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Cloud Economics drive the IT Infrastructure of Tomorrow

| ITBusinessNet.com
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Cloud Economics drive the IT Infrastructure of Tomorrow

The cloud continues to dominate IT as businesses make their infrastructure decisions based on cost and agility. Public cloud, where shared infrastructure is paid for and utilized only when needed, is the most popular model today. However, more and more organizations are addressing security concerns by creating their own private clouds. As businesses deploy private cloud infrastructure they are adopting techniques used in the public cloud to control costs. Gone are the traditional arrays and network switches of the past, replaced with software-defined data centers running on industry standard servers.

Efficiency features make the cloud model more effective by reducing costs and increasing data transfer speeds. One such feature, which is particularly effective in cloud environments, is inline data reduction. This is a technology that can be used to lower the costs of data in flight and at rest. In fact, data reduction delivers unique benefits to each of the cloud deployment models.

Public Clouds

The public cloud’s raison d’etre is its ability to deliver IT business agility, deployment flexibility and elasticity. As a result new workloads are increasingly deployed in public clouds.  Worldwide public IT cloud service revenue in 2018 is predicted to be $127B.

Data reduction technology minimizes public cloud costs. For example, deduplication and compression typically cut capacity requirements of block storage in enterprise public cloud deployments by up to 6:1.  These savings are realized in reduced storage consumption and operating costs in public cloud deployments.

Consider AWS costs employing data reduction;

If you provision a 300TB of EBS General Purpose SSD (gp2) storage for 12 hours per day over a 30 day month in a region that charges $0.10 per GB-month, you would be charged $15,000 for the storage.

With data reduction, that monthly cost of $15,000 would be reduced to $2,500.  Over a 12 month period you will save $150,000.   Capacity planning is a simpler problem when it is 1/6th its former size.  Bottom line, data reduction increases agility and reduces costs of public clouds.

One data reduction application that can readily be applied in public cloud is Permabit’s Virtual Disk Optimizer (VDO) which is a pre-packaged software solution that installs and deploys in minutes on Red Hat Enterprise Linux and Ubuntu LTS Linux distributions.  To deploy VDO in Amazon AWS, the administrator provisions Elastic Block Storage (EBS) volumes, installs the VDO package into their VMs and applies VDO to the block devices represented for their EBS volumes.  Since VDO is implemented in the Linux device mapper, it is transparent to the applications installed above it.

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Using Data Reduction at the OS layer in Enterprise Linux Environments

| Stock Market
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Enterprises and cloud service providers that have built their infrastructure around Linux should deploy data reduction in the operating system to drive costs down, say experts at Permabit Technology Corporation, the company behind Permabit Virtual Data Optimizer (VDO).  Permabit VDO is the only complete data reduction software for Linux, the world’s most popular server Operating System (OS). Permabit’s VDO software fills a gap in the Linux feature set by providing a cost effective, alternative to the data reduction services delivered as part of the two other major OS platforms – Microsoft Windows and VMware. IT architects are driven to cut costs as they build out their next generation infrastructure with one or more of these OS platforms in  public and/or private cloud deployments and one obvious way to do so is with data reduction.

When employed as a component of the OS, data reduction can be applied universally without lock-in of proprietary solutions. Adding compression, deduplication, and thin provisioning to the core OS, data reduction benefits can be leveraged by any application or infrastructure services running on that OS. This ensures that savings accrue across the entire IT infrastructure, delivering TCO advantages no matter where the data resides. This is the future of data reduction – as a ubiquitous service of the OS.

“We’re seeing movement away from proprietary storage solutions, where data reduction was a key differentiated feature, toward OS-based capabilities that are applied across an entire infrastructure,” said Tom Cook, Permabit CEO.  “Early adopters are reaping financial rewards through reduced cost of equipment, space, power and cooling. Today we are also seeing adoption of data reduction in the OS by more conservative IT organizations who are driven to take on more initiatives with tightly constrained IT budgets.”

VDO, with inline data deduplication, HIOPS Compression®, and fine-grained thin provisioning, is deployed as a device-mapper driver for Linux. This approach ensures compatibility with a full complement of direct-attached/ephemeral, block, file and object interfaces. VDO data reduction is available for Red Hat Enterprise Linux and Canonical Ubuntu Linux LTS distributions.

Advantages of in-OS data reduction technology include:

  • Improved density for public/private /hybrid cloud storage, resulting in lower storage and service costs
  • Vendor independent to function across hardware running the target OS
  • Seamless data mobility between on-premise and cloud resources
  • Up to six times lower IT infrastructure OpEx
  • Transparent to end users accessing data
  • Requires no modifications to existing applications, file systems, virtualization features, or data protection capabilities

With VDO, these advantages are being realized on Linux today. VDO deployments have been completed (or are currently in progress) with large telecommunications companies, government agencies, financial services firms and IaaS providers who have standardized on Linux for their data centers. With data reduction in Linux, enterprises achieve vendor independence across all Linux based storage, increased mobility of reduced data and hyper scale economics. What an unbeatable combination!

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Busting the handcuffs of traditional data storage

| SiliconANGLE
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Premise

The largest and most successful Web companies in the world have proven a new model for managing and scaling a combined architecture of compute and storage. If you’ve heard it once, you’ve heard it a hundred times: “The hyperscale guys don’t use traditional disk arrays.”

Giants such as Facebook Inc. and Google Inc. use a design of local distributed storage to solve massive data problems. The key differentiation of this new architecture is extreme scalability and simplicity of management, enabled by automation. Over the years, Wikibon has referred to this approach as “Software-led Infrastructure,” which is analogous to so-called Software-Defined Storage.

Excluding the most mission-critical online transaction processing markets served by the likes of Oracle Corp. and IBM Corp.’s DB2, it’s becoming clear this software-led approach is poised to penetrate mainstream enterprises because it is more cost-effective and agile than traditional infrastructure. Up until recently, however, such systems have lacked the inherent capabilities needed to service core enterprise apps.

This dynamic is changing rapidly. In particular, Microsoft Corp. with Azure Stack and VMware Inc. with its vSAN architecture are demonstrating momentum with tightly integrated and automated storage services. Linux, with its open source ecosystem, is the remaining contender to challenge VMware and Microsoft for mainstream adoption of on-premises and hybrid information technology infrastructure, including data storage.

Upending the ‘iron triangle’ of arrays

Peter Burris, Wikibon’s head of research, recently conducted research that found IT organizations suffer from an infrastructure “iron triangle” that is constraining IT progress. According to Burris, the triangle comprises entrenched IT administrative functions, legacy vendors and technology-led process automation.

In his research, Burris identified three factors IT organizations must consider to break the triangle:

  • Move from a technology to a service administration model;
  • Adopt True Private Cloud to enhance real automation and protect intellectual property that doesn’t belong in the cloud; and
  • Elevate vendors that don’t force false “platform” decisions, meaning technology vendors have a long history of  “adding value” by renaming and repositioning legacy products under vogue technology marketing umbrellas.

The storage industry suffers from entrenched behaviors as much as any other market segment. Traditional array vendors are trying to leverage the iron triangle to slow the decline of legacy businesses while at the same time ramping up investments in newer technologies, both organically and through acquisition. The Linux ecosystem –the lone force that slowed down Microsoft in the 1990s – continues to challenge these entrenched IT norms and is positioned for continued growth in the enterprise.

But there are headwinds.

In a recent research note published on Wikibon (login required), analyst David Floyer argued there are two main factors contributing to the inertia of traditional storage arrays:

  • The lack of equivalent functionality for storage services in this new software-led world; and
  • The cost of migration of existing enterprise storage arrays – aka the iron triangle.

Linux, Floyer argues, is now ready to grab its fair share of mainstream, on-premises enterprise adoption directly as a result of newer, integrated functionality that is hitting the market. As these software-led models emerge in an attempt to replicate cloud, they inevitably will disrupt traditional approaches just as the public cloud has challenged the dominant networked storage models such as Storage Area Network and Network-Attached Storage that have led the industry for two decades.

Linux is becoming increasingly competitive in this race because it is allowing practitioners to follow the game plan Burris laid out in his research, namely:

1) Building momentum on a services model – (i.e. delivering robust enterprise storage management services that are integrated into the OS);

2) Enabling these services to be invoked by an orchestration/automation framework (e.g., OpenStack, OpenShift) or directly by an application leveraging microservices (i.e., True Private Cloud); and

3) The vendors delivering these capabilities have adopted an open ecosystem approach (i.e. they’re not forcing false platform decisions, rather they’re innovating and integrating into an existing open platform). A scan of the OpenStack Web site gives a glimpse of some of the customers attempting to leverage this approach.

Floyer’s research explores some of the key services required by Linux to challenge for market leadership, with a deeper look at the importance of data reduction as a driver of efficiency and cost reduction for IT organizations.

Types of services

In his research, Floyer cited six classes of storage service that enterprise buyers have expected, which have traditionally been available only within standalone arrays. He posited that these services are changing rapidly, some with the introduction of replacement technologies and others that will increasingly be integrated into the Linux operating system, which will speed adoption. A summary of Floyer’s list of storage services follows:

  • Cache management to overcome slow hard disk drives which are being replaced by flash (with data reduction techniques) to improve performance and facilitate better data sharing
  • Snapshot Management for improved recovery
  • Storage-level Replication is changing due to the effects of flash and high speed interconnects such as 40Gb or 100Gb links. Floyer cited WANdisco’s Paxos technology and the Simplivity (acquired by HPE) advanced file system as technologies supporting this transformation.
  • Encryption, which has traditionally been confined to disk drives, overhead-intensive and leaves data in motion exposed. Encryption has been a fundamental capability within the Linux stack for years and ideally all data would be encrypted. However encryption overheads have historically been too cumbersome. With the advent of graphics processing units and field-programmable gate arrays from firms such as Nvidia Corp., encryption overheads are minimized enabling end-to-end encryption, with the application and database as the focal point for both encryption and decryption, not the disk drive.
  • Quality of Service, which is available in virtually all Linux arrays but typically only sets a floor under which performance may not dip. Traditional approaches for QoS lack granularity to set ceilings (for example) and allow bursting programmatically through a complete and well-defined REST API (to better service the needs of individual applications – versus a one-size-fits all approach). NetApp Inc.’s Solidfire has, from its early days, differentiated in this manner and is a good example of a true software-defined approach that allows provisioning both capacity and performance dynamically through software. Capabilities like this are important to automate the provisioning and management of storage services at scale, a key criterion to replicate public cloud on-prem.
  • Data Reduction – Floyer points out in his research that there are four areas of data reduction that practitioners should understand, including zero suppression, thin provisioning, compression and data de-duplication. Data sharing is a fifth and more nuanced capability that will become important in the future. According to Floyer:

To date… “The most significant shortfall in the Linux stack has been the lack of an integrated data reduction capability, including zero suppression, thin provisioning, de-duplication and compression.”

According to Floyer, “This void has been filled by the recent support of Permabit’s VDO data reduction stack (which includes all the data reduction components) by Red Hat.”

VDO stands for Virtual Data Optimizer. In a recent conversation with Wikibon, Permabit Chief Executive Tom Cook explained that as a Red Hat Technology partner, Permabit obtains early access to Red Hat software, which allows VDO testing and deep integration into the operating system, underscoring Floyer’s argument.

Why is this relevant? The answer is cost.

The cost challenge

Data reduction is a wonky topic to chief information officers, but the reason it’s so important is that despite the falling cost per bit, storage remains a huge expense for buyers, often accounting for between 15 and 50 percent of IT infrastructure capital expenditures. As organizations build open hybrid cloud architectures and attempt to compete with public cloud offerings, Linux storage must not only be functionally robust, it must keep getting dramatically cheaper.

The storage growth curve, which for decades has marched to the cadence of Moore’s Law, is re-shaping and growing at exponential rates. IoT, M2M communications and 5G will only serve to accelerate this trend.

Data reduction services have been a huge tailwind for more expensive flash devices and are fundamental to reducing costs going forward. Traditionally, the common way Linux customers have achieved efficiencies is to acquire data reduction services (e.g., compression and de-dupe) through an array – which may help lower the cost of the array, but it perpetuates the Iron triangle. And longer-term, it hurts the overall cost model.

As underscored in Floyer’s research, the modern approach is to access sets of services that are integrated into the OS and delivered via Linux within an orchestration/automation framework that can manage the workflow. Some cloud service providers (outside of the hyperscale crowd) are sophisticated and have leveraged open-source services to achieve hyperscalelike benefits. Increasingly, these capabilities are coming to established enterprises via the Linux ecosystem and are achieving tighter integration as discussed earlier.

More work to be done

Wikibon community data center practitioners typically cite three primary areas that observers should watch as indicators of Linux maturity generally and software-defined storage specifically:

  1. The importance of orchestration and automation

To truly leverage these services, a management framework is necessary to understand what services have been invoked, to ensure recovery is in place (if needed) and give confidence that software-defined storage and associated services can deliver consistently in a production environment.

Take encryption as an example along with data reduction. To encrypt you must reduce the data before you encrypt because encryption tries to eliminate the very patterns that, for example, data de-duplication is trying to find. This example illustrates the benefits of integrated services. Specifically, if something goes wrong during the process, the system must have deep knowledge of exactly what happened and how to recover. The ideal solution in this example is to have encryption, de-dupe and compression integrated as a set of services embedded in the OS and invoked programmatically by the application where needed and where appropriate.

2. Application performance

Wikibon believes that replicating hyperscalerlike models on-prem will increasingly require integrating data management features into the OS. Technologists in the Wikibon community indicate that the really high performance workloads will move to a software-led environment leveraging emerging non-volatile memory technologies such as NVMe and NVMf. Many believe the highest performance workloads will go into these emerging systems and over time, eliminate what some call the “horrible storage stack” – meaning the overly cumbersome storage protocols that have been forged into the iron triangle for years. This will take time, but the business value effects could be overwhelming with game-changing performance and low latencies as disruptive to storage as high frequency trading has been to Wall Street — ideally without the downside.

3. Organizational issues

As Global 2000 organizations adopt this new software-led approach, there are non-technology-related issues that must be overcome. “People, process and technology” is a bit of a bromide, but we hear it all the time: “Technology is the easy part…. People and process are the difficult ones.” The storage iron triangle will not be easily disassembled. The question remains: Will the economics of open source and business model integrations such as those discussed here overwhelm entrenched processes and the people who own them?

On the surface, open source services are the most likely candidates to replicate hyperscale environments because of the collective pace of innovation and economic advantages. However, to date, a company such as VMware has demonstrated that it can deliver more robust enterprise services faster than the open-source alternatives — but not at hyper-scale.

History is on the side of open source. If the ecosystem can deliver on its cost, scalability and functionality promises, it’s a good bet that the tech gap will close rapidly and economic momentum will follow. Process change and people skills will likely be more challenging.

(Disclosure: Wikibon is a division of SiliconANGLE Media Inc., the publisher of Siliconangle.com. Many of the companies referenced in this post are clients of Wikibon. Please read my Ethics Statement.)

 

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Addressing Bandwidth Challenges in the Hybrid Cloud

| By: (53)

Any application infrastructure that relies on a single data center is only as safe as that data center’s physical resources and the competence of its staff.  Witness the recent S3 outage at Amazon. When you choose to deploy in a single public cloud, you are delegating infrastructure management to your provider. When you’re exclusively running in-house, private cloud infrastructure, you’re entrusting that management to your own organization.  Either way mistakes…

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Reduce Cloud’s Highly Redundant Data

| By: (60)

Storage is the foundation of cloud services. All cloud services – delineated as scalable, elastic, on-demand, and self-service – begin with storage. Almost universally, cloud storage services are virtualized and hybrid cloud architectures that combine on-premise resources with colocation, private and public clouds result in highly redundant data environments.  IDC’s FutureScape report finds “Over 80% of enterprise IT organizations will commit to hybrid cloud architectures, encompassing multiple public cloud services,…

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Hybrid Cloud

Hybrid Cloud Gains in Popularity, Survey Finds

| Light Reading
Hybrid Cloud

The hybrid model of cloud computing is gaining more popularity in the enterprise, as businesses move more workloads and applications to public cloud infrastructures and away from private deployments.

Those are some of the findings from RightScale’s annual “State of the Cloud” report, which the company released Wednesday. It’s based on interviews with 1,000 IT professionals, with 48% of them working in companies with more than 1,000 employees.

The biggest takeaway from the report is that enterprises and their IT departments are splitting their cloud dollars between public and private deployments, and creating demands for a hybrid approach.

“The 2017 State of the Cloud Survey shows that while hybrid cloud remains the preferred enterprise strategy, public cloud adoption is growing while private cloud adoption flattened and fewer companies are prioritizing building a private cloud,” according to a blog post accompanying the report. “This was a change from last year’s survey, where we saw strong gains in private cloud use.”

Specifically, 85% of respondents reported having a multi-cloud, hybrid strategy, and that’s up from the 82% who reported a similar approach in 2016. At the same time, private cloud adoption dropped from 77% in 2016 to 72% in 2017.

In the survey, 41% of respondents reported running workloads in public clouds, while 38% said they run workloads in private clouds. In large enterprises, those numbers reverse, with 32% of respondents running workloads in public clouds, and 43% running workloads within private infrastructures.

“It’s important to note that the workloads running in private cloud may include workloads running in existing virtualized environments or bare-metal environments that have been ‘cloudified,’ ” according to the report.

When it comes to adopting cloud technologies and services, there are less barriers and concerns this year compared to 2016. The lack of resources and expertise to implement a cloud strategy was still the top concern.

In addition the report notes that in every cloud expertise level the Top 5 Challenges” indicate there is a substantial concern with “managing costs”.  One vehicle that can help manage costs is to apply data reduction technologies to your cloud deployment. Permabit VDO can be applied to public and/or private clouds quickly and easily enabling cost reduction of 50% or more in on-premise, in-transit and public cloud deployments.

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Why Deduplication Matters for Cloud Storage

| dzone.com
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Most people assume cloud storage is cheaper than on-premise storage. After all, why wouldn’t they? You can rent object storage for $276 per TB per year or less, depending on your performance and access requirements. Enterprise storage costs between $2,500 to $4,000 per TB per year, according to analysts at Gartner and ESG.

This comparison makes sense for primary data, but what happens when you make backups or copies of data for other reasons in the cloud? Imagine that an enterprise needs to retain 3 years of monthly backups of a 100TB data set. In the cloud, this can be easily equated to 3.6 PB of raw backup data, or a monthly bill of over $83,000. That’s about $1 million a year before you even factor in and data access or retrieval charges.

That is precisely why efficient deduplication is hugely important for both on-premise and cloud storage, especially when enterprises want to retain their secondary data (backup, archival, long-term retention) for weeks, months, and years. Cloud storage costs can add up quickly, surprising even astute IT professionals, especially as data sizes get bigger with web-scale architectures, data gets replicated and they discover it can’t be deduplicated in the cloud.

The Promise of Cloud Storage: Cheap, Scalable, Forever Available

Cloud storage is viewed as cheap, reliable and infinitely scalable – which is generally true. Object storage like AWS S3 is available at just $23/TB per month for the standard tier, or $12.50/TB for the Infrequent Access tier. Many modern applications can take advantage of object storage. Cloud providers offer their own file or block options, such as AWS EBS (Elastic Block Storage) that starts at $100/TB per month, prorated hourly. Third-party solutions also exist that connect traditional file or block storage to object storage as a back-end.

Even AWS EBS, at $1,200/TB per year, compares favorably to on-premise solutions that cost 2-3 times as much, and require high upfront capital expenditures. To recap, enterprises are gravitating to the cloud because the OPEX costs are significantly lower, there’s minimal up-front cost, and you pay as you go (vs. traditional storage where you have to buy far ahead of actual need)

How Cloud Storage Costs Can Get Out of Hand: Copies, Copies Everywhere

The direct cost comparison between cloud storage and traditional on-premise storage can distract from managing storage costs in the cloud, particularly as more and more data and applications move there. There are three components to cloud storage costs to consider:

  • Cost for storing the primary data, either on object or block storage
  • Cost for any copies, snapshots, backups, or archive copies of data
  • Transfer charges for data

We’ve covered the first one. Let’s look at the other two.

Copies of data. It’s not how much data you put into the cloud — uploading data is free, and storing a single copy is cheap. It’s when you start making multiple copies of data — for backups, archives, or any other reason — that costs spiral if you’re not careful. Even if you don’t make actual copies of the data, applications or databases often have built-in data redundancy and replicate data (or in database parlance, a Replication Factor).

In the cloud, each copy you make of an object incurs the same cost as the original. Cloud providers may do some dedupe or compression behind the scenes, but this isn’t generally credited back to the customer. For example, in a consumer cloud storage service like DropBox, if you make a copy or ten copies of a file, each copy counts against your storage quota.

For enterprises, this means data snapshots, backups, and archived data all incur additional costs. As an example, AWS EBS charges $0.05/GB per month for storing snapshots. While the snapshots are compressed and only store incremental data, they’re not deduplicated. Storing a snapshot of that 100 TB dataset could cost $60,000 per year, and that’s assuming it doesn’t grow at all.

Data access. Public cloud providers generally charge for data transfer either between cloud regions or out of the cloud. For example, moving or copying a TB of AWS S3 data between Amazon regions costs $20, and transferring a TB of data out to the internet costs $90. Combined with GET, PUT, POST, LIST and DELETE request charges, data access costs can really add up.

Why Deduplication in the Cloud Matters

Cloud applications are distributed by design and are deployed on non-relational massively scalable databases as a standard. In non-relational databases, most data is redundant before you even make a copy. There are common blocks, objects, and databases like MongoDB or Cassandra have replication factor (RF) of 3 to ensure data integrity in a distributed cluster, so you start out with three copies.

Backups or secondary copies are usually created and maintained via snapshots (for example, using EBS snapshots as noted earlier). The database architecture means that when you take a snapshot, you’re really making three copies of the data. Without any deduplication, this gets really expensive.

Today there are solutions to solve the public cloud deduplication or data reduction conundrum. Permabit VDO can be easily deployed in public and/or private cloud solutions  Take a look at the following blog from Tom Cook http://permabit.com/data-efficiency-in-public-clouds/ or for the technical details look at one from Louis Imershein http://permabit.com/effective-use-of-data-reduction-in-the-public-cloud/. Both provide examples and details on why and how to drive deduplication and compression solutions in a public cloud.

 

 

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Effective use of data reduction in the Public Cloud

| By: (53)

  Permabit CEO, Tom Cook recently wrote about how data reduction technology can simplify the problems associated with provisioning adequate storage resources in the public cloud, while balancing performance and efficiency.  The good news is, taking advantage of data reduction software in the public cloud is easier than ever. For example,Permabit’s Virtual Disk Optimizer (VDO) is a pre-packaged software solution that installs and deploys in minutes on Red Hat Enterprise…

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Data Efficiency in Public Clouds

| By: (60)

Public cloud deployments deliver agility, flexibility and elasticity. This is why new workloads are increasingly deployed in public clouds.  Worldwide public IT cloud service revenue in 2018 is predicted to be $127B.  It’s powerful to spin up a data instance instantaneously, however managing workloads and storage still requires analysis, planning and monthly provisioning.  It would be extremely advantageous if public cloud storage capacity could automatically grow and condense to optimize…

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