A future of powerful clouds

In a very good way, the future is filled with clouds.

In the realm of information technology, this statement is especially true. Already, the majority of organizations worldwide are taking advantage of more than one cloud provider. IBM calls this a “hybrid multicloud” environment – “hybrid” meaning both on- and off-premises resources are involved, and “multicloud” denoting more than one cloud provider.

Businesses, research facilities and government entities are rapidly moving to hybrid multicloud environments for some very compelling reasons. Customers and constituents are online and mobile. Substantial CapEx can be saved by leveraging cloud-based infrastructure. Driven by new microservices-based architectures, application development can be faster and less complex. Research datasets may be shared more easily. Many business applications are now only available from the cloud.

Container technologies are the foundation of microservices-based architectures and a key enabler of hybrid multicloud environments. Microservices are a development approach where large applications are built as a suite of modular components or services. Over 90 percent of surveyed enterprises are using or have plans to use microservices.

Containers enable applications to be packaged with everything needed to run identically in any environment. Designed to be very flexible, lightweight and portable, containers will be used to run applications in everything from traditional and cloud data centers, to cars, cruise ships, airport terminals and even gateways to the Internet of Things (IoT).

Container technologies offer many benefits. Because of their lower overhead, containers offer better application start-up performance. They provide near bare metal speeds so management operations (boot, reboot, stop, etc.) can be done in seconds — or even milliseconds — while typical virtual machine (VM) operations may take minutes to complete. And the benefits don’t stop there. Applications typically depend on numerous libraries for correct execution. Seemingly minor changes in library versions can result in applications failing, or even worse, providing inconsistent results. This can make moving applications from one system to another — or out on to the cloud — problematic.

Containers, on the other hand, can make it very easy to package and move an application from one system to another. Users can run the applications they need, where they need them, while administrators can stop worrying about library clashes or helping users get their applications working in specific environments.

Nearly half of all enterprises are planning to start utilizing containers as soon as practical. In terms of use cases, the majority of these IT leaders say they will employ containers to build cloud-native applications. Nearly a third of surveyed organizations plan to use containers for cloud migrations and modernizing legacy applications. That suggests that beyond using them to build new microservices-based applications, containers are starting to play a critical role in migrating applications to the cloud.

But no one will be leveraging containers to build and manage enterprise hybrid multicloud environments without a powerful, purpose-built infrastructure.

IBM and Red Hat are two industry giants that have recognized the crucial role that IT infrastructure will play in enabling the container-driven multicloud architectures needed to support the ERP, database, big data and artificial intelligence (AI)-based applications that will power business and research far into the future.

Along with proven reliability and leading-edge functionality, two key ingredients of any effective infrastructure supporting and enabling multicloud environments are simplicity and automation. Red Hat OpenShift Container Platform and the many IBM Storage Solutions designed to support the Platform are purpose-engineered to automate and simplify the majority of management, monitoring and configuration tasks associated with the new multicloud environments. Thus, IT operations and application development staff can spend less time keeping the lights on and more time innovating.

Red Hat is the market leader in providing enterprise container platform software. The Red Hat OpenShift Container Platform is an enterprise-ready Kubernetes container platform with full-stack automated operations to manage hybrid cloud and multicloud deployments. The Platform includes an enterprise-grade Linux operating system plus container runtime, networking, monitoring, container registry, authentication and authorization solutions. These components are tested together for unified operations on a complete Kubernetes platform spanning virtually any cloud.  

IBM and Red Hat have been working together to develop and offer storage solutions that support and enhance OpenShift functionality. In fact, IBM was one of the first enterprise storage vendors on Red Hat’s OperatorHub. IBM Storage for Red Hat OpenShift solutions provide a comprehensive, validated set of tools, integrated systems and flexible architectures that enable enterprises to implement modern container-driven hybrid multicloud environments that can reduce IT costs and enhance business agility.

IBM Storage solutions are designed to address modern IT infrastructure requirements. They incorporate the latest technologies, including NVMe, high performance scalable file systems and intelligent volume mapping for container deployments. These solutions provide pre-tested and validated deployment and configuration blueprints designed to facilitate implementation and reduce deployment risks and costs.

Everything from best practices to configuration and deployment guidance is available to make IBM Storage solutions easier and faster to deploy. IBM Storage provides solutions for a very wide range of container-based IT environments, including Kubernetes, Red Hat OpenShift, and the new IBM Cloud Paks. IBM is continually designing, testing, and adding to the performance, functionality, and cost-efficiency of solutions such as IBM Spectrum Virtualize and IBM Spectrum Scale software, IBM FlashSystem and Elastic Storage Server data systems and IBM Cloud Object Storage.

To accelerate business agility and gain more value from the full spectrum of ERP, database, AI, and big data applications, organizations of all types and sizes are rapidly moving to hybrid multicloud environments. Container technologies are helping to drive this transformation. IBM Storage for Red Hat OpenShift automates and simplifies container-driven hybrid multicloud environments.

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AI today: Data, training and inferencing

I discussed artificial intelligence, machine learning and deep learning and some of the terms used when discussing them. Today, I’ll focus on how data, training and inferencing are key aspects to those solutions.

The large amounts of data available to organizations today have made possible many AI capabilities that once seemed like science fiction. In the IT industry, we’ve been talking for years about “big data” and the challenges businesses face in figuring out how to process and use all of their data. Most of it — around 80 percent — is unstructured, so traditional algorithms are not capable of analyzing it.

A few decades ago, researchers came up with neural networks, the deep learning algorithms that can unveil insights from data, sometimes insights we could never imagine. If we can run those algorithms in a feasible time frame, they can be used to analyze our data and uncover patterns in it, which might in turn aid in business decisions. These algorithms, however, are compute intensive.

Training neural networks

A deep learning algorithm is one that uses a neural network to solve a particular problem. A neural network is a type of AI algorithm that takes an input, has this input go through its network of neurons — called layers — and provides an output. The more layers of neurons it has, the deeper the network is. If the output is right, great. If the output is wrong, the algorithm learns it was wrong and “adapts” its neuron connections in such a way that, hopefully, the next time you provide that particular input it gives you the right answer.

Fig 1: Illustration of computer neural networks

This ability to retrain a neural network until it learns how to give you the right answer is an important aspect of cognitive computing. Neural networks learn from data they’re exposed to and rearrange the connection between the neurons.

The connections between the neurons is another important aspect, and the strength of the connection between neurons can vary (that is, their bond can be strong, weak or anywhere in between). So, when a neural network adapts itself, it’s really adjusting the strength of the connections among its neurons, so that next time it can provide a more accurate answer. To get a neural network to provide a good answer to a problem, these connections need to be adjusted by exhaustively exercising repeated training of the network — that is, exposing it to data. There can be zillions of neurons involved, and adjusting their connections is a compute-intensive matrix-based mathematical procedure.

We need data and compute power

Most organizations today, as we discussed, have tons of data that can be used to train these neural networks. But there’s still the problem of all of the massive and intensive math required to calculate the neuron connections during training. As powerful as today’s processors are, they can only perform so many math operations per second. A neural network with a zillion neurons trained over thousands of training iterations will still require a zillion thousand operations to be calculated. So now what?

Thanks to the advancements in industry (and I personally like to think that the gaming industry played a major role here), there’s a piece of hardware that’s excellent at handling matrix-based operations called the Graphics Processing Unit (GPU). GPUs can calculate virtually zillions of pixels in matrix-like operations in order to show high-quality graphics on a screen. And, as it turns out, the GPU can work on neural network math operations in the same way.

Please, allow me to introduce our top math student in the class: the GPU!

Fig 2: An NVIDIA SMX2 GPU module

A GPU is a piece of hardware capable of performing math computations over a huge amount of data at the same time. It’s not as fast as a central processing unit (CPU), but if one gives it a ton of data to process, it does so massively in parallel and, even though each operation runs more slowly, the parallelism of applying math operations to more data at once beats the CPU performance by far, allowing you to get your answers faster.

Big data and the GPU have provided the breakthroughs we needed to put neural networks to good practice. And that brings us to where we are with AI today. Organizations can now apply this combination to their business and uncover insights from their vast universe of data by training a neural network for that.

To successfully apply AI in your business, the first step is to make sure you have lots of data. A neural network performs poorly if trained with little data or with inadequate data. The second step is to prepare the data. If you’re creating a model capable of detecting malfunctioning insulators in power lines, you must provide it data about working ones and all types of malfunctioning ones. The third step is to train a neural network, which requires lots of computation power. Then after you train a neural network and it performs satisfactorily, it can be put to production to do inferencing.

Inferencing

Inferencing is the term that describes the act of using a neural network to provide insights after is has been trained. Think of it like someone who’s studying something (being trained) and then, after graduation, goes to work in a real-world scenario (inferencing). It takes years of study to become a doctor, just as like it takes lots of processing power to train a neural network. But doctors don’t take years to perform a surgery on a patient, and, likewise, neural networks take sub-seconds to provide an answer given real world data. This happens because the inferencing phase of a neural network-based solution doesn’t require much processing power. It requires only a fraction of the processing power needed for training. As a consequence, you don’t need a powerful piece of hardware to put a trained neural network to production, but you could use a more modest server, called an inference server, whose only purpose is to execute a trained AI model.

What the AI lifecycle looks like:

Deep learning projects have a peculiar lifecycle because of the way the training process works.

Fig 3: A deep learning project’s lifecycle

Organizations these days are facing the challenge of how to apply deep learning to analyzing their data and obtaining insights from it. They need to have enough data to train a neural network model. That data has to be representative to the problem they’re trying to solve; otherwise the results won’t be accurate. And they need a robust IT infrastructure made up of GPU-rich clusters of servers to train their AI models on. The training phase may go on for several iterations until the results are satisfactory and accurate. Once that happens, the trained neural network is put to production on much less powerful hardware. The data processed during the inferencing phase can retro feed the neural network model to correct it or enhance it according to the latest trends being created in newly acquired data. Therefore, this process of training and retraining happens iteratively over time. A neural network that’s never retrained will age over time and potentially become inaccurate with new data.

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Making the move to value-based health

A few years ago, the IBV Institute for Business Value published a report which predicted the convergence of population health management and precision medicine into a new healthcare model we called Precision Health and Wellness. We believed a key component of that model would be a continued transition to outcome-based results and lower costs.

Fast forward to 2019 and our latest research found that not only had those predictions become real but that the speed of change had increased. Globally, healthcare systems are looking at how to maintain access, quality, and efficiency. Emphasis has shifted from volume of services toward patient outcomes, efficiency, wellness, and cost savings. And there is a recognition that the focus on “care” alone will be not deliver the degree of outcome improvement and cost reduction needed by providers or payers. Instead they will need engage the individuals, employers, communities, and social organizations as key partners in the process.

By using collaboration models, shared information, and innovative technology solutions across these stakeholders, better outcomes can be achieved across the whole lifespan of the individual—not just in doctors’ offices and hospitals, but in their daily lives, homes, and communities. It is this extension of health and wellness beyond the traditional clinical environment that takes care to the next level – value-based health.

Value -based health entails keeping individuals healthy and well even when not receiving healthcare services. Engaging people and communities in health, identifying and addressing social determinants of health, and making sure community resources are available and accessible are cornerstones of value-based health.

In order to determine what is needed to transition from traditional value-based care towards value-based health, we spoke with a thousand healthcare executives in payer and provider organizations around the world.

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Accelerating data for NVIDIA GPUs

These days, most AI and big data workloads need more compute power and memory than one node can provide. As both the number of computing nodes and the horsepower of processors and GPUs increases, so does the demand for I/O bandwidth. What was once a computing challenge can now become an I/O challenge.

For those scaling up their AI workloads and teams, high-performance filesystems are being deployed because it addresses that I/O challenge. These deliver the bandwidth needed to feed the systems to keep them busy.

This week, NVIDIA announced a new solution–Magnum IO–that complements the capabilities of leading-edge data management systems such as IBM Spectrum Scale and helps address AI and big data analytics I/O challenges.

NVIDIA Magnum IO is a collection of software APIs and libraries to optimize storage and network I/O performance in multi-GPU, multi-node processing environments. NVIDIA developed Magnum IO in close collaboration with storage industry leaders, including IBM. The NVIDIA Magnum IO innovative software stack includes several NVIDIA GPUDirect technologies (Peer-to-Peer, RDMA, Storage, and Video) and communications APIs (NCCL, OpenMPI, and UCX). NVIDIA​ GPUDirect Storage is a key feature of Magnum IO, enabling a direct path between GPU memory and storage to improve system throughput and latency, therefore enhancing GPU and CPU utilization.

NVIDIA Magnum IO is designed to be a powerful complement to the IBM Spectrum Storage family. IBM Spectrum Scale, for example, was developed from the beginning for very high-performance environments. It incorporates support for Direct Memory Access technologies. Now, NVIDIA Magnum IO  is extending I/O technologies to speed NVIDIA GPU I/O.

For technology solution providers such as IBM and NVIDIA, the key is to integrate processors, GPUs, and appropriate software stacks into a unified platform designed specifically for AI. NVIDIA is also a major player in this space – 90 percent of accelerator-based systems incorporate NVIDIA GPUs for computation.

Recently, IBM and NVIDIA have been working together to develop modern IT infrastructure solutions that can help power AI well into the future. The synergies created by the IBM and NVIDIA collaboration have already been demonstrated at the largest scales. Currently, the two most powerful supercomputers on the planet – Summit at Oak Ridge and Sierra at Lawrence Livermore National Labs – are built from IBM Power processors, NVIDIA GPUs and IBM Storage. A key to these installations is the fact that they were assembled using only commercially available components. Leveraging this crucial ingredient, there are a range of solutions being offered by IBM and our Business Partners–from IBM supported versions of the complete stack to SuperPOD reference architectures featuring IBM Spectrum Scale and NVIDIA DGX systems.

Developing technology designed to increase data pipeline bandwidth and throughput is only part of the story. These solutions provide comprehensive reference architectures that incorporate a wide range of IBM Spectrum Storage family members, including IBM Cloud Object Storage for scalable data, IBM Spectrum Discover to manage and enhance metadata, and IBM Spectrum Protect to provide modern multicloud system security. The focus is on user productivity and support for the entire data pipeline.

The announcement of NVIDIA Magnum IO highlights the benefits of ecosystem collaboration to bring innovation to AI. As enterprises move rapidly toward adopting AI, they can do so with confidence and support of IBM Storage.

Magnum IO SC19: Accelerating data for NVIDIA GPUs with IBM Spectrum Scale

Source: ibm.com

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My Strategy to Prepare for IBM Cognos Controller Developer (C2020-605) Certification Exam

About IBM Cognos Controller Developer Certification

IBM Cognos Controller is given with an integration component, Financial Analytics Publisher, that automates the process of obtaining data in near real-time from IBM Cognos Controller into IBM Cognos TM1. After the data is a source in IBM Cognos TM1, it can then be inserted as a data reference for IBM Cognos BI for enterprise reporting objectives.

The IBM Cognos Controller data in IBM Cognos TM1 is restored on a near real-time data through an incremental publishing process from the transactional controller database.

The Financial Analytics Publisher element is added on top of the IBM Cognos Controller and manages a temporary storage area before populating an IBM Cognos TM1 cube. When configured, the IBM Cognos TM1 cube is continuously updated, and you can set how often the service frames run.

From the IBM Cognos TM1 cube, the IBM Cognos Controller data can be obtained by several reporting tools, including IBM Cognos studios.

IBM C2020-605 Exam Summary:

• Name: IBM Certified Developer - Cognos 10 Controller

• Code: C2020-605

• Duration: 120 minutes

• Exam Questions: 94

• Passing Score: 65%

• Exam Price: 200 USD

IBM C2020-605 Exam Topics:

• Create Company Structures (5%)

• Create Account Structures (12%)

• Set up General Configuration (14%)

• Enable Data Entry and Data Import (19%)

• Create Journals and Closing Versions (5%)

• Prepare for Currency Conversion (11%)

• Configure the Control Tables (9%)

• Eliminate and Reconcile Intercompany transactions and acquisitions (10%)

• Consolidate a Group's Reported Values (5%)

• Secure the Application and the Data (4%)

• Create Reports to Analyze Data (6%)

Preparation Tips for IBM Cognos Controller Developer (C2020-605) Certification Exam

1. If You Can Not Do the Past Papers Ask Someone For Help

Study groups work well, given you do not think this will mean other people are doing your studying for you.

You have to go and study a subject or try an exam paper by yourself first, then meet together to explain your answers. Don’t work through the past exam questions in the group.

The attraction to let other people do the work is too strong. You require to learn to do it yourself.

2. Do Not Be Tired

If you have to wait up all night to do last minute revision, you have already failed. It does not work. You end up so tired in the exam you cannot work anything out. It might work for the examination in a year, but you would not be able to hold it up during the C2020-605 exam.

3. Eat Protein Before Long Exam

An exam is just as much a physical exercise as a race. Perhaps not quite as much, but you can not ignore your body if you want your brain to work at its best. Filling it full of sugar, or some energy drink just before will work fine for the first hour or so, but by the end of a C2020-605 exam, you will have run entirely out of energy. You require some food that will slowly release energy.

4. Get the Important Facts into Short-term Memory

In the last 24 hours, it is too late to try and get anything new. What you can do is read some facts into short-term memory. This is the time to go through the notes, looking at those critical points sections. If you have not already done it as part of your revision and you should have done it, write out a sheet with just the key facts.

5. Practice Tests

If you want to prepare with highly up-to-date questions, then my strong suggestion to you starts your preparation now from AnalyticsExam.com. It is the best site from where you can get the valid, authentic, and verified dumps to prepare for your IBM Cognos 10 Controller Developer C2020-605 Exam.

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It is a proven good helper to help you get success IBM Cognos 10 Controller Developer C2020-605 Exam. So what you still waiting for, go to AnalyticsExam.com and get your helping material now.

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The Evolving Role of the Data Architect – Lift Up Your Career

Data architects are generally senior-level professionals and are highly admired in huge companies. A data architect is an individual who is responsible for designing, creating, expanding, and leading an organization's data architecture.

Data architects describe how the data will be collected, utilized, integrated, and managed by various data entities and IT systems, as well as any applications using or processing that data in some way.

Data architects must be original problem-solvers who use a considerable amount of programming tools to innovate and create new solutions to store and manage data.

At larger organizations, data architects are more removed from the physical storage and implementation of the data.  They may have a unit of database administrators, data analysts, data modelers working for or alongside them.

Data Architect Duties:

A data architect may be needed to:

• Collaborate with IT organizations and administration to devise a data strategy that approaches industry demands.
• Build an inventory of data required to complete the architecture.

Analysis of new opportunities for data acquisition:

• Develop data forms for database structures
• Found a solution, end-to-end concept for how data will flow through an organization
• Design and support database development models
• Recognize and evaluate prevailing data management technologies
• Integrate technical functionality (e.g., scalability, security, performance, data retrieval, reliability, etc.)
• Design, construct, document and deploy database structures and applications (e.g., enormous relational databases)
• Manage a corporate storehouse of all data architecture artifacts and methods
• Meld new methods with actual warehouse structures
• Execute steps to ensure data efficiency and accessibility
• Continually observe, refine and report on the production of data management systems

Abilities required to become a Data Architect:

Data architects are extremely trained workers, who are fluent in a broad range of programming signals as well as other technologies, and must be skilled communicators with extreme business insights. Data architects must have strong attention to detail, as any difficulties in coding can cost a business millions to improve.

Technical skills involved with being a data architect include strength in:

• Data visualization and data immigration
• Utilized math and statistics
• RDMSs (relational database management systems) or foundational database abilities
• Machine learning
• Operating systems, including Linux, UNIX, Solaris, and MS-Windows
• Database administration system software, especially Microsoft SQL Server
• Programming languages, especially Python and Java, as well as C/C++ and Perl
• Backup/archival software

Prosperous data architects have some other business abilities. Though they must have a depth and width of knowledge in the field, data architects must also be inventive queries-solvers, who can innovate new solutions and change with developing the technology.

As data architects are often senior executives on a project, they must be capable to adequately lead members of a team, such as data engineers, data modelers, and database administrators. They must also be able to communicate explications to associates with non-technical backgrounds.

How to Become a Data Architect

1. Examine additional certifications and additional learning.

• There are many opportunities to develop your expertise and knowledge as a data architect from organizations such as IBM, Salesforce.
• IBM Certified Data Architect – Big Data
• This IBM professional certification program needs that applicants maintain a myriad of required skills from understanding cluster control and data replication to data lineage.

2. Develop and improve in your professional and business abilities from data scooping to analytical problem-solving.

• Application server software
• Development environment software
• Data mining
• Database administration system software
• Technical Abilities for Data Architects
• User interface and query software
• Backup/archival software
• UNIX, Linux, Solaris, and MS-Windows
• Python, C/C++ Java, Perl
• Data visualization
• Machine learning

Business Skills for Data Architects:

Analytical Problem-Solving: Comparing high-level data difficulties with a clear eye on what is necessary; employing the right program/techniques to make the best use of time and human resources.

Management Knowledge: Knowing the way your chosen business functions and how data are collected, analyzed and used; maintaining adaptability in the face of important data improvements.

Compelling Communication: Carefully listening to administration, data investigators, and associated team to come up with the best data design; explaining complex ideas to non-technical associates.

Expert Management: Effectively directing and advising a team of data modelers, data engineers, database administrators, and junior architects.

To become a data architect, you should begin with a bachelor’s degree in computer science, computer engineering, or a similar field. Coursework should include coverage of data programming, management, significant data improvements, technology architectures, and systems analysis. For senior positions, a master’s degree is usually preferred.

Conclusion:

Data architects are usually skilled at logical data modeling, physical data modeling, data policies development, data procedure, data warehousing, data doubting languages and recognizing and choosing a system that is best for addressing data storage, retrieval, and administration.

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Automate disaster recovery using IBM VM Recovery Manager

Business continuity is a top priority for every enterprise. And, at the foundation, it’s all about having a solid plan in place to deal with disruptions and potential threats.

If you’re an IT planner, you know that data protection and disaster recovery (DR) — the aspects of business continuity that are most relevant to IT professionals — can be major concerns. An outage of one or more of your mission-critical systems could cause significant business downtime, including loss of revenue and irreparable damage to long-term customer relationships.

So how can you stay ahead of the curve?

Planning ahead for disaster recovery: Five key questions

There are five key questions on the mind of technology planners when considering a disaster recovery solution:

1. Are there any tools that can automate disaster recovery?
2. How can we keep the remote location updated with the current state of the primary location?
3. Which methodology should we use?
4. How can we reduce the costs of redundant systems and licenses?
5. What’s the best way to test and verify disaster recovery site readiness?

IBM VM Recovery Manager can answer these questions. It’s an easy-to-use, automated and cost-effective disaster recovery solution for applications hosted on IBM Power Systems. In this blog post, I’ll illustrate how IBM Systems Lab Services helped an enterprise client address DR concerns for its SAP HANA landscape using IBM VM Recovery Manager.

How IBM VM Recovery Manager can help

IBM Systems Lab Services recently worked with a client that’s a multinational SAP consulting and hosting service provider, hosting SAP HANA and other SAP landscapes on IBM Power Systems servers for its customers. The client was looking for an easy-to-use, automated solution for disaster recovery.

Initially, they considered HANA System Replication for its database, but with this option, the similar replication would not be available for the SAP Central Services (SCS) and SAP Application Server (AS) components. Therefore, other solutions would have to be used, creating a more complex environment to manage.

Storage replication was another option considered for replicating all critical data to a remote site. However, management of storage replication during a DR operation is highly complex, involves manual processes, is more time consuming and requires additional training for your staff on these expert skills.

Both of these solutions require duplicate instances in the DR site, demand advanced skills, require further resources and come with higher license costs. On top of that, neither provides a DR rehearsal capability while production is running.

IBM Systems Lab Services conducted a proof of concept to demonstrate the benefits of IBM VM Recovery Manager for disaster recovery, including how it would address the client’s specific concerns.

VM Recovery Manager:

◈ Moves SAP HANA databases and other SAP instances to the disaster recovery site with a single command for planned or unplanned site movement. In turn, relieving the client of the complexities of server and storage management during DR operation and sparing them the need for additional staff training.
◈ Validates DR site readiness with its DR rehearsal capability, thereby giving the client confidence about its DR readiness.
◈ Offers other features like monitoring and auto update of configuration changes to the VMs like processor/memory, disk additions and so forth — capabilities that take away the administrative overhead from the staff.

In the end, the client was very happy with the product demonstration and decided to implement VM Recovery Manager as its DR solution — with IBM Systems Lab Services there every step of the way from design to implementation. From there, the Lab Services team provided knowledge transfer and enablement for the client’s team to manage the solution covering all of its SAP systems, including databases and applications.

The following pictures illustrate VM Recovery in normal operation and after a site recovery.

Picture 1: Production VMs running on primary site

Picture 2: Production VMs moved to secondary site and running after a DR move

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Identifying potential storage infrastructure problems

Whenever I talk with infrastructure management teams about concerns and pain points they have with their storage management system, I get these questions:

◈ How can we maintain high availability and avoid system downtime?
◈ Is there any way to proactively identify a potential problem?
◈ How do we ensure maximum storage performance and efficiency?
◈ How do we prepare management for performance or capacity limitations?

To answer these questions and more, I recommend they take a storage infrastructure health assessment.

Many organizations have deployed an infrastructure monitoring platform for their storage health, but a storage health regular assessment is still recommended. That’s because storage infrastructure involves a lot of assets from different parties, which are growing and changing constantly. These changes can cause performance inconsistency, error and even unplanned outages. But with regular storage assessments, these issues can be proactively identified and avoided.

What’s covered in a storage assessment?

A typical storage health assessment includes:

◈ Health check: Validate the functional integrity of related systems
◈ Configuration: Validate whether the current configurations of related systems follow best practices and identify any potential impact to performance or availability
◈ Firmware: Check whether the current firmware is exposed to known issues, vulnerabilities that may cause an incident or security compliance violation
◈ Interoperability: Validate whether various systems and tools in the storage environment are certified to work together
◈ Capacity and performance evaluation: Based on the current peak capacity and performance results, identify performance bottlenecks and evaluate whether the storage system will reach its capacity or performance limit in the near future

What systems are assessed?

A storage health assessment should cover the following systems:

◈ Storage systems such as SAN disk storage, network-attached storage (NAS) gateway/appliance, storage virtualization (for example, SAN Volume Controller), tape library and drives
◈ SAN devices: SAN director, SAN switch, FCIP router
◈ Servers attached to storage: UNIX and x86 servers running various operating systems (only devices and configurations related to storage)

Ready to start your storage health assessment?

IBM Systems Lab Services offers storage infrastructure health assessment services to IBM storage clients to ensure their storage environments are healthy and running according to IBM best practices. We perform storage health assessments in three phases:

The service time (shown at the bottom of the preceding chart) depends on the size of your storage environment. We recommend that you run it at least once per year.

IBM Systems Lab Services also offers a Storage Infrastructure Optimization (SIO) service, which provides tactical and strategic recommendations for improvements to storage infrastructures.

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Top IBM Power Systems myths: Linux on x/86 is different from Linux on Power

There are many misconceptions about IBM Power Systems in the marketplace today, and this blog series is all about dispelling some of the top myths. In the last post, I put aside the myth that IBM Power Systems has no cloud strategy. In this post, we’ll look at a myth that has been propagated by many of our x/86-based competitors and consultants. This myth wants you to believe that if you invest in a Linux on IBM Power Systems solution, you’ll be getting an inferior product, one that’s not “real” Linux, or that your applications won’t work the way they should.

There is one aspect of this myth that’s true. Linux on Power solutions are:

◈ Almost always faster
◈ More reliable
◈ Usually smaller (require fewer cores and fewer physical systems)
◈ More secure

In all other aspects, from Linux distributions and release levels to system management and monitoring tools and development environments, they are the same.

Consider the following points:

Endianness

Not long ago, there was a difference between Power and x/86 based systems that affected not only Linux distributions but all operating systems, applications and databases that ran on those systems. Our industry borrowed from Jonathan Swift’s Gulliver’s Travels, using the terms “big endian” and “little endian” to describe the way computers represent data. x/86 systems have always been “little endian” and Power was always “big endian.” That caused a problem because software created for little endian systems could not run on big endian systems without modification, and vice versa.

In April 2104, IBM announced little endian (LE) support for POWER8, and today all of our POWER9 systems support a 64-bit LE environment.

While LE support removes the endian differences, LE code compiled for an x/86 system needs to be recompiled for a Power-based LE system.

Linux distributions

There are hundreds of Linux-based distributions available in the market today. While many can run on IBM Power Systems, IBM officially supports RedHat Enterprise Linux, Ubuntu and SUSE Linux Enterprise Server. Community versions of Linux like Debian, openSUSE, CentOS, Fedora and others are also available.

These distributions are built around a package management system and include the Linux kernel, Gnu shell utilities, system management tools, a desktop environment as well as open source software and sometimes proprietary packages.

There are Linux distributions with features designed to take advantage of some of the unique capabilities of IBM Power Systems architecture, and the startup process for a Linux distribution may be a bit different between x/86 and Power. However, once it is installed the features and functionality are identical.

Virtualization options

There are usually several options for virtualization within a Linux distribution. KVM is the default option for Ubuntu and is the foundation for Red Hat virtualization. PowerVM is the only virtualization option for SUSE Linux Enterprise Server 12 at this time. KVM on Power and RHEV options provide the same functionality on Power as they do on an x/86 platform, while PowerVM is designed to take advantage of IBM Power architecture.

System administration and monitoring tools

All Linux distributions supported on IBM Power platforms provide system administration and monitoring tools. Cockpit, Performance Co-Pilot (PCP) and NMAP are examples of the tools provided by Red Hat. SUSE Linux Enterprise server supplies an advanced system management module that includes CFEngine, Puppet, Salt and The Machinery Tool. Landscape is Ubuntu’s systems management package.

There are many open source and proprietary solutions available like Chef, JuJu, Ansible, Wireshark and countless others that can be added to a Linux distribution. In fact, the top five open source tools for Linux systems administration are available on Power Linux systems providing the same level of functionality and support as they do for x/86 based systems.

Application development environments

Linux on Power solutions have all of the tools developers need to build their application portfolios. For example:

◈ Container management solutions like Docker, Kubernetes and Rancher

◈ Programming languages including Python, php, Ruby, Scala, Java, Erlang and C/C++

◈ Atom, Bluefish, Eclipse, Netbeans, Geany, AnJuta and Glade—all considered to be the top integrated development environments

◈ Git/Git-Hub, Apache Subversion, Darcs, Mercurial, Monotone and CVS—some of the best version control systems available

◈ Eight of the top ten text editors, including Atom, VIM, gedit, GNU EMACS and Nano

◈ Many other capabilities, including diff tools like meld and kdiff, debug tools and multi-media editors like Adrour, Audacity and Gimp

Desktop environments and application portfolios

There are also a wealth of open source and proprietary applications, databases and desktop options available for Power Systems clients to choose from. Here are a few examples:

◈ KDE, Mate, GNOME, Cinnamon, Budgie, LXDE and XFCE, which are considered to be the top desktop environments in 2018 for Linux

◈ Thunderbird, Geary and Evolution e-mail clients

◈ Pidgin and Empathy instant messaging applications

◈ Calligra Suite, Libre Office and WPS Office

◈ MariaDB, Postgre SQL, EnterpriseDB, MongoDB, Cassandra, Redis are among the top open source databases available, as well as IBM Db2

◈ Dolibarr and Odoo, two of the most popular open source ERP packages

◈ Spark, elasticsearch, Apache Solr and Hadoop for analytics

◈ SAP HANA’s more than 2000 clients, many of whom left the x/86 world to take advantage of the IBM Power architecture

Is Linux on x/86 different from Linux on Power?

Linux on Power is the same as Linux on x/86, except for better performance, reliability, security and a smaller physical footprint. These are the differences that should matter most and be the key reasons why a Linux on Power solution is a better choice than an x/86-based Linux solution.

IBM Systems Lab Services has a team of experienced consultants ready to help you get the most out of your Linux on Power system.

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IBM Spectrum LSF goes multicloud

IBM is moving swiftly to implement multicloud capabilities across both our IBM Spectrum Storage and IBM Spectrum Computing portfolios. In an important step for our high-performance computing (HPC) solutions, today we’re announcing the release of a deployment guide that facilitates the use of IBM Spectrum LSF Suite with Amazon Web Services (AWS).

IBM Spectrum LSF Suite is a comprehensive set of solutions supporting traditional HPC and high-throughput environments, as well as big data, artificial intelligence (AI), GPU, machine learning, and containerized workloads among many others. IBM Spectrum LSF, the core of the Suite, is a workload and resource management platform for demanding, distributed HPC environments. It provides a comprehensive set of intelligent, policy-driven scheduling features that help maximize utilization of compute infrastructure resources while optimizing application performance.

IBM Spectrum LSF Suite comes in three editions and includes additional capabilities such as  LSF resource connector, which enables policy-driven cloud bursting to all major cloud services, including IBM Cloud, AWS, Google and Azure.

The newly released deployment guide builds on an existing relationship with AWS. IBM is providing expertise, services, and management capabilities that will give IBM Spectrum LSF customers fast, flexible access to AWS offerings.

The new deployment guide helps users build a wide range of customizable IBM Spectrum LSF cluster configurations that can enable users to take advantage of cloud computing. In particular, HPC environments can leverage the cloud during times of peak activity. To accommodate spikes in demand, traditional HPC environments often divide up jobs and stretch out scheduling–but this can lengthen time to insight. IBM Spectrum LSF solutions can help address this challenge by enabling dynamic access to cloud resources.

Two of the most common IBM Spectrum LSF cluster solutions are the LSF Stretch Cluster and the LSF Multi Cluster configurations. In the LSF Stretch Cluster architecture, the master scheduler and other core functionality remain with the on-premises IBM Spectrum LSF cluster, but the cluster resources can be dynamically “stretched” over a WAN to include cloud resources.

The Multi Cluster configuration, on the other hand, essentially creates two clusters, one on premises and one in the cloud. This architecture can simplify communication and coordination between the on-premises and cloud-based clusters.

Both configurations offer certain advantages and trade-offs, and both configurations are covered in detail by the new deployment guide.

With the release of the new LSF cloud deployment guide, enterprises and HPC facilities can more easily build the IBM Spectrum LSF cluster architectures that are best for them. Then they can leverage the power of IBM Spectrum Scale–the high-performance data management member of the IBM Spectrum Storage family–to enable the storage portion of the overall solution. The multicloud reach of IBM Spectrum Scale includes Spectrum Scale on AWS, available on AWS Marketplace. Currently available as a Bring Your Own License offering, the service is targeted at IBM customers who want to gain access to the elasticity of AWS for their high-performance computing workloads, allowing deployment of highly available, scalable cluster file systems on AWS. IBM provides a Cloud Formation script that deploys IBM Spectrum Scale across a cluster of AWS virtual server instances.

Release of the new LSF cloud deployment guide marks yet another milestone in the ongoing expansion of IBM Spectrum LSF multicloud capabilities, but it’s not the only important news for IBM customers. IBM is also announcing variable use licensing for IBM Spectrum LSF. This new “bite-sized” licensing will allow users to purchase licenses for IBM Spectrum LSF in blocks of CPU “core hours.” One block equals 1000 core hours. The new licensing will make IBM Spectrum LSF even easier and more flexible to deploy. IBM Spectrum LSF users will be able to run the solution almost anywhere and pay only for what they use rather than predicting and hoping.

Across the entire IBM Spectrum Computing portfolio, plenty of innovation is occurring, with plenty more on the roadmap. As enterprises and HPC facilities search for new cloud usage paradigms, they will likely look to intelligent solutions that leverage multicloud architectures and make them easier and less complex to adopt.

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