Storage Performance Considerations for AI Data Paths

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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Figuring out storage for Kubernetes and containers

Oops! I forgot about you!

To me, containers and container orchestration (CO) engines such as Kubernetes, Mesos, Docker Swarm are fantastic. They scale effortlessly and are truly designed for cloud native applications (CNA).

But one thing irks me. Storage management for containers and COs. It was as if when they designed and constructed containers and the containers orchestration (CO) engines, they forgot about the considerations of storage and storage management. At least the persistent part of storage.

Over a year ago, I was in two minds about persistent storage, especially when it comes to the transient nature of microservices which was so prevalent and were inundating the cloud native applications landscape. I was searching for answers in my blog. The decentralization of microservices in containers means mass deployment at the edge, but to have the pre-processed and post-processed data stick to the persistent storage at the edge device is a challenge. The operative word here is “STICK”.

Two different worlds

Containers were initially designed and built for lightweight applications such as microservices. The runtime, libraries, configuration files and dependencies are all in one package. They were meant to do simple tasks quickly and scales to thousands easily. They could be brought up and brought down in little time and did not have to bother about the persistent data stored by the host. The state of the containers were also not important to the application tasks at hand.

Today containers like Docker have matured to run enterprise applications and the state of the container is important. The applications must know the state and the health of the container. The container could be in online mode, online but not accepting data mode, suspended mode, paused mode, interrupted mode, quiesced mode or halted mode. Each mode or state of the container is important to the running applications and the container can easily brought up or down in an instance of a command. The stateful nature of the containers and applications is critical for the business. The same situation applies to container orchestration engines such as Kubernetes.

Container and Kubernetes Storage

Docker provides 3 methods to local storage. In the diagram below, it describes:

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WekaIO controls their performance destiny

[Preamble: I have been invited by GestaltIT as a delegate to their Tech Field Day for Storage Field Day 18 from Feb 27-Mar 1, 2019 in the Silicon Valley USA. My expenses, travel and accommodation were covered by GestaltIT, the organizer and I was not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

I was first introduced to WekaIO back in Storage Field Day 15. I did not blog about them back then, but I have followed their progress quite attentively throughout 2018. 2 Storage Field Days and a year later, they were back for Storage Field Day 18 with a new CTO, Andy Watson, and several performance benchmark records.

Blowout year

2018 was a blowout year for WekaIO. They have experienced over 400% growth, placed #1 in the Virtual Institute IO-500 10-node performance challenge, and also became #1 in the SPEC SFS 2014 performance and latency benchmark. (Note: This record was broken by NetApp a few days later but at a higher cost per client)

The Virtual Institute for I/O IO-500 10-node performance challenge was particularly interesting, because it pitted WekaIO against Oak Ridge National Lab (ORNL) Summit supercomputer, and WekaIO won. Details of the challenge were listed in Blocks and Files and WekaIO Matrix Filesystem became the fastest parallel file system in the world to date.

Control, control and control

I studied WekaIO’s architecture prior to this Field Day. And I spent quite a bit of time digesting and understanding their data paths, I/O paths and control paths, in particular, the diagram below:

Starting from the top right corner of the diagram, applications on the Linux client (running Weka Client software) and it presents to the Linux client as a POSIX-compliant file system. Through the network, the Linux client interacts with the WekaIO kernel-based VFS (virtual file system) driver which coordinates the Front End (grey box in upper right corner) to the Linux client. Other client-based protocols such as NFS, SMB, S3 and HDFS are also supported. The Front End then interacts with the NIC (which can be 10/100G Ethernet, Infiniband, and NVMeoF) through SR-IOV (single root IO virtualization), bypassing the Linux kernel for maximum throughput. This is with WekaIO’s own networking stack in user space. Continue reading

StorPool – Block storage managed well

[Preamble: I have been invited by GestaltIT as a delegate to their Tech Field Day for Storage Field Day 18 from Feb 27-Mar 1, 2019 in the Silicon Valley USA. My expenses, travel and accommodation were covered by GestaltIT, the organizer and I was not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

Storage technology is complex. Storage infrastructure and data management operations are not trivial, despite what the hyperscalers like Amazon Web Services and Microsoft Azure would like you to think. As the adoption of cloud infrastructure services grow, the small and medium businesses/enterprises (SMB/SME) are usually left to their own devices to manage the virtual storage infrastructure. Cloud Service Providers (CSPs) addressing the SMB/SME market are looking for easier, worry-free, software-defined storage to elevate their value to their customers.

Managed high performance block storage

Enter StorPool.

StorPool is a scale-out block storage technology, capable of delivering 1 million+ IOPS with sub-milliseconds response times. As described by fellow delegate, Ray Lucchesi in his recent blog, they were able to achieve these impressive performance numbers in their demo, without the high throughput RDMA network or the storage class memory of Intel Optane. Continue reading

The Malaysian Openstack storage conundrum

The Openstack blippings on my radar have ratcheted up this year. I have been asked to put together the IaaS design several times, either with the flavours of RedHat or Ubuntu, and it’s a good thing to see the Openstack interest level going up in the Malaysian IT scene. Coming into its 8th year, Openstack has become a mature platform but in the storage projects of Openstack, my observations tell me that these storage-related projects are not as well known as we speak.

I was one of the speakers at the Openstack Malaysia 8th Summit over a month ago. I started my talk with question – “Can anyone name the 4 Openstack storage projects?“. The response from the floor was “Swift, Cinder, Ceph and … (nobody knew the 4th one)” It took me by surprise when the floor almost univocally agreed that Ceph is one of the Openstack projects but we know that Ceph isn’t one. Ceph? An Openstack storage project?

Besides Swift, Cinder, there is Glance (depending on how you look at it) and the least known .. Manila.

I have also been following on many Openstack Malaysia discussions and discussion groups for a while. That Ceph response showed the lack of awareness and knowledge of the Openstack storage projects among the Malaysian IT crowd, and it was a difficult issue to tackle. The storage conundrum continues to perplex me because many whom I have spoken to seemed to avoid talking about storage and viewing it like a dark art or some voodoo thingy.

I view storage as the cornerstone of the 3 infrastructure pillars  – compute, network and storage – of Openstack or any software-defined infrastructure stack for that matter. So it is important to get an understanding the Openstack storage projects, especially Cinder.

Cinder is the abstraction layer that gives management and control to block storage beneath it. In a nutshell, it allows Openstack VMs and applications consume block storage in a consistent and secure way, regardless of the storage infrastructure or technology beneath it. This is achieved through the cinder-volume service which is a driver most storage vendors integrate with (as shown in the diagram below).

Diagram in slides is from Mirantis found at https://www.slideshare.net/mirantis/openstack-architecture-43160012

Diagram in slides is from Mirantis found at https://www.slideshare.net/mirantis/openstack-architecture-43160012

Cinder-volume together with cinder-api, and cinder-scheduler, form the Block Storage Services for Openstack. There is another service, cinder-backup which integrates with Openstack Swift but in my last check, this service is not as popular as cinder-volume, which is widely supported by many storage vendors with both Fibre Channel and iSCSi implementations, and in a few vendors, with NFS and SMB as well. Continue reading

Cohesity SpanFS – a foundational shift

[Preamble: I was a delegate of Storage Field Day 15 from Mar 7-9, 2018. 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]

Cohesity SpanFS impressed me. Their filesystem was designed from ground up to meet the demands of the voluminous cloud-scale data, and yes, the sheer magnitude of data everywhere needs to be managed.

We all know that primary data is always the more important piece of data landscape but there is a growing need to address the secondary data segment as well.

Like a floating iceberg, the piece that is sticking out is the more important primary data but the larger piece beneath the surface of the water, which is the secondary data, is becoming more valuable. Applications such as file shares, archiving, backup, test and development, and analytics and insights are maturing as the foundational data management frameworks and fast becoming the bedrock of businesses.

The ability of businesses to bounce back after a disaster; the relentless testing of large data sets to develop new competitive advantage for businesses; the affirmations and the insights of analyzing data to reduce risks in decision making; all these are the powerful back engine applicability that thrust businesses forward. Even the ability to search for the right information in a sea of data for regulatory and compliance reasons is part of the organization’s data management application.

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Of Object Storage, Filesystems and Multi-Cloud

Data storage silos everywhere. The early clarion call was to eliminate IT data storage silos by moving to the cloud. Fast forward to the present. Data storage silos are still everywhere, but this time, they are in the clouds. I blogged about this.

Object Storage was all the rage when it first started. AWS, with its S3 (Simple Storage Service) offering, started the cloud storage frenzy. Highly available, globally distributed, simple to access, and fitted superbly into the entire AWS ecosystem. Quickly, a smorgasbord of S3-compatible, S3-like object-based storage emerged. OpenStack Swift, HDS HCP, EMC Atmos, Cleversafe (which became IBM SpectrumScale), Inktank Ceph (which became RedHat Ceph), Bycast (acquired by NetApp to be StorageGrid), Quantum Lattus, Amplidata, and many more. For a period of a few years prior, it looked to me that the popularity of object storage with an S3 compatible front has overtaken distributed file systems.

What’s not to like? Object storage are distributed, they are metadata rich (at a certain structural level), they are immutable (hence secure from a certain point of view), and some even claim self-healing (depending on data protection policies). But one thing that object storage rarely touted dominance was high performance I/O. There were some cases, but they were either fronted by a file system (eg. NFSv4.1 with pNFS extensions), or using some host-based, SAN-client agent (eg. StorNext or Intel Lustre). Object-based storage, in its native form, has not been positioned as high performance I/O storage.

A few weeks ago, I read an article from Storage Soup, Dave Raffo. When I read it, it felt oxymoronic. SwiftStack was just nominated as a visionary in the Gartner Magic Quadrant for Distributed File Systems and Object Storage. But according to Dave’s article, Swiftstack did not want to be “associated” with object storage that much, even though Swiftstack’s technology underpinning was all object storage. Strange.

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DellEMC SC progressing well

[Preamble: I was a delegate of Storage Field Day 14. 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]

I haven’t had a preview of the Compellent technology for a long time. My buddies at Impact Business Solutions were the first to introduce the Compellent technology called Data Progression to the local Malaysian market and I was invited to a preview back then. Around the same time, I also recalled another rather similar preview invitation by PTC Singapore for the 3PAR technology called Adaptive Provisioning (it is called Adaptive Optimization now).

Storage tiering was on the rise in the 2009-2010 years. Both Compellent and 3PAR were neck and neck leading the conversation and mind share of storage tiering, and IBM easyTIER and EMC FAST (Fully Automated Storage Tiering) were nowhere to be seen or heard. Vividly, the Compellent Data Progression technology was much more elegant compared to the 3PAR technology. While both intelligent storage tiering technologies were equally good, I took that the 3PAR founders were ex-Sun Microsystems folks, and Unix folks sucked at UX. In this case, Compellent’s Data Progression was a definitely a leg up better than 3PAR.

History aside, this week I have the chance to get a new preview of the Compellent technology again. Compellent was now rebranded as the SC series and was positioned as the mid-range storage arrays of DellEMC. And together with the other Storage Field Day 14 delegates, I have the pleasure to experience the latest SC Data Progression technology update, as well their latest SC All-Flash.

In Data Progression, one interesting feature which caught my attention was the RAID Tiering. This was a dynamic auto expand and auto contract set of RAID tiersRAID 10 and RAID 5/6 in the Fast Tier and RAID 5/6 in the Lower Tier. RAID 10, RAID 5 and RAID 6 on the same set of drives (including SSDs), and depending on the “hotness” of the data, the location of the data blocks switched between the several RAID tiers in the Fast Tier. Over a longer period, the data blocks would relocate transparently to the Capacity Tier from the Fast Tier.

The Data Progression technology is extremely efficient. The movement of the data between the RAID Tiers and between the Performance/Capacity Tiers are in pages instead of blocks, making the write penalty and bandwidth to a negligible minimum.

The Storage Field Day 14 delegates were also privileged to be the first to get into the deep dive of the new All-Flash SC, just days of the announcement of the All-Flash SC. The All Flash SC redefines and refines the Data Progression to the next level. Among the new optimization, NAND Flash in the SC (both SLCs and MLCs, read-intensive and write-intensive) set the Data Progression default page size from 2MB to 512KB. These smaller 512KB pages enabled reduced bandwidth for tiering between the write-intensive and the read-intensive tier.

I didn’t get the latest SC family photos yet, but I managed to grab a screenshot of the announcement from The Register of the new DellEMC SC Series.

I was very encouraged with the DellEMC Midrange Storage presentation. Besides giving us a fantastic deep dive about the DellEMC SC All-Flash Storage, I was also very impressed by the candid and straightforward attitude of the team, led by their VP of Product Management, Pierluca Chiodelli. An EMC veteran, he was taking up the hard questions onslaught by the SFD14 delegate like a pro. His team’s demeanour was critical in instilling confidence and trust in how the bloggers and the analysts viewed Dell EMC merger, and how the SC and the Unity series would pan out in the technology roadmap.

Unlike the fiasco I went through with the DellEMC Forum 2017 in Malaysia, where I was disturbed with 3 calls in 3 consecutive days by DellEMC Malaysia, I was left with a profound respect for this DellEMC Storage team. They strongly supported their position within the DellEMC storage universe, and imparted their confidence in their technology solution in the marketplace.

Without a doubt, in my point of view, this DellEMC Mid-Range Storage team was the best I have enjoyed in Storage Field Day 14. Thank you.