What’s a Multi-cloud Really?  Some Insider Notes from VMworld 2017

(Excerpt from original post on the Taneja Group News Blog)

As comfortable 65-70 degree weather blankets New England here as we near end of summer, flying into Las Vegas for VMworld at 110 degrees seemed like dropping into hell. Last time I was in that kind of heat I was stepping off a C-130 into the Desert Shield/Desert Storm theater of operations. At least here, as everyone still able to breathe immediately says -“at least it’s a dry heat.”

…(read the full post)

Persistent data storage in containerized environments

An IT industry analyst article published by SearchStorage.


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The most significant challenge to the rise of containerized applications is quickly and easily providing enterprise-class persistent storage for containers.

Mike Matchett

The pace of change in IT is staggering. Fast growing data, cloud-scale processing and millions of new internet of things devices are driving us to find more efficient, reliable and scalable ways to keep up. Traditional application architectures are reaching their limits, and we’re scrambling to evaluate the best new approaches for development and deployment. Fortunately, the hottest prospect — containerization — promises to address many, if not all, of these otherwise overwhelming challenges.

In containerized application design, each individual container hosts an isolatable, and separately scalable, processing component of a larger application web of containers. Unlike monolithic application processes of the past, large, containerized applications can consist of hundreds, if not thousands, of related containers. The apps support Agile design, development and deployment methodologies. They can scale readily in production and are ideally suited for hosting in distributed, and even hybrid, cloud infrastructure.

Unfortunately, containers weren’t originally designed to implement full-stack applications or really any application that requires persistent data storage. The original idea for containers was to make it easy to create and deploy stateless microservice application layers on a large scale. Think of microservices as a form of highly agile middleware with conceptually no persistent data storage requirements to worry about.

Persistence in persisting

Because the container approach has delivered great agility, scalability, efficiency and cloud-readiness, and is lower-cost in many cases, people now want to use it for far more than microservices. Container architectures provide such a better way to build modern applications that we see many commercial software and systems vendors transitioning internal development to container form and even deploying them widely, often without explicit end-user or IT awareness. It’s a good bet that most Fortune 1000 companies already host third-party production IT applications in containers, especially inside appliances, converged approaches and purpose-built infrastructure.

It’s a good bet that most Fortune 1000 companies already host third-party container applications within production IT.

You might find large, containerized databases and even storage systems. Still, designing enterprise persistent storage for these applications is a challenge, as containers can come and go and migrate across distributed and hybrid infrastructure. Because data needs to be mastered, protected, regulated and governed, persistent data storage acts in many ways like an anchor, holding containers down and threatening to reduce many of their benefits.

Container architectures need three types of storage…(read the complete as-published article there)

A serverless architecture could live in your data center

An IT industry analyst article published by SearchITOperations.


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Just because you don’t see the server doesn’t mean it’s not there. Serverless frameworks are superseding containers, but is the extra abstraction worth it?

Mike Matchett

Have you figured out everything you need to know about managing and operating container environments already? How to host them in your production data centers at scale? Transform all your legacy apps into containerized versions? Train your developers to do agile DevOps, and turn your IT admins into cloud brokers? Not quite yet?

I hate to tell you, but the IT world is already moving past containers. Now you need to look at the next big thing: serverless computing.

I don’t know who thought it was a good idea to label this latest application architecture trend serverless computing. Code is useless, after all, unless it runs on a computer. There has to be a server in there somewhere. I guess the idea was to imply that when you submit application functionality for execution without caring about servers, it feels completely serverless.

In cloud infrastructure as a service, you don’t have to own or manage your own physical infrastructure. With cloud serverless architecture, you also don’t have to care about virtual machines, operating systems or even containers.

Serving more through serverless architecture?

So what is serverless computing? It’s a service in which a programmer can write relatively contained bits of code and then directly deploy them as standalone, function-sized microservices. You can easily set up these microservices to execute on a serverless computing framework, triggering or scheduling them by policy in response to supported events or API calls.

A serverless framework is designed to scale well with inherently stateless microservices — unlike today’s containers, which can host stateful computing as well as stateless code. You might use serverless functions to tackle applications that need highly elastic, event-driven execution or when you create a pipeline of arbitrary functionality to transform raw input into polished output. This event-pipeline concept meshes well with expected processing needs related to the internet of things. It could also prove useful with applications running in a real-time data stream.

A well-known public cloud example of serverless computing is Amazon Web Service’s Lambda. The Lambda name no doubt refers to anonymous lambda functions used extensively in functional programming. In languages such as JavaScript or Ruby, a function can be a first-class object defined as a closure of some code function within a prescribed variable scope. Some languages have actual lambda operators that a programmer can use to dynamically create new function objects at runtime (e.g., as other code executes).

So with a serverless framework, where does the actual infrastructure come into the picture? It’s still there, just under multiple layers of abstraction. Talk about software-defined computing. With this latest evolution into serverless computing, we now have perhaps several million lines of system- and platform-defining code between application code and hardware. It’s a good thing Moore’s Law hasn’t totally quit on us…(read the complete as-published article there)

Spark speeds up adoption of big data clusters and clouds

An IT industry analyst article published by SearchITOperations.


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Infrastructure that supports big data comes from both the cloud and clusters. Enterprises can mix and match these seven infrastructure choices to meet their needs.

Mike Matchett

If enterprise IT has been slow to support big data analytics in production for the decade-old Hadoop, there has been a much faster ramp-up now that Spark is part of the overall package. After all, doing the same old business intelligence approach with broader, bigger data (with MapReduce) isn’t exciting, but producing operational time predictive intelligence that guides and optimizes business with machine precision is a competitive must-have.

With traditional business intelligence (BI), an analyst studies a lot of data and makes some hypotheses and a conclusion to form a recommendation. Using the many big data machine learning techniques supported by Spark’s MLlib, a company’s big data can dynamically drive operational-speed optimizations. Massive in-memory machine learning algorithms enable businesses to immediately recognize and act on inherent patterns in even big streaming data.

But the commoditization of machine learning itself isn’t the only new driver here. A decade ago, IT needed to stand up either a “baby” high performance computing cluster for serious machine learning or learn to write low-level distributed parallel algorithms to run on the commodity-based Hadoop MapReduce platform. Either option required both data science and exceptionally talented IT admins that could stand up and support massive physical scale-out clusters in production. Today there are many infrastructure options for big data clusters that can help IT deploy and support big data-driven applications.

Here are seven types of big data infrastructures for IT to consider, each with core strengths and differences:…(read the complete as-published article there)

Machine learning algorithms make life easier — until they don’t

An IT industry analyst article published by SearchITOperations.


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Algorithms govern many facets of our lives. But imperfect logic and data sets can make results worse instead of better, so it behooves all of us to think like data scientists.

Mike Matchett

Algorithms control our lives in many and increasingly mysterious ways. While machine learning algorithms change IT, you might be surprised at the algorithms at work in your nondigital life as well.

When I pull a little numbered ticket at the local deli counter, I know with some certainty that I’ll eventually get served. That’s a queuing algorithm in action — it preserves the expected first-in, first-out ordering of the line. Although wait times vary, it delivers a predictable average latency to all shoppers.

Now compare that to when I buy a ticket for the lottery. I’m taking a big chance on a random-draw algorithm, which is quite unlikely to ever go my way. Winning is not only uncertain, but improbable. Still, for many folks, the purchase of a lottery ticket delivers a temporary emotional salve, so there is some economic utility — as you might have heard in Economics 101.

People can respond well to algorithms that have guaranteed certainty and those with arbitrary randomness in the appropriate situations. But imagine flipping those scenarios. What if your deli only randomly selected people to serve? With enough competing shoppers, you might never get your sliced bologna. What if the lottery just ended up paying everyone back their ticket price minus some administrative tax? Even though this would improve almost everyone’s actual lottery return on investment, that kind of game would be no fun at all.

Without getting deep into psychology or behavioral economics, there are clearly appropriate and inappropriate uses of randomization. When we know we are taking a long-shot chance at a big upside, we might grumble if we lose. But our reactions are different when the department of motor vehicles closes after we’ve already spent four hours waiting.

Now imagine being subjected to opaque algorithms in various important facets of your life, as when applying for a mortgage, a car loan, a job or school admission. Many of the algorithms that govern your fate are seemingly arbitrary. Without transparency, it’s hard to know if any of them are actually fair, much less able to predict your individual prospects. (Consider the fairness concept the next time an airline randomly bumps you from a flight.)
Machine learning algorithms overview — machines learn what?

So let’s consider the supposedly smarter algorithms designed at some organizational level to be fair. Perhaps they’re based on some hard, rational logic leading to an unbiased and random draw, or more likely on some fancy but operationally opaque big data-based machine learning algorithm.

With machine learning, we hope things will be better, but they can also get much worse. In too many cases, poorly trained or designed machine learning algorithms end up making prejudicial decisions that can unfairly affect individuals.

I’m not exaggerating when I predict that machine learning will touch every facet of human existence.

This is a growing — and significant — problem for all of us. Machine learning is influencing a lot of the important decisions made about us and is steering more and more of our economy. It has crept in behind the scenes as so-called secret sauce or as proprietary algorithms applied to key operations.

But with easy-to-use big data, machine learning tools like Apache Spark and the increasing streams of data from the internet of things wrapping all around us, I expect that every data-driven task will be optimized with machine learning in some important way…(read the complete as-published article there)

Open Wide and Say Ahh!

(Excerpt from original post on the Taneja Group News Blog)

I’ve been immersed in “Open” for the last two weeks here in Boston, attending both Red Hat Summit 2017 and then OpenStack Summit. There are quite a few things worth paying attention to, especially if you are an enterprise IT shop still wondering how your inevitable cloud (and services) transformation is really going to play out, including accelerating application migration to containers and the rise of platform Management as a Service.

…(read the full post)

Storage technologies evolve toward a data-processing platform

An IT industry analyst article published by SearchDataCenter.


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Emerging technologies such as containers, HCI and big data have blurred the lines between compute and storage platforms, breaking down traditional IT silos.

Mike Matchett

With the rise of software-defined storage, in which storage services are implemented as a software layer, the whole idea of data storage is being re-imagined. And with the resulting increase in the convergence of compute with storage, the difference between a storage platform and a data-processing platform is further eroding.

Storage takes new forms

Let’s look at a few of the ways that storage is driving into new territory:

  • Now in containers! Almost all new storage operating systems, at least under the hood, are being written as containerized applications. In fact, we’ve heard rumors that some traditional storage systems are being converted to containerized form. This has a couple of important implications, including the ability to better handle massive scale-out, increased availability, cloud-deployment friendliness and easier support for converging computation within the storage.
  • Merged and converged. Hyper-convergence bakes software-defined storage into convenient, modular appliance units of infrastructure. Hyper-converged infrastructure products, such as those from Hewlett Packard Enterprise’s SimpliVity and Nutanix, can greatly reduce storage overhead and help build hybrid clouds. We also see innovative approaches merging storage and compute in new ways, using server-side flash (e.g., Datrium), rack-scale infrastructure pooling (e.g., Drivescale) or even integrating ARM processors on each disk drive (e.g., Igneous).
  • Bigger is better. If the rise of big data has taught us anything, it’s that keeping more data around is a prerequisite for having the opportunity to mine value from that data. Big data distributions today combine Hadoop and Spark ecosystems, various flavors of databases and scale-out system management into increasingly general-purpose data-processing platforms, all powered by underlying big data storage tools (e.g., Hadoop Distributed File System, Kudu, Alluxio).
  • Always faster. If big is good, big and fast are even better. We are seeing new kinds of automatically tiered and cached big data storage and data access layer products designed around creating integrated data pipelines. Many of these tools are really converged big data platforms built for analyzing big and streaming data at internet of things (IoT) scales.

The changing fundamentals

Powering many of these examples are interesting shifts in underlying technical capabilities. New data processing platforms are handling more metadata per unit of data than ever before. More metadata leads to new, highly efficient ways to innovate …(read the complete as-published article there)

Enterprise cloud storage boosted by Oracle Cloud Converged Storage

An IT industry analyst article published by SearchOracle.


article_Enterprise-cloud-storage-boosted-by-Oracle-Cloud-Converged-Storage
Hybrid cloud storage systems sound great in theory. So why has it taken vendors so long to produce one that’s actually ‘hybrid’?

Mike Matchett

True hybrid enterprise cloud storage systems promise the best of both on-premises infrastructure and public cloud. They offer the performance, security and governance of local storage necessary for many mission-critical business applications — not to mention the on-demand elastic scalability and utility economics of public clouds.

But finding true hybrid cloud tools to support critical, enterprise-class, database-driven applications can be difficult. This gaping hole in the market has left enterprise IT pros and business application owners stuck on legacy infrastructures.

However, there has been significant progress. Oracle Cloud Converged Storage, released last month, integrates storage services across its database, enterprise applications, enterprise storage infrastructure and public cloud. Practically, its latest on-premises Oracle ZFS storage arrays internally and organically extend into public Oracle Cloud storage (which is also made up of ZFS storage arrays) — no gateway integration or third-party software required.

Oracle Cloud Converged Storage looks like what many of us thought hybrid storage was supposed to look like. Yet, no high-performance, enterprise storage system has ever actually delivered a true native hybrid cloud capability. Why haven’t other vendors, such as IBM and Dell EMC, offered hybrid cloud storage to leverage their enterprise storage and cloud tools? Those vendors require a hardware or software gateway to move data to the public cloud, unlike Oracle, which doesn’t require one.

This failure to deliver what customers really want may be due to internal competition between legacy infrastructure and separately managed cloud divisions…(read the complete as-published article there)

Data center storage architecture gets smarter with AI

An IT industry analyst article published by SearchStorage.


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Trends, such as event-triggered computing, as exemplified by Lambda Architectures, converge on data center storage to hasten data center intelligence evolution.

Mike Matchett

Infrastructure is getting smarter by the day. It’s reached the point where I’m afraid artificially intelligent IT will soon turn the tables and start telling me how to manage my own personal “lifecycle.” Well, I would be afraid if I believed all those AI vendors suddenly claiming they offer AI-powered infrastructure.

Now, we all want smarter, more automated, self-optimizing infrastructure — especially with storage — but I don’t see storage infrastructure components engaging in a human conversation with people about anything anytime soon. Storage is definitely getting smarter in more practical ways, however, and these changes are being seen in places such as data center storage architecture.

I’m excited by the hot storage trend toward embedding machine learning algorithms aimed at key optimization, categorization, search and pattern detection tasks. Corporate data assets are growing, and so is the potential value that comes from gathering and analyzing big data. It’s difficult to manually find those nuggets of data gold, though. And with the coming onslaught of the internet of things (IoT), data prospecting challenges will add mining huge amounts of fast streaming, real-time machine-generated and operational transactional data to the mix.

To help us take advantage of these potential information riches, storage vendors have started inserting intelligent algorithms into the storage layer directly…(read the complete as-published article there)