H ow to Navigate the Converged & Hy per - converged Market This expert guide s erves as your compa ss as you t raverse today ’ s cr owded market — from n ew use cases to stacking u p ve ndors Page 1 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) In this e - guide: T h e c o n v e r g e d a n d h y p e r - c o n v e r g e d ( C I / H C I ) m a r k e t i s n o w m a t u r i n g t o a p o i n t w h e r e the list of vendors that do not have HCI offerings is shorter than those that do. Larger companies such as Dell, EMC, HP and VMware have joined HCI pioneers suc h as Nutanix, Scale Computing and SimpliVity in the crowded market. E v e n companies that previously focused on dedicated scale - out storage products have also joined in — DataCore, GridStore, Maxta, Nimboxx and Pivot3, to name a few. This proliferation of competitors has made it difficult to select the rig ht hyper - converged product for your environment a n d y o u r e m e r g i n g workloads l i k e A I — u n t i l n o w T h i s e - g u i d e s e r v e s a s y o u r f a s t - p a s s t o t h e C I / H C I m a r k e t , o f f e r i n g a n i n - d e p t h l o o k a t : • E s s e n t i a l f e a t u r e s t o l o o k f o r i n a s o l u t i o n • H e a d - t o - h e a d v e n d o r c o m p a r i s o n s • W h a t i s H C I 2 0 ? • H o w H C I accommodates n e w w o r k l o a d s l i k e A I • A n d m o r e Page 2 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) T a b l e o f C o n t e n t s : Hyper - converged vendors offer new use cases, products Hyper - converged infrastructure vendors adapt to AI workloads Hyper - converged infrastructure starts to offer greater choice What does HCI 2.0 mean for hyper - convergence? Navigate today's hyper - converged market Which converged infrastructure vendors are businesses considering? 3 vendors to consider for an HCI appliance Page 3 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s H yper - converged vendors offer new use cases, products Arun Taneja, Founder You know a tech has gone mainstream when its product category starts to split along four lines. That's the case with hyper - convergence and is evidence that it's gaining strength and acceptance. The hyper - converged market represents approximately $1B in sales and is expected to grow in excess of 50% compound annual growth rate over the next three years. Hyper - convergence emerged as an alternative to building an IT infrastructure in layers. Instead of buying compute, storage and other infrastructure elements se parately, vendors like Nutanix and SimpliVity tightly integrated them in a scale - out architecture . Compared to converged p roducts, such as HPE ConvergedSystem, NetApp FlexPod and VCE, deployment and management was much easier, Capex was lower by a factor of four and scaling was trivial. But hyper - convergence is about more than tight integration of compute and storage . It's about support of multiple hypervisors, virtualization operations, true application mobility across hypervisors, capacity planning across the i nfrastructure, integrated end - to - end data center Page 4 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) management and so on. Cloud integration is becoming another distinguishing feature, with an ultimate vision of replacing entire infrastructures with a new web - scale hyper - converged architecture. The hyper - co nverged market is splitting along four lines: • Primary storage • Primary plus secondary storage • Secondary storage only • Edge applications and storage Hyper - convergence primary storage This is the best - understood category as it's the essence of the original the me: A scale - out architecture for primary storage applications where the boundary between compute and storage is blurred and both are combined in a single node. For all products in this category, the focus shifts from managing storage to managing a VM -- everything becomes VM - centric. Hyper - convergence of primary and secondary storage When hyper - convergence started taking off, we saw one vendor -- SimpliVity -- start differentiat ing itself along the secondary storage dimension . They presented their product not only to solve the primary storage problem but also for many secondary storage use cases. Data protection, replication, WAN optimization, deduplication, compression and other functions normally associated with secondary data handling were all part of SimpliVity's story. Page 5 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) Of course, most other vendors have at least some of these functiona lities built into their products. But SimpliVity made a clear architectural distinction: by deduplicating and compressing data inline at the time of creation or ingestion, they would integrate these secondary uses of data so tightly that one wouldn't have to buy separate products for any of the functions. It demonstrated complete hyper - convergence of primary and secondary storage use cas es. To be sure, Nutanix too provides snapshot - based backup/restore and replication , but prefers to work with third - party data protection vendors to solve large - scale secondary storage issues. They place a much strong er emphasis on other aspects of hyper - convergence mentioned above, including Web - like scalability and support of their own hypervisor, Acropolis. Today, you can buy a product in the hyper - converged market focused on primary storage and separately buy indiv idual products for secondary storage, or buy one that has it all tightly integrated. Hyper - convergence of secondary storage This hyper - convergence segment was created on the premise that what's good for primary storage is also good for secondary storage. C learly, such a product would make a solid alternative to primary storage hyper - convergence as well as combined primary/secondary hyper - convergence, and would cover a wide spectrum of infrastructural functionality. The hyper - converged market represents approximately $1B in sales and is expected to grow in excess of 50% compound annual growth rate over the next three years. Page 6 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) Noting that secondary storage functionalit y today is far more than backup/restore, replication, data deduplication, compression and WAN optimization, new vendors such as Cohesity and Rubrik embraced those and other functionalities such as DevOps, copy management, big data analytics and more to eff ectively create a new category. While the two vendors look similar on paper, their architectures and strategies differ quite a bit. Cohesity, for instance, offers (or plans to) all that functionality but will still work with third - party products via open A PIs; Rubrik, on the other hand, will strictly offer their own functionality. But they share a premise: tightly converge as many of the secondary use cases as possib le and work cooperatively with other hyper - converged market offerings. Hyper - converged Edge "Edge" is the latest variation, with only one purveyor today: Riverbed. The company focuses on ROBO sites, where even hyper - converged systems are too much to manage with little or no IT expertise available. Their simpler configuration for ROBO is essentially IT - free but offers all the benefits of local application performance and convenience. Riverbed pioneered WAN optimization and consolidation with its SteelHead technology . The original idea was to remove all IT infrastructure in the ROBO and serve the application entirely from the data center. Data would reside 100% in the data center and the best of all data protection methods, including DR, would be applied to it. With a SteelHead appliance at the edge of the data center and one in each ROBO, the appliances would use WAN optimization to mitigate the effects of long distance latency. The result would be "local - like" application performance for ROBO users with only user devices at the remote sites. One would get excellent application performance with no IT infrastructure to man age. Page 7 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) But not all applications fit nicely into this category. WANs are notorious for being flaky, and may go up and down a few tim es a day. For some applications, work cannot be stopped, even for short intervals. Some applications simply couldn't be consolidated in the data center; they needed to run locally in the branch offices. If the exceptions called for IT infrastructure in the ROBO, and required data to be stored onsite in the branch office, then we were back to square one with IT infrastructure to manage in each ROBO. Riverbed recognized these challenges and developed SteelFusion, an appliance that would sit in the data cente r and at each ROBO, and the application, in the form of a VM, would be "projected" to the branch offices to run locally on the appliance with true local performance. But the master application and its data (VM) would be located in the data center where it could be managed and protected. The ROBO appliance -- SteelFusion Edge -- is the SteelHead WAN optimization integrated with virtualization and storage cache technologies. Any new data created or changed at the branch office would be instantly applied to th e master VM, using all the principles of latency mitigation and WAN optimization built into SteelFusion. Most importantly, the application would continue to run even if the WAN failed, and performance would be truly local. If the branch office disappeared for some reason, the applications could be fired up elsewhere using another SteelFusion Edge appliance. The application and IT management would stay in the data center with the branch office remaining "IT free." The data center could operate on separate co mpute, storage and networking, or be converged (as in VCE or FlexPod) or be hyper - converged, but the branch office would use Riverbed's SteelFusion Edge. A function of The hyper - converged market is no longer a monolithic category. It's forming tributaries and subtle variations are cropping up. Page 8 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) the product called FusionSync automatically fails over all ROBO operations to a secondar y data center in case of the loss of the primary data center. A one - click failback is used to return operations to the primary data center when it becomes operational again. The hyper - converged market is no longer a monolithic category. It's forming tributaries and subtle variations are cropping up. I consider this ratification that this style of computing has serious merit as demonstrated by the many users who are already enj oying its benefits. But no two IT shops are alike, so you'll have to decide which variation of hyper - convergence is right for your company. ▼ Next Article Page 9 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) Hyper - converged infrastructure vendors adapt to AI workloads Robert Sheldon, Contributor Artificial intelligence and machine learning are significantly impacting IT infrastructures nowadays -- not only as a result of the increasing number of AI and machine learning workloads, but also because man agement tools now integrate AI and machine learning to better control mission - critical workloads and the systems that support them. Given this strong movement toward AI and machine learning , it should come as no surprise these technologies are now affecting hyper - converged infrastructure (HCI), leading to hyper - converged infrastructure vendors developing more robust systems that enterprises can better integrate into their overall IT framework. Supporting AI workloads At its most basic, AI carries out the simulation of human intelli gence on machines -- most notably, computing systems -- by using rules - based learning and reasoning to analyze data and arrive at approximate or definitive conclusions. AI also includes self - correcting mechanisms to continually refine its analytics as more data becomes available. The field of AI contains a number of disciplines, including machine learning, which is a software - specific form of AI that enables applications to predict outcomes without requiring explicit programming. At its core, machine learni ng is a set of intelligent algorithms that perform statistical analysis on data, looking for patterns that can be used to predict outcomes and subsequently take actions. Page 10 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) Workloads specific to AI and machine learning are claiming more compute and storage resources than ever, leaving IT teams to scramble for AI - compatible resources to deliver the necessary application performance. In response, hyper - conver ged infrastructure vendors are updating their offerings to accommodate AI and machine learning workloads by incorporating hardwa re and software components that meet the demands for processing large quantities of data, often in real or near - real time. For example, Dell EMC built its latest VxRail appliances on 14th generation PowerEdge servers that support high - CPUs and Nvidia P40 GPUs, in addition to 25 Gbps connectivity and NVMe flash drives. According to Dell, it designed its latest HCI appliances for today's mission - critical workloads, significantly outperforming the previous Dell EMC VxRail G Series by providin g more processing power and , as well as delivering greater IOPS and faster response times, all of which are essential to AI workloads. Dell isn't alone among hyper - converged infrastructure vendors. Hewlett Packard Enterprise (HPE) has updated its SimpliVity HCI series to support fluctuating, reso urce - intensive workloads, such as AI and machine learning. For example, SimpliVity systems are now available with HPE's Composable Fabric, a software - defined networking system that's integrated in the HCI stack. Composable Fabric automates routine network management tasks, such as provisioning the network fabric in response to real - time compute and storage events. It can also automatically discover hyper - converged nodes, virtual controllers and virtual machines. Workloads specific to AI and machine learning are claiming more compute and storage re sources than ever. Page 11 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) Cisco, meanwhile, is touting its release of Cisco HyperFlex 3.5 as an AI - friendly HCI environment, with support for Nvidia's most advanced data center GPU, Tesla V100. In addition, the Cisco systems can scale using compute - only nodes equipped with GPUs to help better accommodate AI workload requirements. The HyperFlex systems also include the FlexVolume driver to provide persistent volumes for Kubernetes containers , making it easier to deploy, manage and scale containers that support AI applications. Another example among hyper - co nverged infrastructure vendors supporting AI and machine learning are IBM and Nutanix. They have joined forces to deliver an HCI system for enterprises to implement private clouds to support AI and machine applications, as well as other mission - critical wo rkloads. The HCI platform builds in Nutanix's AHV virtualization, which is based on the vendor's Acropolis HCI platform and hy pervisor. Bringing AI to HCI In addition to better supporting AI and machine learning workloads, HCI systems themselves are benefiting from AI technologies. AI helps HCI platforms manage systems and workloads, as well as automate everyday tasks. Furthermor e, advancements in AI and machine learning are leading to more intelligent data tools for working across the entire IT infrastructure, a trend known as AI for IT operations ( AIOps ) AIOps incorporates machine learning and other AI technologies, as well as big data analytics, to streamline administrative operations and optimize workload management, particularly as it applies to resource utilization. AIOps takes a holistic approach to data center management that spans the entire environment, analyzing data collected from both HCI and non - HCI data points in order to uncover patterns , identify anomalies and predict outcomes. Ideally, AIOps treats HCI environments just like any other resource, whether virtualized or running on bare metal, making it possible to map workloads to the most appropriate compute, Page 12 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) storage a nd network resources. AIOps also helps address issues that result from siloed IT resources, such as individual HCI systems. An HCI appliance might typically include comprehensive software for managing the system itself, but connecting that system to other systems in a meaningful way is much more dif ficult, especially with traditional IT tools. AIOps addressees this issue by coordinating collected data from all systems, whether multiple HCI appliances, virtual servers, storage nodes or any number of other systems. Page 13 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) Using predictive analytics, AIOps can help streamline capacity and resource allocation , while leading to more accurate and faster insights. In addition, the softwar e can automate routine processes and more quickly detect and respond to service disruptions, system failures, security threats and other issues. For example, FixStream AIOps can incorporate Nutanix HCI systems into its monitoring and analytics, offering such capabilities as automating resource discovery, maintaining inventories in near - real time, generating reports dynamically and id entifying application flows in and out of an HCI environment. Despite such offerings, AIOps is still a relatively young technology, and its full impact has yet to be felt in the enterprise. Most organizations still rely on traditional tools to manage dispa rate resources, including HCI environments. Even so, as AI management technologies continue to improve, the impact of AIOps on IT infrastructure should be keenly felt in the world of HCI. Meanwhile, efforts with less lofty goals than AIOps are also under w ay, with vendors starting to provide management tools that incorporate AI technologies. For example, some tools use AI to help streamline specific operations, such as optimizing hardware resources, balancing workloads across available storage nodes, moving data from HCI environments to secondary storage or tracking personally identifiable information across HCI clusters to ensure compliance. The limitless world of AI As the size and quantity of AI workloads have steadily evolved and increased, so too have t he HCI systems that support those workloads, with hyper - converged infrastructure vendors enhancing their products to better accommodate mission - critical applications. In comparison, Page 14 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) AIOps and AI - based tooling -- for optimizing and managing infrastructures -- still have a long way to go to catch up. AI is nonetheless starting to make significant inroads into IT management , pr omising to better manage both HCI and non - HCI resources. Not only will this make resource and workload administration more effective, it also promises to pull HCI systems into the larger picture, where all resources are treated as components of a unified s ystem across the entire IT infrastructure, thereby helping to deliver workloads more efficiently and securely, while better utilizing the resources at hand. ▼ Next Article Page 15 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) Hyper - converged infrastructure starts to offer greater choice Carol Sliwa, Senior News Writer A hyper - converged infrastructure tightly integrates storage, compute, networking and server virtualization resources in the same box, and now products are starting to offer more poin ts of differentiation. Arun Taneja, founder and consulting analyst at Taneja Group in Hopkinton, Massachusetts, surveyed the hyper - converged product landscape in this podcast interview. He explained the distinction between hyper - converged and converged systems , updated the list of products that meet his definition of hyper - convergence , discussed the latest choices users will find for hypervisors and hardware , and offered his predictions on the direction hyper - converged storage products could take. What sets apart hyper - converged storage or a hyper - converged system from a converged infrastructure ? Arun Taneja: Converged systems, or converged infrastructure , take the fundamental pieces of compute, storage, networking and server virtualization, possibly from different companies, and bring them together and make them operate as closely as [they] ca n as a whole. In other words, the vendor that will put that converged infrastructure unit together will perhaps put a management layer on top of it and a variety of other things to make the deployment and the management of those four pieces easier. Page 16 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) Hyper - converged storage or hyper - converged infrastructure or hyper - converged systems -- different terms being used meaning the same thing -- essentially means that one vendor has taken it upon [itself] to build an entire infrastructure and put it in a box . So, it not only has compute and networking and storage and server virtualization in a box, but it would also have other elements like backup software, snapshot capability, data deduplication, inline compression and WAN optimization. [One vendor] takes all the various pieces that make up a traditional infrastructure in the market today and put it in a sin gle box . That is very different than converged infrastructure, which is really three or four separate pieces that are brought together, but [each piece is] still distinct in its own right. In a hyper - converged environment , you don't see any seams. It is entirely seamless. What products fall into this category of hyper - converged storage ? Taneja: By my definition, there are three products available in the marketplace today that meet the criteria we've established for hyper - converged storage . You have Nutanix , which is considered to be the granddaddy of hyper - convergence be cause they were the first to market, and they are the pioneer. Then you have SimpliVity and Scale Computing , which is really focused at a much lower level of the organizations. I also think in terms of VSAN or Virtual SAN from VMware. Even though it may or may not meet all the criteria of hyper - converged storage , I think from a customer persp ective, [it] would be viewed as hyper - converged storage But I can also say that almost all the major vendors that have servers , storage and networking all available as part of their total arsenal, they're all looking at producing someth ing that would be considered hyper - converged storage . So, over time, I think a lot more possibilities will exi st, but at the moment, I consider the four that I mentioned to be prime examples of hyper - converged storage Page 17 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) How much choic e do users have these days with hypervisors for their hyper - converged storage ? Taneja: The starting poin t for Nutanix, for example, was VMware, so that is the predominant installed base for Nutanix today But Nutanix also made an announcement that they now have Hyper - V - based offerings. SimpliVity also started with VMware, and that is fundamentally their offering today. I do expect that SimpliVity, like Nutanix, will add additional hypervisors, but whether i t ends up to be KVM or Hyper - V, I don't know. Then the third one, Scale Computing , actually is distinct in that they were KVM based from the very beginning for very good reasons -- they are targeting the lower end of the market, which is very price sensitive. KVM, of c ourse, is open source and gives them that lower price point. And Virtual SAN from VMware, by definition, is VMware only. The customer will have one choice. Is hyper - converged storage generally associated with commodity hardware? Taneja: The answer is yes a nd no, and let me explain why. Today, if I look at it, Nutanix is based on commodity hardware. Scale Computing is based on commodity hardware. And SimpliVity is based primarily on commodity hardware, but they do hav e a very specialized ASIC that is designed for very fast inline data deduplication. SimpliVity obviously determined that the only way to do inline data deduplication without impacting the application performance was to actually have specialized hardware to go with it. Today, the answer is a mixed answer. However, as Intel compute capability continues to increase, and we've seen Moore's Law apply for the past three decades, why wouldn't it apply moving forward? At some point in time, it would not be a big de al to really see all hyper - converged storage be commodity based because, ultimately, I think that is the objective. One Page 18 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) of the primary reasons a customer would want to go to hyper - converged storage is lower prices that are demonstrated by commodity hardware. In what ways other than hardware do hyper - converged storage vendors differentiate themselves from each other? Taneja: Look at the starting points of these different vendors, and you will see where they have done exceptionally good work. Take, fo r example, SimpliVity. The roots go back to data deduplication for them. They kind of started with the premise that whe n the data is created, the data should be deduplicated and then kept in that condensed format through its entire lifecycle. That became a hallmark of their differentiation. Granted, they did that with the specialized hardware, but they excel in that catego ry. Some might have better WAN optimization between two separate sites. Someone else might have better replication capabil ities . Others might have better snapshot capabilities. All of these hyper - converged storage vendors produce what I call [virtual machine ] VM - centric solutions. In other words, you don't deal with LUNs and volumes anymore. You deal with VMs and decide what VM is important. And then all the storage and all th e networking and everything happens behind the scenes automatically. So, how efficient those things are clearly becomes a point of differentiation. There's a wide amount of differentiation, but you will not be able to tell that without looking deeper. What are the limitations of hyper - converged storage ? Taneja: All of these disparate technologies -- for example, data deduplication, compression, WAN optimization, backup, snapshot -- remember, most of these things grew up almost in separate companies. There were companies built on data deduplication alone. There are separate companies built on WAN optimization alone, and when you do a hyper - converged system, you're really bringing al l those technologies in one box Page 19 of 35 In this e - guide • I n t r o d u c t i o n ( p g 1 ) • T a b l e o f C o n t e n t s ( p g 2 ) • P a r t 1 : H C I u n d e r t h e m i c r o s c o p e : N e w u s e c a s e s , w o r k l o a d s , a n d d e v e l o p m e n t s ( p g 3 ) • P a r t 2 : W e i g h i n g y o u r o p t i o n s i n t h e C I / H CI m a r k e t ( p g 2 2 ) Clearly, each of these elements may or may not be best of breed in the traditional sense of the word. But the value of bringing all of them together in a hyper - converged format cannot be understated. A lot of customers I know would actually take [lower quality] individual elements from a best - of - breed perspective because the combination makes their life significantly easier than it is today. For what types of organizations and what use cases does hyper - converged storage make the most sense? Taneja: Hyper - converged systems are capable of taking on just about any application that exists in the environment. And in terms of the size of the organization, there is no particular reason why it is not usable by a small organization , a midsize organization or the largest of the large organizations. However, since the primary purpose for hyper - converged systems is to manage a virtualized environment, those applications that are running on physical infrastructure would probably want to stay on the physical infrastructure. So, other than applications running in physical infrastructure today, I do n't think there's a particular application that cannot be brought over on the hyper - converged infrastructure ▼ Next Article