. So you can go into Intersight, and … you could claim your existing FlexPod that has been deployed in the past and create a Stack View.”

“So you can actually look at your combined FlexPod real estate through the Intersight pane of glass,” he explained. “And you can do inventory, you can do health checks, look at performance capacity alarms, those types of things.” The same platform will then provide automated day one and beyond management of the system, with a cloud-like experience.  It seems that systems up to two years old, possibly, three, can be “claimed” under the program, as well as yet to be launched systems.

NetApp’s vp and general manager for hybrid cloud infrastructure and OEM solutions, Mike Arterbury, said the “deep integration into Intersight allows you to not just click through to NetApp’s tooling, but to actually see the entirety of the estate and act on it through a single pane of glass, even out to the public cloud.”

By leveraging NetApp’s ONTAP and its Astra Kubernetes orchestration technology, “you can create and provision a persistent data store for containerized applications. You’re going to be able… to build using the same familiar tooling of ONTAP in a private cloud, in Flexpod or a public cloud in the hyperscalers.”

Arterbury said systems in the revamped line, like earlier generations, would be built for particular use cases. Initial targets include: business continuity, for example, by enabling a hot replica in the cloud; application development, with apps being developed and tested in the public cloud, then run on scale on prem;  and, thirdly, backup and recovery.

The XCS is currently in pilot, with general availability expected in the summer. When systems do begin hitting the market, they will be available under a cloud like opex purchasing model with a promised “low bar of entry”, with the ability to burst up as needed – whether that’s to over-provisioned on prem capacity in the first instance, and into the cloud as necessary.

WEKA chooses Pi Day to serve up latest rev

Pi
Pi

WEKA unwrapped version 3.14 of its eponymous parallel file system yesterday, appropriately enough on Pi Day.

In a blog announcing the update, marketing veep Colin Gallagher waxed on how Pi represents “infinite possibilities and challenges us to find meaning in seemingly random numbers.”

How does this relate to the latest update of the platform? Well, there are obvious parallels to AI and data science. Gallagher said the 3.x series is about extending interoperability and choice, and better resource sharing as customers build out their AI deployments.

But what’s really holding up AI deployments right now is the supply chain crisis. Gallagher said some customers were left facing “extended” waits for servers last year because of NIC shortages.

So Gallagher said WEKA was adding support for three new cards: Mellanox ConnectX-6 Dx, Intel’s E810 series, and Broadcom 57810s. This should give customers more variety and flexibility when it came to choosing servers, and help to reduce lead times, the company said.

The WEKA Data Platform has also been updated “to take full advantage of more and more CPU cores by using multiple containers running our WekaFS software to take full advantage of our partners’ increasingly powerful servers.”

“It also provides the means to increase scalability to 8000 nodes, improving on how a large cluster can be created,” Gallagher wrote.

Gallagher said the update adds more granular controls for customers running multiple AI workloads on a WEKA system, in the shape of client side QoS to throttle bandwidth from any client into the WEKA system.

“When you combine this QoS functionality with granular capacity quotas and organizational role restrictions, you have a powerful set of tools to help manage resources on the Weka system,” he said.

The update also extends WEKA’s multiprotocol support, with interoperability for S3 workloads, alongside the ability to share the same filesystem across NFS, SMB, and Posix.