I started working years ago to engage large datacenters, learn what their problems are and try to craft solutions for their problems. It’s taken years, but we engaged them, learned, changed how we thought about storage and began creating solutions that are being deployed at scale.
We’ve started to do the same with the Chinese Internet giants. They’re growing at an incredible rate. They have similar problems, but it’s surprising how different their solution approaches are. Each one is unique. And we’re constantly learning from these guys.
So to wrap up the blog series on my interview with CIO & CEO magazine, here are the last two questions to explain a bit more.
CEO & CIO: Please use examples to tell the stories about the forward-looking technologies and architectures that LSI has jointly developed with Internet giants.
While our host bus adapters (HBAs) and MegaRAID® solutions have been part of the hyperscale Internet companies’ infrastructure since the beginning, we have only recently worked very closely with them to drive joint innovation. In 2009 I led the first LSI engagement with what we then called “mega datacenters.” It took a while to understand what they were doing and why. By 2010 we realized there were specialized needs, and began to imagine new hardware products that worked with these datacenters. Out of this work came the realization that flash was important for efficiency and capability, and the “invention” of LSI® Nytro™ product portfolio. (More are in the pipeline). We have worked closely with hyperscale datacenters to evolve and tune these solutions, to where Nytro products have become the backbone of their main revenue platforms. Facebook has been a vitally important partner in evolving our Nytro platform – teaching us what was truly needed, and now much of their infrastructure runs on LSI products. These same products are a good fit for other hyperscale customers, and we are slowly winning many of the large ones.
Looking forward, we are partnered with several Internet giants in the U.S. and China to work on cold storage solutions, and more importantly shared DAS (Distributed DAS: D-DAS) solutions. We have been demonstrating prototypes. These solutions enable pooled architectures and rack scale architecture, and can be made to work tightly with software-defined datacenters (SDDCs). They simplify management and resource allocation – making task deployment more efficient and easier. Shared DAS solutions increase infrastructure efficiency and improves lifecycle management of components. And they have the potential to radically improve application performance and infrastructure costs.
Looking further into the future, we see even more radical changes in silicon supporting transport protocols and storage models, and in rack scale architectures supporting storage and pooled memory. And cold storage is a huge though, some would say, boring problem that we are also focused on – storing lots of data for free and using no power to do it… but I really can’t talk about any of that.
CEO & CIO: LSI maintains good contact with big Internet companies in China. What are the biggest differences between dealing with these Internet enterprises and dealing with traditional partners?
Yes, we have a very good relationship with large Chinese Internet companies. In fact, I will be visiting Tencent, Alibaba and Baidu in a few weeks. One of the CTOs I would like to say is a friend. That is, we have fun talking together about the future.
These meetings have evolved. The first meetings LSI had about two years ago were sales calls, or support for OEM storage solutions. These accomplished very little. Once we began visiting as architects speaking to architects, real dialogs began. Our CEO has been spending time in China meeting with these Internet companies both to learn, and to make it clear that they are important to us, and we want a chance to solve their problems. But the most interesting conversations have been the architectural ones. There have been very clear changes in the two years I have traveled within China – from standard enterprise to hyperscale architectures.
We’ve received fascinating feedback on architecture, use, application profiles, platforms, problems and goals. We have strong engagement with the U.S. Internet giants. At the highest level, the Chinese Internet companies have similar problems and goals. But the details quickly diverge because of revenue per user, resources, power availability, datacenter ownership and Internet company age. The use of flash is very different.
The Chinese Internet giants are at an amazing change point. Most are ready for explosive growth of infrastructure and deployment of cloud services. Most are changing from standard OEM systems and architectures to self-designed hyperscale systems after experimenting with Scorpio and microserver deployments. Several, like JD.com (an Amazon-like company) are moving from hosted to self-built infrastructure. And there seems to be a general realization that the datacenter has changed from a compute-centric model to a dataflow model, where storage and network dictate how much work gets done more than the CPU does. These giants are leveraging their experience and capability to move very quickly, and in a few cases are working to create true pooled rack level architectures much like Facebook and Google have started in the U.S. In fact, Baidu is similar to Facebook in this approach, but is different in its longer term goals for the architecture.
The Chinese companies are amazingly diverse, even within one datacenter, and arguments on architectural direction are raging within these Internet giants – it’s healthy and exciting. However, the innovations that are coming are similar to those developed by large U.S. Internet companies. Personally I have found these Internet companies much more exciting and satisfying to work with than traditional OEMs. The speed and cadence of advancement, the recognition of problems and their importance, the focus on efficiency and optimization have been much more exciting. And the youthful mentality and view to problems, without being burdened by “the way we’ve always done this” has been wonderful.
Also see these blogs of mine over the past year, where you can read more about some of these changes:
“Postcard from Shenzhen: China’s hyperscale datacenter growth, mixed with a more traditional approach”
“China in the clouds, again”
“China: A lot of talk about resource pooling, a better name for disaggregation”
Or see them (and others) all here.
Summary: So it’s taken years, but we engaged U.S. Internet giants, learned about their problems, changed how we thought about storage and began creating solutions that are now being deployed at scale. And we’re constantly learning from these guys. Constantly, because their problems are constantly changing.
We’ve now started to do the same with the Chinese Internet giants. They have similar problems, and will need similar solutions, but they are not the same. And just like the U.S. Internet giants, each one is unique.
Tags: Alibaba, Amazon, Baidu, CEO & CIO Magazine, China, cloud services, cold storage, D-DAS, DAS, datacenter, datacenter ecosystem, direct attached storage, distributed DAS, Facebook, flash, flash storage, Google, HBA, host bus adapter, hyperscale datacenter, Internet, JD.com, MegaRAID, OEM, original equipment manufacturer, Scorpio, Tencent
My “Size matters: Everything you need to know about SSD form factors” blog in January spawned some interesting questions, a number of them on Z-height.
What is a Z-height anyway?
For a solid state drive (SSD), Z-height describes its thickness and is generally its smallest dimension. Z-height is a redundant term, since Z is a variable representing the height of an SSD. The “Z” is one of the variables – X, Y and Z, synonymous with length, width and height – that describe the measurements of a 3-dimensional object. Ironically, no one says X-length or Y-width, but Z-height is widely used.
What’s the state of affairs with SSD Z-height?
The Z-height has typically been associated with the 2.5″ SSD form factor. As I covered in my January form factor blog, the initial dimensions of SSDs were modeled after hard disk drives (HDDs). The 2.5” HDD form factor featured various heights depending on the platter count – the more disks, the greater the capacity and the thicker the HDD. The first 2.5” full capacity HDDs had a maximum Z-height of 19mm, but quickly dropped to a 15mm maximum to enable full-capacity HDDs in thinner laptops. By the time SSDs hit high-volume market acceptance, the dimensional requirements for storage had shrunk even more, to a maximum height of 12.5mm in the 2.5” form factor. Today, the Z-height of most 2.5″ SSDs generally ranges from 5.0mm to 9.5mm.
With printed circuit board (PCB) form factor SSDs—those with no outer case—the Z-height is defined by the thickness of the board and its components, which can be 3mm or less. Some laptops have unique shape or height restrictions for the SSD space allocation. For example, the MacBook Air’s ultra-thin profile requires some of the thinnest SSDs produced.
A new standard in SSD thickness
The platter count of an HDD determines its Z-height. In contrast, an SSD’s Z-height is generally the same regardless of capacity. The proportion of SSD form factors deployed in systems is shifting from the traditional, encased SSDs to the new bare PCB SSDs. As SSDs drift away from the older form factors with different heights, consumers and OEM system designers will no longer need to consider Z-height because the thickness of most bare PCB SSDs will be standard.
The introduction of LSI® SF3700 flash controllers has prompted many questions about the PCIe® (PCI Express) interface and how it benefits solid state storage, and there’s no better person to turn to for insights than our resident expert, Jeremy Werner, Sr. Director of Product and Customer Management in LSI’s Flash Components Division (SandForce):
Most client-based SSDs have used SATA in the past, while PCIe was mainly used for enterprise applications. Why is the PCIe interface becoming so popular for the client market?
Jeremy: Over the past few decades, the performance of host interfaces for client devices has steadily climbed. Parallel ATA (PATA) interface speed grew from 33MB/s to 100MB/s, while the performance of the Serial ATA (SATA) connection rose from 1.5Gb/s to 6Gb/s. Today, some solid state drives (SSDs) use the PCIe Gen2 x4 (second-generation speeds with four data communication lanes) interface, supporting up to 20Gb/s (in each direction). Because the PCIe interface can simultaneously read and write (full duplex) and SATA can only read or write at one time (half-duplex), PCIe can potentially double the 20Gb/s speeds in a mixed (read and write) workload, making it nearly seven times faster than SATA.
Will the PCIe interface replace SATA for SSDs?
Jeremy: Eventually the replacement is likely, but it will probably take many years in the single-drive client PC market given two hindrances. First, some single-drive client platforms must use a common HDD and SSD connection to give users the choice between the two devices. And because the 6Gb/s SATA interface delivers much higher speeds than than hard disk drives, there is no immediate need for HDDs to move to the faster PCIe connection, leaving SATA as the sole interface for the client market. And, secondly, the older personal computers already in consumers’ homes that need an SSD upgrade support only SATA storage devices, so there’s no opportunity for PCIe in that upgrade market.
By contrast, the enterprise storage market, and even some higher-end client systems, will migrate quickly to PCIe since they will see significant speed increases and can more easily integrate PCIe SSD solutions available now.
It is noteworthy that some standards, like M.2 and SATA Express, have defined a single connector that supports SATA or PCIe devices. The recently announced LSI SF3700 is one example of an SSD controller that supports both of those interfaces on an M.2 board.
What is meant by the terms “x1, x2, x4, x16” when referencing a particular PCIe interface?
Jeremy: These numbers are the PCIe lane counts in the connection. Either the host (computer) or the device (SSD) could limit the number of lanes used. The theoretical maximum speed of the connection (not including protocol overhead) is the number of lanes multiplied by the speed of each lane.
What is protocol overhead?
Jeremy: PCIe, like many bus interfaces, uses a transfer encoding scheme – a set number of data bits represented by a slightly larger number of bits called a symbol. The additional bits in the symbol constitute the inefficient overhead of metadata required to manage the transmitted user data. PCIe Gen3 features a more efficient data transfer encoding with 128b/132b (3% overhead) instead of the 8b/10b (20% overhead) of PCIe Gen2, increasing data transfer speeds by up to 21%.
What is defined in the PCIe 2.0 and 3.0 specifications, and do end users really care?
Jeremy: Although each PCIe Gen3 lane is faster than PCIe Gen2 (8Gb/s vs 5Gb/s, respectively), lanes can be combined to boost performance in both versions. The changes most relevant to consumers pertain to higher speeds. For example, today consumer SSDs top out at 150K random read IOPS at 4KB data transfer sizes. That translates to about 600MB/s, which is insufficient to saturate a PCIe Gen2 x2 link, so consumers would see little benefit from a PCIe Gen3 solution over PCIe Gen2. The maximum performance of PCIe Gen2 x4 and PCIe Gen3 x2 devices is almost identical because of the different transfer encoding schemes mentioned previously.
Are there mandatory features that must be supported in any of these specifications?
Jeremy: Yes, but nearly all of these features have little impact on performance, so most users have no interest in the specs. It’s important to keep in mind that the PCIe speeds I’ve cited are defined as the maximums, and the spec has no minimum speed requirement. This means a PCIe Gen3 solution might support only a maximum of 5Gb/s, but still be considered a PCIe Gen3 solution if it meets the necessary specifications. So buyers need to be aware of the actual speed rating of any PCIe solution.
Is a PCIe Gen3 SSD faster than a PCIe Gen2 SSD?
Jeremy: Not necessarily. For example, a PCIe Gen2 x4 SSD is capable of higher speeds than a PCIe Gen3 x1 SSD. However, bottlenecks other than the front-end PCIe interface will limit the performance of many SSDs. Examples of other choke points include the bandwidth of the flash, the processing/throughput of the controller, the power or thermal limitations of the drive and its environment, and the ability to remove heat from that environment. All of these factors can, and typically do, prevent the interface from reaching its full steady-state performance potential.
In what form factors are PCIe cards available?
Jeremy: PCIe cards are typically referred to as plug-in products, much like SSDs, graphics cards and host-bus adapters. PCIe SSDs come in many form factors, with the most popular called “half-height, half-length.” But the popularity of the new, tiny M.2 form factors is growing, driven by rising demand for smaller consumer computers. There are other PCIe form factors that resemble traditional hard disk drives, such as the SFF-8639, a 2.5” hard disk drive form factor that features four PCIe lanes and is hot pluggable. What’s more, its socket is compatible with the SAS and SATA interfaces. The adoption of the SATA Express 2.5” form factor has been limited, but could be given a boost with the availability of new capabilities like SRIS (Separate Refclk with Independent SSC), which enables the use of lower cost interconnection cables between the device and host.
Are all M.2 cards the same?
Jeremy: No. All SSD M.2 cards are 22 mm wide (while some WAN cards are 30 mm wide), but the specification allows for different lengths (30, 42, 60, 80, and 110 mm). What’s more, the cards can be single- or double-sided to account for differences in the thickness of the products. Also, they are compatible with two different sockets (socket 2 and socket 3). SSDs compatible with both socket types, or only socket 2, can connect only two lanes (x2), while SSDs compatible with only socket 3 can connect up to four (x4).
In my last few blogs, I covered various aspects of SSD form factors and included many images of the types that Jeremy mentioned above. I also delve deeper into details of the M.2 form factor in my blog “M.2: Is this the Prince of SSD form factors?” One thing about PCIe is certain: It is the next step in the evolution of computer interfaces and will give rise to more SSDs with higher performance, lower power consumption and better reliability.
How did he do that?
Growing up, I watched a little TV. Okay, a lot of TV as I did not have my DVR or iPad and a man who would one day occupy the White House as VP had not yet invented the Internet. Of the many shows I watched, MacGyver was one of my favorites. He would take ordinary objects and use them to solve complicated problems in a way no one could have imagined. Out of all the things he used, his trusty Swiss army knife was the most awesome. With all its blades, tools and accessories, it could solve multiple problems at the same time. It was easy to use, did not take up a lot of space and was very cost-effective.
Nytro MegaRAID – the Swiss Army knife of server storage
LSI has its own multi-function, get-yourself-out-of-a-fix workhorse – the Nytro MegaRAID® card, part of the Nytro product family. It combines caching intelligence, RAID protection and flash on a single PCIe® card to accelerate applications, so it can be deployed to solve problems across a broad number of applications.
A feature for every challenge!
The Nytro MegaRAID card is built on the same trusted technology as the MegaRAID cards deployed in datacenters worldwide. That means, it is enterprise architected and hardened and datacenter tested. Its Swiss Army knife-like features include, as I mentioned, on-board flash storage that can be configured to monitor the flow of data from an application to the attached RAID protected storage, intelligently identify hot, or the most frequently accessed, data, and automatically move a copy of that data to the flash storage to accelerate applications. The next time the application needs that data, the information is fetched from flash, not the much slower traditional hard disk drive (HDD) storage.
Hard drives can lead to slowdowns in another way, too, when the mechanics wear out and fail. When they do, your storage (and application) performance can dramatically decrease – in a RAID storage environment, this is called degraded mode. The good news is that the Nytro MegaRAID card stores much of an application’s frequently used data in its intelligent flash based cache, boosting the performance of a connected HDD in degrade mode by as much as 10x, depending on the configuration. The Swiss Army knife follow-on benefit is that when you replace the failed drive, Nytro MegaRAID speeds RAID storage rebuilds by as much as 4x. RAID rebuilds add to IT admin time, and IT time is money, so that’s money you get to keep in your pocket.
The Nytro MegaRAID card also can be configured so you can use half of its onboard flash as a pair of mirrored boot drives. In big data environments, this mirroring frees up two boot drives for use as data storage to help increase your server storage density (aka available storage capacity), often significantly, while dramatically improving boot time. What’s more, that same flash can be deployed instead as primary storage to complement your secondary HDD storage with higher speeds, providing a superfast repository for key files like virtual desktop infrastructure (VDI) golden images or key database log files.
One MacGyver Swiss Army knife, one Nytro MegaRAID card – both easy-to-use solutions for a number of complex problems.
Tags: application acceleration, big data, data protection, database, flash, flash card, flash-based cache, hard disk drive, HDD, MacGyver, Nytro MegaRAID card, PCIe card, RAID, server storage, Swiss Army knife, VDI, virtual desktop infrastructure
I was asked some interesting questions recently by CEO & CIO, a Chinese business magazine. The questions ranged from how Chinese Internet giants like Alibaba, Baidu and Tencent differ from other customers and what leading technologies big Internet companies have created to questions about emerging technologies such as software-defined storage (SDS) and software-defined datacenters (SDDC) and changes in the ecosystem of datacenter hardware, software and service providers. These were great questions. Sometimes you need the press or someone outside the industry to ask a question that makes you step back and think about what’s going on.
I thought you might interested, so this blog, the first of a 3-part series covering the interview, shares details of the first two questions.
CEO & CIO: In recent years, Internet companies have built ultra large-scale datacenters. Compared with traditional enterprises, they also take the lead in developing datacenter technology. From an industry perspective, what are the three leading technologies of ultra large-scale Internet data centers in your opinion? Please describe them.
There are so many innovations and important contributions to the industry from these hyperscale datacenters in hardware, software and mechanical engineering. To choose three is difficult. While I would prefer to choose hardware innovations as their big ones, I would suggest the following as they have changed our world and our industry and are changing our hardware and businesses:
Autonomous behavior and orchestration
An architect at Microsoft once told me, “If we had to hire admins for our datacenter in a normal enterprise way, we would hire all the IT admins in the world, and still not have enough.” There are now around 1 million servers in Microsoft datacenters. Hyperscale datacenters have had to develop autonomous, self-managing, sometimes self-deploying datacenter infrastructure simply to expand. They are pioneering datacenter technology for scale – innovating, learning by trial and error, and evolving their practices to drive more work/$. Their practices are specialized but beginning to be emulated by the broader IT industry. OpenStack is the best example of how that specialized knowledge and capability is being packaged and deployed broadly in the industry. At LSI, we’re working with both hyperscale and orchestration solutions to make better autonomous infrastructure.
High availability at datacenter level vs. machine level
As systems get bigger they have more components, more modes of failure and they get more complex and expensive to maintain reliability. As storage is used more, and more aggressively, drives tend to fail. They are simply being used more. And yet there is continued pressure to reduce costs and complexity. By the time hyperscale datacenters had evolved to massive scale – 100’s of thousands of servers in multiple datacenters – they had created solutions for absolute reliability, even as individual systems got less expensive, less complex and much less reliable. This is what has enabled the very low cost structures of the cloud, and made it a reliable resource.
These solutions are well timed too, as more enterprise organizations need to maintain on-premises data across multiple datacenters with absolute reliability. The traditional view that a single server requires 99.999% reliability is giving way to a more pragmatic view of maintaining high reliability at the macro level – across the entire datacenter. This approach accepts the failure of individual systems and components even as it maintains data center level reliability. Of course – there are currently operational issues with this approach. LSI has been working with hyperscale datacenters and OEMs to engineer improved operational efficiency and resilience, and minimized impact of individual component failure, while still relying on the datacenter high-availability (HA) layer for reliability.
It’s such an overused term. It’s difficult to believe the term barely existed a few years ago. The gift of Hadoop® to the industry – an open source attempt to copy Google® MapReduce and Google File System – has truly changed our world unbelievably quickly. Today, Hadoop and the other big data applications enable search, analytics, advertising, peta-scale reliable file systems, genomics research and more – even services like Apple® Siri run on Hadoop. Big data has changed the concept of analytics from statistical sampling to analysis of all data. And it has already enabled breakthroughs and changes in research, where relationships and patterns are looked for empirically, rather than based on theories.
Overall, I think big data has been one of the most transformational technologies this century. Big data has changed the focus from compute to storage as the primary enabler in the datacenter. Our embedded hard disk controllers, SAS (Serial Attached SCSI) host bus adaptors and RAID controllers have been at the heart of this evolution. The next evolutionary step in big data is the broad adoption of graph analysis, which integrates the relationship of data, not just the data itself.
CEO & CIO: Due to cloud computing, mobile connectivity and big data, the traditional IT ecosystem or industrial chain is changing. What are the three most important changes in LSI’s current cooperation with the ecosystem chain? How does LSI see the changes in the various links of the traditional ecosystem chain? What new links are worth attention? Please give some examples.
Cloud computing and the explosion of data driven by mobile devices and media has and continues to change our industry and ecosystem contributors dramatically. It’s true the enterprise market (customers, OEMs, technology, applications and use cases) has been pretty stable for 10-20 years, but as cloud computing has become a significant portion of the server market, it has increasingly affected ecosystem suppliers like LSI.
Timing: It’s no longer enough to follow Intel’s ticktock product roadmap. Development cycles for datacenter solutions used to be 3 to 5 years. But these cycles are becoming shorter. Now, demand for solutions is closer to 6 months – forcing hardware vendors to plan and execute to far tighter development cycles. Hyperscale datacenters also need to be able to expand resources very quickly, as customer demand dictates. As a result they incorporate new architectures, solutions and specifications out of cycle with the traditional Intel roadmap changes. This has also disrupted the ecosystem.
End customers: Hyperscale datacenters now have purchasing power in the ecosystem, with single purchase orders sometimes amounting to 5% of the server market. While OEMs still are incredibly important, they are not driving large-scale deployments or innovating and evolving nearly as fast. The result is more hyperscale design-win opportunities for component or sub-system vendors if they offer something unique or a real solution to an important problem. This also may shift profit pools away from OEMs to strong, nimble technology solution innovators. It also has the potential to reduce overall profit pools for the whole ecosystem, which is a potential threat to innovation speed and re-investment.
New players: Traditionally, a few OEMs and ISVs globally have owned most of the datacenter market. However, the supply chain of the hyperscale cloud companies has changed that. Leading datacenters have architected, specified or even built (in Google’s case) their own infrastructure, though many large cloud datacenters have been equipped with hyperscale-specific systems from Dell and HP. But more and more systems built exactly to datacenter specifications are coming from suppliers like Quanta. Newer network suppliers like Arista have increased market share. Some new hyperscale solution vendors have emerged, like Nebula. And software has shifted to open source, sometimes supported for-pay by companies copying the Redhat® Linux model – companies like Cloudera, Mirantis or United Stack. Personally, I am still waiting for the first 3rd-party hardware service emulating a Linux support and service company to appear.
Open initiatives: Yes, we’ve seen Hadoop and its derivatives deployed everywhere now – even in traditional industries like oil and gas, pharmacology, genomics, etc. And we’ve seen the emergence of open-source alternatives to traditional databases being deployed, like Casandra. But now we’re seeing new initiatives like Open Compute and OpenStack. Sure these are helpful to hyperscale datacenters, but they are also enabling smaller companies and universities to deploy hyperscale-like infrastructure and get the same kind of automated control, efficiency and cost structures that hyperscale datacenters enjoy. (Of course they don’t get fully there on any front, but it’s a lot closer). This trend has the potential to hurt OEM and ISV business models and markets and establish new entrants – even as we see Quanta, TYAN, Foxconn, Wistron and others tentatively entering the broader market through these open initiatives.
New architectures and new algorithms: There is a clear movement toward pooled resources (or rack scale architecture, or disaggregated servers). Developing pooled resource solutions has become a partnership between core IP providers like Intel and LSI with the largest hyperscale datacenter architects. Traditionally new architectures were driven by OEMs, but that is not so true anymore. We are seeing new technologies emerge to enable these rack-scale architectures (RSA) – technologies like silicon photonics, pooled storage, software-defined networks (SDN), and we will soon see pooled main memory and new nonvolatile main memories in the rack.
We are also seeing the first tries at new processor architectures about to enter the datacenter: ARM 64 for cool/cold storage and web tier and OpenPower P8 for high power processing – multithreaded, multi-issue, pooled memory processing monsters. This is exciting to watch. There is also an emerging interest in application acceleration: general-purposing computing on graphics processing units (GPGPUs), regular expression processors (regex) live stream analytics, etc. We are also seeing the first generation of graph analysis deployed at massive scale in real time.
Innovation: The pace of innovation appears to be accelerating, although maybe I’m just getting older. But the easy gains are done. On one hand, datacenters need exponentially more compute and storage, and they need to operate 10x to 1000x more quickly. On the other, memory, processor cores, disks and flash technologies are getting no faster. The only way to fill that gap is through innovation. So it’s no surprise there are lots of interesting things happening at OEMs and ISVs, chip and solution companies, as well as open source community and startups. This is what makes it such an interesting time and industry.
Consumption shifts: We are seeing a decline in laptop and personal computer shipments, a drop that naturally is reducing storage demand in those markets. Laptops are also seeing a shift to SSD from HDD. This has been good for LSI, as our footprint in laptop HDDs had been small, but our presence in laptop SSDs is very strong. Smart phones and tablets are driving more cloud content, traffic and reliance on cloud storage. We have seen a dramatic increase in large HDDs for cloud storage, a trend that seems to be picking up speed, and we believe the cloud HDD market will be very healthy and will see the emergence of new, cloud-specific HDDs that are radically different and specifically designed for cool and cold storage.
There is also an explosion of SSD and PCIe flash cards in cloud computing for databases, caches, low-latency access and virtual machine (VM) enablement. Many applications that we take for granted would not be possible without these extreme low-latency, high-capacity flash products. But very few companies can make a viable storage system from flash at an acceptable cost, opening up an opportunity for many startups to experiment with different solutions.
Summary: So I believe the biggest hyperscale innovations are autonomous behavior and orchestration, HA at the datacenter level vs. machine level, and big data. These are radically changing the whole industry. And what are those changes for our industry and ecosystem? You name it: timing, end customers, new players, open initiatives, new architectures and algorithms, innovation, and consumption patterns. All that’s staying the same are legacy products and solutions.
These were great questions. Sometimes you need the press or someone outside the industry to ask a question that makes you step back and think about what’s going on. Great questions.
Tags: Alibaba, Apple Siri, Arista, ARM 64, Baidu, big data, Casandra, CEO & CIO Magazine, China, cloud storage, Cloudera, cold storage, cool storage, datacenter, datacenter ecosystem, Dell, flash, Foxconn, Google File System, Google MapReduce, Hadoop, hard disk drive, HDD, high availability, HP, hyperscale datacenter, Intel, Internet, latency, Microsoft, Mirantis, Nebula, OEM, Open Compute, OpenPower P8, OpenStack, original equipment manufacturer, Quanta, rack scale, RAID, Redhat Linux, SAS, SDDC, SDN, SDS, Serial Attached SCSI, software-defined datacenter, software-defined networks, software-defined storage, solid state drive, SSD, Tencent, TYAN, United Stack, virtual machine, VM, Wistron
I was recently speaking to a customer about data reduction technology and I remembered a conversation I had with my mother when I was a teenager. She used to complain how chaotic my bedroom looked, and one time I told her “I was illustrating the second law of thermodynamics” for my physics class. I was referring to the mess and the tendency of things to evolve towards the state of maximum entropy, or randomness. I have to admit I only used that line once with my mom because it pissed her off and she likened me to an intelligent donkey.
I never expected those early lessons in theoretical physics to be useful in the real world, but as it turns out entropy can be a significant factor in determining solid state drive (SSD) performance. When an SSD employs data reduction technology, the degree of entropy or randomness in the data stream becomes inversely related to endurance and performance—the lower the data entropy, the higher the endurance and performance of the SSD.
Entropy affects data reduction
In this context I am defining entropy as the degree of randomness in data stored by an SSD. Theoretically, minimal or nonexistent entropy would be characterized by data bits of all ones or all zeros, and maximum entropy by a completely random series of ones and zeros. In practice, the entropy of what we often call real-world data falls somewhere in between these two extremes. Today we have hardware engines and software algorithms that can perform deduplication, string substitution and other advanced procedures that can reduce files to a fraction of their original size with no loss of information. The greater the predictability of data – that is, the lower the entropy – the more it can be reduced. In fact, some data can be reduced by 95% or more!
Files such as documents, presentations and email generally contain repeated data patterns with low randomness, so are readily reducible. In contrast, video files (which are usually compressed) and encrypted files (which are inherently random) are poor candidates for data reduction.
A reminder is in order not to confuse random data with random I/O. Random (and sequential) I/Os describe the way data is accessed from the storage media. The mix of random vs. sequential I/Os also influences performance, but in a different way than entropy, described in my blog “Teasing out the lies in SSD benchmarking.”
Why data reduction matters in an SSD
The NAND flash memory inside SSDs is very sensitive to the cumulative amount of data written to it. The more data written to flash, the shorter the SSD’s service life and the sooner its performance will degrade. Writing less data, therefore, means better endurance and performance. You can read more about this topic in my two blogs “Can data reduction technology substitute for TRIM” and “Write Amplification – Part 2.”
Real-world examples in client computing
Take an encrypted text document. The file started out as mostly text with some background formatting data. All things considered, the original text file is fairly simple and organized. The encryption, by design, turns the data into almost completely random gibberish with almost no predictability to the file. The original text file, then, has low entropy and the encrypted file high entropy.
Intel Labs examined entropy in the context of compressibility as background research to support its Intel SSD 520 Series. The following chart summarizes Intel’s findings for the kinds of data commonly found on client storage drives, and the amount of compression that might be achieved:
According to Intel, “75% of the file types observed can be typically compressed 60% or more.” Granted, the kind of files found on drives varies widely according to the type of user. Home systems might contain more compressed audio and video, for example – poor candidates, as we mentioned, for data reduction. But after examining hundreds of systems from a wide range of environments, LSI estimates that the entropy of typical user data averages about a 50%, suggesting that many users would see at least a moderate improvement in performance and endurance from data reduction because most data can be reduced before it is written to the SSD.
Real-world examples in the enterprise
Enterprise IT managers might be surprised at the extent to which data reduction technology can increase workload performance. While gauging the level of improvement with any precision would require data-specific benchmarking, sample data can provide useful insights. LSI examined the entropy of various data types, shown in the chart below. I found the high reducibility of the Oracle® database file very surprising because I had previously been told by database engineers that I should expect higher entropy. I later came to understand these enterprise databases are designed for speed, not capacity optimization. Therefore it is faster to store the data in its raw form rather than use a software compression application to compress and decompress the database on the fly and slow it down.
Putting it all together
The chief goals of PC and laptop users and IT managers have long been, and remain, to maximize the performance and lifespan of storage devices – SSDs and HDDs – and at a competitive price point. The challenge for SSD users is to find a device that delivers on all three fronts. LSI® SandForce® DuraWrite™ technology helps give SSD users exactly what they want. By reducing the amount of data written to flash memory, DuraWrite increases SSD endurance and performance without additional cost – even if it doesn’t help organize your teenager’s bedroom.
It’s the start of the new year, and it’s traditional to make predictions – right? But predicting the future of the datacenter has been hard lately. There have been and continue to be so many changes in flight that possibilities spin off in different directions. Fractured visions through a kaleidoscope. Changes are happening in the businesses behind datacenters, the scale, the tasks and what is possible to accomplish, the value being monetized, and the architectures and technologies to enable all of these.
A few months ago I was asked to describe the datacenter in 2020 for some product planning purposes. Dave Vellante of Wikibon & John Furrier of SiliconANGLE asked me a similar question a few weeks ago. 2020 is out there – almost 7 years. It’s not easy to look into the crystal ball that far and figure out what the world will look like then, especially when we are in the midst of those tremendous changes. For some context I had to think back 7 years – what was the datacenter like then, and how profound have the changes been over the past 7 years?
And 7 years ago, our forefathers…
It was a very different world. Facebook barely existed, and had just barely passed the “university only” membership. Google was using Velcro, Amazon didn’t have its services, cloud was a non-existent term. In fact DAS (direct attach storage) was on the decline because everyone was moving to SAN/NAS. 10GE networking was in the future (1GE was still in growth mode). Linux was not nearly as widely accepted in enterprise – Amazon was in the vanguard of making it usable at scale (with Werner Vogels saying “it’s terrible, but it’s free, as in free beer”). Servers were individual – no “PODs,” and VMware was not standard practice yet. SATA drives were nowhere in datacenters.
An enterprise disk drive topped out at around 200GB in capacity. Nobody used the term petabyte. People, including me, were just starting to think about flash in datacenters, and it was several years later that solutions became available. Big data did not even exist. Not as a term or as a technology, definitely not Hadoop or graph search. In fact, Google’s seminal paper on MapReduce had just been published, and it would become the inspiration for Hadoop – something that would take many years before Yahoo picked it up and helped make it real.
Analytics were statistical and slow, and you had to be very explicitly looking for something. Advertising on the web was a modest business. Cold storage was tape or MAID, not vast pools of cheap disks in the cloud at absurdly low price points. None of the Chinese web-cloud guys existed… In truth, at LSI we had not even started looking at or getting to know the web datacenter guys. We assumed they just bought from OEMs…
No one streamed mainstream media – TV and movies – and there were no tablets to stream them to. YouTube had just been purchased by Google. Blu-ray was just getting started and competing with HD-DVD (which I foolishly bought 7 years ago), and integrated GPS’s in your car were a high-tech growth area. The iPhone or Android had not launched, Danger’s Sidekick was the cool phone, flip phones were mainstream, there was no App store or the billions of sales associated with that, and a mobile web browser was virtually useless.
Dell, IBM, and HP were the only real server companies that mattered, and the whole industry revolved around them, as well as EMC and NetApp for storage. Cisco, Lenovo and Huawei were not server vendors. And Sun was still Sun.
7 years from now
So – 7 years from now? That’s hard to predict, so take this with a grain of salt… There are many ways things could play out, especially when global legal, privacy, energy, hazardous waste recycling, and data retention requirements come into play, not to mention random chaos and invention along the way.
Compute-centric to dataflow-centric
Major applications are changing (have changed) from compute-centric to dataflow architectures. That is big data. The result will probably be a decline in the influence of processor vendors, and the increased focus on storage, network and memory, and optimized rack-level architectures. A handful of hyperscale datacenters are leading the way, and dragging the rest of us along. These types of solutions are already being deployed in big enterprise for specialized use cases, and their adoption will only increase with time. In 7 years, the main deployment model will echo what hyperscale datacenters are doing today: disaggregated racks of compute, memory and storage resources.
The datacenter is now being viewed as a profit growth enabler, rather than a cost center. That implies more compute = more revenue. That changes the investment profile and the expectations for IT. It will not be enough for enterprise IT departments to minimize change and risk because then they would be slowing revenue growth.
Customers and vendors
We are in the early stages of a customer revolt. Whether it’s deserved or not is immaterial, though I believe it’s partially deserved. Large customers have decided (and I’m doing broad brush strokes here) that OEMs are charging them too much and adding “features” that add no value and burn power, that the service contracts are excessively expensive and that there is very poor management interoperability among OEM offerings – on purpose to maintain vendor lockin. The cost structures of public cloud platforms like Amazon are proof there is some merit to the argument. Management tools don’t scale well, and require a lot of admin intervention. ISVs are seen as no better. Sure the platforms and apps are valuable and critical, but they’re really expensive too, and in a few cases, open source solutions actually scale better (though ISVs are catching up quickly).
The result? We’re seeing a push to use whitebox solutions that are interoperable and simple. Open source solutions – both software and hardware – are gaining traction in spite of their problems. Just witness the latest Open Compute Summit and the adoption rate of Hadoop and OpenStack. In fact many large enterprises have a policy that’s pretty much – any new application needs to be written for open source platforms on scale-out infrastructure.
Those 3 OEMs are struggling. Dell, HP and IBM are selling more servers, but at a lower revenue. Or in the case of IBM – selling the business. They are trying to upsell storage systems to offset those lost margins, and they are trying to innovate and vertically integrate to compensate for the changes. In contrast we’re seeing a rapid increase planned from self-built, self-architected hyperscale datacenters, especially in China. To be fair – those pressures on price and supplier revenue are not necessarily good for our industry. As well, there are newer entrants like Huawei and Cisco taking a noticeable chunk of the market, as well as an impending growth of ISV and 3rd party full rack “shrink wrapped” systems. Everybody is joining the party.
Storage, cold storage and storage-class memory
Stepping further out on the limb, I believe (but who really knows) that by 2020 storage as we know is no longer shipping. SMB is hollowed out to the cloud – that is – why would any small business use anything but cloud services? The costs are too compelling. Cloud storage is stratified into 3 levels: storage-class memory, flash/NVM and cool/cold bulk disk storage. Cold storage is going to be a very, very important area. You need to save that data, but spend zero power, and zero $ on storing it. Just look at some of the radical ideas like Facebook’s Blu-ray jukebox to address that, which was masterminded by a guy I really like – Gio Coglitore – and I am very glad is getting some rightful attention. (http://www.wired.com/wiredenterprise/2014/02/facebook-robots/)
I believe that pooled storage class memory is inevitable and will disrupt high-performance flash storage, probably beginning in 2016. My processor architect friends and I have been daydreaming about this since 2005. That disruption’s OK, because flash use will continue to grow, even as disk use grows. There is just too much data. I’ve seen one massive vendor’s data showing average servers are adding something like 0.2 hard disks per year and 0.1 SSDs per year – and that’s for the average server including diskless nodes that are usually the most common in hyperscale datacenters. So growth in spite of disruption and capacity growth.
Data will be pooled, and connected by fabric as distributed objects or key/value pairs, with erasure coding. In fact, Object store (key/value – whatever) may have “obsoleted” block storage. And the need for these larger objects will probably also obsolete file as we’re used to it. Sure disk drives may still be block based, though key/value gives rise to all sorts of interesting opportunities to support variable size structures, obscure small fault domains, and variable encryption/compression without wasting space on disk platters. I even suspect that disk drives as we know them will be morphing into cold store specialty products that physically look entirely different and are made from different materials – for a lot of reasons. 15K drives will be history, and 10K drives may too. In fact 2” drives may not make sense anymore as the laptop drive and 15K drive disappear and performance and density are satisfied by flash.
Enterprise becomes private cloud that is very similar structurally to hyperscale, but is simply in an internal facility. And SAN/NAS products as we know them will be starting on the long end of the tail as legacy support products. Sure new network based storage models are about to emerge, but they’re different and more aligned to key/value.
Rack-scale architectures will have taken over clustered deployments. That means pooled resources. Processing will be pools of single socket SoC servers enabling massive clusters, rather than lots of 2- socket servers. These SoCs might even be mobile device SoCs at some point or at least derived from that – the economics of scale and fast cadence of consumer SoCs will make that interesting, maybe even inevitable. After all, the current Apple A7 in the iphone 5S is a dual core, 64-bit V8 ARM at 1.4GHz and the whole iPhone costs as much as mainstream server processor chips. In a few years, an 8 or 16 core equivalent at 1.5GHz or 2GHz is not hard to imagine, and the cost structure should be excellent.
Rapidly evolving open source applications will have morphed into eventually consistent dataflow tasks. Or they will be emerging in-memory applications working on vast data structures in the pooled storage class memory at the rack or larger scale, which will add tremendous monetary value to businesses. Whatever the evolutionary paths – the challenge for the next 10 years is optimizing dataflow as the amount used continues to exponentially grow. After all – data has value in aggregate, so why would you throw anything away, even as the amount we generate increases?
Clusters will be autonomous. Really autonomous. As in a new term I love: “emergent.” It’s when you can start using big data analytics to monitor the datacenter, and make workload/management and data placement decisions in real time, automatically, and the datacenter begins to take on un-predicted characteristics. Deployment will be autonomous too. Power on a pod of resources, and it just starts working. Google does that already.
Layer 2 datacenter network switches will either be disappearing or will have migrated to a radically different location in the rack hierarchy. There are many ways this can evolve. I’m not sure which one(s) will dominate, but I know it will look different. And it will have different bandwidth. 100G moving to 400G interconnect fabric over fiber.
So there you have it. Guaranteed correct…
Different applications and dataflow, different architectures, different processors, different storage, different fabrics. Probably even a re-alignment of vendors.
Predicting the future of the datacenter has not been easy. There have been, and are so many changes happening. The businesses behind them. The scale, the tasks and what is possible to accomplish, the value being monetized, and the architectures and technologies to enable all of these. But at least we have some idea what’s ahead. And it’s pretty different, and exciting.
Tags: 10 gigabit ethernet, 2020, Amazon, Apple, China, Cisco, cloud storage, cold storage, datacenter, Dell, EMC, Facebook, flash, Google, Hadoop, HP, Huawei, hyperscale datacenter, IBM, iPhone, kaleidoscope, Lenovo, NAS, NetApp, non-volatile memory, NVM, Open Compute, OpenStack, rack scale architecture, SAN, SoC, Sun, VMware, YouTube
A major reason enterprise customers see high latency and poorer than expected performance when implementing flash technology is that the flash partition is not aligned on a sector boundary that allows the flash device to access its data efficiently. When creating a Logical Volume (LVM), things can even get more complicated. Proper partition alignment is critical to performance when implementing flash in your enterprise.
An aligned partition is one that starts on a sector number that’s evenly divisible by 4k, or 8k, or a starting sector that is divisible by eight. Aligned input-out (IO) operations will start at sector 8 for 4k alignments, 16 for an 8k alignments, and so forth, with sector 2048 for 1M alignments.
If a flash partition is unaligned – its IO operations start at a sector number not divisible by eight – the device will perform two IOs over adjacent blocks instead of one. These extra IOs will degrade performance of the flash device. In our testing, we have seen up to 4x performance gains by properly aligning the flash device.
Out with the old … in with the new
There are many articles, websites, and Linux system administrators best practice documents describing how to create a logical volume (LVM) – an abstraction of a number of flash devices into a single storage volume that enables dynamic volume resizing and makes it easier to replace, re-partition and back up individual devices in Linux. However, most of these practices were developed before the advent of PCIe® flash devices. I have worked with customers who have used these old practices of creating LVMs and some of them are seeing very poor performance when implementing flash in their environments.
My conversations with customers and documents I’ve read on creating LVMs have revealed that the first step in creating a LVM – to create a physical volume (PV) – needs refinement. The reason is the PV create process can use a raw device, a partitioned device, or a mix. I would suggest getting into the habit of aligning all flash devices on a physical sector boundary so that all PVs are aligned. The PV command is typically specified as either “pvcreate /dev/sdX,” which allocates the whole device (non-partitioned) to the PV, or “pvcreate /dev/sdX1,” which uses a partition to create the PV. If the PV is created using a mix of raw devices and partitioned devices, or multiple partitioned devices, is there alignment over all the PVs? Maybe! Maybe not! That’s the problem!
Aligning for higher speed
I recommend a new approach to creating LVMs when using flash technology. My suggestion is to align each of the flash devices on a 1M boundary before creating the PV. Here are the steps to help make sure that you are using boundary-aligned devices when creating a LVM:
echo “2048,,8e” | sfdisk – uS /dev/sdX – force
Implementing flash in the enterprise is a great way to produce low latencies while providing high IOPs and throughput. By following these steps, you will successfully set up an LVM over multiple flash devices that are aligned on a proper boundary to get the best performance.
A customer recently asked me if the SF3700, our latest flash controller, supports SATA Express and fired away with a bunch of other questions about the standard. The depth of his curiosity suggested a broader need for education on the basics of the standard.
To help me with the following overview of SATA Express, I recruited Sumit Puri, Sr. Director of Strategic Marketing for the Flash Components Division at LSI (SandForce). Sumit is a longtime contributor to many storage standards bodies and has been working with SATA- IO – the group responsible for SATA Express – for many years. He has first-hand knowledge of SATA- IO’s work.
Here are his insights into some of the fundamentals of SATA Express.
What is SATA Express?
Sumit: There’s quite a bit of confusion in the industry about what SATA Express defines. In simple terms, SATA Express is a specification for a new connector type that enables the routing of both PCIe® and SATA signals. SATA Express is not a command or signaling protocol. It should really be thought of as a connector that mates with legacy SATA cables and new PCIe cables.
Why was SATA Express created?
Sumit: SATA Express was developed to help smooth the transition from the legacy SATA interface to the new PCIe interface. SATA Express gives system vendors a common connector that supports both traditional SATA and PCIe signaling and helps OEMs streamline connector inventory and reduce related costs.
What is the protocol used in SATA Express?
Sumit: One of the misconceptions about SATA Express is that it’s a protocol specification. Rather, as I mentioned, it’s a mechanical specification for a connector and the matching cabling. Protocols that support SATA Express include SATA, AHCI and NVME.
What are the form factors for SATA Express?
Sumit: SATA Express defines connectors for both a 2.5” drive and the host system. SATA Express connects the drive and system using SATA cables or the newly defined PCIe cables.
What connector configuration is used for SATA Express?
Sumit: Because SATA Express supports both SATA and PCIe signaling as well as the legacy SATA connectors, there are multiple configuration options available to motherboard and device manufacturers. The image below shows plug (a) which is built for attaching to a PCIe device. Socket (b) would be part of a cable assembly for receiving plug (a) or a standard SATA plug, and Socket (c) would mount to a backplane or motherboard fir receiving plug (a) or a standard SATA plug. The last two connectors are a mating pair designed to enable cabling (e) to connect to motherboards (d).
When will hosts begin supporting SATA Express?
Sumit: We expect systems to begin using SATA Express connectors early this year. They will primarily be deployed in desktop environments, which require cabling. In contrast, we expect limited use of SATA Express in notebook and other portable systems that are moving to cableless card-edge connector designs like the recently minted M.2 form factor. We also expect to see scant use of SATA Express in enterprise backplanes. Enterprise customers will likely transition to other connectors that support higher speed PCIe signaling like the SFF-8639, a new connector that was originally included in the SATA Express specification but has since been removed.
Will LSI support SATA Express?
Sumit: Absolutely. Our SF3700 flash controller will be fully compatible with the newly defined SATA Express connector and support either SATA or PCIe. Our current SF-2000 SATA flash controllers support SATA cabling used on SATA Express, but not PCIe.
Will LSI also support SRIS?
Sumit: PCIe devices enabled with SRIS (Separate Refclk Independent SSC) can self-clock so need no reference clock from the host, allowing system builders to use lower cost PCIe cables. SRIS is an important cost-saving feature for cabling that supports PCIe signaling. It doesn’t support card-edge connector designs. Today the SF3700 supports PCIe connectivity, and LSI will support SRIS in future releases of SF3700 and other products.
Why is it called SATA Express?
Sumit: SATA Express blends the names of the two connectors and captures the hybridization of the physical interconnects. The name reflects the ability of legacy SATA connectors to support higher PCIe data rates to simplify the transition to PCIe devices. SATA Express can pull double duty, supporting both PCIe and SATA signaling in the same motherboard socket. The same SATA Express socket accepts both traditional SATA and new PCIe cables and links to either a legacy SATA or SATA Express device connector.
How fast can SATA Express run?
Sumit: The PCIe interface defines the top SATA Express speed. A PCIe Gen2 x2 device supports up to 900 MB/s of throughput, a PCIe Gen3 x2 device up to 1800 MB/s of throughput – both significantly higher than 550 Mb/s speed ceiling of today’s SATA devices.
Is SATA Express similar to M.2?
Sumit: There are two key similarities. Both support SATA and PCIe on the same host connector, and both are designed to help transition from SATA to PCIe over time.
SATA Express delivers the future of connector speeds today
SATA Express was born of the stuff of all great inventions. Necessity. The challenge SATA-IO faced in doubling SATA 6 Gb/s speeds was herculean. The undertaking would have been too time-consuming to support the next-generation connection speeds that PCIe answers. It would have been too involved, requiring an overhaul of the SATA standard. Even in the brightest scenario, the effort would have produced a power guzzler at a time when greater power efficiency is a must for system builders. SATA-IO found a better path, an elegant bridge to PCIe speeds in the form of SATA Express.
Solid state drive (SSD) makers have introduced many new layout form factors that are not possible with hard disk drives (HDDs). My blog Size matters: Everything you need to know about SSD form factors talks about the many current SSD form factors, but I gave the new M.2 form factor only a glimpse. The specification and its history merit a deeper look.
A few years ago the PCI Special Interest Group (PCI-SIG), teaming with The Serial ATA International Organization (SATA-IO), started to develop a new form factor standard to replace Mini-PCIe and mSATA since specifications from both of these organizations are required to build SATA M.2 SSDs. The new layout and connector would be used for applications including WiFi, WWAN, USB, PCIe and SATA, with SSD implementations using either PCIe or SATA interfaces. The groups set out to create a narrower connector that supports higher data rates, a lower profile and boards of varying lengths to accommodate various very small notebook computers.
This new form factor also aimed to support micro servers and similar high-density systems by enabling the deployment of dozens of M.2 boards. Unique notches in the edge connector known as “keys” would be used to differentiate the wide array of products using the M.2 connector and prevent the insertion of incompatible products into the wrong socket.
The name change
Initially the M.2 form factor was called Next Generation Form Factor, or NGFF for short. NGFF was designed to follow the dimensional specifications of M.2, a different specification from NGFF, which at that time was being defined by the PCI SIG. Soon after NGFF was announced, confusion between the identical form factors reigned, prompting the name change of NGFF to M.2. Many people in the industry have been slow to adopt the new M.2 name and you often see articles that describe these solutions as M.2, formally known as NGFF.
In the world of connectors or sockets, a “key” prevents the insertion of a connector into an incompatible socket to ensure the proper mating of connectors and sockets. The M.2 specification has defined 11 key configurations, seven for use sometime in the future. A socket can only have one key, but the plug-in cards can have keyways cut for multiple keys if they support those socket types. Of the four defined keys available for current use, two support SSDs. Key ID B (pins 12-19) gives PCIe SSDs up to two lanes of connectivity and key ID M (pins 59-66) provides PCIe SSDs with up to four lanes of connectivity. Both can accommodate SATA devices. All of the key patterns are uniquely configured so that the card cannot be flipped over and inserted incorrectly.
Unfortunately these keys alone do not tell the user enough about an SSD to help in the selection of replacement or upgrade drives. For example, a computer with an M.2 socket for PCIe x2 support features a B key so that no M.2 boards with PCIe x4 requirements (M key) can fit. However, even though a SATA M.2 card with a B key can fit in the same system, the host will not recognize SATA signals from the motherboard’s PCIe socket. With this signal incompatibility, users need to carefully read other socket specifications either printed on the motherboard or included in the system configuration information to see if the socket is PCIe or SATA.
The profile and lengths
Pin spacing on the M.2 card connector is higher in density than prior connector specifications, enabling a narrower board and thinner, lighter mobile computing systems that are smaller and weigh less. What’s more, the M.2 specification defines a module with components populating only one side of the board, leaving enough space between the main system board and the module for other components. The number of flash chips used by SSDs varies with storage capacity. The less the storage capacity requirement of an SSD, the shorter the module can be used, leaving system manufacturers more space for other components.
It’s all in the name
When I hear people call this specification by the name M.2 formally known as NGFF, I cannot help but think about the time when the rock artist Prince changed his name to an unpronounceable symbol and everyone was stuck calling him The Artist Formerly Known as Prince. In his case I believe he was going for the publicity of the confusion.
As for the renaming of NGFF to M.2, I really don’t think that was the goal. In fact I believe it was intended to simplify brand identity by eliminating a second name for the same specification. No matter what we call this new form factor, it appears destined to thrive in both the mobile computing and high-density server markets.