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000-202 Enterprise Storage Technical Support(R) Specialist V1

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IBM Enterprise Storage Technical Support(R)

business Storage methods Market to Witness large boom by 2025 | leading Key avid gamers- IBM, Hewlett Packard commercial enterprise, EMC, Dell, Buffalo

Enterprise Storage Systems Market to Witness Huge Growth by 2025 | Leading Key Players- IBM, Hewlett Packard Enterprise, EMC, Dell, Buffalo

"business Storage programs Market"

dwell up to date with commercial enterprise Storage programs Market offered via HTF MI. investigate how key trends and rising drivers are shaping this business growth.

HTF MI launched a brand new market study on international enterprise Storage programs Market with a hundred+ market statistics Tables, Pie Chat, Graphs & Figures unfold through Pages and straightforward to understand extensive evaluation. "world business Storage techniques Market by way of classification (, Direct connected Storage (DAS), Storage enviornment network (SAN), network attached Storage (NAS) & mixed/ Hybrid Storage atmosphere), by end-clients/software (Retail, safety, funding/ fiscal features & other), trade measurement, agencies, and vicinity - Forecast and outlook to 2025". At current, the market is developing its presence. The analysis record items an entire assessment of the Market and carries a future fashion, existing boom elements, concentrated opinions, details, and industry certified market statistics. The research study provides estimates for global commercial enterprise Storage programs Forecast till 2025*. one of the crucial key avid gamers profiled are IBM, Hewlett Packard commercial enterprise, EMC enterprise, Dell, Buffalo, Isilon programs, 3PAR, Hitachi information methods, LSI organisation, NetGear, Overland Storage, Oracle, Panasas, SGI supplier, Intel, Seagate, integrated equipment expertise, Western Digital & Lenovo and so forth.

Get access to pattern pages @ https://www.htfmarketreport.com/sample-document/1781182-global-business-storage-programs-market-3

The international enterprise Storage programs market document extra makes a speciality of precise trade leaders and explores all necessities aspects competitive landscape. It explains strong enterprise innovations and tactics, consumption propensity, regulatory policies, contemporary moves taken via rivals, in addition to abilities funding opportunities and market threats additionally. The report emphasis crucial monetary particulars of principal manufacturers together with 12 months-smart sale, revenue increase, CAGR, creation cost analysis, and cost chain constitution.

In 2017, the world commercial enterprise Storage methods market measurement become USD XX and is forecast to reach Million YY USD in 2025, growing to be at a CAGR of xx%. The aims of this examine is to outline, market phase having opportunity, and to challenge the dimension of the commercial enterprise Storage techniques market in line with enterprise, product category, software and key areas.

anyway, the record also covers segment data, together with: class section, trade segment and so forth. cover distinctive section market measurement. also cowl distinct industries valued clientele’ counsel, which is awfully important for the main gamers. in order for you more counsel, please contact HTF MI at earnings@htfmarketreport.com.

**The market is valued in line with weighted average promoting cost (WASP) and comprises any relevant taxes on producers. All forex conversions used in the creation of this report have been calculated the use of constant annual ordinary 2018 foreign money fees. 

competitors analysis

global enterprise Storage programs Market - seller landscape: The analysts authoring the booklet explain the character and future changes in the competitive state of affairs of the international organizations which are profiled in the book ebook, some of key gamers that contains in the examine are IBM, Hewlett Packard enterprise, EMC enterprise, Dell, Buffalo, Isilon systems, 3PAR, Hitachi facts techniques, LSI enterprise, NetGear, Overland Storage, Oracle, Panasas, SGI enterprise, Intel, Seagate, built-in equipment know-how, Western Digital & Lenovo

The analyze is segmented by following Product classification , Direct connected Storage (DAS), Storage area network (SAN), community attached Storage (NAS) & combined/ Hybrid Storage environment

most important applications/end-clients industry are as follows Retail, protection, investment/ economic features & other

Enquire for customization in report @ https://www.htfmarketreport.com/enquiry-earlier than-purchase/1781182-international-commercial enterprise-storage-programs-market-three

area Segmentation: united states, Europe, China, Japan, Southeast Asia, India & crucial & South the united states

** personalized document with specified 2-level country level destroy-up can even be provided.North the usa (united states, Canada) South the usa (Brazil, Argentina, leisure of South the us)Asia (China, Japan, India, Korea, RoA) Europe (Germany, united kingdom, France, Italy, Spain, Russia, leisure of Europe) Others (center East, Africa)

during this analyze, the years considered to estimate the market measurement of international business Storage programs are as follows:• history yr: 2013-2017• Base yr: 2017• Estimated yr: 2018• Forecast year 2018 to 2025

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Key Stakeholders/world stories: • enterprise Storage systems producers• commercial enterprise Storage techniques Distributors/traders/Wholesalers• enterprise Storage methods Sub-component manufacturers• trade affiliation• Downstream providers

Following would be the Chapters to screen the global enterprise Storage programs market.

Chapter 1, to explain Definition, specifications and Classification of enterprise Storage systems, functions of commercial enterprise Storage techniques, Market section by using regions;Chapter 2, to research the Manufacturing cost constitution, uncooked cloth and Suppliers, Manufacturing method, trade Chain constitution;Chapter three, to display the Technical information and Manufacturing plants analysis of business Storage programs, capacity and industrial production Date, Manufacturing plant life Distribution, R&D popularity and technology source, raw materials Sources evaluation;Chapter four, to reveal the normal Market evaluation, capability evaluation (enterprise section), income analysis (business phase), earnings fee evaluation (enterprise section);Chapter 5 and 6, to demonstrate the Regional Market evaluation that comprises North the us, united states, Canada, Mexico, Asia-Pacific, China, India, Japan, South Korea, Australia, Indonesia, Singapore, rest of Asia-Pacific, Europe, Germany, France, UK, Italy, Spain, Russia, leisure of Europe, primary & South the united states, Brazil, Argentina, relaxation of South the usa, core East & Africa, Saudi Arabia, Turkey & rest of middle East & Africa, business Storage techniques phase Market analysis (by way of type);Chapter 7 and eight, to analyze the business Storage techniques phase Market analysis (by application) fundamental manufacturers evaluation of commercial enterprise Storage systems;Chapter 9, Market fashion analysis, Regional Market fashion, Market style by Product classification [, Direct Attached Storage (DAS), Storage Area Network (SAN), Network Attached Storage (NAS) & Mixed/ Hybrid Storage Environment], Market vogue by means of application [Retail, Security, Investment/ Financial Services & Other];Chapter 10, Regional marketing class analysis, overseas exchange classification analysis, give Chain evaluation;Chapter 11, to analyze the buyers analysis of international commercial enterprise Storage methods;Chapter 12,13, 14 and 15, to describe business Storage systems earnings channel, distributors, traders, dealers, analysis Findings and Conclusion, appendix and data source.

read distinct Index of full research look at at @ https://www.htfmarketreport.com/experiences/1781182-global-commercial enterprise-storage-programs-market-three 

Thanks for analyzing this article, you can also get individual chapter intelligent area or area sensible file version like North america, Europe or Asia. also, you probably have any special requirements, please let us know and they can offer you the document as you need.

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For IBM, Storage Is A Lighthouse For Its records-driven, Multi-Cloud World

IBM Storage announcement held in Rome

IBM

This blog became written with the aid of Steve McDowell, storage and HCI practice lead for Moor Insights & approach.

The cloud computing world is one filled with sudden pivots, swift turns, and sharp boomerangs. The early hopes that cloud computing, with its alluring cost/improvement equations and ease of management, would substitute the commercial enterprise facts center had been brief-lived.

As corporations migrated workloads to the a number of cloud suppliers, classes had been learned, and a brand new fact set in. Workloads require records, and information has gravity. It’s now not a simple be counted to circulation an application to the cloud and hope that your latest storage architecture gives the right set of services to help it. You should installation storage architectures designed to bridge on-premises infrastructure with the cloud. Storage in a multi-cloud atmosphere isn't a place for the meek.

The complexity of mixing on-premise and cloud has come into sharp center of attention over the past a number of months as every player within the enterprise IT price chain strikes into new territory.  The note "cloud" itself has develop into nebulous, as public cloud providers circulation infrastructure and capabilities on-premises, ordinary OEMs enter the potential-on-demand company, and application increasingly turns into the defining glue tying it all collectively.

Google's Cloud next developer convention turned into held this week in San Francisco, where the business introduced a group of utility capabilities referred to as "Anthos" to manage purposes and workloads throughout deepest facts centers and Google Cloud services. Anthos even promises to aid workloads on its opponents' clouds, Amazon internet functions and Microsoft service provider's Azure. Anthos is, no longer extraordinarily, based on containers and Kubernetes, with a storage story that depends on vendors correctly assisting the Container Storage Interface (CSI).

Deploying solutions akin to Google’s Anthos, or even Amazon’s Outpost on-web page cloud offering, requires complicated integration with a gradual eye against balancing compute and storage. These multi-cloud implementations aren't flip-key, as a substitute relying on tight coordination between companions to installation an business-competent answer.

while the cloud suppliers and server OEMs vacillate on what they each and every believe is the appropriate steadiness of on-premises and cloud applied sciences, every retaining high-margin turf within the procedure, they may still talk about IBM, who has emerged as an unlikely lighthouse for the records-pushed multi-cloud world. IBM , if you're not conscious, is the quantity four public cloud company international. The business doesn't get down within the grime and fight for each little bit of company in the same method that Amazon and Google do. IBM instead focuses its cloud efforts where it has all the time focused its business: servicing the wants of the enterprise.

on my own amongst cloud suppliers and storage expertise carriers, IBM has under no circumstances vacillated on its imaginative and prescient. IBM has always viewed multi-cloud as an clever mix of infrastructure and cloud, managed with the aid of a comprehensive blend of utility and capabilities. It has made it effortless for its customers to set up multi-cloud options, whether these cloud workloads are containerized or leverage greater common digital computer technology. last week in Rome, IBM’s storage team persisted its efforts to permit commercial enterprise or not it's multi-cloud event.

All about the software

The statistics-driven multi-cloud world revolves around the utility stack,  which makes the capabilities precise and manageable. For IBM, that software is its Spectrum Storage suite of items.

the most beautiful of IBM’s storage announcements is its enhancements to IBM Spectrum Virtualize, now offering aid for Amazon AWS public cloud. IBM Spectrum Virtualize for Public Cloud allows for for hybrid multi-cloud mobility to and from AWS and IBM clouds, with non-disruptive migration to, from, and between clouds. IBM Spectrum Virtualize for Public Cloud is hosted on a pair of AWS EC2 compute cases, where it will possibly virtualize and manage EBS block storage, and photo to and from S3 storage. The software offers information mobility from IBM’s Storwize family unit, FlashSystem 9100, SVC, and VersaStack.  It’s an across-the-board solution play for IBM.

all through its Rome event, IBM additionally up to date its Spectrum Scale facts-administration device to boost efficiency for SMB and NFS, while also aiding new stages of scalability and resiliency.

one of the most greater fascinating bulletins from IBM is its new software aid for blockchain expertise inside IBM storage options. As blockchain evolves into a crucial skill for managing chains of have confidence, i will be able to see many functions leveraging this know-how in a multi-cloud world. i'm anxious to see this evolve and consider how companies leverage the ability.

one of the most first-rate issues about IBM's storage choices is the blueprints the company provides to help business IT and IBM's partners straight away installation solutions with self belief. IBM Spectrum Virtualize for Public Cloud extends the library of blueprints, with new choices defining workload mobility with VMware's NSX, business continuity, and cyber-resiliency with "air-gapped" snapshots.

NVMe in all places

application is the primary fearful gadget of the multi-cloud infrastructure, however that application can simplest ever be as in a position because the hardware elements its tasked to manage.  performance within the storage world is defined by using the capabilities provided by means of the blend of flash memory and the NVMe interconnect. IBM turned into a really early adopter of NVMe-based flash storage, deploying its customized FlashCore modules to convey very excessive-throughput, low-latency, options into its performance product.

The element about multi-cloud is that it doesn’t all the time require the highest-performing arrays. Deploying multi-cloud solutions requires the performance mandatory for a given workload, with enough scalability to survive future evolutions of that workload. To that end, IBM introduced enhancements to its Storwize V5000 family unit, bringing stronger capabilities to the reduce-conclusion of its storage choices, and providing end-to-conclusion NVMe to its V5100 series.

the new IBM Storwize V5100F and v5100 deliver NVMe to a in the past not possible fee-point. The arrays convey virtually 2.5x greater performance than the outdated V5030F, present 9x extra cache than old iterations, and have help for server-type reminiscence. The densities are equally compelling, with the arrays capable of carry as much as 2PB of flash in just 2U.  That capacity can scale-up to 23PB, and scale-out to 32PB with 2-approach clustering enabled. The IBM Storwize V5100F redefines how be sure to think about affordable performance and density.

IBM also updated its Storwize 5100, bringing new stages of scalability and density to the lessen-cost range of its offerings. The updated IBM Storwize V5010E double the IOPS of its predecessor whereas scaling to 12PB. The up to date IBM Storwize V5030E additionally presents a nice bump, providing 20% more advantageous max IOPs, with scalability up to 32PB.

IBM also provided updates to its FlashSystem A9000/A9000R to supply more advantageous aid for multi-tenant environments. The up-to-date FlashSystem now allows for sharing of physical storage supplies among numerous digital networks, while also helping VLAN tagging on its iSCSI ports. These facets should lead to superior protection and an ordinary reduction of fees in multi-tenant environments. These are critical enhancements for MSPs and others who share elements between disparate consumer companies.

Tying together all of IBM’s storage portfolio is its rich suite of Spectrum Storage utility, designed to combine IBM storage infrastructure with the multi-cloud world. The mixture of IBM Spectrum Storage application and the up-to-date arrays offers you an end-to-end solution equipped for containerized, AI-driven workloads. at the identical time, this set of updates gives IBM one of the vital broadest stages of NVMe-enabled flash storage in the industry.

Concluding innovations

because the average server OEMs and the general public cloud providers hone in on a set of architectures for the records-driven multi-cloud world, it is obvious that the solution turned into right in entrance of us the entire time. IBM has blended cloud and infrastructure from the very early days of its cloud choices. The enterprise promises the most cohesive set of capabilities and options that scale on-premise and cloud hosted workloads.

IBM's storage crew, in particular, has been aggressive in riding this imaginative and prescient. Its line of storage arrays are one of the crucial most competitive in the industry, but when you couple these arrays with the power of the IBM Spectrum Storage software suite, it turns into unbeatable. IBM stands just about by myself in offering a complete latitude of storage options that span statistics center hardware, inner most cloud, and public cloud. Its fresh embody of Amazon AWS and other public cloud opponents is a robust circulate that benefits IBM's commercial enterprise client base. option is all the time respectable.

Steve McDowell is a Moor Insights & strategy Senior Analyst overlaying storage applied sciences.

Disclosure: My firm, Moor Insights & method, like every analysis and analyst corporations, offers or has offered research, evaluation, advising, and/or consulting to many high-tech groups within the industry, together with Microsoft, IBM, Google, and VMware, which may well be mentioned listed here. I don't hold any fairness positions with any groups stated in this column.


IBM Storwize: New improvements for Midmarket, Channel

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Enterprise Storage Technical Support(R) Specialist V1

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Top 7 storage certifications for IT pros

The Storage Networking Industry Association (SNIA) is a non-profit, membership-driven organization dedicated to providing standards and education/certification on IT storage technologies. It's been around since 1997 and is both well connected in the industry and highly respected therein.

The SNIA certification program offers the SNIA Certified Storage Engineer (SCSE), SNIA Certified Storage Architect (SCSA) and SNIA Certified Storage Networking Expert (SCSN-E) credentials. The SCSN-E is the pinnacle cert in this line-up, and identifies IT professionals who can design, deploy and manage a storage network in a multivendor environment.

This is a rigorous certification that can take years of experience and study to achieve. Those who persevere and earn the SCSN-E join an elite group of storage networking professionals at the top of their games.

Requirements: Pass three exams: SNIA Storage Network Foundations exam (S10-101) or substitute CompTIA Storage+ Powered by SNIA certification, SNIA Storage Networking Management/Administration exam S10-201 or S10-210, and SNIA Architect - Assessment, Planning & Design exam S10-300 or S10-310. Must also complete two SNIA Certification Partner product credentials. Partners include Brocade, Cisco, Dell, EMC, HP ASE, Hitachi Data Systems and NetApp. (See list at http://www.snia.org/education/certification/scsne.)

Exam costs: $200 per SNIA exam + the cost of partner product credentials (which vary): $600 plus.


Edge computing: The state of the next IT transformation

edge-computing-intro-header.jpg Image: metamorworks, Getty Images/iStockphoto

In recent years, computing workloads have been migrating: first from on-premises data centres to the cloud and now, increasingly, from cloud data centres to 'edge' locations where they are nearer the source of the data being processed. The goal? To boost the performance and reliability of apps and services, and reduce cost of running them, by shortening the distance data has to travel, thereby mitigating bandwidth and latency issues.

That's not to say that on-premises or cloud centres are dead -- some data will always need to be stored and processed in centralised locations. But digital infrastructures are certainly changing. According to Gartner, for example, 80 percent of enterprises will have shut down their traditional data centre by 2025, versus 10 percent in 2018. Workload placement, which is driven by a variety of business needs, is the key driver of this infrastructure evolution, says the analyst firm:

With the recent increase in business-driven IT initiatives, often outside of the traditional IT budget, there has been a rapid growth in implementations of IoT solutions, edge compute environments and 'non-traditional' IT. There has also been an increased focus on customer experience with outward-facing applications, and on the direct impact of poor customer experience on corporate reputation. This outward focus is causing many organizations to rethink placement of certain applications based on network latency, customer population clusters and geopolitical limitations (for example, the EU's General Data Protection Regulation [GDPR] or regulatory restrictions).

There are challenges involved in edge computing, of course -- notably centering around connectivity, which can be intermittent, or characterised by low bandwidth and/or high latency at the network edge. That poses a problem if large numbers of smart edge devices are running software -- machine learning apps, for example -- that needs to communicate with central cloud servers, or nodes in the intervening 'fog'. Solutions are on the way, however.

With edge computing sitting at the peak of Gartner's 2018 Hype Cycle for Cloud Computing, there's plenty of scope for false starts and disillusionment before standards and best practices are settled upon, and mainstream adoption can proceed. This introduction to ZDNet's special report looks to set the scene and assess the current state of play.

Definitions

Edge computing is a relatively new concept that has already been associated with another term, 'fog computing', which can lead to confusion among non-specialist observers. Here are some definitions that will hopefully clarify the situation.

Futurum ResearchUnlike Cloud Computing, which depends on data centers and communication bandwidth to process and analyze data, Edge Computing keeps processing and analysis near the edge of a network, where the data was initially collected. Edge Computing (a category of Fog Computing that focuses on processing and analysis at the network node level)...should be viewed as a de facto element of Fog Computing.

State of the Edge 2018The delivery of computing capabilities to the logical extremes of a network in order to improve the performance, operating cost and reliability of applications and services. By shortening the distance between devices and the cloud resources that serve them, and also reducing network hops, edge computing mitigates the latency and bandwidth constraints of today's Internet, ushering in new classes of applications. In practical terms, this means distributing new resources and software stacks along the path between today's centralized data centers and the increasingly large number of devices in the field, concentrated, in particular, but not exclusively, in close proximity to the last mile network, on both the infrastructure and device sides.

451 Research/OpenFog Consortium[Fog] begins on one 'end' with edge devices (in this context, they define edge devices as those devices where sensor data originates, such as vehicles, manufacturing equipment and 'smart' medical devices) that have the requisite compute hardware, operating system, application software and connectivity to participate in the distributed analytics Fog. It extends from the edge to 'near edge' functions, such as local datacenters and other compute assets, multi-access-edge (MEC) capabilities within an enterprise or operator radio access network, intermediate computing and storage capabilities within hosting service providers, interconnects and colocation facilities, and ultimately to cloud service providers. These locations have integrated or host 'Fog nodes', which are devices capable of participating in the overall distributed analytics system.

David Linthicum (Chief Cloud Strategy Officer at Deloitte Consulting)"With edge, compute and storage systems reside at the edge as well, as close as possible to the component, device, application or human that produces the data being processed. The purpose is to remove processing latency, because the data needn't be sent from the edge of the network to a central processing system, then back to the edge...Fog computing, a term created by Cisco, also refers to extending computing to the edge of the network. Cisco introduced its fog computing in January 2014 as a way to bring cloud computing capabilities to the edge of the network...In essence, fog is the standard, and edge is the concept. Fog enables repeatable structure in the edge computing concept, so enterprises can push compute out of centralized systems or clouds for better and more scalable performance."

Here's how the OpenFog Consortium visualises the relationship between data-generating 'things' at the network edge, cloud data centres at the core, and the fog infrastructure in between:

openfogdiagram.png

openfogdiagram.png

Image: OpenFog Consortium Market estimates

According to B2B analysts MarketsandMarkets, the edge computing market will be worth $6.72 billion by 2022, up from an estimated $1.47bn in 2017 -- a CAGR (Compound Annual Growth Rate) of 35.4 percent. Key driving factors are the advent of the IoT and 5G networks, an increase in the number of 'intelligent' applications, and growing load on cloud infrastructure:

Edge computing market dynamics

Drivers
  • Growing load on cloud infrastructure
  • Wide range of applications in different industries
  • Increase in the number of intelligent applications
  • Restraints
  • Security and privacy concerns related to edge computing
  • Opportunities Challenges
  • Inadequate industry standards and interoperability issues
  • [Data: MarketsandMarkets]

    Among the vertical segments considered by MarketsandMarkets, Telecom and IT is expected to have the biggest market share during the 2017-2022 forecast period. That's because enterprises faced with high network load and increasing demand for bandwidth will need to optimise and extend their Radio Access Network (RAN) in order to deliver an efficient Mobile (or Multi-access) Edge Computing (MEC) environment for their apps and services.

    The fastest-growing segment of the edge computing market during the forecast period is likely to be retail, says MarketsandMarkets: high volumes of data generated by IoT sensors, cameras and beacons that feed into smart applications will be more efficiently collected, stored and processed at the network edge, rather than in the cloud or an on-premises data centre.

    Grand View Research takes a more conservative view, estimating that the edge computing market will be worth $3.24 billion by 2025, although that's still a 'phenomenal' CAGR of 41 percent over the 2017-2025 forecast period. Regionally, North America will lead the market due to increasing penetration of IoT devices in the US and Canada, said the research firm, while the vertical segment with the highest CAGR will be healthcare and life sciences, thanks to "storage capabilities and real-time computing offered by edge computing solutions". SMEs will witness the highest CAGR (46.5%) over the forecast period, said Grand View Research, thanks to the ability of edge computing solutions to reduce operating costs.

    The most optimistic growth estimate comes from 451 Research, in an October 2017 study -- Size and Impact of Fog Computing Market -- commissioned by the OpenFog Consortium. This wide-ranging research puts the market opportunity for fog computing at $18.2 billion by 2022, up from $1.03bn in 2018 and $3.7bn in 2019 -- a CAGR of 104.9 percent between 2018 and 2022.

    451-openfogmarket-forecast-1.png

    451-openfogmarket-forecast-1.png

    Data: 451 Research & OpenFog Consortium / Chart: ZDNet

    According to 451 Research, the leading verticals for fog computing in 2022, in terms of market share, will be utilities, transportation, healthcare, industrial and agriculture.

    451-openfogmarket-forecast-22.png

    451-openfogmarket-forecast-22.png

    Image: 451 Research & OpenFog Consortium

    When it comes to the fog computing ecosystem in 2022, 451 Research breaks down the components like this:

    451-openfogmarket-forecast-3.png

    451-openfogmarket-forecast-3.png

    Data: 451 Research & OpenFog Consortium / Chart: ZDNet

    Hardware components are well out in front, with a 42.1 percent slice of the 2022 pie, followed by fog applications/platforms (21.5%) and fog services (20.4%). No wonder hardware vendors and cloud application/services providers are queueing up to get involved in the fast-developing edge/fog market.

    Despite their different emphases, these forecasts make it clear that the 'perfect storm' for edge computing is being created by a rapidly increasing number of internet-connected devices and the imminent advent of high-bandwidth, low-latency 5G networks. Ericsson's June 2018 Mobility Report summarises the expected developments in these areas.

    Whereas PCs, laptops, tablets and (to a lesser extent) mobile phones show flat growth between 2017 and 2023, IoT devices are taking off: those with wide-area connections will see 30 percent CAGR, with short-range IoT devices showing significant but slower growth (17% CAGR). This results in an almost 80 percent (79.4%) increase in the number of connected devices between 2017 (17.5 billion) and 2023 (31.4 billion):

    ericsson-connected-devices.png

    ericsson-connected-devices.png

    * Cellular IoT devices are a subset of Wide-area IoT devices.

    Data: Ericsson Mobility Report, June 2018 / Chart: ZDNet

    As far as 5G is concerned, Ericsson expects the first data-only devices from the second half of 2018 and the first 5G smartphones in 2019. By 2023, following the advent of third-generation chipsets in 2020, the company forecasts that 1 billion 5G devices will be connected worldwide.

    ericsson-5g-devices.png

    ericsson-5g-devices.png

    CPE/FWT: Customer-Provided Equipment/Fixed Wireless Terminal

    Image: Ericsson Mobility Report, June 2018

    The first module-based 5G IoT devices, supporting ultra-low latency communications for industrial process monitoring and control, are expected during 2020, says Ericsson.

    Standards & organisations

    Any new IT initiative requires standards and best practices, and the early stages are often characterised by multiple groups and consortia with different agendas (despite often significant overlap in membership). Edge/fog computing is no exception.

    Fog computing, a term coined by Cisco, is backed by the OpenFog Consortium, which was founded in 2015 by Arm, Cisco, Dell, Intel, Microsoft and the Princeton University Edge Laboratory. Its mission statement reads (in part):

    Our efforts will define an architecture of distributed computing, network, storage, control and resources that will support intelligence at the edge of IoT, including autonomous and self-aware machines, things, devices, and smart objects. OpenFog members will also identify and develop new operational models. Ultimately, their work will help to enable and drive the next generation of IoT.

    Edge computing is promoted by the EdgeX Foundry, an open-source project hosted by The Linux Foundation. EdgeX Foundry's goals include: building and promoting EdgeX as a common platform unifying IoT edge computing; certifying EdgeX components to ensure interoperability and compatibility; providing tools to quickly create EdgeX-based IoT edge solutions; and collaborating with relevant open-source projects, standards groups and industry alliances.

    According to EdgeX Foundry, "The project's sweet spot is edge nodes such as embedded PCs, hubs, gateways, routers, and on-premises servers to address key interoperability challenges where 'south meets north, east, and west' in a distributed IoT fog architecture".

    EdgeX Foundry's technical steering committee includes representatives from IOTech, ADI, Mainflux, Dell, The Linux Foundation, Samsung Electronics, VMWare and Canonical.

    There are two other industry bodies in this area: the Japan-focused EdgeCross Consortium, which was founded in November 2017 by Omron Corporation, Advantech, NEC, IBM Japan, Oracle Japan and Mitsubishi Electric; and the Industrial Internet Consortium, founded in 2014 by AT&T, Cisco, General Electric, Intel, and IBM.

    What the surveys say

    Edge Computing Index: From Edge to Enterprise

    futurumreport-cover.png

    futurumreport-cover.png

    Futurum Research surveyed over 500 North American companies (ranging from 500 to 50,000 employees) in late 2017 to discover their position on edge computing -- adoption and deployment, investment intent, and more. All respondents exerted influence on edge computing investment decisions, said Futurum, with 41.8 percent being 'operational staff' and 25.6 percent at 'director, manager, team lead' level; only 8.6 percent were classed as 'executive, C-suite, owner, partner' though.

    Futurum reported that nearly three-quarters (72.7%) of companies had already implemented an edge computing strategy, or were in the process of doing so. Furthermore, almost all (93.3%) intended to invest in edge computing in the next 12 months:

    futuruminvestmentoutlookchart.png

    futuruminvestmentoutlookchart.png

    Data: Futurum Research / Chart: ZDNet

    Futurum also curates a general Digital Transformation Index, which in 2018 put 68 percent of companies in the 'leaders' and 'adopters' categories. So the fact that 72.7 percent of respondents are already investing in edge computing shows that this is a hot topic for tech-savvy businesses. However, Futurum also noted that "the eagerness of 93.3% of businesses to invest in edge computing in the next 12 months does not speak to the size of their investment".

    The positive vibes among Futurum's respondents continued when they were asked about the importance of edge computing data streams in their business processes, with 71.8 percent describing these as 'critically' (22.2%) or 'very' (49.6%) important:

    futurumimportancechart.png

    futurumimportancechart.png

    Data: Futurum Research / Chart: ZDNet

    What were the key drivers of this enthusiasm for edge computing? For Futurum's respondents, it was 'improved application performance', followed by 'real-time analytics/data streaming':

    futurumuse-caseschart.png

    futurumuse-caseschart.png

    Data: Futurum Research / Chart: ZDNet

    The analyst firm interpreted these priorities as a reflection of the need for operational efficiency, suggesting that the relatively low ranking for IoT strategy -- often quoted as a canonical edge computing use case -- "will likely increase in the coming years".

    Only 15.6 percent of Futurum's respondents aimed to keep edge computing and cloud computing separate -- a decision often driven by data and system security concerns, and a focus on compartmentalised operations, the research firm said. That leaves nearly 64 percent (63.9%) who had already deployed (28.3%) or were seeking (35.6%) combined edge/data centre analytics solutions, plus 20.5 percent who were unsure whether to combine these functions or keep them separate:

    futurumintegrationchart.png

    futurumintegrationchart.png

    Data: Futurum Research / Chart: ZDNet

    The 'unsure' and 'seeking' responses amount to 56.1 percent of the survey sample, which clearly represents a significant opportunity for edge computing providers.

    2018 Outlook for Fog Computing

    openfogsurvey-cover.png

    openfogsurvey-cover.png

    The OpenFog Consortium surveyed its 61 member organisations on the state of fog computing in December 2017, finding that an impressive 70 percent of CEOs were aware of the fog computing initiatives happening on their watch.

    Budgets for fog computing in 2018 were generally increasing (40%) or staying the same (51%), with just 5 percent of respondents reporting a decrease. Initiatives were primarily based in the R&D department (51%) and overwhelmingly had IoT applications as their primary focus area (70%).

    Security was the number-one concern among OpenFog respondents (32%), followed by worries about early/unproven technology, interoperability and unclear ROI. The main drivers of interest in fog computing were latency and bandwidth issues. Respondents expected manufacturing, smart cities and transportation to be the top industry segments adopting fog computing, followed by energy, healthcare and smart homes.

    Key vendors

    Edge/fog computing can pull workloads away from cloud data centres, so it's no surprise to see the cloud giants taking steps to prevent those workloads from escaping their orbit.

    AmazonIntroduced at Amazon's 2016 re:Invent developer conference, AWS Greengrass builds on the company's existing IoT and Lambda (serverless computing) offerings to extend AWS to intermittently connected edge devices.

    "With AWS Greengrass, developers can add AWS Lambda functions to a connected device right from the AWS Management Console, and the device executes the code locally so that devices can respond to events and take actions in near real-time. AWS Greengrass also includes AWS IoT messaging and synching capabilities so devices can send messages to other devices without connecting back to the cloud," said Amazon. "AWS Greengrass allows customers the flexibility to have devices rely on the cloud when it makes sense, perform tasks on their own when it makes sense, and talk to each other when it makes sense -- all in a single, seamless environment."

    aws-greengrass.png

    aws-greengrass.png

    Image: Amazon Web Services

    These are 'smart' edge devices, of course: Greengrass requires at least 1GHz of compute (either Arm or x86), 128MB of RAM, plus additional resources for OS, message throughput and AWS Lambda execution. According to Amazon, "Greengrass Core can run on devices that range from a Raspberry Pi to a server-level appliance".

    MicrosoftIntroduced at Microsoft's BUILD 2017 developer conference and generally available since June 2018, Azure IoT Edge allows cloud workloads to be containerised and run locally on smart devices ranging from a Raspberry Pi to an industrial gateway.

    Azure IoT Edge comprises three components: IoT Edge modules; the IoT Edge runtime; and IoT Hub. IoT Edge modules are containers that run Azure services, third-party services or custom code; they are deployed to IoT Edge devices and execute locally. The IoT Edge runtime runs on each IoT Edge device, managing the deployed modules, while IoT Hub is a cloud-based interface for remotely monitoring and managing IoT Edge devices.

    Here's how the different Azure IoT Edge elements fit together:

    azure-iot-edge.png

    azure-iot-edge.png

    Images: Microsoft

    With general availability, Microsoft added new capabilities to Azure IoT Edge, including: open-source support; device provisioning, security and management services; and a simplified developer experience.

    google-edge-tpu.jpg

    google-edge-tpu.jpg

    Google's Edge TPU ASIC, compared to a 1 cent coin.

    Image: Google

    GoogleIn July 2018, Google announced two products for developing and deploying smart connected devices at scale: Edge TPU and Cloud IoT Edge. Edge TPU is a purpose-built small-footprint ASIC chip designed to run TensorFlow Lite machine-learning models on edge devices. Cloud IoT Edge is the software stack that extends Google's cloud services to IoT gateways and edge devices.

    Cloud IoT Edge has three main components: a runtime for gateway-class devices (with at least one CPU) to store, translate, process and extract intelligence from edge data, while interoperating with the rest of Google's Cloud IoT platform; the Edge IoT Core runtime that securely connects edge devices to the cloud; and the Edge ML runtime, based on TensorFlow Lite, that performs machine-learning inference using pre-trained models.

    google-cloud-iot-edge.png

    google-cloud-iot-edge.png

    Image: Google

    Both Edge TPU and Cloud IoT Edge are at the alpha testing stage at the time of writing (September 2018).

    Outlook

    The edge/fog computing transformation is one of those shifts in focus that happen periodically in computing -- from mainframes to desktop PCs, to on-premises data centres, to cloud data centres, for example. Now we're looking at a mix of existing elements, along with billions of smart IoT devices, bound together by an intervening 'fog' of gateways and nodes. Device connectivity has been a bottleneck holding back this transformation, but that's about to get a huge boost with the advent of 5G mobile networks.

    Any industry sector that can derive benefit from the timely analysis of IoT data streams -- and that's pretty much all of them -- will be interested in edge/fog computing. That's why there are huge opportunities for vendors at all levels of the technology stack -- standards, networking, compute, storage, applications and services.

    With ever more data being generated, processed and stored in ever more locations, issues surrounding infrastructure management and data security, privacy and governance will become even more important than they are today. Let's hope those issues are addressed sooner rather than later.

    RECENT AND RELATED CONTENT

    Understanding Edge ComputingThe edge is that theoretical space where a data center resource can be accessed in the minimum amount of time. The location could be in the data center, on the desktop, or wherever there's a need.

    Microsoft Azure gets new tools for edge computing and machine learning (TechRepublic)Announced at the Microsoft Ignite conference today, these new features are designed to streamline the collection, processing and analysis of large volumes of data.

    VMware taps IoT to extend hybrid and multi-cloud environments to the edge (TechRepublic)At VMworld 2018, VMware unveiled its extended edge computing strategy to better control, secure, and scale customers' edge and IoT applications and solutions.

    IT leader's guide to edge computing (Tech Pro Research)Companies of all sizes and across various industries are moving to edge computing to generate, collect, and analyze data so they can take immediate action on that information. This guide looks at the pros and cons of edge computing and how its real-world usage has been working out.


    How To Do Stateless Compute, Clustered Storage Like A Hyperscaler

    There are so many companies that claim that their storage systems are inspired by those that have been created by the hyperscalers – particularly Google and Facebook – that it is hard to keep track of them all.

    But if they had to guess, and they do because the search engine giant has never revealed the nitty gritty on the hardware architecture and software stack underpinning its storage, they would venture that the foundation of the current Google File System and its Colossus successor looks a lot like what storage upstart Datrium has finally, after many years of development, brought to market for the rest of us.

    We would guess further that the relationship between compute and storage on Google’s infrastructure looks very much like what Datrium has put together with the third iteration of its DVX stack, which is, by necessity and by nature, a platform in its own right.

    It is hard to say for sure, of course, because companies like Google can be secretive. While a lot of hyperconverged server-storage suppliers such as Nutanix with its Enterprise Computing Platform claim their inspiration came from Google, they don’t think that Google uses a stack anywhere as complex as the hyperconverged server-SAN half-bloods, and the people from server virtualization juggernaut VMware and storage archiving specialist Data Domain, which was acquired by EMC in 2009 and which is now part of Dell, who started Datrium in 2012 and who have raised $110 million in four rounds of funding since that time to commercialize their ideas about converging primary and backup storage while also disaggregating compute from several levels of storage and making the whole shebang cheaper and easier to manage.

    “You will find, that if you know people at Google, they have had separated compute and data nodes for a long time, and the same is true of Facebook, Amazon, and so on,” Brian Biles, CEO at Datrium, tells The Next Platform. “And the reason is that at scale you have to operate that way because the rebuild times on storage are just ridiculous, and they have distributed erasure coding for data protection and the system is optimized for low latency I/O because of loads like Gmail. They also optimize for simple maintenance of their nodes. And you can even see it with Amazon with their services, where you store to instance flash but you persist in an object store that is separate. This is the same model, but it is just made private.”

    Biles co-founded Datrium Hugo Patterson, who was the chief architect and then CTO at Data Domain and who before that was a member of the technical staff at network-attached storage pioneer NetApp. Boris Weissman is also a co-founder, and was previously a Java engineer at Sun Microsystems, a technical lead at LoudCloud, the cloud startup whose control system was productized as OpsWare and which was acquired by Hewlett Packard Enterprise back in 2007 for $1.6 billion, and then a principal engineer at VMware working on the core hypervisor for over a decade. Sazzala Reddy, a chief architect at EMC and Data Domain, and Ganesh Venkitachalam, who was a principal engineer at VMware and IBM, round out the company’s founders. They know a thing or two about server virtualization and storage virtualization.

    Datrium DVX embodies all of the best ideas they have, and it is distinct from the hyperconverged storage out there on the market in some important ways. The reason why they are bringing up Datrium now, even though it is five years old, is that the company’s hybrid compute-storage platform is now available at petascale, which it was not before. Let’s talk about what DVX is first and then how it is different from other hyperconverged storage.

    Like many modern converged infrastructure, DVX runs on commodity X86 servers using a combination of flash and disk storage and generic Ethernet networking to lash together nodes in a cluster. The DVX stack is comprised of compute nodes and data nodes, and either can, in theory, be based on any time of server customers prefer, with as many or as few sockets as they desire. For now, Datrium is letting customers pick their own compute nodes but is designing its own specific data nodes. The compute nodes are equipped with processors and main memory and have high-speed flash on them, and the combination of the two provides a place where applications and their hottest data run. Only active data is stored on the compute nodes, and anything that needs to be persisted is pushed out to the data nodes over the network. Importantly, the compute nodes are stateless, meaning they do not talk to each other or depend on each other in any way and the storage is also not dependent or hard-wired to them in any way.

    The compute nodes are typically based on two-socket Xeon servers these days, and they can support VMware ESXi or Red Hat KVM hypervisors as a virtualization abstraction layer and also can run Docker containers on the bare metal if customers want to be truly Google-like. Both Red Hat Enterprise Linux 7.3 and the cheaper CentOS 7.3 alternative are supported, by the way, and the VMware stack can support Docker containers inside of the VMs if customers want to do that and nodes running KVM can also support containers atop KVM. The bare metal Docker 1.12 stack runs with RHEL or CentOS. Importantly, the compute nodes, not the data nodes, run its storage stack, which includes distributed erasure coding for data protection as well as the de-duplication and data compression technologies that Data Domain helped develop and commercialize a decade ago.

    Biles is not revealing all of the feeds and speeds on the data node, but it is a dual-controller OEM disk enclosure based on a modest X86 processor with some NVRAM packed in to persist a replica of the hot data on the compute hosts and to front end a dozen 7,200 RPM SATA disks that provide the raw persistent capacity. Biles jokes that this enclosure has just enough oomph to drive the disks, which at 100 MB/sec of bandwidth for each drive is just enough to saturate a 10 Gb/sec Ethernet link. Each data node uses inexpensive 4 TB disk drives, for a total raw capacity of 48 TB for the enclosure and after all of the de-duplication and compression works on the data and after the overhead of erasure coding is taken out, there is about 100 TB on the data node. This, quite frankly, is not a balance set of compute and data, and the Datrium folks knew that.

    So for the past several years, they have been perfecting a wide erasure coding technique that can distribute data chunks evenly across a pool of data nodes that have been networked together, and adapting the de-duplication pool so it spans multiple data nodes. Most of the code for this updated erasure coding and de-duplication stack runs on the compute nodes, not the data nodes, so there is no reason to suddenly need very hefty data nodes as is the case with a lot of hyperconverged storage. And equally importantly, that global object storage pool that underpins the DVX stack lets each compute node spread its data across the compute nodes in a stateless fashion so none of the compute nodes are dependent on the other for access to their persistent data. This is what Datrium calls split provisioning.

    The upshot is that the DVX platform now offers the ability to independently scale compute and storage, and with the most recent 3.0 release, it can scale both a lot future. In the initial release that has been shipping since early last year, the compute cluster could scale to 32 nodes, but they only shared one data node. This was severely limiting on the use cases. But starting with this update, the compute can scale to 128 nodes, with one, two, four, or eight sockets each customers choose, and the data nodes can scale to ten in the storage cluster.

    All of the elements of the DVX stack can be linked using modest and dirt cheap 10 Gb/sec Ethernet adapters and switches on the nodes, but in some cases, lower latency or higher bandwidth switching might help. It call comes down to cases. Biles says that support for 25 GB/sec and 40 Gb/sec is coming in the near future.

    With the expanded DVX stack, the data nodes can scale to up to 1 PB of effective capacity, and importantly for enterprise customers, handle up to 1.2 million concurrent snapshots of datasets.

    This snapshotting is part of the backup and protection that is integrated into the DVX stack, and it is one of the reasons why customers who are used to having primary data storage and backup storage (something like the virtual tapes on disk arrays that Data Domain created) will spend a lot less money if they just have a single stack with snapshots for cloning data and erasure coding for spreading the data out for performance and high persistence.

    Those 128 compute nodes can crank through 200 GB/sec of reads off of the storage nodes, and in aggregate they can process 18 million IOPS against the local flash on their nodes, which in turn pull data off the disk nodes when needed.

    Here is the performance profile of the DVX cluster as it scales compared to that of a typical all-flash array, according to Datrium:

    The write performance of the DVX stack scales with the number of data nodes and, to a certain extent, to the NVRAM that is in the enclosure. Here is how Datrium says the DVX clusters compare to EMC’s XtremIO clustered flash arrays.

    The important thing to note here is that Datrium is getting the same or better performance on reads and writes as all-flash arrays using a mix of DRAM and flash on stateless compute nodes and NVRAM and disks on data nodes. Now they understand why Google is so keen on advancing the state of the art of disk technology. If you need exabytes of capacity, as hyperscalers do, you can’t afford to go all flash. Enterprises, which have relatively modest datasets and often have no more than a few petabytes, can do this. And, as the sales of Pure Storage and other all-flash array makers demonstrates, this is precisely what they are doing. It is just funny that the most advanced companies in the world have no choice but to keep a technology that is sixty years old already alive for another decade.

    That leaves us with explaining how this kind of simple convergence is different from hyperconvergence.

    First of all, the compute nodes are completely stateless, and that means that applications running on one node can be isolated from those on the other. They are isolated from each other’s faults and crashes and not woven into a single compute fabric. The machines can be of distinct types and therefore the performance requirements of the software can be matched to the hardware. this is not the case with hyperconverged stacks, which tend to be homogeneous. Second, because storage is broken from compute in a stateless manner, there is fault isolation between the compute and data nodes. With hyperconverged software, if a node goes down, it is a problem for the entire cluster as that node’s data is restored. The other interesting difference is that the hyperconverged stacks out there need to be in an all-flash configuration, which can be very pricey, for de-duplication, compression, and other data reduction techniques to work at an acceptable performance, but Datrium’s DVX can do it on a mix of flash, NVM, and disk. And finally, hyperconverged stacks tie compute to storage within a node, and you can’t scale up one without the other. (This is something the hyperconvergers need to fix.)

    The two DVX components for compute and storage are sold and priced separately, and given the fact that they scale independently this makes sense. The software for the compute nodes cost $12,000 per server, and the data node costs $94,000 each. So a top-end cluster with 128 nodes might cost $25,000 for a hefty server node with lots of cores, RAM, and flash plus another $12,000 for the DVX compute software, or $4.74 million across the compute infrastructure with maybe 7,200 cores and as many virtual machines and ten times as many containers, with terabytes of memory and tens of terabytes of flash. The ten data nodes, you are talking another $940,000 in addition to that, and after some modest discounting, you might get it all for maybe 25 percent off or around $4 million. That assumes the use of open source software, and adding commercial ESXi or KVM would balloon this cost even more. Still, that is a lot less expensive than buying primary flash and disk inside of compute nodes and then having to buy a separate petascale class backup array.

    A company like Google can get its iron for half that price, but it has to create its own software, and that is not free, either, even if it is fun. Imagine what 100,000 nodes, like the hyperscalers put into one datacenter, actually costs with all the hardware, software, and people costs fully burdened.



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