Information flows everywhere – part 1

Read an article today from Scientific American (Sewage is helping cities flush out the opioid crisis) about how using chemical analysis of wastewater can be used to assess the extent of the opioid crisis in their city.

Wastewater information highway

There’s a lab at ASU (Arizona State University) that chemically analyzes samples of wastewater to determine the amount of drugs that a city’s population excretes. They can provide a near real-time assessment of the proportion of drugs in city sewage and thereby, in a city’s population.

The problem with public drug use surveys and hospital data gathering is that they take time.  Moreover, surveys and hospital data gathering typically come long after drugs problem have become a serious problem in a city’s population.

Wastewater sample drug analysis can be done in a matter of days and can be redone as often as needed. Such data could be used to track intervention activities and see if they have a real impact (positive or negative) on drug use in a population.

Neighborhood health

In addition, by sampling sewage at a neighborhood level, one can gain an assessment of drug problems at any sub-division of a city that’s needed.

The above article talks about an MIT program with Cary, NC (from Biobot.io)  that is designing robots to traverse sewer pipes and analyze wastewater chemical makeup in real time, reporting this back to ground stations around the city.

With such an approach, one could almost zero in (depending on sewer pipe networks) on any neighborhood in a city, target specific interventions at that level and measure impact in (digestion delayed) real time. Doing so, cities or states for that matter, could  experiment with different interventions on a neighborhood by neighborhood basis and gain statistical evidence on drug problem intervention effectiveness.

But, you can analyze wastewater for any number of variables, such as viruses, bacteria, enzymes, etc. Any of which can lead to a better understanding of a population’s health.

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Two things I want to leave you with:

First, public health has had a major impact on human health and has doubled our lifespan in 200 years. All modern cities have water treatment plants today to insure water quality and thereby, have reduced the incidence of cholera and other waterborne epidemics in their cities. Wastewater analysis has the potential for significant improvements in population health monitoring. Just like water treatment, wastewater analysis will someday become common public health practice in modern cities throughout the world.

Second, I was at a conference this week which presented a slide that there was no cold data anymore (Pure//Accelerate 2018). This was in reference to  re-analyzing old, cold data can often lead to insights and process improvements that were not obvious at first glance.

But it’s not just data anymore. Any activity done by man needs to be analyzed for (inherent & invisible) information flows that could be extracted to make the world a better place.

Photo Credit(s):

NetApp’s new NVMeoF/FC AFF & Cloud Data Volumes for every cloud

We attended a NetApp analyst event in their CA HQ last week and they had some interesting announcements as well other information to share. 1st up new faster ONTAP storage.

NVMeoF AFF

NetApp announced this week that their latest generation AFF (All Flash FAS) systems will support FC NVMeoF. We asked if this was just for NVMe SSDs or did it apply to all AFF media. The answer was it’s just another host interface which the customer can license for NVMe SSDs (available only on AFF F800) or SAS SSDs (A700S, A700, and A300). The only AFF not supporting the new host interface is their lowend AFF A220.

As for which NVMeoF, they only support FC at the moment, and it’s our belief that the FC NVMeoF spec is most well defined these days and the FC switch hardware (Brocade-Broadcom since Gen 5, now shipping Gen 6, Cisco not sure) already has NVMeoF support.

NetApp also mentioned support for 100GbE (A800 & A700S only) and 32Gbs FC hardware (all AFF systems but A220). So, presumably they offer NVMeoF for both 32Gbps and 16Gbps FC.

No word on when this will be available for Ethernet FCoE or iSCSI (iNVMe?) but with all the major storage vendors bar one, moving to NVMe SSDs it’s only a matter of time before they also support Ethernet NVMeoF.

As for AFF NVMeoF performance, the answer wasn’t entirely satisfactory. The indication was that the interface reduced response time by 10 usecs or so for NVMe SSDs over SAS SSDs. But I didn’t see any other performance information to substantiate that.

We did see on their AFF datasheet that with NVMe SSDs and NVMeoF FC, the AFF A800 response time was sub 200usec with throughput of 300GB/s (in a 24 node cluster, 12 HA pairs). This means they add only about 100usec for ONTAP data services, a decent trade off from our perspective. Later in their datasheet they say the A800 is capable of 1.3M IOPS and sub-500usec latencies. Unsure why they quoted both numbers.

Cloud Data Volumes

NetApp is taking storage to the cloud. They just announced that NetApp Cloud Data Volumes will be available as a native service under Google Cloud Platform (GCP). NetApp Cloud Data Volume is a storage-as-a-service offering that provides on demand ONTAP file services in the cloud.

For GCP,  both Google and NetApp will be offering the service. Dianne Green, GCP VP said Cloud Data Volumes are a bit like Kubernetes, disruption without disrupting. Customers can easily migrate their onprem file based applications to the cloud without having to worry about the performance of their data or data protection for that matter.

Getting the data there is another matter, but NetApp has other services like CloudSync and someday (maybe for Cloud Data Volumes), SnapMirror, which can help customers move data to and from the cloud.

Currently Cloud Data Volumes are in public preview as an Microsoft Azure Enterprise NFS (and SMB) service. It’s also in beta (I think) in AWS marketplace. And availability on GCP is still restricted. There’s a lot of emphasis at NetApp events on Cloud Data Volumes given its current status on public cloud providers but we think they are trying to gain some experience before they roll it out to the rest of the world.

However,  Jean English, NetApp CMO mentioned that NetApp’s Cloud Data Service business unit has over 1800 customers and currently supports a multi-PB storage footprint in various clouds. Note, this is not just Cloud Data Volumes but comprises all NetApp Cloud Data Services, which includes ONTAP Cloud, NPS, CloudSync, AltaVault, etc. Nonetheless, it’s an impressive indicator of just how far they have come in applying their storage magic to the public cloud in a short time. The hyperscalers (read public cloud providers) say NetApp is 2 or more years ahead of all the other competition and from what we can see, it’s true.

One of the key differentiators between NetApp Cloud Data Volumes and ONTAP Cloud is performance SLAs. Cloud Data Volume customers can select and purchase a specified performance SLA. We believe it comes at three levels and is normally purchased on a pay as you go, consumption based, service offering. However, it’s also available to be billed periodically, other purchase options may be available as well.

When asked what storage was behind the service, the only thing NetApp would confirm was that it was ONTAP storage, present in public cloud data centers in various regions. So Cloud Data Volumes is available in only specific regions but I would expect that to expand over time.

Data Visualization Center

They also christened their new Data Visualization Center (DVC) and we had a multi-course meal at the Bistro at the center. The DVC had a wrap around, 1.5 floor tall screen which showed some of NetApp customer success stories. Inside the screen was a more immersive setting and there was plenty of VR equipment in work spaces alongside customer conference rooms.

Full Disclosure: NetApp paid for all our travel, hotel and food during the analyst event and gave us all Google Home Minis as going away presents and NetApp is a long time customer of my firm.

Scratch file use in HPC @ORNL, a statistical analysis

Attended SC17 (Supercomputing Conference) this past week and I received a copy of the accompanying research proceedings. There are a number of interesting papers in the research and I came across one, Scientific User Behavior and Data Sharing Trends in a Peta Scale File System by Seung-Hwan Lim, et al from Oak Ridge National Laboratory (ORNL) and the use of files at the Oak Ridge Leadership Computing Facility (OLCF) which was very interesting.

The paper statistically describes the use of a Scratch files in a multi PB file system (Lustre) at OLCF from January 2015 to August 2016. The OLCF supports over 32PB of storage, has a peak aggregate of over 1TB/s and Spider II (current Lustre file system) consists of 288 Lustre Object Storage Servers, all interconnected and connected to all the supercomputing cluster of  servers via an InfiniBand network. Spider II supports all scratch storage requirements for active/queued jobs for the Titan (#4 in Top 500 [super computer clusters worldwide] list) and other clusters at ORNL.

ORNL uses an HPSS (High Performance Storage System) archive for permanent storage but uses the Spider II file system for all scratch files generated and used during supercomputing applications.  ORNL is expecting Spider III (2018-2023) to host 10 billion files.

Scratch files are purged from Spider II after 90 days of no access.The paper is based on metadata analysis captured during scratch purging process for 500 days of access.

The paper displays a number of statistics and metrics on the use of Spider II:

  • Less than 3% of projects have a directory depth >15, the maximum directory depth was recorded at 432, with most projects having a shallow (<10) directory depth.
  • A project typically has 10X the files that a specific researcher has and a median file count/researcher is 2000 files with a median project having 20,000 files.
  • Storage system performance is actively managed by many projects. For instance, 20 out of 35 science domains manually managed their Lustre cluster configuration to improve throughput.
  • File count continues to grow and reached a peak of 1B files during the time being analyzed.
  • On average only 3% of files were accessed readonly, 10% of files updated (read-write) and 76% of files were untouched during a week period. However, median and maximum file age was 138 and 214 days respectively, which means that these scratch files can continue to be accessed over the course of 200+ days.

There was more information in the paper but one item missing is statistics on scratch file size distribution a concern.

Nonetheless, in paints an interesting picture of scratch file use in HPC application/supercluster environments today.

Comments?

Axellio, next gen, IO intensive server for RT analytics by X-IO Technologies

We were at X-IO Technologies last week for SFD13 in Colorado Springs talking with the team and they showed us their new IO and storage intensive server, the Axellio. They want to sell Axellio to customers that need extreme IOPS, very high bandwidth, and large storage requirements. Videos of X-IO’s sessions at SFD13 are available here.

The hardware

Axellio comes in 2U appliance with two server nodes. Each server supports  2 sockets of Intel E5-26xx v4 CPUs (4 sockets total) supporting from 16 to 88 cores. Each server node can be configured with up to 1TB of DRAM or it also supports NVDIMMs.

There are two key differentiators to Axellio:

  1. The FabricExpress™, a PCIe based interconnect which allows both server nodes to access dual-ported,  2.5″ NVMe SSDs; and
  2. Dense drive trays, the Axellio supports up to 72 (6 trays with 12 drives each) 2.5″ NVMe SSDs offering up to 460TB of raw NVMe flash using 6.4TB NVMe SSDs. Higher capacity NVMe SSDS available soon will increase Axellio capacity to 1PB of raw NVMe flash.

They also probably spent a lot of time on packaging, cooling and power in order to make Axellio a reliable solution for edge computing. We asked if it was NEBs compliant and they told us not yet but they are working on it.

Axellio can also be configured to replace 2 drive trays with 2 processor offload modules such as 2x Intel Phi CPU extensions for parallel compute, 2X Nvidia K2 GPU modules for high end video or VDI processing or 2X Nvidia P100 Tesla modules for machine learning processing. Probably anything that fits into Axellio’s power, cooling and PCIe bus lane limitations would also probably work here.

At the frontend of the appliance there are 1x16PCIe lanes of server retained for networking that can support off the shelf NICs/HCAs/HBAs with HHHL or FHHL cards for Ethernet, Infiniband or FC access to the Axellio. This provides up to 2x100GbE per server node of network access.

Performance of Axellio

With Axellio using all NVMe SSDs, we expect high IO performance. Further, they are measuring IO performance from internal to the CPUs on the Axellio server nodes. X-IO says the Axellio can hit >12Million IO/sec with at 35µsec latencies with 72 NVMe SSDs.

Lab testing detailed in the chart above shows IO rates for an Axellio appliance with 48 NVMe SSDs. With that configuration the Axellio can do 7.8M 4KB random write IOPS at 90µsec average response times and 8.6M 4KB random read IOPS at 164µsec latencies. Don’t know why reads would take longer than writes in Axellio, but they are doing 10% more of them.

Furthermore, the difference between read and write IOP rates aren’t close to what we have seen with other AFAs. Typically, maximum write IOPs are much less than read IOPs. Why Axellio’s read and write IOP rates are so close to one another (~10%) is a significant mystery.

As for IO bandwitdh, Axellio it supports up to 60GB/sec sustained and in the 48 drive lax testing it generated 30.5GB/sec for random 4KB writes and 33.7GB/sec for random 4KB reads. Again much closer together than what we have seen for other AFAs.

Also noteworthy, given PCIe’s bi-directional capabilities, X-IO said that there’s no reason that the system couldn’t be doing a mixed IO workload of both random reads and writes at similar rates. Although, they didn’t present any test data to substantiate that claim.

Markets for Axellio

They really didn’t talk about the software for Axellio. We would guess this is up to the customer/vertical that uses it.

Aside from the obvious use case as a X-IO’s next generation ISE storage appliance, Axellio could easily be used as an edge processor for a massive fabric of IoT devices, analytics processor for large RT streaming data, and deep packet capture and analysis processing for cyber security/intelligence gathering, etc. X-IO seems to be focusing their current efforts on attacking these verticals and others with similar processing requirements.

X-IO Technologies’ sessions at SFD13

Other sessions at X-IO include: Richard Lary, CTO X-IO Technologies gave a very interesting presentation on an mathematically optimized way to do data dedupe (caution some math involved); Bill Miller, CEO X-IO Technologies presented on edge computing’s new requirements and Gavin McLaughlin, Strategy & Communications talked about X-IO’s history and new approach to take the company into more profitable business.

Again all the videos are available online (see link above). We were very impressed with Richard’s dedupe session and haven’t heard as much about bloom filters, since Andy Warfield, CTO and Co-founder Coho Data, talked at SFD8.

For more information, other SFD13 blogger posts on X-IO’s sessions:

Full Disclosure

X-IO paid for our presence at their sessions and they provided each blogger a shirt, lunch and a USB stick with their presentations on it.

 

There’s a new cluster filesystem on the block, Elastifile

At SFD12 last month we talked with the team from Elastifile. They are a new startup out of Israel working on a better cluster file system.

Elastifile was designed to support 1000s of nodes, 100,000 of users/client and 1000s of data containers (file systems/mount points), together with an infinite (64 bit) number of files and directories and up to Exabytes (10**18) in capacity. They also offer a 100% SSD file store capability. I encourage you to view the videos of their presentations at SFD12 to learn more.

Elastifile features

Elastifile supports data compression and optionally deduplication with NAND/Flash (e. g., low-/high-endurance) storage tiering, cloud storage tiering and multi-site storage. They also provide NFSv3/v4, SMB, AWS S3 and HDFS as native access protocols for their file storage.

They also offer non-disruptive hardware/software upgrades, n-way (2- or 3-way) data and metadata redundancy, self-healing capabilities, snapshots, and synchronous/asynchronous data replication or mirroring. Further, they provide multi-tenancy and QoS support.

Elastifile can be used in hyper converged mode as well as a dedicated storage server mode. For backend storage, they support heterogeneous, physical (block, I think?) storage systems as well as direct access storage in cluster nodes

Internals matter

Elastifile’s architecture supports accessor, owner and data nodes. But these can all be colocated on the same server or segregated across different servers.

Owner nodes, own all the metadata objects for a file or directory and caches the metadata working set in i’s memory. Ownership file or directory metadata may change in the case of hardware failures.

Elastifile supports a dynamic write data path, which means they determine, in real time, where to write file data rather than having the data locations identified before hand. They call this distributed write anywhere semantics.

Notably they don’t do data caching (with NVMe it doesn’t make sense) however, as noted above, they do use metadata caching

Internally, Elastifile uses variable length objects for both file data and metadata.

  • File data is composed of three object types: a file metadata (FileMD) object, mapping data objects, and file data objects. FileMD’s hold the normal file metadata (name, file size, create, access & modify ToDs, etc.) as well as pointing to all the Mapping Object (OIDs). Mapping objects exist for each 0.5MB of file data and consist of a 128 element table, each element mapping 4KB of file address space to a data object (OID). Each  data object holds the 4KB of compressed file data and journal log entries.
  • Director metadata is composed of directory metadata (DirMD) object and Directory listing objects. Directory listing objects maps file/directory names to FileMD or DirMD OIDs. Directory listing objects are accessed via an extensible hash table and contain a list of filenames/directory names within the directory

The Elastifile software architecture consists of three layers:

  • A protocol layer which terminates file system access protocols and translates requests into internal requests. The hashing and data compression of file data occur at this level.
  • A metadata layer which provides file system/directory name mapping to objects for owned files/directories and maintains file/directory metadata updates/journals/checkpoints.
  • A data layer which provides transaction consistency and a n-way redundant persistent data storage for (file or metadata) objects.

Metadata operations are persisted via journaled transactions and which are distributed across the cluster. For instance the journal entries for a mapping data object updates are written to the same file data object (OID) as the actual file data, the 4KB compressed data object.

There’s plenty of discussion on how they manage consistency for their metadata across cluster nodes. Elastifile invented and use Bizur, a key-value consensus based DB. Their chief architect Ezra Hoch (@EzraHoch) did a blog post and paper on Bizur for more information

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New file systems generally take many years to mature and get out into the market, cluster file systems even longer. Elastifile started in 2013, by some very smart engineers, is already on the market, just 4 years later. That’s impressive enough, but with their list of advanced functionality plus cloud storage tiering and multi-site operations all shipping in the current product is mind-blowing.

One lingering question is, does a market exist for another cluster file system? All flash is interesting but most of the current CFS’s do this and ship this today. Cloud storage tiering is interesting and a long term need but some CFSs already have this and others are no doubt implementing it as we speak. CFS’s use of objects for internal data and metadata management is not new and may make internals cleaner but don’t really provide a lot of customer benefit.

Exascale raw capacity, support for 100K users, 1000s of nodes, 1000s of file systems and an infinite # of files/directories is interesting. But most CFSs claim this level of support already, although this is more aspirational for some. And proving support at this scale is difficult, if not impossible.

On the other hand, Bizur is really neat. Its primary benefit is during recovery from hardware failures. For a CFS with 1000s of nodes, failures likely occur quite often. So Bizur’s advantage here may pay significant customer dividends.

Is that enough to to market a new CFS?

To see what other SFD12 bloggers have written on Elastifile, please see:

The fragility of public cloud IT

I have been reading AntiFragile again (by Nassim Taleb). And although he would probably disagree with my use of his concepts, it appears to me that IT is becoming more fragile, not less.

For example, recent outages at major public cloud providers display increased fragility for IT. Yet these problems, although almost national in scope, seldom deter individual organizations from their migration to the cloud.

Tragedy of the cloud commons

The issues are somewhat similar to the tragedy of the commons. When more and more entities use a common pool of resources, occasionally that common pool can become degraded. But because no-one really owns the common resources no one has any incentive to improve the situation.

Now the public cloud, although certainly a common pool of resources, is also most assuredly owned by corporations. So it’s not a true tragedy of the commons problem. Public cloud corporations have a real incentive to improve their services.

However, the fragility of IT in general, the web, and other electronic/data services all increases as they become more and more reliant on public cloud, common infrastructure. And I would propose this general IT fragility is really not owned by any one person, corporation or organization, let alone the public cloud providers.

Pre-cloud was less fragile, post-cloud more so

In the old days of last century, pre-cloud, if a human screwed up a CLI command the worst they could happen was to take out a corporation’s data services. Nowadays, post-cloud, if a similar human screws up a CLI command, the worst that can happen is that major portions of the internet services of a nation go down.

Strange Clouds by michaelroper (cc) (from Flickr)

Yes, over time, public cloud services have become better at not causing outages, but they aren’t going away. And if anything, better public cloud services just encourages more corporations to use them for more data services, causing any subsequent cloud outage to be more impactful, not less

The Internet was originally designed by DARPA to be more resilient to failures, outages and nuclear attack. But by centralizing IT infrastructure onto public cloud common infrastructure, we are reversing the web’s inherent fault tolerance and causing IT to be more susceptible to failures.

What can be done?

There are certainly things that can be done to improve the situation and make IT less fragile in the short and long run:

  1. Use the cloud for non-essential or temporary data services, that don’t hurt a corporation, organization or nation when outages occur.
  2. Build in fault-tolerance, automatic switchover for public cloud data services to other regions/clouds.
  3. Physically partition public cloud infrastructure into more regions and physically separate infrastructure segments within regions, such that any one admin has limited control over an amount of public cloud infrastructure.
  4. Divide an organizations or nations data services across public cloud infrastructures, across as many regions and segments as possible.
  5. Create a National Public IT Safety Board, not unlike the one for transportation, that does a formal post-mortem of every public cloud outage, proposes fixes, and enforces fix compliance.

The National Public IT Safety Board

The National Transportation Safety Board (NTSB) has worked well for air transportation. It relies on the cooperation of multiple equipment vendors, airlines, countries and other parties. It performs formal post mortems on any air transportation failure. It also enforces any fixes in processes, procedures, training and any other activities on equipment vendors, maintenance services, pilots, airlines and other entities that can impact public air transport safety. At the moment, air transport is probably the safest form of transportation available, and much of this is due to the NTSB

We need something similar for public (cloud) IT services. Yes most public cloud companies are doing this sort of work themselves in isolation, but we have a pressing need to accelerate this process across cloud vendors to improve public IT reliability even faster.

The public cloud is here to stay and if anything will become more encompassing, running more and more of the worlds IT. And as IoT, AI and automation becomes more pervasive, data processes that support these services, which will, no doubt run in the cloud, can impact public safety. Just think of what would happen in the future if an outage occurred in a major cloud provider running the backend for self-guided car algorithms during rush hour.

If the public cloud is to remain (at this point almost inevitable) then the safety and continuous functioning of this infrastructure becomes a public concern. As such, having a National Public IT Safety Board seems like the only way to have some entity own IT’s increased fragility due to  public cloud infrastructure consolidation.

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In the meantime, as corporations, government and other entities contemplate migrating data services to the cloud, they should consider the broader impact they are having on the reliability of public IT. When public cloud outages occur, all organizations suffer from the reduced public perception of IT service reliability.

Photo Credits: Fragile by Bart Everson; Fragile Planet by Dave Ginsberg; Strange Clouds by Michael Roper

Hardware vs. software innovation – round 4

We, the industry and I, have had a long running debate on whether hardware innovation still makes sense anymore (see my Hardware vs. software innovation – rounds 1, 2, & 3 posts).

The news within the last week or so is that Dell-EMC cancelled their multi-million$, DSSD project, which was a new hardware innovation intensive, Tier 0 flash storage solution, offering 10 million of IO/sec at 100µsec response times to a rack of servers.

DSSD required specialized hardware and software in the client or host server, specialized cabling between the client and the DSSD storage device and specialized hardware and flash storage in the storage device.

What ultimately did DSSD in, was the emergence of NVMe protocols, NVMe SSDs and RoCE (RDMA over Converged Ethernet) NICs.

Last weeks post on Excelero (see my 4.5M IO/sec@227µsec … post) was just one example of what can be done with such “commodity” hardware. We just finished a GreyBeardsOnStorage podcast (GreyBeards podcast with Zivan Ori, CEO & Co-founder, E8 storage) with E8 Storage which is yet another approach to using NVMe-RoCE “commodity” hardware and providing amazing performance.

Both Excelero and E8 Storage offer over 4 million IO/sec with ~120 to ~230µsec response times to multiple racks of servers. All this with off the shelf, commodity hardware and lots of software magic.

Lessons for future hardware innovation

What can be learned from the DSSD to NVMe(SSDs & protocol)-RoCE technological transition for future hardware innovation:

  1. Closely track all commodity hardware innovations, especially ones that offer similar functionality and/or performance to what you are doing with your hardware.
  2. Intensely focus any specialized hardware innovation to a small subset of functionality that gives you the most bang, most benefits at minimum cost and avoid unnecessary changes to other hardware.
  3. Speedup hardware design-validation-prototype-production cycle as much as possible to get your solution to the market faster and try to outrun and get ahead of commodity hardware innovation for as long as possible.
  4. When (and not if) commodity hardware innovation emerges that provides  similar functionality/performance, abandon your hardware approach as quick as possible and adopt commodity hardware.

Of all the above, I believe the main problem is hardware innovation cycle times. Yes, hardware innovation costs too much (not discussed above) but I believe that these costs are a concern only if the product doesn’t succeed in the market.

When a storage (or any systems) company can startup and in 18-24 months produce a competitive product with only software development and aggressive hardware sourcing/validation/testing, having specialized hardware innovation that takes 18 months to start and another 1-2 years to get to GA ready is way too long.

What’s the solution?

I think FPGA’s have to be a part of any solution to making hardware innovation faster. With FPGA’s hardware innovation can occur in days weeks rather than months to years. Yes ASICs cost much less but cycle time is THE problem from my perspective.

I’d like to think that ASIC development cycle times of design, validation, prototype and production could also be reduced. But I don’t see how. Maybe AI can help to reduce time for design-validation. But independent FABs can only speed the prototype and production phases for new ASICs, so much.

ASIC failures also happen on a regular basis. There’s got to be a way to more quickly fix ASIC and other hardware errors. Yes some hardware fixes can be done in software but occasionally the fix requires hardware changes. A quicker hardware fix approach should help.

Finally, there must be an expectation that commodity hardware will catch up eventually, especially if the market is large enough. So an eventual changeover to commodity hardware should be baked in, from the start.

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In the end, project failures like this happen. Hardware innovation needs to learn from them and move on. I commend Dell-EMC for making the hard decision to kill the project.

There will be a next time for specialized hardware innovation and it will be better. There are just too many problems that remain in the storage (and systems) industry and a select few of these can only be solved with specialized hardware.

Comments?

Picture credit(s): Gravestones by Sherry NelsonMotherboard 1 by Gareth Palidwor; Copy of a DSSD slide photo taken from EMC presentation by Author (c) Dell-EMC

4.5M IO/sec@227µsec 4KB Read on 100GBE with 24 NVMe cards #SFD12

At Storage Field Day 12 (SFD12) this week we talked with Excelero, which is a startup out of Israel. They support a software defined block storage for Linux.

Excelero depends on NVMe SSDs in servers (hyper converged or as a storage system), 100GBE and RDMA NICs. (At the time I wrote this post, videos from the presentation were not available, but the TFD team assures me they will be up on their website soon).

I know, yet another software defined storage startup.

Well yesterday they demoed a single storage system that generated 2.5 M IO/sec random 4KB random writes or 4.5 M IO/Sec random 4KB reads. I didn’t record the random write average response time but it was less than 350µsec and the random read average response time was 227µsec. They only did these 30 second test runs a couple of times, but the IO performance was staggering.

But they used lots of hardware, right?

No. The target storage system used during their demo consisted of:

  • 1-Supermicro 2028U-TN24RT+, a 2U dual socket server with up to 24 NVMe 2.5″ drive slots;
  • 2-2 x 100Gbs Mellanox ConnectX-5 100Gbs Ethernet (R[DMA]-NICs); and
  • 24-Intel 2.5″ 400GB NVMe SSDs.

They also had a Dell Z9100-ON Switch  supporting 32 X 100Gbs QSFP28 ports and I think they were using 4 hosts but all this was not part of the storage target system.

I don’t recall the CPU processor used on the target but it was a relatively lowend, cheap ($300 or so) dual core, Intel standard CPU. I think they said the total target hardware cost $13K or so.

I priced out an equivalent system. 24 400GB 2.5″ NVMe Intel 750 SSDs would cost around $7.8K (Newegg); the 2 Mellanox ConnectX-5 cards $4K (Neutron USA); and the SuperMicro plus an Intel Cpu around $1.5K. So the total system is close to the ~$13K.

But it burned out the target CPU, didn’t it?

During the 4.5M IO/sec random read benchmark, the storage target CPU was at 0.3% busy and the highest consuming process on the target CPU was the Linux “Top” command used to display the PS status.

Excelero claims that the storage target system consumes absolutely no CPU processing to service an 4K read or write IO request. All of IO processing is done by hardware (the R(DMA)-NICs, the NVMe drives and PCIe bus) which bypasses the storage target CPU altogether.

We didn’t look at the host cpu utilization but driving 4.5M IO/sec would take a high level of CPU power even if their client software did most of this via RDMA messaging magic.

How is this possible?

Their client software running in the Linux host is roughly equivalent to an iSCSI initiator but talks a special RDMA protocol (patent pending by Excelero, RDDA protocol) that adds an IO request to the NVMe device submission queue and then rings the doorbell on the target system device and the SSD then takes it off the queue and executes it. In addition to the submission queue IO request they preprogram the PCIe MSI interrupt request message to somehow program (?) the target system R-NIC to send the read data/write status data back to the client host.

So there’s really no target CPU processing for any NVMe message handling or interrupt processing, it’s all done by the client SW and is handled between the NVMe drive and the target and client R-NICs.

The result is that the data is sent back to the requesting host automatically from the drive to the target R-NIC over the target’s PCIe bus and then from the target system to the client system via RDMA across 100GBE and the R-NICS and then from the client R-NIC to the client IO memory data buffer over the client’s PCIe bus.

Writes are a bit simpler as the 4KB write data can be encapsulated into the submission queue command for the write operation that’s sent to the NVMe device and the write IO status is relatively small amount of data that needs to be sent back to the client.

NVMe optimized for 4KB IO

Of course the NVMe protocol is set up to transfer up to 4KB of data with a (write command) submission queue element. And the PCIe MSI interrupt return message can be programmed to (I think) write a command in the R-NIC to cause the data transfer back for a read command directly into the client’s memory using RDMA with no CPU activity whatsoever in either operation. As long as your IO request is less than 4KB, this all works fine.

There is some minor CPU processing on the target to configure a LUN and set up the client to target connection. They essentially only support replicated RAID 10 protection across the NVMe SSDs.

They also showed another demo which used the same drive both across the 100Gbs Ethernet network and in local mode or direct as a local NVMe storage. The response times shown for both local and remote were within  5µsec of each other. This means that the overhead for going over the Ethernet link rather than going local cost you an additional 5µsec of response time.

Disaggregated vs. aggregated configuration

In addition to their standalone (disaggregated) storage target solution they also showed an (aggregated) Linux based, hyper converged client-target configuration with a smaller number of NVMe drives in them. This could be used in configurations where VMs operated and both client and target Excelero software was running on the same hardware.

Simply amazing

The product has no advanced data services. no high availability, snapshots, erasure coding, dedupe, compression replication, thin provisioning, etc. advanced data services are all lacking. But if I can clone a LUN at lets say 2.5M IO/sec I can get by with no snapshotting. And with hardware that’s this cheap I’m not sure I care about thin provisioning, dedupe and compression.  Remote site replication is never going to happen at these speeds. Ok HA is an important consideration but I think they can make that happen and they do support RAID 10 (data mirroring) so data mirroring is there for an NVMe device failure.

But if you want 4.5M 4K random reads or 2.5M 4K random writes on <$15K of hardware and happen to be running Linux, I think they have a solution for you. They showed some volume provisioning software but I was too overwhelmed trying to make sense of their performance to notice.

Yes it really screams for 4KB IO. But that covers a lot of IO activity these days. And if you can do Millions of them a second splitting up bigger IOs into 4K should not be a problem.

As far as I could tell they are selling Excelero software as a standalone product and offering it to OEMs. They already have a few customers using Excelero’s standalone software and will be announcing  OEMs soon.

I really want one for my Mac office environment, although what I’d do with a millions of IO/sec is another question.

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