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Trusted Business Advisors, Expert Technology Analysts

Research Areas

Systems

Includes Storage Arrays, NAS, File Systems, Clustered and Distributed File Systems, FC Switches/Directors, HBA, CNA, Routers, Components, Semiconductors, Server Blades.

Taneja Group analysts cover all form and manner of storage arrays, modular and monolithic, enterprise or SMB, large and small, general purpose or specialized. All components that make up the SAN, FC-based or iSCSI-based, and all forms of file servers, including NAS systems based on clustered or distributed file systems, are covered soup to nuts. Our analysts have deep backgrounds in file systems area in particular. Components such as Storage Network Processors, SAS Expanders, FC Controllers are covered here as well. Server Blades coverage straddles this section as well as the Infrastructure Management section above.

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Profile

The Best All-Flash Array for SAP HANA

These days the world operates in real-time all the time. Whether making airline reservations or getting the best deal from an online retailer, information is expected to be up to date with the best information at your fingertips. Businesses are expected to meet this requirement, whether they sell products or services. Having real-time, actionable information can dictate whether a business survives or dies. In-memory databases have become popular in these environments. The world's 24X7 real-time demands cannot wait for legacy ERP and CRM application rewrites.  Companies such as SAP devised ways to integrate disparate databases by building a single super-fast uber-database that could operate with legacy infrastructure while simultaneously creating a new environment where real-time analytics and applications can flourish. These capabilities enable businesses to succeed in the modern age, giving forward-thinking companies a real edge in innovation.

SAP HANA is an example of an application environment that uses in-memory database technology and allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows HANA to process data stored in RAM as opposed to reading it from a disk. At the heart of SAP HANA is a database that operates on both OLAP and OLTP database workloads simultaneously. SAP HANA can be deployed on-premises or in the cloud. Originally, on-premises HANA was available only as a dedicated appliance. Recently SAP has expanded support to best in class components through their SAP Tailored Datacenter Integration (TDI) program. In this solution profile, Taneja Group examined the storage requirements needed for HANA TDI environments and evaluated storage alternatives including the HPE 3PAR StoreServ All Flash. We will make a strong case as to why all-flash arrays like the HPE 3PAR version are a great fit for SAP HANA solutions.

Why discuss storage for an in-memory database? The reason is simple: RAM loses its mind when the power goes off. This means that persistent shared storage is at the heart of the HANA architecture for scalability, disaster tolerance, and data protection. The performance attributes of your shared storage dictate how many nodes you can cluster into a SAP HANA environment which in turn affects your business outcomes. Greater scalability capability means more real-time information is processed. SAP HANA workload shared storage requirements are unique in being write intensive with low latency for small files and sequential throughput performance for large files. However, the overall storage capacity is not extreme which makes this workload an ideal fit for all-flash arrays that can meet performance requirements with the smallest quantity of SSDs. Typically you would need 10X the equivalent spinning media drives just to meet the performance requirements, which then leaves you with a massive amount of capacity that cannot be used for other purposes.

In this study, we examined five leading all-flash arrays including the HPE 3PAR StoreServ 8450 All Flash. We found that that the unique architecture of the 3PAR array could meet HANA workload requirements with up to 73% fewer SSDs, 76% less power, and 60% less rack space. 

Publish date: 11/29/16
Free Reports

Datrium’s Optimized Platform for Virtualized IT: “Open Convergence” Challenges HyperConvergence

The storage market is truly changing for the better with new storage architectures finally breaking the rusty chains long imposed on IT by traditional monolithic arrays. Vast increases in CPU power found in newer generations of servers (and supported by ever faster networks) have now freed key storage functionality to run wherever it can best serve applications. This freedom has led to the rise of all software-defined storage (SDS) solutions that power modular HyperConverged infrastructure (HCI). At the same time, increasingly affordable flash resources have enabled all-flash array options that promise both OPEX simplification and inherent performance gains. Now, we see a further evolution of storage that intelligently converges performance-oriented storage functions on each server while avoiding major problems with HyperConverged “single appliance” adoption.

Given the market demand for better, more efficient storage solutions, especially those capable of large scale, low latency and mixed use, we are seeing a new generation of vendors like Datrium emerge. Datrium studied the key benefits that hyperconvergence previously brought to market including the leverage of server-side flash for cost-effective IO performance, but wanted to avoid the all-in transition and the risky “monoculture” that can result from vendor-specific HCI. Their resulting design runs compute-intensive IO tasks scaled-out on each local application server (similar to parts of SDS), but persists and fully protects data on cost-efficient, persistent shared storage capacity. We have come to refer to this optimizing tiered design approach as “Server Powered Storage” (SPS), indicating that it can take advantage of the best of both shared and server-side resources.

Ultimately this results in an “Open Convergence” approach that helps virtualized IT environments transition off of aging storage arrays in an easier, flexible and more natural adoption path than with a fork-lift HyperConvergence migration. In this report we will briefly review the challenges and benefits of traditional convergence with SANs, the rise of SDS and HCI appliances, and now this newer “open convergence” SPS approach as pioneered by Datrium DVX. In particular, we’ll review how Datrium offers benefits ranging from elastic performance, greater efficiency (with independent scaling of performance vs. capacity), VM-centric management, enterprise scalability and mixed workload support while still delivering on enterprise requirements for data resiliency and availability.


DATA Challenges in Virtualized Environments

Virtualized environments present a number of unique challenges for user data. In physical server environments, islands of storage were mapped uniquely to server hosts. While at scale that becomes expensive, isolating resources and requiring a lot of configuration management (all reasons to virtualize servers), this at least provided directly mapped relationships to follow when troubleshooting, scaling capacity, handling IO growth or addressing performance.

However, in the virtual server environment, the layers of virtual abstraction that help pool and share real resources also obfuscate and “mix up” where IO actually originates or flows, making it difficult to understand who is doing what. Worse, the hypervisor platform aggregates IO from different workloads hindering optimization and preventing prioritization. Hypervisors also tend to dynamically move virtual machines around a cluster to load balance servers. Fundamentally, server virtualization makes it hard to meet application storage requirements with traditional storage approaches.

Current Virtualization Data Management Landscape

Let’s briefly review the three current trends in virtualization infrastructure used to ramp up data services to serve demanding and increasingly larger scale clusters:

  • Converged Infrastructure - with hybrid/All-Flash Arrays (AFA)
  • HyperConverged Infrastructure - with Software Defined Storage (SDS)
  • Open Converged Infrastructure - with Server Powered Storage (SPS)

Converged Infrastructure - Hybrid and All-Flash Storage Arrays (AFA)

We first note that converged infrastructure solutions simply pre-package and rack traditional arrays with traditional virtualization cluster hosts. The traditional SAN provides well-proven and trusted enterprise storage. The primary added value of converged solutions is in a faster time-to-deploy for a new cluster or application. However, ongoing storage challenges and pain points remain the same as in un-converged clusters (despite claims of converged management as these tend to just aggregate dashboards into a single view).

The traditional array provides shared storage from which virtual machines draw for both images and data, either across Fibre Channel or IP network (NAS or iSCSI). While many SAN’s in the hands of an experienced storage admin can be highly configurable, they do require specific expertise to administer. Almost every traditional array has by now become effectively hybrid, capable of hosting various amounts of flash, but if the array isn’t fully engineered for flash it is not going to be an optimal choice for an expensive flash investment. Hybrid arrays can offer good performance for the portion of IO that receives flash acceleration, but network latencies are far larger than most gains. Worse, it is impossible for a remote SAN to know which IO coming from a virtualized host should be cached/prioritized (in flash)– it all looks the same and is blended together by the time it hits the array.

Some organizations deploy even more costly all-flash arrays, which can guarantee array-side performance for all IO and promise to simplify administration overhead. For a single key workload, a dedicated AFA can deliver great performance. However, we note that virtual clusters mostly host mixed workloads, many of which don’t or won’t benefit from the expensive cost of persisting all data on all flash array storage. Bottomline - from a financial perspective, SAN flash is always more expensive than server-side flash. And by placing flash remote across a network in the SAN, there is always a relatively large network latency which denigrates the benefit of that array side flash investment.

HyperConverged Infrastructures - Software Defined Storage (SDS)

As faster resources like flash, especially added to servers directly, came down in price, so-called Software Defined Storage (SDS) options proliferated. Because CPU power has continuously grown faster and denser over the years, many traditional arrays came to be actually built on plain servers running custom storage operating systems. The resulting storage “software” often now is packaged as a more cost-effective “software-defined” solution that can be run or converged directly on servers (although we note most IT shops  prefer buying ready-to-run solutions, not software requiring on-site integration).

In most cases software-defined storage runs within virtual machines or containers such that storage services can be hosted on the same servers as compute workloads (e.g. VMware VSAN). An IO hungry application accessing local storage services can get excellent IO service (i.e. no network latency), but capacity planning and performance tuning in these co-hosted infrastructures can be exceedingly difficult. Acceptable solutions must provide tremendous insight or complex QoS facilities that can dynamically shift IO acceleration with workloads as they might move across a cluster (eg. to keep data access local).  Additionally, there is often a huge increase in East-West traffic between servers.

Software Defined Storage enabled a new kind of HyperConverged Infrastructure (HCI). Hyperconvergence vendors produce modular appliances in which a hypervisor (or container management), networking and (software-defined) storage all are pre-integrated to run within the same server. Because of vendor-specific storage, network, and compute integration, HCI solutions can offer uniquely optimized IO paths with plug-and-play scalability for certain types of workloads (e.g. VDI).

For highly virtualized IT shops, HCI simplifies many infrastructure admin responsibilities. But HCI presents new challenges too, not least among them is that migration to HCI requires a complete forklift turnover of all infrastructure. Converting all of your IT infrastructure to a unique vendor appliance creates a “full stack” single vendor lock-in issue (and increased risk due to lowered infrastructure “diversity”).

As server-side flash is cheaper than other flash deployment options, and servers themselves are commodity resources, HCI does help optimize the total return on infrastructure CAPEX – especially as compared to traditional silo’d server and SAN architectures. But because of the locked-down vendor appliance modularity, it can be difficult to scale storage independently from compute when needed (or even just storage performance from storage capacity). Obviously, pre-configured HCI vendor SKU’s also preclude using existing hardware or taking advantage of blade-type solutions.

With HCI, every node is also a storage node which at scale can have big impacts on software licensing (e.g. if you need to add nodes just for capacity, you will also pay for compute licenses), overbearing “East-West” network traffic, and in some cases unacceptable data availability risks (e.g. when servers lock/crash/reboot for any reason, an HCI replication/rebuild can be a highly vulnerable window).

OPEN Converged Infrastructure - Server Powered Storage (SPS)

When it comes to performance, IO still may need to transit a network incurring a latency penalty. To help, there are several third party vendors of IO caching that can be layered in the IO path – integrated with the server or hypervisor driver stack or even placed in the network. These caching solutions take advantage of server memory or flash to help accelerate IO. However, layering in yet another vendor and product into the IO path incurs additional cost, and also complicates the end-to-end IO visibility. Multiple layers of caches (vm, hypervisor, server, network, storage) can disguise a multitude of ultimately degrading performance issues.

Ideally, end-to-end IO, from within each local server to shared capacity, should all fall into a single converged storage solution – one that is focused on providing the best IO service by distributing and coordinating storage functionality where it best serves the IO consuming applications. It should also optimize IT’s governance, cost, and data protection requirements. Some HCI solutions might claim this in total, but only by converging everything into a single vendor appliance. But what if you want a easier solution capable of simply replace aging arrays in your existing virtualized environments – especially enabling scalability in multiple directions at different times and delivering extremely low latency while still supporting a complex mix of diverse workloads?

This is where we’d look to a Server Powered Storage (SPS) design. For example, Datrium DVX still protects data with cost-efficient shared data servers on the back-end for enterprise quality data protection, yet all the compute-intensive, performance-impacting functionality is “pushed” up into each server to provide local, accelerated IO. As Datrium’s design leverages each application server instead of requiring dedicated storage controllers, the cost of Datrium compared to traditional arrays is quite favorable, and the performance is even better than (and as scalable as) a 3rd party cache layered over a remote SAN.

In the resulting Datrium “open converged” infrastructure stack, all IO is deduped and compressed (and locally served) server-side to optimize storage resources and IO performance, while management of storage is fully VM-centric (no LUN’s to manage). In this distributed, open and unlocked architecture, performance scales with each server added to naturally scale storage performance with application growth.

Datrium DVX makes great leverage for a given flash investment by using any “bring-your-own” SSDs, far cheaper to add than array-side flash (and can be added to specific servers as needed/desired). In fact, most vm’s and workloads won’t ever read from the shared capacity on the network – it is write-optimized persistent data protection and can be filled with cost-effective high-capacity drives.

Taneja Group Opinion

As just one of IT’s major concerns, all data bits must be persisted and fully managed and protected somewhere at the end of the day. Traditional arrays, converged or not, just don’t perform well in highly virtualized environments, and using SDS (powering HCI solutions) to farm all that critical data across fungible compute servers invokes some serious data protection challenges. It just makes sense to look for a solution that leverages the best aspects of both enterprise arrays (for data protection) and software/hyperconverged solutions (that localize data services for performance).

At the big picture level, Server Powered Storage can be seen as similar (although more cost-effective and performant) to a multi-vendor solution in which IT layers server-side IO acceleration functionality from one vendor over legacy or existing SANs from another vendor. But now we are seeing a convergence (yes, this is an overused word these days, but accurate here) of those IO path layers into a single vendor product. Of course, a single vendor solution that fully integrates distributed capabilities in one deployable solution will perform better and be naturally easier to manage and support (and likely cheaper).

There is no point in writing storage RFP’s today that get tangled up in terms like SDS or HCI. Ultimately the right answer for any scenario is to do what is best for applications and application owners while meeting IT responsibilities. For existing virtualization environments, new approaches like Server Powered Storage and Open Convergence offer considerable benefit in terms of performance and cost (both OPEX and CAPEX). We highly recommend that before one invests in expensive all-flash arrays, or takes on a full migration to HCI, that an Open Convergence option like Datrium DVX be considered as a potentially simpler, more cost-effective, and immediately rewarding solution.


NOTICE: The information and product recommendations made by the TANEJA GROUP are based upon public information and sources and may also include personal opinions both of the TANEJA GROUP and others, all of which we believe to be accurate and reliable. However, as market conditions change and not within our control, the information and recommendations are made without warranty of any kind. All product names used and mentioned herein are the trademarks of their respective owners. The TANEJA GROUP, Inc. assumes no responsibility or liability for any damages whatsoever (including incidental, consequential or otherwise), caused by your use of, or reliance upon, the information and recommendations presented herein, nor for any inadvertent errors that may appear in this document.

Publish date: 11/23/16
Profile

Optimizing VM Storage Performance & Capacity - Tintri Customers Leverage New Predictive Analytics

Today we are seeing big impacts on storage from the huge increase in the scale of an organization’s important data (e.g. Big Data, Internet Of Things) and the growing size of virtualization clusters (e.g. never-ending VM’s, VDI, cloud-building). In addition, virtualization adoption tends to increase the generalization of IT admins. In particular, IT groups are focusing more on servicing users and applications and no longer want to be just managing infrastructure for infrastructure’s sake. Everything that IT does is becoming interpreted, analyzed, and managed in application/business terms, including storage to optimize the return on their total IT investment. To move forward, an organization’s storage infrastructure not only needs to grow internally smarter, it also needs to become both VM and application aware.

While server virtualization made a lot of things better for the over-taxed IT shop, delivering quality storage services in hypervisor infrastructures with traditional storage created difficult challenges. In response Tintri pioneered per-VM storage infrastructure. The Tintri VMstore has eliminated multiple points of storage friction and pain. In fact, it’s now becoming a mandatory checkbox across the storage market for all arrays to claim some kind of VM-centricity. Unfortunately, traditional arrays are mainly focused on checking off rudimentary support for external hypervisor APIs that only serve to re-package the same old storage. The best fit to today’s (and tomorrow’s) virtual storage requirements will only come from fully engineered VM-centric storage and application-aware approaches as Tintri has done.

However, it’s not enough to simply drop in storage that automatically drives best practice policies and handles today’s needs. We all know change is constant, and key to preparing for both growth and change is having a detailed, properly focused view of today’s large scale environments, along with smart planning tools that help IT both optimize current resources and make the best IT investment decisions going forward. To meet those larger needs, Tintri has rolled out a Tintri Analytics SaaS-based offering that applies big data analytical power to the large scale of their customer’s VMstore VM-aware metrics.

In this report we will look briefly at Tintri’s overall “per-VM” storage approach and then take a deeper look at their new Tintri Analytics offering. The new Tintri Analytics management service further optimizes their app-aware VM storage with advanced VM-centric performance and capacity management. With this new service, Tintri is helping their customers receive greater visibility, insight and analysis over large, cloud-scale virtual operations. We’ll see how “big data” enhanced intelligence provides significant value and differentiation, and get a glimpse of the payback that a predictive approach provides both the virtual admin and application owners. 

Publish date: 11/04/16
Report

Qumulo Tackles the Machine Data Challenge: Six Customers Explain How

We are moving into a new era of data storage. The traditional storage infrastructure that we know (and do not necessarily love) was designed to process and store input from human beings. People input emails, word processing documents and spreadsheets. They created databases and recorded business transactions. Data was stored on tape, workstation hard drives, and over the LAN.

In the second stage of data storage development, humans still produced most content but there was more and more of it, and file sizes got larger and larger. Video and audio, digital imaging, websites streaming entertainment content to millions of users; and no end to data growth. Storage capacity grew to encompass large data volumes and flash became more common in hybrid and all-flash storage systems.

Today, the storage environment has undergone another major change. The major content producers are no longer people, but machines. Storing and processing machine data offers tremendous opportunities: Seismic and weather sensors that may lead to meaningful disaster warnings. Social network diagnostics that display hard evidence of terrorist activity. Connected cars that could slash automotive fatalities. Research breakthroughs around the human brain thanks to advances in microscopy.

However, building storage systems that can store raw machine data and process it is not for the faint of heart. The best solution today is massively scale-out, general purpose NAS. This type of storage system has a single namespace capable of storing billions of differently sized files, linearly scales performance and capacity, and offers data-awareness and real-time analytics using extended metadata.

There are a very few vendors in the world today who offer this solution. One of them is Qumulo. Qumulo’s mission is to provide high volume storage to business and scientific environments that produce massive volumes of machine data.

To gauge how well Qumulo works in the real world of big data, we spoke with six customers from life sciences, media and entertainment, telco/cable/satellite, higher education and the automotive industries. Each customer deals with massive machine-generated data and uses Qumulo to store, manage, and curate mission-critical data volumes 24x7. Customers cited five major benefits to Qumulo: massive scalability, high performance, data-awareness and analytics, extreme reliability, and top-flight customer support.

Read on to see how Qumulo supports large-scale data storage and processing in these mission-critical, intensive machine data environments.

Publish date: 10/26/16
Profile

Petabyte-Scale Backup Storage Without Compromise: A Look at Scality RING for Enterprise Backup

Traditional backup storage is being challenged by the immense growth of data. These solutions including tape, RAID devices that are gated by controllers and dedicated storage appliances simply aren’t designed for today’s enterprise backup storage at petabyte levels, especially when that data lives in geographically distributed environments. This insufficiency is due in large part to inefficiency and limited data protection, as well as the limited scalability and the lack of flexibility of these traditional storage solutions.

These constraints can lead to multiple processes and many storage systems to manage. Storage silos develop as a result, creating complexity, increasing operational costs and adding risk. It is not unusual for companies to have 10-20 different storage systems to achieve petabyte storage capacity, which is inefficient from a management point of view. And if companies want to move data from one storage system to another, the migration process can take a lot of time and place even more demand on data center resources.

And the concerns go beyond management complexity. Companies face higher capital costs due to relatively high priced proprietary storage hardware, and worse, limited fault tolerance, which can lead to data loss if a system incurs simultaneous disk failures. Slow access speeds also present a major challenge if IT teams need to restore large amounts of data from tape while maintaining production environments. As a result, midsized companies, large enterprises and service providers that experience these issues have begun to shift to software-defined storage solutions and scale-out object storage technology that addresses the shortcomings of traditional backup storage.

Software-defined scale out storage is attractive for large-scale data backup because these storage solutions offer linear performance and hardware independence – two core capabilities that drive tremendous scalability and enable cost-effective storage solutions. Add to this the high fault tolerance of object storage platforms, and it’s easy to see why software-defined object storage solutions are rapidly becoming the preferred backup storage approach for petabyte-scale data environments. A recent Taneja Group survey underscores the benefits of software-defined scale out storage. IT professionals indicated that the top benefits of software-defined, scale-out architecture on industry standard servers are a high level of flexibility (34%), low cost of deployment (34%), modular scalability (32%), and ability to purchase hardware separate from software (32%).

Going a step further, the Scality backup storage solution built upon the Scality RING platform offers the rare combination of scalability, durability and affordability plus the flexibility to handle mixed workloads at petabyte-scale. Scality backup storage achieves this by supporting multiple file and object protocols so companies can backup files, objects and VMs, leveraging a scale-out file system that delivers linear performance as system capacity increases, offering advanced data protection for extreme fault tolerance, enabling hardware independence for better price performance and providing auto balancing that enables migration-free hardware upgrades.

In this paper, we will look at the limitations of backup appliances and Network-Attached Storage (NAS) and the key requirements for backup storage at petabyte-scale. We will also study the Scality RING software-defined architecture and provide an overview of the Scality backup storage solution.

Publish date: 10/18/16
Free Reports

For Lowest TCO and Maximum Agility Choose the VMware Cloud Foundation Hybrid SDDC Platform

The race is on at full speed.  What race?  The race to bring public cloud agility and economics to a data center near you. Ever since the first integrated systems came onto the scene in 2010, vendors have been furiously engineering solutions to make on-premises infrastructure as cost effective and as easy to use as the public cloud, while also providing the security, availability, and control that enterprises demand. Fundamentally, two main architectures have evolved within the race to modernize data centers that will create a foundation enabling fully private and hybrid clouds. The first approach uses traditional compute, storage, and networking infrastructure components (traditional 3-tier) overlaid with varying degrees of virtualization and management software. The second more recent approach is to build a fully virtualized data center using industry standard servers and networking and then layer on top of that a full suite of software-based compute, network, and storage virtualization with management software. This approach is often termed a Software-Defined Data Center (SDDC).

The goal of an SDDC is to extend virtualization techniques across the entire data center to enable the abstraction, pooling, and automation of all data center resources. This would allow a business to dynamically reallocate any part of the infrastructure for various workload requirements without forklifting hardware or rewiring. VMware has taken SDDC to a new level with VMware Cloud Foundation.  VMware Cloud Foundation is the only unified SDDC platform for the hybrid cloud, which brings together VMware’s compute, storage, and network virtualization into a natively integrated stack that can be deployed on-premises or run as a service from the public cloud. It establishes a common cloud infrastructure foundation that gives customers a unified and consistent operational model across the private and public cloud.

VMware Cloud Foundation delivers an industry-leading SDDC cloud infrastructure by combining VMware’s highly scalable hyper-converged software (vSphere and VSAN) with the industry leading network virtualization platform, NSX. VMware Cloud Foundation comes with unique lifecycle management capabilities (SDDC Manager) that eliminate the overhead of system operations of the cloud infrastructure stack by automating day 0 to day 2 processes such as bring-up, configuration, workload provisioning, and patching/upgrades. As a result, customers can significantly shorten application time to market, boost cloud admin productivity, reduce risk, and lower TCO.  Customers consume VMware Cloud Foundation software in three ways: factory pre-loaded on integrated systems (VxRack 1000 SDDC); deployed on top qualified Ready Nodes from HPE, QCT, Fujitsu, and others in the future, with qualified networking; and run as a service from the public cloud through IBM, vCAN partners, vCloud Air, and more to come.

In this comparative study, Taneja Group performed an in-depth analysis of VMware Cloud Foundation deployed on qualified Ready Nodes and qualified networking versus several traditional 3-tier converged infrastructure (CI) integrated systems and traditional 3-tier do-it-yourself (DIY) systems. We analyzed the capabilities and contrasted key functional differences driven by the various architectural approaches. In addition, we evaluated the key CapEx and OpEx TCO cost components.  Taneja Group configured each traditional 3-tier system's hardware capacity to be as close as possible to the VMware Cloud Foundation qualified hardware capacity.  Further, since none of the 3-tier systems had a fully integrated SDDC software stack, Taneja Group added the missing SDDC software, making it as close as possible to the VMware Cloud Foundation software stack.  The quantitative comparative results from the traditional 3-tier DIY and CI systems were averaged together into one scenario because the hardware and software components are very similar. 

Our analysis concluded that both types of solutions are more than capable of handling a variety of virtualized workload requirements. However, VMware Cloud Foundation has demonstrated a new level of ease-of-use due to its modular scale-out architecture, native integration, and automatic lifecycle management, giving it a strong value proposition when building out modern next generation data centers.  The following are the five key attributes that stood out during the analysis:

  • Native Integration of the SDDC:  VMware Cloud Foundation natively integrates vSphere, Virtual SAN (VSAN), and NSX network virtualization.
  • Simplest operational experience: VMware SDDC Manager automates the life-cycle of the SDDC stack including bring up, configuration, workload provisioning, and patches/upgrades.
  •  
  • Isolated workload domains: VMware Cloud Foundation provides unique administrator tools to flexibly provision subsets of the infrastructure for multi-tenant isolation and security.
  • Modular linear scalability: VMware Cloud Foundation employs an architecture in which capacity can be scaled by the HCI node, by the rack, or by multiple racks. 
  • Seamless Hybrid Cloud: Deploy VMware Cloud Foundation for private cloud and consume on public clouds to create a seamless hybrid cloud with a consistent operational experience.

Taneja Group’s in-depth analysis indicates that VMware Cloud Foundation will enable enterprises to achieve significant cost savings. Hyper-converged infrastructure, used by many web-scale service providers, with natively integrated SDDC software significantly reduced server, storage, and networking costs.  This hardware cost saving more than offset the incremental SDDC software costs needed to deliver the storage and networking capability that typically is provided in hardware from best of breed traditional 3-tier components. In this study, we measured the upfront CapEx and 3 years of support costs for the hardware and software components needed to build out a VMware Cloud Foundation private cloud on qualified Ready Nodes.  In addition, Taneja Group validated a model that demonstrates the labor and time OpEx savings that can be achieved through the use of integrated end-to-end automatic lifecycle management in the VMware SDDC Manager software.

 

By investing in VMware Cloud Foundation, businesses can be assured that their data center infrastructure can be easily consumed, scaled, managed, upgraded and enhanced to provide the best private cloud at the lowest cost. Using a pre-engineered modular, scale-out approach to building at web-scale means infrastructure is added in hours, not days, and businesses can be assured that adding infrastructure scales linearly without complexity.  VMware Cloud Foundation is the only platform that provides a natively integrated unified SDDC platform for the hybrid cloud with end-to-end management and with the flexibility to provision a wide variety of workloads at the push of a button.

In summary, VMware Cloud Foundation enables at least five unparalleled capabilities, generates a 45% lower 3-year TCO than the alternative traditional 3-tier approaches, and delivers a tremendous value proposition when building out a modern hybrid SDDC platform. Before blindly going down the traditional infrastructure approach, companies should take a close look at VMware Cloud Foundation, a unified SDDC platform for the hybrid cloud.

Publish date: 10/17/16
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