|Exam Name||:||Foundations of IBM Cloud Computing(R) Architecture V2|
|Questions and Answers||:||100 Q & A|
|Updated On||:||February 22, 2019|
|PDF Download Mirror||:||Pass4sure 000-281 Dump|
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000-281 exam Dumps Source : Foundations of IBM Cloud Computing(R) Architecture V2
Test Code : 000-281
Test Name : Foundations of IBM Cloud Computing(R) Architecture V2
Vendor Name : IBM
Q&A : 100 Real Questions
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IBM enabled Watson AI to run across the enterprise records centers and mainstream public cloud structures. Branded as Watson anyplace, the flagship statistics and analytics platform that changed into firstly constructed for the IBM Cloud will also be deployed in loads of environments together with VMware, IBM Cloud inner most, AWS, Azure, and Google Cloud Platform.
on the think 2019 conference hosted in San Francisco, Ginni Rometty, IBM’s CEO introduced Watson anywhere.
IBM Watson anyplace is constructed on true of Kubernetes, the open supply orchestration engine that may also be deployed in different environments. in view that the Watson anyplace platform is constructed as a set of microservices designed to run on Kubernetes, it's bendy and transportable.
IBM bets on large on Kubernetes. As a member of the Cloud Native Computing groundwork and a essential contributor to the open supply mission, it presents Kubernetes as a carrier in its public cloud. IBM Cloud deepest, the hybrid cloud platform from big Blue is solely constructed on Kubernetes.
in accordance with IBM, the microservices-based mostly Watson anyplace delivers two solutions -
Watson OpenScale: IBM's open AI platform for managing varied situations of AI, no remember where they have been developed – including the capacity to explain how AI choices are being made in true time, for more desirable transparency and compliance.
Watson Assistant: IBM's AI tool for building conversational interfaces into functions and contraptions. extra advanced than a normal chatbot, Watson Assistant intelligently determines when to look for a outcomes, when to ask the consumer for clarification, and when to offload the person to a human for personal counsel. also, the Watson Assistant Discovery Extension enables businesses to release hidden insights in unstructured records and documents.
IBM Cloud private for statistics is an extension of the hybrid cloud focused on information and analytics. according to IBM, it simplifies and unifies how consumers collect, organize and analyze statistics to accelerate the price of data science and AI. The multi-cloud platform provides a large range of core records microservices, with the choice so as to add greater from a becoming services catalog.
IBM Watson anywhere is seamlessly built-in with Cloud inner most for statistics. The aggregate allows consumers to manage conclusion-to-conclusion statistics workflows to support be sure that information is effectively purchasable for AI.
almost immediately after the announcement from IBM on Watson any place, Pat Gelsinger, VMware’s CEO, outlined Watson’s integration with Workspace One, an endpoint management product that provides start and management of apps across instruments. Watson anyplace will turn into accessible within Workspace ONE clever Hub, a taste of the endpoint management platform integrated with AirWatch.
With the upcoming acquisition of crimson Hat, IBM is expected to guess large on OpenShift, purple Hat’s own enterprise distribution of Kubernetes that runs well-nigh in each public cloud.
IBM hopes that the multi-cloud strategy of Watson helps the company compete greater with its opponents.
Navigating cloud computing features can also be difficult, specially when they come from varied suppliers. therefore, setting up a fantastic basis turns into even more critical in keeping successful operations across today’s multicloud landscape. For IBM, that foundation is Kubernetes, the open-supply tool for managing containerized software applications at scale.
“IBM has taken a large guess on Kubernetes two and a half years in the past,” pointed out Daniel Berg (pictured), uncommon engineer, IBM Cloud Kubernetes service, at IBM. “[We] on no account in reality looked lower back; it’s our primary groundwork for our platform features.”
Berg spoke with Dave Vellante (@dvellante) and Stu Miniman (@stu), co-hosts of theCUBE, SiliconANGLE Media’s cell livestreaming studio, right through the IBM think event in San Francisco. They discussed IBM’s Kubernetes functions and the company challenges of relocating operations between inner most and public clouds. (* Disclosure beneath.)Bridging the multicloud hole
The IBM Cloud Kubernetes provider at the moment has two distributions: IBM Cloud deepest, or ICP, which operates on-premises, and a managed service in the public cloud. So, what are the benefits of solely conserving a personal cloud with Kubernetes? The container management platform modernizes and organizes years-historical content.
“We’ve modernized it, put it in containers, install it, and manipulate it on Kubernetes. The great element is that content material you could bring on-premises the place it’s crucial probably the most and run it in ICP — and also take that and run it in our public cloud,” Berg explained.
Kubernetes is effortless to set up, set up, and get all started. youngsters, it isn't without its issues. With more desirable proliferation comes enhanced problem in managing the different clusters, Berg stated. “There are still some complexities, because … you’ve bought building clusters; you’ve got test clusters,” he referred to.
To alleviate the concern, IBM launched a new product known as Multicloud supervisor, which offers a control aircraft to manage components throughout many different clouds and disparate systems. it works with ICP and IBM Kubernetes service but is also appropriate with Amazon, Google, Azure and OpenShift. Multicloud manager also helps with protection compliance and enforcement, so it offers protection at any place it is missing.
For corporations finding it less than feasible to maintain consistency and specifications while customizing for certain facts needs, Berg stated the benefits of diverse distributions. “if you need whatever thing that’s enormously, highly specific to a given use case otherwise you have modifications for your infrastructure that you need to have more flexibility, that’s the place IBM Cloud private comes in,” he mentioned.Two clouds are more desirable than one
relocating to public cloud in a single fell swoop is a Herculean assignment, even for large companies akin to Amazon and Google. here's where hybrid cloud comes in. In IBM’s case, it combines ICP and OpenShift to provide OpenShift clients IBM’s content, integrated monitoring, and built-in logging onto the platform for which they're already standardized.
“as a result of we built and are standardized on Kubernetes, we give Kubernetes carrier and we try this at scale and cozy, as well as tremendously accessible,” Berg cited.
Berg did warn towards the use of only one cloud supplier, and he additionally recommended companies to be consistent in what they need out of their providers. “but the factor that customers do need to examine, and what they do deserve to standardize throughout an commercial enterprise, is some of the core tenets and core applied sciences,” he introduced.
Visibility into workloads is also critical to a corporation’s operations, Berg explained. And IBM Cloud Monitoring does this. The device isn't entertaining to Kubernetes, both. somewhat, it will also be prolonged into digital machines and distinctive types of workloads. IBM’s monitoring is vastly helped by using its partnership with Sysdig Inc., Berg brought.
“you can’t construct a cloud-native answer devoid of monitoring, correct? Monitoring and log … it’s like peanut butter and jelly. You’ve bought to have them,” Berg concluded.
Watch the finished video interview beneath, and be sure to take a look at extra of SiliconANGLE’s and theCUBE’s coverage of the IBM think event. (* Disclosure: IBM subsidized this phase of theCUBE. Neither IBM nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)photograph: SiliconANGLE considering that you’re here …
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The announcements IBM made finally week's feel 2019 conference round Watson AI capabilities are neatly timed to meet evolving cloud computing calls for.
IBM referred to that via their Watson any place initiative they're making Watson AI functions obtainable throughout AWS, Azure and GCP, moreover their own IBM Cloud offerings.
For cases where groups can also wish to increase and/or run AI-based applications in deepest clouds or their own statistics centers, the enterprise is licensing Watson to be capable of run locally.
Ever considering that the upward push to prominence of cloud computing, now we have seen agencies grapple with a way to most advantageous consider about and leverage this new means of computing. Some groups, particularly net-concentrated ones, dove in head first and still have their entire existence dependent on services like Amazon's AWS (Amazon web features) (NASDAQ:AMZN), Microsoft's Azure (NASDAQ:MSFT), and Google's Cloud Platform (GCP) (NASDAQ:GOOG) (NASDAQ:GOOGL). for most traditional groups, despite the fact, the procedure of relocating toward the cloud hasn't been essentially as clear, nor as convenient. because of big investments of their personal actual statistics facilities, heaps of legacy purposes, and a lot of different customized utility investments that weren't firstly designed with the cloud in intellect, the transition to cloud computing has been a whole lot slower.
one of the stumbling blocks in moving to the cloud for these traditional companies is that the shift has often required a monolithic alternate to a completely new, distinctive category of computing. needless to say, it truly is not convenient to do, specially if the option you are moving to is considered as a singular choice, with few alternate options. In certain, as a result of AWS changed into so dominant in the early days of cloud computing, many businesses have been afraid of getting locked into this new atmosphere.
As option cloud computing choices from Microsoft, Google, IBM (NYSE:IBM), Oracle (NYSE:ORCL), SAP (NYSE:SAP) and others began to kick in, although, agencies started to see that a variety of attainable alternatives had been available. What's been happening within the cloud computing world over the last 12-18 months is more than just a simple enhance in aggressive alternatives. it's a big enlargement in considering a way to approach computing in the cloud. With multi-cloud, for instance, organizations are actually embracing, as opposed to rejecting, the concept of having various kinds of workloads hosted by means of different carriers.
In a way, we're seeing cloud computing evolve in an identical path to overall computing developments, but at a a whole lot faster pace. The initial AWS choices, for instance, weren't that conceptually diverse from mainframe-primarily based efforts, focused around a platform controlled via a single supplier. The aggregate of latest choices from different vendors as well as several types of supported workloads could be viewed as a theoretical similar to more heterogenous computing models. The stream to containers and microservices across multiple cloud computing providers in many ways mirrors the client-server evolution stage of computing. ultimately, the contemporary building of "serverless" fashions for cloud computing could be regarded roughly analogous to the advancements in side computing.
in this context, announcements that IBM made finally week's feel 2019 convention round their Watson AI features are smartly timed to meet evolving cloud computing calls for. in particular, the company talked about that via their Watson any place initiative they were going to be making Watson AI capabilities purchasable across AWS, Azure, and GCP, apart from their personal IBM Cloud offerings. in addition, for cases the place organizations may are looking to improve and-or run AI-based mostly applications in deepest clouds or their personal records facilities, the company is licensing Watson to be capable of run locally.
constructing on the company's Cloud deepest for records as a base platform, IBM is offering a call of Watson APIs or direct access to the Watson Assistant across all the in the past mentioned cloud platforms, in addition to programs running pink Hat OpenShift or Open Stack across plenty of distinctive environments.
This offers groups the pliability they are now anticipating to entry these features throughout a variety of cloud computing offerings. in fact, companies can get the AI computing substances they need, despite the classification of cloud computing efforts they've chosen to make. even if it be including cognitive functions capabilities to an present legacy application it is been lifted and shifted to the cloud, or architecting an entirely new microservices-based carrier leveraging cloud-native systems and protocols, the range of flexibility being provided to businesses looking to circulate more of their efforts to the cloud are turning out to be dramatically.
companies who need to address these needs will must undertake this extra flexible category of considering and adapt or enhance capabilities that in shape now not simplest the fact of the multi-cloud world, but the range of selections that these new alternate options are beginning to permit. The implications of multi-cloud are enormously better, although, than simply having a call of carriers or choosing to host certain workloads with one seller and other workloads with a further. Multi-cloud is really enabling businesses to feel about cloud computing in a extra bendy, approachable means. it be precisely the variety of building the trade needs to take cloud computing into the mainstream.
Disclaimer: one of the vital writer's customers are providers within the tech trade.
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Today IBM announced an ambitious plan to create a global research hub to develop next-generation AI hardware and expand their joint research efforts in nanotechnology. As part of a $3 billion commitment, the IBM Research AI Hardware Center will be the nucleus of a new ecosystem of research and commercial partners collaborating with IBM researchers to further accelerate the development of AI-optimized hardware innovations.
IBM headquarters for the AI Hardware Center will be on the SUNY Polytechnic campus in Albany, New York, and the Center will advance a range of technologies from chip-level devices, materials and architecture to the software supporting AI workloads. These efforts will push the limits of chip technology needed to meet the emerging demands of cloud computing, Big Data, and cognitive computing systems.
The groundbreaking work that these engineers will conduct at SUNY Polytechnic Institute reflects IBM’s long-term commitment to inventing the future of microelectronics,” said Dr. John Kelly, Senior Vice President, Solutions Portfolio and Research at IBM. “The IBMers working at SUNY Poly possess unique skills and capabilities, positioning our company to drive development of the next generation of chips and to fuel a new era of computing.”
The $3 billion investment that IBM announced in July 2014 focuses on the development of basic materials science to make it possible to shrink semiconductors to 7 nanometers and beyond, as well as support research into completely new areas beyond traditional silicon architectures, such as synaptic computing, quantum devices, carbon nanotubes, and photonics, that could transform computing of all kinds.
Partnerships within an open ecosystem are key to advancing hardware and software innovation that are the foundation of AI. New IBM Research AI Hardware Center partnerships announced today will aid in those continuing efforts. Samsung is a strategic IBM partner in both manufacturing and research. Mellanox Technologies is a leading supplier of high-performance, end-to-end smart interconnect solutions that accelerate many of the world’s leading AI and machine learning platforms. Synopsys is the leader in software platforms, emulation and prototyping solutions, and IP for developing the high-performance silicon chips and secure software applications that are driving advancements in AI.
IBM and SUNY Poly have built a highly successful, globally recognized partnership at the multi-billion dollar Albany NanoTech Complex, highlighted by the institution’s Center for Semiconductor Research (CSR), a $500 million program that also includes the world’s leading nanoelectronics companies. The CSR is a long-term, multi-phase, joint R&D cooperative program on future computer chip technology. IBM was a founding member of Governor Andrew M. Cuomo’s Global 450 Consortium. It continues to provide student scholarships and fellowships at the university to help prepare the next generation of nanotechnology scientists, researchers and engineers.
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The tantalizing prospect of cloud computing is changing the way people in IT think. Instead of massive and ever growing data centers, it may now be possible to simply tap into potentially unlimited resources residing externally, in the "cloud." Of course, these visions have already begun to take tangible form in cloud services such as Amazon EC2 and Microsoft Azure. However, according to analysts and others, the potential of the cloud -- at least for data-intensive applications -- will be limited without the application of a crucial enabling technology -- distributed data grids.
A distributed data grid, also called a distributed data cache, operates between the database and the in-memory of an application and provides a temporary repository for data, enhancing performance by improving access and eliminating bottlenecks.
Analyst Mike Gualtieri and his colleague at Forrester Research, John R. Rymer, have proposed an additional term -- elastic caching -- which captures a particularly useful characteristic of some data grids. Their recent report, The Forrester Wave: Elastic Caching Platforms, Q2 2010, describes the technology and some of the key vendors in the space.
Gualtieri says it is important to recognize that within the concept of data caching there are a range of solutions. The best way to describe a distributed cache is something that acts across one or more nodes. The reason Gualtieri terms some types of distributed cache elastic is that they can add and remove nodes while running. "And we think that is important and more descriptive of the defining characteristics of a data grid," he says.
By contrast, there are a number of potent but non-elastic distributed caching schemes, one of which is Memcached, an open source caching product that is widely used at Facebook and other Web properties. "Memcached is distributed but not elastic, you can decide that you have enough data so that you will require eight servers or 80 servers, but if it turns out you need more or fewer you have to shut down in order to add or remove them," he explains.
Many people associate cloud computing with scale, notes Gualtieri. Certainly the cloud allows you to scale instances of machines -- but you can't easily scale applications and data in the cloud because applications and data haven't been architected to take advantage of the "extra horsepower."
Likewise, if you think of a typical relational database packed with customer or order information, when it comes to the cloud, that database becomes your bottleneck. "If you are getting more and more transactions against that database you can try to speed it up by adding five more servers, but how do you split the data? You can't," says Gualtieri. "So elastic caching is really interesting because it has a huge impact for the cloud -- it is a solution for scaling data," he adds.
Because of its elasticity, nodes can be added in real time; if you start with four servers and add four more, these platforms will rebalance the data fairly evenly across the nodes and if any node goes down you are not down because they replicate the data. "So elastic caching also provides fault tolerance and high availability at a fraction of the cost of what it would take just to re-architect a database," he adds.
According to Gualtieri, the quest to deliver cloud scalability has also spawned a few other variations, notably the NO SQL movement. "On first glance it sounds like an attempt to get rid of SQL but the term actually stands for Not Only SQL," he says. Of course, he notes, traditional relational database are great at transactional integrity; they always provide consistent data.
By contrast, notes Gualtieri, the NO SQL crowd talks about a concept called eventual consistency. For example, when someone does an update on Twitter or Facebook, it isn't absolutely necessary that every user on the internet sees it that second -- as long as it arrives eventually. "It isn't like decrementing $100, you need a relational database for that," says Gualtieri.
For all the data that doesn't need absolute timeliness or consistency, NO SQL can provide that eventual consistency. "You give up some of the transactional integrity but what you get is an inexpensive way to scale a large amount of non-transactional data," he says.
Historically, notes Gualtieri, NO SQL grew out of the attempts by Amazon and EBay to master issues of scale. "What has happened over the years is that these technologies and similar ones have made their way into open source projects," one of which -- Cassandra -- is an open source NO SQL "that is very much like elastic caching in that the data is distributed, spread across multiple nodes, and it is fault tolerant," he explains. However, he adds, in general, NO SQL is not as well defined or developed as elastic caching -- and most NO SQL products are open source.
Elastic caching products
Coming back to the elastic caching vendors, Gualtieri's report pegs IBM (WebSphere eXtreme Scale), Terracota (Ehcache FX edition), GigaSpaces (XAP), and Oracle (Coherence) as leaders in the sector.
Cameron Purdy, vice president of development at Oracle says "The goal with Coherence is to dramatically simplify the usage of the software and the learning curve for building, deploying and operating data grids, regardless of their size."
Purdy reiterates the point that if a cloud is to achieve true capacity-on-demand, it needs to be able to shift its server footprint by tens, hundreds or even thousands of servers. "Any time an application is running on more than one server, it has state that it has to manage across those servers, and that is what a data grid enables, and specifically that is what Coherence enables," he says.
Purdy says Oracle Coherence is not a relational database management system (RDBMS). Instead, Coherence manages application data in the form that the application works with that data, such as "objects" in languages such as Java, C# and C++. Oracle Coherence manages live application data (application state, sessions, caches, etc.) in memory, using multiple servers to provide both scalability and availability in managing that application data. Furthermore, Coherence does so automatically as the server footprint grows and shrinks, all without loss of data or interruption of service.
The Forrester report also named a second tier of "strong performers," namely GemStone Systems (GemFire), Alachisoft (NCache), and ScaleOut Software (State Server).
William Bain, founder and CEO of ScaleOut Software, and a veteran of the parallel computing industry, says "We combine distributed caching with parallel data analysis." Bain says elasticity is central to the idea of distributed caching. "What distributed caching offers is the ability to have lots of threads on lots of servers accessing a common pool of data -- and as applications grow in scope they have the ability to have scalable storage," he says.
Like Gualtieri, Bain says Memcached "runs out of steam" when the data is being updated rapidly and being read, as occurs in e-commerce shopping carts. "In a distributed cache it can be read and updated by many web servers and web farms -- and it can scale," he says.
Advice on data caching
Bain says CTOs and their architects should make an effort to come up to speed on distributed caching and data grids and understand the power they provide in scaling application states. "By becoming savvy on this technology they will be able to move applications to the cloud and scale seamlessly across a large pool of virtual servers," he says. On the other hand, Bain predicts that those who don't take advantage of distributed caching will find it is hard to eliminate all the bottlenecks to scalability and storage in multiserver environments.
Expanding on that concept, Purdy says in order for an application to take full advantage of a data grid, the application should have a strong domain model. In other words, the information that the application consumes, creates and uses internally to run should be expressed as -- and managed as -- a set of defined entities with well-understood relationships among those entities.
"In modern object oriented languages such as Java, these entities are typically implemented as Java classes, and are often referred to simply as domain objects," he notes. According to Purdy, applications that have a strong domain model tend to be very easy to move to a data grid, while applications that do not encapsulate data access and representation -- such as those that sprinkle direct SQL database access throughout the application -- are difficult to adapt to data grids.
Summarizing, Gualtieri says his general advice is if you are considering cloud computing, you must ask if the application architecture is elastic and then ask three follow-up questions. The first is how can you scale your database? The second is how can your data shrink and grow to take advantage of the cloud, and how can your application code shrink and grow? The third question is about performance -- what is your performance strategy in the cloud?
"The reason I mention performance is because people hear about the opportunity to have more instances in the cloud, but more instances doesn't necessarily translate into more performance because you won't necessarily know what platforms you are running on," he says. In fact, he advises planning to do load and performance testing in the cloud. "You can't assume that just because it is in the cloud it will perform better -- start with the data first, because that is going to be a bottleneck," he adds.
But there’s more! With this major update to our existing IBM Cloud Private offering, we made a number of enhancements that I’d like to highlight for you.
REVAMPED USER EXPERIENCE: More consistent with IBM Bluemix — providing access to your apps, operations consoles, and a much enhanced app catalog of IBM and third-party software. It’s easy to get started if you’re already familiar with Kubernetes, or use one of the guided wizards to create your apps.
RICH APP CATALOG: IBM and open source software to make it easy to get access to the services you need in development and production. We’re using the open Helm format to package these “charts” out of the box and to allow you to add your own charts to your cluster as well.
IBM WebSphere Liberty, IBM Node SDK, Db2, Db2 Data Warehouse, Data Science Experience, IBM Integration Broker, and many others are available with this release, with more to come. In addition, PostgresSQL; MongoDB; Elasticsearch, Logstash, and Kibana (ELK); Prometheus; and Grafana are also available.
PROVISION APPS AND MIDDLEWARE FROM THE WEB CONSOLE OR COMMAND LINE: With just a few pieces of information to customize the chart for your needs. In this example, we’re provisioning MongoDB — we indicate how much persistent storage we need and the system takes care of the rest. Many software packages support out of the box options to scale up or down based on the needs of the developer or operator.
When you deploy any of these helm charts, all of the resources that are created are easily available from a single dashboard, with support to drill in to get more details. We can drill in to see the pieces that were setup for us — persistent storage, secrets, configmaps, deployments, stateful sets, or whatever was required for the software package.
OPEN SERVICE BROKER APIs OUT OF THE BOX: In addition to your own private Helm chart repository, we have integrated the Open Service Broker API-compliant community project, Service Catalog. By using the Service Catalog, you can extend your IBM Cloud Private platform through Service Brokers, which make it easy to provision slices of middleware based on app needs. The most basic Service Brokers might just deploy a Helm chart for the software package; others might create a database or a set of message queues for an app. Service Brokers offer more fine grained control over how you enable developers to use the platform.
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