E8 Storage Archives - Storage Gaga

Storage Performance Considerations for AI Data Paths

By cfheoh | June 17, 2019 - 10:50 am |June 17, 2019 100Gigabit Ethernet, Algorithm, Analytics, API, Artificial Intelligence, Big Data, Cloud, Composable Infrastructure, Data, Data Fabric, Data Management, Data Privacy, Data Security, Digital Transformation, Drivescale, E8 Storage, Edge Computing, Elastifile, Excelero, Filesystems, High Performance Computing, Hyperconvergence, Industry 4.0, Infiniband, Intel, Liqid, Lustre, Machine Learning, Mellanox Technologies, NVMe, Object Storage, Performance Benchmark, Performance Caching, Quantum Corporation, RDMA, Software-defined Datacenter, Storage Optimization, Storage Tiering, ThinkParq, Vast Data, Virtualization, WekaIO

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The hype of Deep Learning (DL), Machine Learning (ML) and Artificial Intelligence (AI) has reached an unprecedented frenzy. Every infrastructure vendor from servers, to networking, to storage has a word to say or play about DL/ML/AI. This prompted me to explore this hyped ecosystem from a storage perspective, notably from a storage performance requirement point-of-view.

One question on my mind

There are plenty of questions on my mind. One stood out and that is related to storage performance requirements.

Reading and learning from one storage technology vendor to another, the context of everyone’s play against their competitors seems to be “They are archaic, they are legacy. Our architecture is built from ground up, modern, NVMe-enabled“. And there are more juxtaposing, but you get the picture – “We are better, no doubt“.

Are the data patterns and behaviours of AI different? How do they affect the storage design as the data moves through the workflow, the data paths and the lifecycle of the AI ecosystem?

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Sexy HPC storage is all the rage

By cfheoh | November 26, 2018 - 10:44 am |November 26, 2018 100Gigabit Ethernet, Analytics, API, Artificial Intelligence, BeeGFS, CIFS, Clusters, Data Management, Deep Learning, DellEMC, Disks, E8 Storage, EMC, Excelero, Filesystems, Hadoop Clusters, High Performance Computing, Hyperconvergence, IBM, Infiniband, Intel, Linux, Lustre, Machine Learning, Mellanox, Memory Cloud, NAS, NetApp, NFS, Panasas, Performance Benchmark, Performance Caching, Pure Storage, RDMA, Scale-out architecture, SMB, Software-defined Datacenter, Storage Field Day, Tech Field Day, ThinkParq, WekaIO

HPC is sexy

There is no denying it. HPC is sexy. HPC Storage is just as sexy.

Looking at the latest buzz from Super Computing Conference 2018 which happened in Dallas 2 weeks ago, the number of storage related vendors participating was staggering. Panasas, Weka.io, Excelero, BeeGFS, are the ones that I know because I got friends posting their highlights. Then there are the perennial vendors like IBM, Dell, HPE, NetApp, Huawei, Supermicro, and so many more. A quick check on the SC18 website showed that there were 391 exhibitors on the floor.

And this is driven by the unrelentless demand for higher and higher performance of computing, and along with it, the demands for faster and faster storage performance. Commercialization of Artificial Intelligence (AI), Deep Learning (DL) and newer applications and workloads together with the traditional HPC workloads are driving these ever increasing requirements. However, most enterprise storage platforms were not designed to meet the demands of these new generation of applications and workloads, as many have been led to believe. Why so?

I had a couple of conversations with a few well known vendors around the topic of HPC Storage. And several responses thrown back were to put Flash and NVMe to solve the high demands of HPC storage performance. In my mind, these responses were too trivial, too irresponsible. So I wanted to write this blog to share my views on HPC storage, and not just about its performance.

The HPC lines are blurring

I picked up this video (below) a few days ago. It was insideHPC Rich Brueckner interview with Dr. Goh Eng Lim, HPE CTO and renowned HPC expert about the convergence of both traditional and commercial HPC applications and workloads.

I liked the conversation in the video because it addressed the 2 different approaches. And I welcomed Dr. Goh’s invitation to the Commercial HPC community to work with the Traditional HPC vendors to help push the envelope towards Exascale SuperComputing.

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Is Pure Play Storage good?

By cfheoh | November 13, 2018 - 3:16 am |November 15, 2018 Acquisition, Analytics, Appliance, Artificial Intelligence, Cloud, Cloudian, Cohesity, Data Management, Data Protection, Data Security, Datera, Datrium, Deep Learning, Dell, DellEMC, Drivescale, E8 Storage, EMC, Excelero, Flash, Gartner, Hammerspace, HDS, Hedvig, Hitachi Vantara, Huawei, IBM, Kaminario, NetApp, Nimble Storage, Nutanix, NVMe, Panasas, Promise Technology, Pure Storage, Ryuusi, Scale-out architecture, Server SAN, Software Defined Storage, Software-defined Datacenter, Solid State Devices, Storage Field Day, Storage Market Share, Tech Field Day, Tegile, Tintri, Unified Storage, Violin Memory, WekaIO, XtremIO

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I post storage and cloud related articles to my unofficial SNIA Malaysia Facebook community (you are welcomed to join) every day. It is a community I started over 9 years ago, and there are active live banters of the posts of the day. Casual, personal were the original reasons why I started the community on Facebook rather than on LinkedIn, and I have been curating it religiously for the longest time.

The Big 5 of Storage (it was Big 6 before this)

Looking back 8-9 years ago, the storage vendor landscape of today has not changed much. The Big 5 hegemony is still there, still dominating the Gartner Magic Quadrant for Enterprise and Mid-end Arrays, and is still there in the All-Flash quadrant as well, albeit the presence of Pure Storage in that market.

The Big 5 of today – Dell EMC, NetApp, HPE, IBM and Hitachi Vantara – were the Big 6 of 2009-2010, consisting of EMC, NetApp, Dell, HP, IBM and Hitachi Data Systems. The All-Flash, or Gartner calls it Solid State Arrays (SSA) market was still an afterthought, and Pure Storage was just founded. Pure Storage did not appear in my radar until 2 years later when I blogged about Pure Storage’s presence in the market.

Here’s a look at the Gartner Magic Quadrant for 2010:

We see Pure Play Storage vendors in the likes of EMC, NetApp, Hitachi Data Systems (before they adopted the UCP into their foray), 3PAR, Compellent, Pillar Data Systems, BlueArc, Xiotech, Nexsan, DDN and Infortrend. And when we compare that to the 2017 Magic Quadrant (I have not seen the 2018 one yet) below:

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The power of E8

By cfheoh | November 21, 2017 - 4:22 pm |November 21, 2017 Analytics, API, Big Data, Data Availability, Data Fabric, Data Management, E8 Storage, Filesystems, High Performance Computing, Hyperconvergence, Infiniband, NVMe, PCIe, Performance Benchmark, Performance Caching, RDMA, Scale-out architecture, Server SAN, Software Defined Storage, Solid State Devices, Storage Optimization

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[Preamble: I was a delegate of Storage Field Day 14 from Nov 8-10, 2017. My expenses, travel and accommodation were paid for by GestaltIT, the organizer and I was not obligated to blog or promote the technologies presented at this event. The content of this blog is of my own opinions and views]

E8 Storage technology update at Storage Field Day 14 was impressive. Out of the several next generation NVMe storage technologies I have explored so far, E8 came out as the most complete. It was no surprise that they won the “Best of Show” in the Flash Memory Summits for the “Most Innovative Flash Memory Technology” in 2016 and “Most Innovative Flash Memory Enterprise Business Application” for 2017.

Who is E8 Storage?

They came out of stealth in August 2016 and have been making waves with very impressive stats. When E8 was announced, their numbers were more than 10 million IOPS, with 100µsecs for reads and 40µsecs for writes. And in the SFD14 demo, they reached and past the 10 million IOPS numbers.

The design philosophy of E8 Storage is different than the traditional dual controller scale-up storage architecture design or the multi-node scale-out cluster design. In fact, from a 30,000 feet view, it is quite similar to a “SAN-client” design advocated by Lustre, leveraging a very high throughput, low latency network.

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Category Archives: E8 Storage