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WANdisco takes the replicant test to the multi-cloud

WANdisco this week made LiveData for MultiCloud available for public consumption.

The idea is simple, namely to store one replicated copy of data in multiple clouds so that you have an alternative ready and waiting if one cloud copy goes down.

LiveData for MultiCloud can span regions in AWS, Azure or a mix of on-premises and cloud S3-compatible storage services. These include most cloud and on-premises object storage systems, including Amazon S3, AWS Snowball, Azure Blob Storage, Azure Data Box, Dell EMC Elastic Cloud Storage, IBM Cloud Object Storage, Alibaba Cloud Object Storage Service, Oracle Cloud Object Storage, Scality Zenko Cloud Server, and many others. 

The software uses WanDisco’s own Distributed Coordination Engine (DConE) blockchain technology to ensure each replication copy is consistent.

DConE enables active-active replication data without single points of failure or scalability bottlenecks. It uses Paxos-based consensus technology as a component to produce an agreed status among several computing systems in different networks. (Find out more about Paxos in this PDF document.)

WANDisco claims its LiveData for MultiCloud product is the only one that can continuously replicate at petabyte scale data with zero loss or downtime across multiple cloud environments. 

WekaIO pumps up the SpecSFS 2014 build volume

WekaIO has notched another benchmark win, scoring 5,700 on the SpecSFS 2014 software build test.

This benchmark tests a variety of storage application types: software builds, video streaming, databases, virtual desktops and electronic design automation.  The chart below shows Weka moving to the top spot from its previous equal fourth position (1,200 builds)  behind NetApp (4,200 and 2,200), DDN (1,500) and E8 (1,200).

Weka used a 6-node Supermicro system for the new benchmark run. Its earlier 1,200 build score used a 4-node Supermicro setup.  A bullish Weka representative told us that it will simply add more nodes and run again if a competitor tops 5,700 builds. We have a comment about this below.

The benchmark also records overall response time (ORT), and we have charted the results again, with ORT (blue bars) compared to builds (red line):

By this measure, Weka has the second lowest ORT at 0.26secs, behind a DDN Spectrum Scale system with 0.19secs. The largest ORT is also a Weka system, running on 60 instances in AWS and clocking 3.06secs.

The general trend is for ORT to decrease as the software builds number  increases.

Comment

Regular readers will know that I am no fan of SpecSFS 2014 because there is no pricing context. This means it is impossible to compare price/performance and also non-scale-out systems are effectively excluded. This weakens the utility of this benchmark for enterprise customers.

If vendors can merely add nodes to a scale-out system to establish new records, SpecSFS 2014 is effectively a scale-out storage system benchmark only.

If price-performance were added to SpecSFS 2014, Weka and rival software-defined storage products running on X86 commodity server boxes are likely to do even better.  Such a move would put Cisco, DDN, E8, Huawei, IBM and NetApp and other scale-out systems running on proprietary hardware at a disadvantage compared to Weka and its ilk.

Datera gets helpful halfway OEM hug from HPE

HPE this week added Datera, a software-defined and scale-out storage supplier, to its HPE Complete program.

Hang on, HPE already works with Hedvig, another software-defined and scale-out storage supplier. What gives?

Datera

Datera began life in 2013 and gained $40m in VC funding three years later.

The company’s product is called Elastic Data Fabric and began shipping in 2017. It is best compared to Amazon’s Elastic Block Store.

Datera engineered its own data path to the physical storage devices, replacing the native paths in the Linux OS. You can get a technical overview of Datera’s software here.

Users see block and object storage that is both high-performance and scale out. Its data placement and load-balancing are automated, using machine learning technology, and the object storage uses Minio software .

This software runs on-premises or in the cloud using all-flash or hybrid nodes. Compatible servers include Datera X86 hardware, Dell, Fujitsu, Supermicro, HPE Apollo and ProLiant.

The hardware and software build a virtual SAN that is comparable to Dell EMC’s VxFlex product, based on ScaleIO software, and supports thousands of nodes, spanning multiple data centres, regions, and extending into the public cloud.  Datera storage can deliver less than 200 microsecond latency and millions of IOPS across these  nodes. VMware’s VSAN is a pygmy scale-out virtual SAN in comparison.

Datera latency varies with the storage media

Datera customers include the world’s top travel ecommerce site (think 8million transactions/sec), a top five global airline, Ultimate Software, eDiscovery SaaS operator Morae Global, and the cloud service provider, Packet. They use Datera to supply storage to apps in bare metal, virtualised and containerised servers. In Packet’s case 18 data centres around the globe are involved, with automated operations.

Datera makes HPE complete

HPE, broadly speaking, offers two kinds of storage: hardware-defined and software-defined. Hardware products include 3PAR block arrays, with File Persona file support, mid-range Nimble block arrays, SimpliVity hyper-converged infrastructure and StoreEasy NAS. There is no HPE equivalent to Dell EMC’s Isilon filer.

On the software-defined storage side HPE is relatively weak and looks to partners to get this software running on its ProLiant and Apollo server hardware. There are varying levels of partnership, from meet-in-the-channel, through reselling to near-OEM.

I have made a diagram to position some of these partnerships:

There are two object storage partnerships; Cloudian and Scality. There are also two file storage partnerships we know of: Qumulo and WekaIO.

Qumulo is an Isilon-class competitor while WekaIO is more of a high-performance computing-class supplier.

As you can see there are two scale-out and multi-protocol storage software partnerships: Hedvig and Datera. I characterise them by positioning two scale-out arrows in different directions.

Hedvig is a multi-protocol (block, file and object) storage platform for broad scale-out across an enterprise’s data centres, branch offices and public cloud locations. Datera is a high-performance, dual-protocol (block and object), scale-out storage facility. There is some overlap between the two products and HPE has shoehorned some messaging differentiation for cleaner positioning.

Basically, Datera gives HPE a way to compete for Dell EMC VxFLEX (ScaleIO)-class  opportunities, at up to 70 per cent lower cost, Datera says.

HPE Complete

HPE Complete provides Datera and HPE interoperability validation and facilitated troubleshooting for issue resolution. It is more than a reselling deal, with HPE both supplying and supporting the product. But it is less than an OEM deal as the third-party product retains its own branding.

Future possibilities

How might things develop? Interesting avenues could be the addition of file storage to Datera and an integration between Datera’s software and HPE’s InfoSight storage and server system management facility, using Rest APIs.

We also expect Datera’s software to support storage-class memory and NVMe over Fabrics. Datera suggests we might expect 80 microsecs latency off an Optane SSD.

And I would not be surprised if there was a funding round for Datera later this year…but this is mere conjecture on my part.

Benchmarks! IBM deploys memcaching with flash and 3D XPoint to match DRAM

Implementing Memcache with NAND or 3D XPoint produces near-DRAM cache performance at a much lower cost, IBM claims.

Memcache is an open source distributed memory system launched in 2003.  These days databases are much larger and DRAM has remained expensive – indeed prices increased 47 per cent from 2016 to 2017, according to IBM

More than 700 applications use Memcache and many public clouds offer a managed Memcache service. For instance, LinkedIn, Airbnb and Twitter use Memcache to avoid accessing databases on storage and so reduce speed query response times.

IBM drives memory to uDepot

IBM Zurich researchers have built Memcache implementations using NVMe flash and also Optane (3D XPoint). They say it could provide close-to-DRAM performance for less money – and retain its contents over a power-loss.

They have built a key:value store called uDepot which is tailored for NVMe flash, and also Optane. Users can expect 20x lower costs than DRAM (c$10/GiB) when using flash ($0.4/GiB) and 4.5x lower hardware costs when using 3D XPoint ($1.25/GiB) without sacrificing performance, and gaining higher cloud caching capacity scalability, the IBMers say.

They implemented uDepot with NVMe flash SSDs as an IBM Cloud service, calling it Data Store for Memcache, and benchmarked it using the memaslap test, against the free version of Amazon’s AWS Elasticache, which uses DRAM.

They found Data Store for Memcache is 33 per cent faster on average (across all concurrent request data points) as a transactions per second  chart shows:

A latency comparison chart shows DataStore for Memcache is close to Elasticache latency:

The unlabelled vertical axis of this chart shows average latency in microseconds.

DataStore for Memcache is available as a no-charge beta offering from the IBM Cloud.

Memcache for questions

IBM has also implemented uDepot using two Intel Optane 3D XPoint drives – Intel P4800X 375GB – and compared that to DRAM and flash Memcache implementations, again using the memaslap test. The company compared five alternative memcache implementations:

  • uDepot Optane with trt-spdk backend
  • uDepot Optane with trt-aio backend
  • memcached with DRAM
  • MemC3 – a newer Memcache implementation with DRAM
  • Fatcache – Memcache implementation coded for SSDs but implemented here with Optane media

The results show uDepot getting  close to memcached and MemC3 performing better than memcached in throughput terms (left-hand chart). Fatcache, with its SSD-based code, lags far behind on the throughput test.

In latency terms (right-hand chart) Fatcache is not so good either. It caches data in DRAM, getting low latency at low queue depths and then latency rapidly increases with the number of concurrent requests from clients. 

The memcached and MemC3 DRAM, and uDepot Optane caching alternatives are closely aligned in latency terms.

For 128 clients, the actual latency and throughput numbers are:

  • MemC3 – 110μs and 1,145kops/s
  • memcached – 126μs and 1,001kops/s
  • uDepot trt-spdk – 128μs and 985kops/s
  • uDepot trt-aio – 139μs and 911kops/s
  • Fatcache – 2,418μs and 53kops/s

The IBM researchers conclude that memcached on DRAM can be replaced  by uDepot on Optane with negligible impact on performance. 

How does uDepot Optane throughput compare to that of uDepot flash? The uDepot Flash throughput at 128 clients is 40,000, reading from the first chart, and it is around 140,000, reading off the right-hand uDepot Optane chart above – 3.5 times better.

These numbers suggest that NVMe Optane drives could be a worthwhile replacement for DRAM in memcache applications.



Two ways in which QLC flash can cause falling SSD prices

Blocks & Files expects the rapid adoption of 96-layer QLC flash for fast-access, read-centric data storage applications, where its lower write endurance, compared to TLC flash, matters less. But will the take-up of QLC flash result in price declines for SSD memory?

That’s what happened with the arrival of TLC flash. This precipitated a fall in SSD $/GB prices in the summer of 2017, as the chart below shows.

TLC (3bits/cell) delivers a 50 per cent increase in capacity on MLC (2bits/cell) flash, which was the dominant technology until its arrival.

The arrival of 3D NAND in 2017 also contributed to lower prices. 3D NAND arranges multiple layers of cells to flash to increase capacity within a flash chip’s footprint.

Both technologies increased the bit capacity of flash chips and of the circular wafers from which flash chips are made. This in turn paved the way for price falls.

QLC in bits

So what about QLC? Here are some rough calculations to show what could happen.

QLC has 4bits/cell – a third more than TLC – and 96 3D layers, 50 per cent more than TLC’s 64 3D layer arrangement.

Combine these two effects – the increased bits and more layers, and  the cost per bit of a 96-layer QLC cell compared to a 64-layer TLC cell, could be up to 50 per cent less.

I have not factored in QLC unit production costs  and my calculations are for illustrative  purposes only. They are not meant to be definitive. Nonetheless, my sources expect this to have a modest impact on pricing

Happy Hedvig gets helping hand from HPE

Profile: Hedvig sells multi-protocol, scale-out, software-defined storage and says it is getting a strong helping hand from HPE to seed its technology in big companies.

Why does HPE want to get involved with this smallish startup?

The need

Hedvig thinks enterprises want to access storage in the same way that public cloud and hyperscale users do. Enterprises do not want hardware array lock-in, multiple overlapping single-silo suppliers or difficult storage management. They want their storage scalable, easy to use and rock-solid reliable.

They want any of their enterprise storage accessing clients to access any storage through a single platform service. By any storage, we mean files, blocks or objects, on hyperconverged server drives or external storage nodes. These reside in local or remote data centres or in the public cloud, all clustered together.

Storage clients can be ordinary servers, virtualised servers or containerised servers and live on-premises or in the cloud.  

Company background

Hedvig engineers come from a public cloud and hyperscale background and have devoted their efforts to design their Distributed Storage Platform to function as a single central storage platform.

The company was started in 2012 by Avinash Lakshman, who helped develop Apache Cassandra for Facebook and Amazon’s Dynamo database. It has taken in $52m in four funding rounds. By contrast, this looks frugal compared with competitors such as Actifio ($311.5m), Cohesity ($410m) and Rubrik ($553m). 

HPE participated in the most recent $21.5m C-round in 2017 though the Hewlett Packard Pathfinder organisation.

Here’s a short video about Hedvig:

A Hedvig YouTube video with founder and CEO Avinash Lakshman taking about the company.

Distributed Storage Platform

The Distributed Storage Platform (DSP) is described as a “private, hybrid, and multi-cloud data management system for virtual and container environments.” It is an abstraction layer, providing block, file and object protocol support to clients, and is based around a so-called elastic Universal Data Plane (UDP) enabling applications to store data in-premises or public cloud tiers using virtual disks. 

The UDP runs on commodity Linux servers, either on-premises or in public clouds, and is managed by an orchestration or management framework. It has a scale-out architecture, growing to 1,000s of stretch clustered nodes, and provides thin provisioning, global compression, global deduplication, encryption, replication, zero-copy snapshots and clones, pinning data to flash, and automation via storage proxies and Rest APIs.

A DSP Data Management service provides self-healing, clustering and storage facilities. A Data Persistence Service maintains storage state and tracks the health of cluster nodes. Clusters can bridge on-premises and cloud nodes.

Hedvig Storage Service

Below this in the Hedvig software stack is a Hedvig Storage Service (HSS) which operates a key:value store and interfaces to the underlying storage media. All random writes are sequentially ordered into a log-structured format and written to the drives. Hedvig says this provides high ingest rates and effective drive utilisation.

The HSS auto-tiers and load-balances across racks, data centres and clouds. It runs data and metadata processes. The metadata side looks after how and where data is written, recording the container, storage pool, replica, and replica locations of all data. It tracks all reads and guarantees all writes. 

Metadata is cached by a so-called Storage Proxy. This provides a fast response to client metadata queries by avoiding any deeper processing by the Hedvig stack. The storage proxy runs at the app tier and routes IO to the storage tier.

In the storage tier, data processes create 16GB data chunks called containers and storage pools – a logical grouping of three drives in a node. The storage tier creates virtual disks, which can be of any size, and are split into containers: one per storage pool in a storage node.

Blocks and Files DSP diagram

Containers are replicated, according to policies at the virtual disk level. Containers can be shared by several virtual disks and are spread around physical drives. If a physical drive fails its contents can be rebuilt by using the replicas. A 4TB disk drive can be rebuilt in 20 minutes this way, in contrast to a RAID rebuild which could take hours.

DSP can be deployed in a hyperconverged environment, with Hedvig storage nodes and Hedvig client users acting as HCI nodes.

DSP is currently in its v3 incarnation and v4.0 is expected by summer this year.

Hedvig customers

Hedvig’s DSP, like Ceph, can cover all three main storage protocols: block, file and object. It is also an on-ramp to the public cloud and operates in the multi-cloud world. But it is not presented as best of breed or fastest-performing for any one protocol. 

That gives it a market focus problem, in that it can do most storage things whereas competitors can say they do fewer storage things better or faster.

Hedvig has focused sales efforts on specific storage problems, such as presenting itself as a backup target, and other secondary data use cases. It then relies on customers expanding their use-cases as they see what DSP can do. It has partnership arrangements with Commvault, IBM (Spectrum Protect) Veeam and Veritas for DSP to be a backup target. 

All-in-all Hedvig hopes that, as enterprises recognise the need for cloud-scale and cloud-simple storage then they turn to Hedvig’s DSP for multi-cloud, multi-location and multi-protocol storage services. 

The thinking is that it will sell direct or via a small channel to large enterprises that need the ability to scale out to 1,000s of nodes and like the idea of a single storage service covering block, file and object needs for their central and many branch offices. Customers like the idea in principle, even if they initially treat Hedvig as a backup target.

Customers tend to want a software-defined storage product, free of hardware lock-in, and a preference for a platform product that covers many of their storage needs.

Hedvig has concentrated on selling to larger enterprises in the financial services, service provider, manufacturing, energy and retail markets, and has notched up Bank Paribas, GE Digital, IAG, Airbus, Scania and LKAB, a huge iron ore mining company, as customers. The company has about 50 customers in total. 

Hedvig beat Dell EMC’s ScaleIO and Pure Storage to win the IAG deal, which involves multiple Azure and AWS regions. We understand IAG focused on Hedvig’s reliability and availability,  possibly due to its British Airways subsidiary suffering outages in the past.

IAG is a multi-petabyte, multi-site, multi-cloud deal. Airbus is a similar configuration but more than double the size of the IAG deal, according to Hedvig. The company is not publishing any figures, though.

We understand some Hedvig potential or actual customers are running Arm server POCs.

Lifting all boats

HPE does not see Hedvig as a high-performance, scale out software-defined storage play. It has teamed up with Datera for those sales opportunities.

HPE uses Hedvig’s software when it needs to compete with Dell EMC’s ScaleIO and similar software-defined products, such as Ceph. That rules out HPE’s 3PAR, Nimble and SimpliVity products, which combine HPE hardware with storage software and there is no equivalent product to Hedvig’s DSP in HPE’s storage portfolio.

So why does HPE plump for Ceph instead of Hedvig? According to Hedvig, its software has more functionality and is easier to use than its open source rival. Therein may lie the answer.

Startups with radically different software-only alternatives to existing hardware+software products have a hard time overcoming the inherent caution of enterprise IT buyers. That is if they can get in front of them.

As a startup, Hedvig gains immediate credibility with the HPE relationship and a huge channel to sell into enterprises.

Hedvig says the HPE alliance helped make 2018 its best revenue year and it expects, and hopes, that 2019 will surpass it.

Comment

Hedvig is not a flashy or go-for-it company. It is an engineering class-act- which its enterprise customers – relatively few to date – recognise. 

It has a small and tight management team with no COO or CFO. We wonder if frugality puts it at a disadvantage with its competitors. In its seventh year of existence, now might be the time to consider bringing in a seasoned CEO to grow sales channels, partnerships and marketing coverage. Just a thought.


WekaIO boosts HPE Apollo AI workload

WekaIO’s file system has given HPE’s Apollo’s servers a boost for artificial intelligence workloads, giving them a leading Resnet-50 score.

Resnet-50 (Residual Neural Network- 50 layers) is an image recognition training workload for machine learning systems.

The Apollo server is an Apollo Gen 10 6500; a dual Xeon SP CPU server equipped with up to 8 Nvidia Tesla V100 GPUs (16GB versions) and the NVLink GPU-to-GPU interconnect. Mellanox EDR (100Gbit/s) InfiniBand was used to interconnect a cluster of eight ProLiant DL360 servers running the WekaIO Matrix filesystem with the Apollo 6500. Each DL360 contained four NVMe SSDs

HPE noted that “the WekaIO shared file system delivers comparable performance to a local NVMe drive and, in all but one test, WekaIO is faster than the local file system when scaled to four or eight GPUs.”

I have assembled and charted results for Dell EMC, DDN A3I, IBM Spectrum AI, NetApp A700 and A800 arrays, and Pure Storage AIRI systems, using servers equipped with varying numbers of Nvidia GPUs. Adding HPE Apollo 6500 servers fitted with GPUs and using WekaIO’s Matrix filesystem to these charts shows that HPE has the top results for the 2, 4 and 8 GPU levels with Resnet-50:

Resnet-50 results at varying GPU counts. Not all suppliers provide values at each GPU count level, which is why there are gaps in the chart.

However, HPE came second to IBM Spectrum AI systems at the 4- and 8-GPU levels in the alternative Resnet-152 workload, but beat it at the 1-GPU level.

There is no Spectrum AI result available at the 2-GPU level.

Why Spectrum AI is better than Apollo 6500/WekaIO at Resnet-152 but worse in Resnet-50 is something for machine learning experts to ponder. An HPE technical paper describes the hardware and software used in the tests above.

For reference, our recorded Resnet-50 and -152 supplier system results are below.

Not all suppliers provide values at each GPU count level, which explains the gaps in the tables.

China embarks on long march to 3D NAND domination

China’s target for self-sufficiency in semiconductor technology is edging closer with the first mass production of 3D NAND chip packaging and testing products in the country.

Unimos Electronics will start mass-producing 32-layer 3D NAND chips at its Wuhan plant later this year.  The company is an affiliate of  state-owned Tsinghua Unigroup, which in turn is the the majority owner of Yangtze Memory Technology (YMTC). YMTC is developing a 64-layer chip and says it can bypass 96 layers to jump from there to 128-layers.

YMTC is set to build manufacturing plants at Chengdu, Nanjing and Suzhou and the way looks clear for YMTC 3D NAND chips to be packaged and tested with Unimos products.

3D NAND vendors include Intel, Micron, Samsung, SK Hynix, Toshiba and Western Digital. They are moving en masse to 96-layers from 64-layers, and 128-layers is seen as the next stage. YMTC’s layering capability is behind the industry but it should catch up if it can jump to 128-layers.

An amassment of storage predictions and announcements

We have four sets of predictions for 2019, followed by 11 brief news items, two customer notes and a CEO promotion at Datto.

Three Archive360 predictions

Archive360 archives data up to the Azure cloud. It reckons these things will happen in 2019:

1. To achieve defensible disposition of live data and ongoing auto-categorization, more companies will turn to a self-learning or “unsupervised” machine learning model, in which the program literally trains itself based on the data set provided. This means there will be no need for a training data set or training cycles.  Microsoft Azure offers machine-learning technology as an included service. 

2. Public cloud Isolated Recovery will help defeat ransomware. It refers to the recovery of known good/clean data and involves generating a “gold copy” pre-infection backup. This backup is completely isolated and air-gapped to keep the data pristine and available for use. All users are restricted except those with proper clearance. WORM drives will play a part in this.

3. Enterprises will turn to cloud-based data archiving in 2019 to respond to eDiscovery requests in a legally defensible manner, with demonstrable chain of custody and data fidelity when migrating data.

Three Arcserve predictions for 2019

1. Public cloud adoption gets scaled back because of users facing unexpected and significant fees associated with the movement and recovery of data in public clouds. Users will reduce public cloud use for disaster recovery (DR) and instead, use hybrid cloud strategies and cloud service providers (CSPs) who can offer private cloud solutions with predictable cost models.

2. Data protection offerings will incorporate artificial intelligence (AI) to predict and avert unplanned downtime from physical disasters before they happen. DR processes will get automated, intelligently restoring the most frequently accessed, cross-functional or critical data first and proactively replicate it to the cloud before a downtime event occurs.

3. Self-managed disaster recovery as a service (DRaaS) will increase in prominence as it costs less than managed DRaaS. Channel partners will add more self-service options to support growing customer demand for contractually guaranteed recovery time and point objectives (RTOs/RPOs) and expanding their addressable market free of the responsibility of managing customer environments.

NetApp’s five  predictions for 2019

These predictions are in a blog which we have somewhat savagely summarised 

1. Most new AI development will use the cloud as a proving ground as there is a rapidly growing body of AI software and service tools there.

2. Internet of Things (IoT) edge processing must be local for real-time decision-making. IoT devices and applications – with built-in services such as data analysis and data reduction – will get better, faster and smarter about deciding what data requires immediate action, what data gets sent home to the core or to the cloud, and what data can be discarded.

3. With containerisation and “server-less” technologies, the trend toward abstraction of individual systems and services will drive IT architects to design for data and data processing and to build hybrid, multi-cloud data fabrics rather than just data centres. Decision makers will rely more and more on robust yet “invisible” data services that deliver data when and where it’s needed, wherever it lives, using predictive technologies and diagnostics. These services will look after the shuttling of containers and workloads to and from the most efficient service provider solutions for the job.

4. Hybrid, multi-cloud will be the default IT architecture for most larger organisations while others will choose the simplicity and consistency of a single cloud provider. Larger organisations will demand the flexibility, neutrality and cost-effectiveness of being able to move applications between clouds. They’ll leverage containers and data fabrics to break lock-in.

5. New container-based cloud orchestration technologies will enable better hybrid cloud application development. It means development will produce applications for both public and on-premises use cases: no more porting applications back and forth. This will make it easier and easier to move workloads to where data is being generated rather than what has traditionally been the other way around.

StorPool predicts six things

1. Hybrid cloud architectures will pick up the pace in 2019. But, for more demanding workloads and sensitive data, on-premise is still king. I.e. the future is hybrid: on-premise takes the lead in traditional workloads and cloud storage is the backup option; for new-age workloads, cloud is the natural first choice and on-prem is added when performance, scale or regulation demands kick-in.

2. Software-defined storage (SDS) will gain majority market share over the next 3 to 5 years, leaving SAN arrays with a minority share. SDS buyers want to reduce vendor lock-in, make significant cost optimisations and accelerate application performance.

3. Fibre Channel (FC) is becoming an obsolete technology and adds complexity in an already complex environment, being a separate storage-only component. In 2019, it makes sense to deploy a parallel 25G standard Ethernet network, instead of upgrading an existing Fibre Channel network. At scale, the cost of the Ethernet network is 3-5 per cent of the whole project and a fraction of cost of a Fibre Channel alternative.

4. We expect next-gen storage media to gain wider adoption in 2019. Its primary use-case will still be as cache in software-defined storage systems and database servers.

On a parallel track, Intel will release large capacity Optane-based NVDIMM devices, which they are promoting as a way to extend RAM to huge capacities, at low cost, through a process similar to swapping. The software stack to take full advantage of this new hardware capability will slowly come together in 2019.

There will be a tiny amount of proper niche usage of Persistent memory, where it is used for more than a very fast SSD.

5. ARM servers enter the data centre. However this will still be a slow pickup, as wider adoption requires the proliferation of a wider ecosystem. The two prime use-cases for ARM-based servers this year are throughput-driven, batch processing workloads in the datacenter and small compute clusters on “the edge.”

6. High core-count CPUs appear. Intel and AMD are on a race to provide high core-count CPUs for servers in the datacenter and in HPC. AMD announced its 64-cores EPYC 2 CPU with overhauled architecture (9 dies per socket vs EPYC’s 4 dies per socket). At the same time, Intel announced its Cascade Lake AP CPUs, which are essentially two Xeon Scalable dies on a single (rather large) chip, scaling up to 48 cores per socket. Both products represent a new level of per-socket compute density. Products will hit the market in 2019.

While good for the user, this is “business as usual” and not that exciting.

Shorts

Data sprawl controller and data-as-a-service provider Actifio said it had a very good 2018 year, surpassing the 3,500 mark in global customers across 38 countries. It recently won an OEM deal with IBM.

Commvault issued results from a conducted at Data Protection World Forum 2018 in London. While four in five data experts and IT professionals believe that the requirement to comply with stronger, more stringent data management regulations (like GDPR), will be a long-term benefit to their organisations, only one in five were fully confident in their business’ level of compliance with current data protection legislation.

Research house DRAMeXchange says NAND flash manufacturers are cutting CAPEX in 2019, aiming to moderate oversupply in the industry by limiting bit output growth. The total 2019 CAPEX in the global NAND flash industry is expected to be $22 billion, about 2 per cent YoY lower than in 2018.

GIGABYTE’s decentralised storage cluster, VirtualStor Extreme, invented with software-defined storage vendor Bigtera, is claimed to simplify storage management with its virtualised system architecture. It can integrate and manage existing storage systems (including the vast majority of storage types on the market such as SAN, NAS, Object, File) to provide flexibility in allocating and using existing storage resources.

GridGain Systems, which provides enterprise-grade in-memory computing based on Apache Ignite, has improved GridGain Cloud, its in-memory-computing-platform-as-a-service (imcPaaS), to include automatic disk-based backup persistence of the in-memory operational dataset. This ensures immediate data access if a cluster restart is ever required.

Quad-level cell (QLC or 4bits/cell) flash is the latest development in 3D NAND with Micron, Toshiba, Western Digital and others producing QLC SSDs. Taiwan-based LITE-ON is joining them with its own line of QLC SSDs coming in the second half of 2019. It thinks it can capture sales with these SSDs that would otherwise have gone to disk drives.

Micron, with its automotive-grade LPDDR4X memory devices, is working with Qualcomm Technologies and its Snapdragon Automotive Cockpit Platforms to develop products for next-generation in-vehicle cockpit compute systems for things like infotainment. 

QNAP has four AMD-powered NAS systems;

  • The TS-1677X is a Ryzen NAS with graphics processing to facilitate AI-oriented tasks.
  • The TS-2477XU-RP is a Ryzen-based rackmount NAS with up to 8 cores/16 threads and integrated dual 10GbitE SFP+ ports,
  • The TS-977XU is a HDD + SSD hybrid-structure AMD Ryzen 1U rackmount NAS with up to 4 cores/4 threads,
  • The low-end TS-963X is a quad-core AMD NAS with 10GBASE-T port and dedicated SSD slots for caching and tiering.

Starwind says its NVMe-oF Target is a protocol tailored to squeeze maximum performance out of NVMe devices and deliver close-to-zero latency for IOPS-hungry applications. By mapping each disk to the particular CPU core and expanding the command queue, you get microsecond-scale latency without overwhelming CPUs and build, probably, the fastest storage ever. Grab a white paper here.

IT infrastructure performance monitor, tester and simulator Virtual Instruments recorded its best year to date in 2018, with record bookings, nearly a 100 per cent growth rate in new customers, over 200 per cent growth in EMEA sales, and 125 per cent growth in channel-initiated business

Replicator WANdisco and IBM have jointly engineered an offering providing replication of IBM Db2 Big SQL data. Db2 Big SQL is a SQL engine for Hadoop with support for HDFS, RDBMS, NoSQL databases, object stores and WebHDFS data sources. It’s the first time WANdisco technology has been applied to SQL data.

Customers

Strava, the GPS tracking app and social network for athletes, is now using Snowflake Computing’s data warehouse in the cloud to find out which features customers use and how they want to use them. It has a 120 TB data warehouse, 13 trillion GPS data points, 15 million uploads/week and 1.5 billion analytics points ingested daily.

Mobileye will use Micron DRAM and NOR flash products in its fifth-generation EyeQ5 system-on-chip (SoC)-based EPM5 platform for fully autonomous driving (up to level 5; full autonomy.) Mobileye is developing its EyeQ5 SoC-based platform to serve as the central computer, performing vision and sensor fusion, as part of its effort to have fully autonomous driving vehicles on the road in 2020.  

People

On-line backup outfit Datto appointed a new CEO following on from founder Austin McChord who quit the CEO role after Vista Equity Partners bought Datto last year.

McChord stays on the board while interim CEO, Tim Weller, pictured left, who was the CFO, now gets confirmed as the CEO. He joined Datta in June 2017 and apparently played a big part in the merger of Datto and Autotask by Vista.

Nick Dyer has been appointed as a Worldwide Field CTO & Evangelist for HPE Nimble Storage. The role is is part Engineering, part Sales Engineer, part Technical Marketing and part Product Management. He’s part of a team which he says HPE decided to invest in in order to kickstart further growth of Nimble storage. HPE and Dyer reckon new coming technology will boost this growth;   Synchronous Replication availability, Cloud Volumes going international (UK in April/May!), Intel Optane “Memory Driven Flash”, StoreOnce integration and “a slew of cool stuff due in the coming months on the Nimble platform.”


Quantum dedupes DXi hardware range from 3 to 2 models

Quantum has refreshed its DXI deduplicating backup-to-storage array appliance line, replacing three hardware models with just two.

Two high-end models, the DXi6900 and DXi6900S (a DXI6900 with added SSDs to store metadata and speed internal operations), have been replaced with a single DXi9000 product.

The low-end DX4700 has been replaced with the DXi4800. The new products store more backup data and ingest it faster than their predecessors.

DXi products offers multi-protocol access: CIFS/NFS, VTL, OST, and VDMS, and are Integrated with all leading backup applications, with deeper integration with Veeam and Veritas NetBackup OST.

Old models

DXi6900  2U system Node, 2U Expansion node

  • 17TB – 510TB usable (pre-dedupe)
  • Up to 18TB/hour
  • Up to 32TB/hr with DXi Accent (backup server does dedupe before sending data to the DXi)
  • HDD – 4TB SED (Self-Encrypting Drives)
Quantum DXi front bezel.

DXi6900S – provided industry-best density at launch and faster performance than DXi6900 by leveraging SSDs for metadata operations. 2U system Node, 2U Expansion node

  • 34TB – 544TB usable 
  • Up to 24TB/hour
  • UP to 37TB/hr with DXi Accent
  • HDD – 8TB
  • 13 x 800GB SSD

DXi4700 – 2U system node, 2U expansion node

  • 5TB – 135TB usable
  • Up to 7.9TB/hour
  • Up to 16.9TB/hour with DXi Accent
  • HDD – 4TB

New products

DXi9000 – for enterprise data centres – 2U system node, 2U expansion node

  • 51TB to 1.02PB
  • Up to 24TB/hour 
  • Up to 37TB/hour with DXi Accent
  • HDD – 12TB SED
  • SSD – 16 x 960GB

The DXi9000 is the first such appliance to use 12TB disk drives, giving it a density advantage, and enabling it to store twice as much as the DXi6900S. Ingest speeds are the same as the DXi6900S.

DXi4800 – for small and remote sites – 2U system node, 2U expansion node

  • 8TB to 171TB usable
  • Up to 12TB/hour 
  • Up to 34TB/hour with DXi Accent
  • HDD – 4 and 8TB SED – better than DXi4700

DXi4800 ingest speed is much faster than the DXi4700.

Quantum’s announcement states: “With the addition of flash to the DXi4800 node, operations such as backup, restore, replication, and space reclamation are significantly accelerated to improve overall data management.”

However, the DXi4800 datasheet specifications do not list any SSDs. They are listed for the DXi9000 and were listed for the previous DXi6900S

Quantum tells us the DXi4800 uses “480GB of SSD, in the node, for metadata operations”.

Exagrid has a much faster high-end deduping appliance. Its  EX63000E product scales to a 2PB full backup in a single system with an ingest rate of over 400TB per hour.

Quantum’s DXi V-Series; virtual appliances using DXi software, with 1TB – 24TB of usable capacity, continues as before.

The new products are available now. Quantum has not released pricing information.

Apeiron throws new CEO in at the deep end

Apeiron has hired HPE veteran Chuck Smith as CEO. His predecessor company founder William Harrison has stepped aside take the CTO role.

Smith worked for two years with Compaq, which HP (now HPE) bought, and then 18 years with HPE. In his last role at the company, he ran the datacenter and hybrid cloud, business leaving in November 2017.

Ape Iron

Apeiron sells the ADS1000 NVMe flash array which can also use Optane (3D XPoint) drives for extra speed.

The host-array connectivity uses hardened layer 2 Ethernet with round trip array network access latency cut to <3 µS. The 2U array provides 20 million IOPS and 72GB/sec throughput.

Chuck Smith talking about HPE ProLiant servers on a YouTube video

Incoming CEO Smith has a job on his hands with this small NVMe storage array network startup. It faces significant, growing competition.

These include other NVMe fabric startups such as E8, Excelero, Pavilion and Vexata. More importantly, mainstream heavyweights storage suppliers are adopting NVMe fabric networking en masse.

Moreover, Smith must make the transition from senior exec at a $30bn rev oil tanker of a company – with all its internal support infrastructure – to running a small, feisty startup that must be able to turn on a dime. 

Apeiron needs to build out its sales, marketing and support infrastructure and strengthen its channel.  Smith can bring huge sales and sales channel experience and steady management to Apeiron and this may be just what the founder and investors want.

But it will cost. The company has raised less than $30m since its inception in 2013. Blocks & Files thinks more funding is necessary.

Amazon buys replicating CloudEndure for AWS

Amazon is buying CloudEndure, an Israeli startup which supplies replication-based, multi-cloud business continuity, migration and disaster recovery services.

CloudEndure was setup in 2012 in Tel Aviv and is now headquartered in New York, USA, with R&D in Israel.

The firm has raised around $18m in funding from VCs and strategic investors, including Dell Technologies Capital, VMware, Mitsui, Infosys, and Magma Venture Partners.

CloudEndure technology uses continuous block-level replication and automatic VM conversion to build an application copy in the target cloud –  AWS, Azure or Google Cloud Platform. This can be spun up quickly when needed. 

The company has OEM deals with Google Cloud (VM Migration Service), Cisco (CloudCenter Disaster Recovery and Migration) and Sungard Availability Services (Cloud Recovery.)

It’s also available on Azure Marketplace as a SaaS Subscription.

According to a  report, Amazon is paying $250m for CloudEndure and the deal is expected to close in a few days. That will give valuation cheer to UK-based competitor WANdisco which also uses replication to send production data to the cloud.