Nov 22, 2021
Nov 9, 2021
In our recent live SNIA Network Storage Forum webcast, “Next-generation Interconnects: The Critical Importance of Connectors and Cables” provided an outstanding tutorial on the latest in the impressive array of data center infrastructure components designed to address expanding requirements for higher-bandwidth and lower-power. They covered common pluggable connectors and media types, copper cabling and transceivers, and real world use cases. If you missed the live event, it is available on-demand.
We ran out of time to answer all the questions from the live audience. As promised, here are answers to them all.
Q. For 25GbE, is the industry consolidating on one of the three options?
A.
The first version of 25 GbE (i.e. 25GBASE-CR1)
was specified by the Ethernet Technology Consortium around 2014. This was
followed approximately two years later by the IEEE 802.3 versions of 25 GbE
(i.e. 25GBASE-CR-S and 25GBASE-CR). As a result of the timing, the first 25 GbE
capable products to market only supported consortium mode. More recently
developed switches and server products support both consortium and IEEE 802.3
modes, with the link establishment protocol favoring the IEEE 802.3 mode when
it is available. Therefore, IEEE 802.3 will likely be the incumbent over the
long term.
Please note that there is no practical difference between Consortium vs. IEEE
802.3 for short reaches (<=3m) between end points, where a 25G-CA-N
cable is used. The longer cable reaches above 3m (CA-25G-L) requires the
IEEE 802.3 modes with forward error correction, which adds latency. The
CA-25G-S type of cable is the least common. See slide 16 and slide 17
from the presentation.
Q. What's the max DAC cables (passive and/or active) length for 100G PAM4?
A. The IEEE P802.3ck specification for 100 Gb/s/lane copper cable PHYs targets a reach of at least 2 meters for a passive copper cable. Because the specification is still in development, the exact reach is still being worked out. Expect >2 meters for passive cables and 3-4 meters for active cables. Note that this is a reduction of reach from previous rates, as illustrated on slide 27, and that DAC cables are not for long range and generally used for very short interconnections between devices. For longer reaches, AOC cables are preferred.
The passive copper cable length is primarily driven by the performance of the SERDES in the host (i.e. switch, server, FPGA, etc) and the construction materials of the cable assembly. Active copper cables (ACCs or AECs) use several different methods of signal conditioning to increase the cable reach; more sophisticated solutions have greater reach, greater latency and greater cost. See slide 35.
Q. What's the latency difference between active and passive DAC (PAM4 encoding)?
A. Passive copper cables do not contain signal conditioning electronics. Active copper cables (ACCs or AECs) use several different methods of signal conditioning to increase the cable reach; more sophisticated solutions have greater reach, greater latency and greater cost. See slide 35. A simple active cable may use one or more linear equalizer IC where as a complex cable uses a full retimer that may use FEC logic embedded inside.
For 50G PAM4 rates, the difference in one-way latency between a passive copper cable and a simple active copper cable is ~20 nsec. The difference between a passive copper cable and a complex active copper cable could be as high as ~80 nsec.
Q. Can you comment about "gearbox" cables (200G 4lane (@56G) to 100G 4 lane (@28G)?
A. A few companies are supplying cables that have 50G PAM4 on one end and 25G-NRZ on the other with gear box. We see it as a niche; used to link new to older equipment.
Q. Showing QSFP instead of OSFP on slide 29 in the image at the top right?
A. Good catch. That was a mistake on the slide. It has been corrected. Thanks.
Nov 9, 2021
Nov 8, 2021
Each year cars become smarter and more automated. In fact, the automotive industry is effectively transforming the vehicle into a data center on wheels. Connectedness, autonomous driving, and media & entertainment all bring more and more storage onboard and into networked data centers. But all the storage in (and for) a car is not created equal. There are 10s if not 100s of different processors on a car today. Some are attached to storage, some are not and each application demands different characteristics from the storage device.
The SNIA Networking Storage Forum (NSF) is exploring this fascinating topic on December 7, 2021 at our live webcast “Revving Up Storage for Automotive” where industry experts from both the storage and automotive worlds will discuss:
Start your engines and register today to join us as we drive into the future!
Nov 8, 2021
Nov 3, 2021
Object storage has been among the most popular topics we’ve covered in the SNIA Networking Storage Forum. On November 16, 2021, we will take this topic on again at our live webcast “Object Storage: Trends, Use Cases.” Moving beyond the mechanics of object storage, our experts panel will focus on recent object storage trends, problems object storage can solve, and real-world use cases including ransomware protection.
So, what’s new? Object storage has traditionally been seen as an archival storage platform, and is now being employed as a platform for primary data. In this webcast, we’ll highlight how this is happening and discuss:
Register today. Our SNIA panel will be available to answer your object storage questions.
I also encourage you to check out these previous presentations we’ve done on object storage:
Nov 3, 2021
Oct 25, 2021
At our recent SNIA Networking Storage Forum (NSF) webcast “How Fibre Channel Hosts and Targets Really Communicate” our Fibre Channel (FC) experts explained exactly how Fibre Channel works, starting with the basics on the FC networking stack, link initialization, port types, and flow control, and then dove into the details on host/target logins and host/target IO. It was a great tutorial on Fibre Channel. If you missed it, you can view it on-demand. The audience asked several questions during the live event. Here are answers to them all:
Q. What is the most common problem that we face in the FC protocol?
A. Much the same as any other network protocol, congestion is the most common problem found in FC SANs. It can take a couple of forms including, but not limited to, host oversubscription and “Fan-in/Fan-out" ratios of host ports to storage ports, but it is probably the single largest generator of support cases. Another common problem is the 'Host cannot see target' kind of problem.
Q. What are typical latencies for N-to-N (node-to-node) Port and N-F-N (one switch between)?
A. Latencies vary from switch type to switch type and also vary based on the type of forwarding that is done. Port to port on a switch, I would say is from 1us to 5us in general.
Q. Has the Fabric Shortest Path First (FSPF) always been there or is there a minimum FC speed at which it was introduced in? Also, how is the FSPF determined? Is it via shortest path only or does it also take into account for speeds of the switches along the path?
A. While Fibre Channel has existed since 1993 at 133Mbit speed, FSPF was developed by the INCITS T11 Technical Committee and was published in 2000 as a cost-based Link State routing protocol. Costs are based on link speeds. The higher the link speed, the lower the cost. The cost is 1012 / Bandwidth(bps) = cost. There have been variations of implementations that allowed the network administrator to artificially set a link cost and force traffic into a path, but the better case is to simply allow FSPF to do its normal work. And yes, the link costs are considered for all of the intermediate devices along the path.
Q. All of this FSPF happens without even us noticing, right? Or do we need to manually configure?
A. Yes, all of the FSPF routing happens without any manual configuration. Most users don't even realize there is an underlying routing protocol.
Q. Is it a best practice to have all ports in the system run at the same speed? We have storage connected at 32Gb interfaces and a hundred clients with 16Gb interfaces. Would this make the switch's job easier?
A. It's virtually impossible to have all ports of a FC SAN (or any network of size) connect at the same speed. In fact, the more common environment is for multiple versions of server and storage technology to have been “organically grown over time” in the datacenter. Even if that was somehow done, then there still can be congestion caused by hosts and targets requesting data from multiple simultaneous sources. So, having a uniform speed doesn't really fix anything even if it might make some things a bit better. That said, it is always helpful to make certain that your HBA device drivers and firmware versions are up to date.
Q. From your experience, is there any place where the IO has gone wrong?
A. Not sure what 'IO gone wrong' means. All frames that transverse the SAN are cyclic redundancy check (CRC) checked. That might happen on each hop or it might just happen at the end devices. But frames that are found to be corrupted should never be incorporated into the LUN.
Q. Is there a fabric notification feature for these backpressure events?
A. Yes, the recent standards have several mechanisms for notification. This is called 'Fabric Performance Impact Notifications (FPIN). It includes several things such as ELS (extended link service) notifications sent through software to identify congestion, link integrity and SCSI command delivery issues. In Gen 7/64Gb platforms it also includes an in-band hardware signal for credit stall and oversubscription conditions. Today both RHEL and AIX support the receipt of FPIN link integrity notifications and integrate it into their respective MPIO interfaces allowing them to load balance/avoid a “sick but not dead” link. Additional operating systems are on the way and the first of the array vendors to support this are expected “soonish.” While there is no "silver bullet" that solves every congestion problem, FPIN as a tool which engages the whole ecosystem instead of leaving the "switch in the middle" to interpret data on its own is a huge potential benefit.
Q. There is so much good information here. Are the slides available?
A. Yes, the session has been recorded and is available on-demand along with the slides at the SNIA Educational Library where you can also search for countless educational content storage.
Oct 25, 2021
Oct 20, 2021
What types of storage are needed for different aspects of AI? That was one of the many topics covered in our SNIA Networking Storage Forum (NSF) webcast “Storage for AI Applications.” It was a fascinating discussion and I encourage you to check it out on-demand. Our panel of experts answered many questions during the live roundtable Q&A. Here are answers to those questions, as well as the ones we didn’t have time to address.
Q. What are the different data set sizes and workloads in AI/ML in terms of data set size, sequential/ random, write/read mix?
A. Data sets will vary incredibly from use case to use case. They may be GBs to possibly 100s of PB. In general, the workloads are very heavily reads maybe 95%+. While it would be better to have sequential reads, in general the patterns tend to be closer to random. In addition, different use cases will have very different data sizes. Some may be GBs large, while others may be <1 KB. The different sizes have a direct impact on performance in storage and may change how you decide to store the data.
Q. More details on the risks associated with the use of online databases?
A. The biggest risk with using an online DB is that you will be adding an additional workload to an important central system. In particular, you may find that the load is not as predictable as you think and it may impact the database performance of the transactional system. In some cases, this is not a problem, but when it is intended for actual transactions, you could be hurting your business.
Q. What is the difference between a DPU and a RAID / storage controller?
A. A Data Processing Unit or DPU is intended to process the actual data passing through it. A RAID/storage controller is only intended to handle functions such as data resiliency around the data, but not the data itself. A RAID controller might take a CSV file and break it down for storage in different drives. However, it does not actually analyze the data. A DPU might take that same CSV and look at the different rows and columns to analyze the data. While the distinction may seem small, there is a big difference in the software. A RAID controller does not need to know anything about the data, whereas a DPU must be programmed to deal with it. Another important aspect is whether or not the data will be encrypted. If the data will encrypted, a DPU will have to have additional security mechanisms to deal with decryption of the data. However, a RAID-based system will not be affected.
Q. Is a CPU-bypass device the same as a SmartNIC?
A. Not entirely. They are often discussed together, but a DPU is intended to process data, whereas a SmartNIC may only process how the data is handled (such as encryption, handle TCP/IP functions, etc.). It is possible for a SmartNIC to also act as a DPU where the data itself is processed. There are new NVMe-oF™ technologies that are beginning to allow FPGA, TPD, DPU, GPU and other devices direct access to other servers’ storage directly over a high-speed local area network without having to access the CPU of that system.
Q. What work is being done to accelerate S3 performance with regard to AI?
A. A number of companies are working to accelerate the S3 protocol. Presto and a number of Big Data technologies use it natively. For AI workloads there are a number of caching technologies to handle the re-reads of training on a local system. Minimizing the performance penalty
Q. From a storage perspective, how do I take different types of data from different storage systems to develop a model?
A. Work with your project team to find the data you need and ensure it can be served to the ML/DL training (or inference) environment in a timely manner. You may need to copy (or clone) data on to a faster medium to achieve your goals. But look at the process as a whole. Do not underestimate the data cleansing/normalization steps in your storage analysis as it can prove to be a bottleneck.
Q. Do I have to "normalize" that data to the same type, or can a model accommodate different data types?
A. In general, yes. Models can be very sensitive. A model trained on one set of data with one set of normalizations may not be accurate if data that was taken from a different set with different normalizations is used for inference. This does depend on the model, but you should be aware not only of the model, but also the details of how the data was prepared prior to training.
Q. If I have to change the data type, do I then need to store it separately?
A. It depends on your data, "do other systems need it in the old format?"
Q. Are storage solutions that are right for one form of AI also the best for others?
A. No. While it may be possible to use a single solution for multiple AIs, in general there are differences in the data that can necessitate different storage. A relatively simple example is large data (MBs) vs. small data (~1KB). Data in that multiple MBs large example can be easily erasure coded and stored more cost effectively. However, for small data, Erasure Coding is not practical and you generally will have to go with replication.
Q. How do features like CPU bypass impact performance of storage?
A. CPU bypass is essential for those times when all you need to do is transfer data from one peripheral to another without processing. For example, if you are trying to take data from a NIC and transfer it to a GPU, but not process the data in any way, CPU bypass works very well. It prevents the CPU and system memory from becoming a bottleneck. Likewise, on a storage server, if you simply need to take data from an SSD and pass it to a NIC during a read, CPU bypass can really help boost system performance. One important note: if you are well under the limits of the CPU, the benefits of bypass are small. So, think carefully about your system design and whether or not the CPU is a bottleneck. In some cases, people will use system memory as a cache and in these cases, bypassing CPU isn’t possible.
Q. How important is it to use All-Flash storage compared to HDD or hybrid?
A. Of course, It depends on your workloads. For any single model, you may be able to make due with HDD. However, another consideration for many of the AI/ML systems is that their use can quite suddenly expand. Once there is some amount of success, you may find that more people will want access to the data and the system may experience more load. So beware of the success of these early projects as you may find your need for creation of multiple models from the same data could overload your system.
Q. Will storage for AI/ML necessarily be different from standard enterprise storage today?
A. Not necessarily. It may be possible for enterprise solutions today to meet your requirements. However, a key consideration is that if your current solution is barely able to handle its current requirements, then adding an AI/ML training workload may push it over the edge. In addition, even if your current solution is adequate, the size of many ML/DL models are growing exponentially every year. So, what you provision today may not be adequate in a year or even several months. Understanding the direction of the work your data scientists are pursuing is important for capacity and performance planning.
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