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000-400 IBM WebSphere Operational decision Management V8.0 Application Development

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000-400 exam Dumps Source : IBM WebSphere Operational decision Management V8.0 Application Development

Test Code : 000-400
Test denomination : IBM WebSphere Operational decision Management V8.0 Application Development
Vendor denomination : IBM
: 51 actual Questions

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IBM IBM WebSphere Operational Decision

IBM to uphold Utilities reduce Tree Trimming Budgets and reduce Outages | actual Questions and Pass4sure dumps

IBM unveiled unusual technology to carve back verve outages by means of assisting energy organizations forecast where trees and other vegetation might likewise intimidate vigour traces. IBM labored with Oncor, the biggest utility industry in Texas and the fifth largest within the u.s., to further an retort tailor-made for the power and utility business, to aid improve operations and provide reputable electric provider for thousands and thousands of clients across the state.

The weather industry Vegetation management - forecast is built on IBM PAIRS Geoscope, a know-how developed by course of IBM analysis.The gadget at once strategies massive, complex geospatial and time-based datasets accumulated with the aid of satellites, drones, aerial flights, millions of IoT sensors and climate models.

The resulting insights can assist organizations fancy Oncor to parade screen vegetation extend throughout their total carrier territory, permitting them to more desirable determine and forecast competencies infringement with power strains. companies can more proactively and precisely scheme for preventive maintenance and speedy response, focusing crews in the maximum-precedence places and validating that imperative trimming turned into achieved as anticipated.

"electrical energy is an significant section of their lives, and hundreds of thousands of Texans depend on Oncor each day. Vegetation poses a major possibility to vigour lines and the surrounding areas, but monitoring it's a challenging and time-consuming manner," says Peter Stoltman, vegetation administration program supervisor, Oncor. "through working with IBM, they are capable of consume analytics and AI to prioritize excessive-possibility areas. This helps us suitable protection operations to improve public defense and repair reliability."

Vegetation is a leading explanation for provider interruption for utility companies. With traditional processes, the primary perception into odds risks includes high priced on-website inspections or realizing the terminal time a local become trimmed. With this unusual solution, hundreds of miles of transmission and distribution traces will live always monitored to give continuous insight about the situation of extend and preservation. besides assisting identify and forecast outage threats, geospatial-temporal insights can assist with common grid reliability and compliance, wildfire prevention, storm management and evaluation.

“Our utilities customers told us that getting minute perception into the situation of vegetation throughout their carrier territory changed into a key challenge,” says Mahesh Sudhakaran, chief digital officer of IBM energy, atmosphere and Utilities. "by using combining PAIRS with AI and industry advantage, they may give valued clientele one integrated answer, delivered through IBM Hybrid Cloud, to aid them forecast and manage vegetation in a value-effective and ingenious means.”

“every enterprise is suffering from climate. however for power corporations and their consumers, it might imply the change between even if they could retain the lights on and heat their homes,” says Cameron Clayton, IBM’s accustomed manager of Watson Media and climate. “The capability to layer climate facts with satellite tv for pc and sensor records gives utility companies potent unusual insights to assist them enhance operations and carve impact on their purchasers.”

PAIRS Geoscope ingests greater than 10 terabytes of unusual records per day and has already served more than 15 million requests from greater than 4,000 researchers, information scientists and developers in 80 international locations. It removes the labor-intensive manner of generating insights from geospatial-temporal information, which is universal for its sheer dimension and complexity. The lack of talent to access, query and resolve this classification of great records in a scalable manner is the motive it become long regarded unsearchable statistics. IBM scientists invented a brand unusual technique to dash and resolve complex queries inside minutes as a substitute of weeks or months, making prior to now unimaginable insights now a fact.

PAIRS Geoscope is commonly accessible for distinctive industries and consume circumstances. besides Vegetation management - Predict, PAIRS is additionally the underlying technology for Watson election Platform for Agriculture, a collection of agribusiness tools that uses the power of AI and geospatial records to wait on farmers do greater suggested choices about their plants.

For greater suggestions, view the net website. 

international Healthcare election assist and IBM Watson Market, 2019: analysis & Outlook via 2016-2026 - | actual Questions and Pass4sure dumps

The "world Healthcare decision wait on and IBM Watson Market size, Market Share, utility evaluation, Regional Outlook, growth tendencies, Key players, competitive recommendations and Forecasts, 2018 To 2026" file has been brought to's offering.

The world healthcare resolution uphold and IBM Watson market are anticipated to expand at a CAGR of eleven.four% from 2018 to 2026.

IBM Watson has developed a core engine which aids the scientific community in understanding scientific literature by cross-referencing and extracting assistance related to genes, diseases, medicine and promote dissimilar access to users global.

Healthcare decision uphold is greatly employed by using medical professionals to uphold them in providing cost-based supervision and mediate strategic resolution touching on the treatment routine. it's positive in featuring services reminiscent of purchaser engagement and fraud detection in executive and privately held clinical facilities.

excellent points corresponding to information warehousing, streamlining administrative operations and stakeholder administration are being imparted to healthcare determination wait on capabilities. population trend evaluation, demographics, can charge-sharing, and recipient profiling are being developed under novel healthcare reform initiatives promoted by means of executive healthcare organizations international.

IT healthcare options are reigning the choices section for healthcare decision uphold market. a tremendous era of scientific statistics, pressing requirement of medical insights to wait on in the election making for medicine of sick sufferers power the IT healthcare solutions market growth.

Genomic reviews are being fortified with previous scientific facts to supply valuable therapeutic options for malignant tumors and extend affected person compliance. synthetic intelligence and cognitive computing are being combined with true-time scientific facts to promote helpful medicine guidelines and sickness analysis.

North the usa is at the moment leading the regional phase for healthcare resolution wait on and IBM Watson market. abode of key gamers comparable to IBM Watson, Accenture, American well methods, AT&T, McKesson employer and many others. provide IT-healthcare options to the medical fraternity of North the us. The burgeoning requirement of novel drug discovery within the light of increasing mortality caused with the aid of persistent diseases drives the healthcare decision wait on market boom in North the us.

Strategic collaboration between pharmaceutical giants and research academia and proactive executive policies in proposing positive remedy guidelines propel the healthcare decision wait on market boom within the European region.

The Asia Pacific can live the quickest starting to live market each and every the course through the forecast length from 2018 to 2026 due to constructing IT-healthcare sector and extending mortality linked to an absence of constructive medicine demeanor on continual disorder.

Key Market movements

  • The rising incidence of persistent disease and the burgeoning requirement of novel drug discovery during the globe
  • developing healthcare infrastructure and extending demand for IT-healthcare solutions within the patient medication
  • Cognitive computing and synthetic intelligence being clubbed with actual scientific evidence to promote dazzling scientific effects
  • issues lined

    Chapter 1. Preface

    1.1. report Scope and description

    1.2. analysis Scope

    1.3. analysis Methodology

    1.4. Market Segmentation

    Chapter 2. govt abstract

    2.1. world Healthcare resolution aid and IBM Watson Market Portraiture

    2.2. international Healthcare determination uphold and IBM Watson Market, by choices, 2017 (US$ Mn)

    2.three. international Healthcare resolution uphold and IBM Watson Market, through Geography, 2017 (US$ Mn)

    Chapter 3. Healthcare decision uphold and IBM Watson Market: Dynamics and Future Outlook

    three.1. Market Overview

    3.2. Drivers

    3.three. Challenges

    3.four. alternatives

    3.5. captivating investment Proposition, by Geography, 2017

    3.6. competitive evaluation: international Healthcare decision assist and IBM Watson Market, through Key avid gamers, 2017

    Chapter four. global Healthcare decision aid and IBM Watson Market, through offerings

    four.1. Overview

    four.2. IT-Healthcare options

    four.three. medical records management

    4.4. Genomics

    four.5. Drug Discovery

    Chapter 5. international Healthcare determination wait on and IBM Watson Market, by course of Geography

    5.1. Overview

    5.2. North america Healthcare decision wait on and IBM Watson Market evaluation, 2016-2026

    5.three. Europe Healthcare decision assist and IBM Watson Market evaluation, 2016-2026

    5.four. Asia Pacific Healthcare resolution aid and IBM Watson Market evaluation, 2016-2026

    5.5. Latin america Healthcare determination aid and IBM Watson Market analysis, 2016-2026

    5.6. middle East & Africa Healthcare decision uphold and IBM Watson Market evaluation, 2016-2026

    Chapter 6. enterprise Profiles

    6.1. Accenture

    6.2. Aetna

    6.3. American neatly methods

    6.four. AT&T

    6.5. Cisco

    6.6. EMC fitness supervision Ltd.

    6.7. IBM Watson

    6.eight. McKesson industry enterprise

    6.9. Optum Inc.

    6.10. Vangent Inc.

    For more counsel about this document visit

    View supply edition on

    source: research and Markets

    ResearchAndMarkets.comLaura timber, Senior Press Managerpress@researchandmarkets.comFor E.S.T office Hours denomination 1-917-300-0470For U.S./CAN Toll Free denomination 1-800-526-8630For GMT workplace Hours denomination +353-1-416-8900Related topics: Healthcare services, synthetic Intelligence

    Copyright industry Wire 2019

    IBM: a course to Play It post revenue Rally | actual Questions and Pass4sure dumps

    IBM (IBM) has been a inventory to live averted in the past a number of years. however, the undeniable fact that so many of its most bullish shareholders Have thrown in the towel on IBM has led to it to Have mostly been forgotten.

    Admittedly, IBM has many problems, both when it comes to governance and expertise. in spite of this, I contend that ordinary, IBM nowadays is still very a lot under undervalued and below-liked.

    fresh Rally post-revenue

    Shareholders positively received IBM's this autumn 2018 results, which confirmed right-line revenue down 1% 12 months-over-12 months at regular currency to $21.8 billion, and largely flat full-12 months over 12 months.  in spite of this, in spite of feeble exact-line performance, IBM's shares rallied roughly eight% post-earnings and Have remained at that flush seeing that. 

    So What basically Drove This Rally?

    mighty guidance made traders flip advantageous on IBM. however IBM presents no reform line forecast, it does offer each base line non-GAAP EPS numbers and its forecast at no cost cash stream for FY 2019.

    whereas IBM's non-GAAP EPS of $13.90 for FY 2019, which exceeded analyst estimates for $13.eighty, actually looks enticing, I Have petite self assurance in the gist of this number. it is made from so many assumptions, which makes it a largely useless determine on which to base any sort of funding determination. then again, IBM's free cash movement expectation of approximately $12 billion is each significant and reassuring.

    in spite of this, one could factor out that this volume of free money stream is flat with what IBM generated in FY 2018. To which, i might counter, that investors are unlikely to collect a cheaply valued inventory when its free cash movement is growing at a robust clip.

    biggest Strategic Pivot in Years

    IBM recently acquired red Hat. on the time, back in October 2018, IBM deployed the equal of a third of its market cap into acquiring pink Hat (approximately $34 billion). This unusual acquisition turned into meant to live a latest-ditch effort by IBM to sojourn primary within the cloud wars.

    pink Hat focuses on offering a multi-cloud platform. IBM hopes that IBM's infrastructure and customer base, at the side of crimson Hat's technology, will create a sturdy providing in order that collectively it might be triumphant in carving out a niche alongside the likes Amazon's (AMZN) AWS, Microsoft's (MSFT) Azure and Alphabet's (GOOGL) Google Cloud.

    besides the fact that children, regardless of being several months after the announcement of this groundbreaking acquisition, IBM has remained quite tight-lipped on the tangible numbers and outcomes this acquisition would subsequently give to IBM. furthermore, CFO James Kavanaugh talked about on the convoke that the acquisition of crimson Hat is anticipated to nearby in the 2nd half on 2019, and as such, the expectations from this industry Have not been fully mirrored in IBM's fiscal 2019 forecast expectations.

    Capital Return: a powerful nice

    As a cost investor, whereas i'm interested in a companies' dynamic developments, i'm passionate about how my agencies proceed about deploying their extra capital. usually, groups which Have a powerful heritage of having powerful cash prance era capabilities commonly become complacent with their extra capital and subsequently arrive to live squandering this capital. currently, notwithstanding, this has no longer been the case with IBM.

    definitely, during q4 2018, while IBM's participate cost became south and hit multi-year lows, IBM used the chance to aggressively repurchase its personal shares. principally, each and every over this descend 2018, IBM deployed $2.1 billion in opposition t participate repurchases. additional, FY 2018 saw IBM set up extra capital against repurchases than each and every through the outdated two plenary fiscal years of 2017, 2016.

    Valuation - competencies Upside?

    As proven within the above table, IBM's cash flows are being meaningfully undervalued. IBM is cheaply valued relative to itself, because it trades on 8x cash flows from operations in comparison with 8.5x for its historic valuation. youngsters, much more meaningful is that IBM at this time trades for just 10x its free money move.

    additionally, for the reason that IBM at this time supports a dividend bow of four.7%, as smartly as the incontrovertible fact that its dividend continues to grow, I wrangle that this, too, features to another aspect of IBM it's undervalued.

    last observe

    I believe that IBM shareholders are likely to discern potent advantageous returns from the roughly $a hundred and twenty billion market cap IBM currently trades at. nonetheless, these returns aren't prone to arrive rapidly, but I reliance that over the next one to two years, IBM's valuation will greater carefully fit its intrinsic cost.

    Amazon, Microsoft and Alphabet are holdings in Jim Cramer's motion alerts PLUS Charitable Have self-confidence Portfolio. are looking to live alerted earlier than Cramer buys or sells AMZN, MSFT or GOOGL? learn more now.

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    IBM WebSphere Operational decision Management V8.0 Application Development

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    IBM Adds Analytics to industry Processes | actual questions and Pass4sure dumps

    Posted October 7, 2011 By Sean Michael Kerner     Feedback

    IBM integrates analytics and industry processes to create agile enterprises.

    Data is at the root of wonderful decision making, but how carry out you collect the birthright data in the birthright location to do those decisions?

    IBM (NYSE:IBM) is enhancing its software portfolio with unusual capabilities for analytics and industry intelligence. The unusual software releases span the IBM Rational and Tivoli product groups.

    "We want to wait on their client infuse analytics and intelligence into their infrastructure, to uphold better decision making and more integrated processes," Nancy Pearson, vice president of market strategy at IBM WebSphere, told "We're doing an integrated launch that takes the capabilities from Rational and Tivoli and puts them together against some key throe points that clients have."

    Those throe points comprise dealing with the complexity inherent in rapid rates of change. Enabling industry agility is likewise a key goal of the unusual IBM push. One of the enhanced solutions is IBM Tivoli Analytics for Service Performance. Pearson explained that Tivoli Analytics enables enterprises to do better decisions throughout the industry process. Additionally, the unusual IBM Tivoli industry Service Manager software release provides the talent to wait on forecast IT service levels. Pearson said that the unusual applications enable enterprises to automate the course they gain insight throughout the industry process.

    Randy Newell, program director at IBM Rational, preeminent that there are likewise enhancements being made in the locality of application portfolio management.

    "What we're providing there is a view that can recognize across a combination of data, systems, networks, applications and processes to provide visibility into better decision making," Newell said.

    That visibility can wait on an organization understand the impact of a change initiative. Newell said the visibility can likewise deliver insight into which projects an organization should actually initiate.

    "Most IT budgets typically fund maintenance activities, so how carry out you actually fund initiatives that are about innovation?" Newell said. "It's about making difficult decisions about which projects you're not going to do, as well as identifying redundancies so you can free up funds for an innovation initiative."

    The unusual Rational Insight 1.1 release is one of the key enhanced products that provides the application development visibility.

    "It allows you to recognize across the application lifecycle and bubble the information up to a dashboard to provide a view of project health," Newell said.

    Another enhanced project is the Rational Automation Framework version 3.0 release, which helps to enable collaborative integration between development and operations. Newell said the framework is an automated deployment utensil that helps to provide industry agility.

    "If you can't build agility into software delivery, then you haven't built an agile industry yet," Newell said. "The hand-off from development to operation to precipitate up application to consume is essentially where they gain from the world of development with Rational into the world of operations, which is the Tivoli world."

    Sean Michael Kerner is a senior editor at, the word service of, the network for technology professionals.

    IBM Completes Acquisition of ILOG, Extending IBM BPM Portfolio | actual questions and Pass4sure dumps

    Jan 13, 2009

    IBM has completed its approximately $340 million tender offer for the shares of ILOG, which offers industry Rule Management System (BRMS), Optimization, Visualization, and Supply Chain Management (SCM) portfolios. The tender offer was initially announced July of 2008. "I believe it is going to live one of their strongest strategic acquisitions that they Have made at IBM," Sandy Carter, IBM vice president for SOA and WebSphere Strategy, Channels and Marketing, tells 5 Minute Briefing.

    Headquartered in Paris, France, ILOG has more than 2,500 customers, 850 employees and operations in 30 countries around the world. ILOG will become section of the IBM WebSphere software portfolio.

    The acquisition will allow IBM to enter several unusual markets, observes Carter. One of them is a standalone rules business. "Today, they Have some rules capability within their WebSphere Suite but this product is much more robust and allows us to sell rules each and every by itself, which is a growing marketplace," she notes. The ILOG BRMS products wait on businesses improve their decision making by letting them suitable and respond dynamically. 

    The acquisition likewise adds in other unusual pieces to IBM's business, including optimization. Based on applied mathematics and computer science, the ILOG optimization products allow enterprises to transform industry objectives, resources and operational constraints into best possible action plans and schedules to enhance service, revenue and profits, according to IBM.  In fact, Carter says, IBM uses that optimization technology in its own supply chain.

    A third unusual piece ILOG adds is in the locality of supply chain management solutions, which comprise Network Design, Inventory Optimization, Production Planning, and Scheduling. And the fourth key piece that the ILOG acquisition adds is visualization, which allows users to hold information and transform it into a visual picture so action can live taken, explains Carter. Germany's national railway company uses ILOG technology to visualize the trains as they dispatch. As a result of the deployment, Carter states, the transportation company has achieved improvement in on-time performance, reductions in staff training time, and more accurate decision-making.

    According to IBM, the ILOG acquisition will strengthen IBM's industry process management (BPM) and SOA position by providing customers a broad set of rule management tools for information and application lifecycle management across a comprehensive platform, including IBM's leading WebSphere application development and management platform.  Many of the ILOG products will likewise become section of IBM's Industry Frameworks, which are integrated solutions that uphold industry specific industry and technology standards.

    For more information, proceed here. To watch a demo on YouTube showing how WebSphere Dynamic Process Edition and ILOG JRules, visualizations and optimization technology combine to offer an end-to-end industry process management solution, allowing a industry leader to do decisions more quickly, proceed here.

    IBM Cloud Automation Manager, a primer. | actual questions and Pass4sure dumps

    The development community is adopting containers at a remarkably breakneck pace. Organizations are looking to containerize their workloads for different reasons. Whether it’s the packaging odds of containers, their smaller size (compared to VMs), or their ‘build once, dash anywhere’ attribute, the main motivation that is commonly shared across individuals and organizations alike — the agility and ephemeral nature of containers.

    But with each and every of their benefits, enterprises deem containers to live bleeding edge, and are cautiously (and curiously) accepting the reality that the world is adopting hybrid architectures composed of containers and VMs that labor side-by-side in multi-cloud platforms. This “new world order” of containers + VMs isn’t without its challenges and complexities when dealing with operations and workload management.

    To this intention, and while it is industry-wide agreed that VMs (and infrastructure in general) are here to sojourn for the foreseeable future, this made some very smart people believe of ways to bring some qualities of the container world to VMs. Welcome to the exciting world of Infrastructure as Code (IaC).

    In this article, I interpolate IBM Cloud Automation Manager, an enterprise-ready Infrastructure as Code (IaC) platform that provides a separate pane of glass to provision VM-based alongside Kubernetes-based workloads by simply using templates (text files).

    IBM Cloud Automation Manager

    IBM Cloud Automation Manager (or CAM for short) was released late 2017 alongside IBM Cloud Private, IBM’s enterprise-grade Kubernetes platform. Simply put, CAM is an enterprise-ready Infrastructure as Code (IaC) manager that allows you to:

  • Automate the provisioning of workloads, whether virtual machines and/or containers and their infrastructure pre-requisites.
  • Compose and orchestrate services made out of both VMs and containers.
  • Integrate multiple Infrastructure as Code and Configuration Manager solutions.
  • Provision workloads on multiple clouds (whether public or private).
  • Integrate with DevOps toolchains.
  • Integrate with day-2 ITSM solutions.
  • As seen in the diagram above, IBM Cloud Automation Manager has 3 main user-visible components:

  • Terraform, which handles the provisioning of cloud and infrastructure resources using providers such as VMware vSphere, Microsoft Azure, IBM Cloud Platform, Amazon Web Services, Google Cloud Platform and even OpenStack.
  • Chef, which does configuration management and compliance automation. Chef normally takes over the provisioning after Terraform to start deployment and configuration of middleware and applications.
  • Service Composer, which is where administrators can author, compose and design services that are made out of not only multiple VMs, but likewise Kubernetes resources by invoking Helm charts.
  • Take this scenario for example: A common drill is to Have front-end apps deployed on containers and databases (or legacy middleware) deployed on VMs, and to hold things up a notch, containers might live vital on a public cloud while databases might reside behind an enterprise firewall.

    Service Composer helps organizations create hybrid workloads of containers, public cloud resources, and on-premise VMs with the click of a button. This simplicity comes in handy at the discontinuance of the DevOps toolchain, when environments need to live deployed on demand for Dev, Test, QA and then Production.

    In simple terms, with IBM Cloud Automation Manager, administrators and developers alike are able to define where and how target environments should recognize like, author declarative code, save and then version to Git. And because environments are written as declarative templates, they can live re-produced and re-deployed over and over.

    What does it solve?

    Well, many things. First of all, administrators can automate most (if not all) of the processes that arrive with provisioning environments, avoiding manual processes that are tedious and oversight prone.

    With CAM and Terraform, administrators Have to simply declare how they want the environment to look, launch the provisioning, and forget each and every about it. Terraform will figure out how to deploy.

    See the following image, which displays a typical deployment procedure:

    Imagine having an application made out of 10 VMs and then having to iterate these steps for each and every environment in a typical enterprise scenario: Dev, QA, UAT, Production and DR, that’s roughly a total of 50 VMs…

    CAM’s automation reduces provisioning time from weeks to hours (dare I allege minutes?).

    Second, it solves the issue of configuration drifts. Gone are the days when applications don’t behave uniformly when the environments they sit on are supposedly identical. Configuration drifts betide when somebody, somewhere messed up and never reflected the config change to comfort of the environments. This happens quite often in a typical Dev, QA, UAT, Production and DR deployment scenario.

    And because Terraform templates are text files that are stored in Git, it simplifies integration into existing DevOps tools (such as Jenkins) to wait on automate the dev-test-prod promotion alongside the application.

    Third, just fancy application code, infrastructure should live versioned. Infrastructure encompasses everything in a software-defined environment, so in the example of VMware we’re talking, amongst other things, Storage I/O Control, Admission Control, vSwitch config, Port Groups, HA Affinity rules, OS customizations (think sysprep), core count, and likewise sometimes the sort of CPU the VM is emulating to enable and disable processor features.

    Infrastructure doesn’t halt there, it likewise includes /etc/hosts files, IP configuration, DNS entries, iptables rules, apt-get/yum repos and then, perhaps, middleware configuration might likewise live section of the responsibility of the infrastructure team, depending on the organization structure.

    Tracking and versioning each and every aspects of an environment while consolidating VM and container artifacts allow configurations to live branched, forked, rolled-back and then promoted from Dev, QA, UAT, and then Production alongside every other ingredient in an app development delivery process.

    Fourth, Infrastructure as Code allows Ops to rebuild instead of fixing when things proceed wrong. It has been proven time and again that rebuilding an entire environment and then rolling back to a known working version will save time and effort helping achieving SLOs and SLAs.

    Finally, it saves on disk space. Currently, organizations reliance on VM templates, VM snapshots and a spaghetti of VM clones to quickly provision environments. This is an operational nightmare and wastes disk space, which incidentally opened the doors for storage appliance companies to interpolate expensive deduplication technologies.

    Infrastructure as Code allows for replacing clones of VM images with text files that store descriptive declarative statement that reflect configurations and discontinuance targets.

    IBM Cloud Automation Manager Concepts

    Before digging into CAM, there are some concepts that need to live explained.

    IBM Cloud Private: IBM Cloud Automation Manager is a Dockerized application that runs on top of IBM Cloud Private, the documentation on how to deploy it can live found here. Although available on DockerHub, CAM is tightly integrated with IBM Cloud Private as its Kubernetes platform for authorization, RBAC and others functions.

    Git: Although Git isn’t bundled as section of CAM, it is highly recommended that each and every the templates and content libraries that are created are stored and versioned on a Git repo. This is likewise a pre-requisites for air-gapped environments that can’t gain where IBM stores its sample templates.

    Terraform: By default, IBM Cloud Automation Manager only manages Terraform. Terraform is the module within CAM that talks to and provisions infrastructure. CAM comes with a bunch of Terraform Providers pre-packaged such as: IBM Cloud, AWS, Azure, GCP and OpenStack and the list is growing. Bringing-Your-Own-Provider (BYOP) can live done by following this link. Terraform has dozens of providers for different types of clouds and infrastructure which can live found here.

    Chef: Chef is a configuration management utensil responsible to deploy and configure simple and complex software from templates (also called cookbooks). There are cookbooks for thousands applications from major software vendors available from

    Note: Terraform and Chef overlap in some situations, IBM Cloud Automation Manager takes supervision of segregating responsibilities, where Terraform will mostly carry out infrastructure and Chef will mostly carry out configuration and software deployment.

    CAM Content Provider (or CAMC Provider): After Terraform completes the provisioning of the VM and if a Chef template is chosen as section of the deployment, CAM engage the CAMC Provider. The CAMC Provider is nothing more than an gateway to a Chef server that takes supervision of configuration management, middleware and application deployment.

    There are few things to note here:

  • The CAMC Provider isn’t installed as section of the CAM installation by default, it’s an optional step that should live executed within CAM. This basically installs a Chef Server on a separate VM.
  • The yardstick Chef that comes with CAMC is pre-populated with some Chef cookbooks likewise referred to as ‘Pre-Built Content’ covering mostly IBM Oracle and LAMP cookbooks. It is however, the responsibility of administrators to populate other cookbooks from the thousands of cookbooks available on Chef Supermarket onto the Chef server and this can live done with Chef’s client: knife. result this link to understand how to properly manage Chef.
  • Although CAM will deploy Chef by default, organizations can elect another configuration manager such as Ansible, Puppet or even their own existing Chef servers. What’s powerful here is that within CAM, organizations can deploy multiple CAMC Providers and multiple configuration managers. For a tutorial on deploying Ansible instead of or alongside Chef, result this link.
  • Helm: Helm charts are to Kubernetes what Terraform templates are to VMs. They likewise are a sort of Infrastructure as Code that allow authoring of Kubernetes resources such as deployments, services, replica sets, stateful sets, volumes, ingress rules etc… With Helm charts, developers can package container information and Kubernetes resources as templates, version them and deploy them on-demand, while avoiding the tedious tasks of managing the Kubernetes aspect of things.

    Software Repository: This is where software binaries will live residing. Normally this should live an NFS participate that has ample capacity to store great binaries such as IBM WebSphere, Oracle, Apache, MySQL and other software. This is significant for packages that are not publicly accessible, not available on a public repo such as via apt-get/yum or if CAM is residing in an air-gapped environment (with no internet connectivity).

    Administrators should populate the Software Repository with binaries based on a directory structure, to carry out that, result the link here. For IBM specific software, I highly recommend using PUCL to prepare the software repo, result this link.

    Following is a diagram that describes how CAM, Terraform and Chef arrive together.

    Templates Library: A template is nothing more than a Terraform template that declares a VM (or other cloud resources) based on some specifications. each and every information about the VM such as CPU, disk size, cloud provider is defined there. The Template Library lists each and every the available templates that are available for provisioning. This is where administrators will spend time creating, authoring and editing Terraform templates.

    Service Library: A service is a collection of templates executed as one process. IBM Cloud Automation Manager can chain the provisioning and creation of CAM Templates and ICP Helm charts while providing the possibility to pre-set variables and composable execution plans. At the time of writing (CAM, provisioning of templates within a service is sequential, however optional parallelism is on the roadmap. What’s more is that within the Service Library, the output parameters from one template can live used as an input to another.

    Service Library is where administrators will spend time creating, authoring, editing and then publishing CAM services to the Service Library.

    Service Composer: Within the Service Library resides the Service Composer. This is CAM’s management console that allows authoring of CAM Services. After the creation of CAM templates (Terraform), administrators can head to the Service Composition section of the Service Library to drag and drop the created CAM templates and/or Kubernetes Helm charts (from IBM Cloud Private) based on a decided sequence and/or decision. Service Composition can likewise convoke comfort APIs for third party integration and transmit emails.

    Decision: A decision is simply a prompt that a CAM user will mug during the provisioning of a service. For example, the service could prompt the user for the size of the VM or whether the workload should live deployed on a Dev or Production environment. During the creation of the service, administrators can elect different CAM templates for each environment. Alternatively, administrators could potentially consume Docker versions of certain software for Dev and Test, but then deploy VM based versions for Production.

    A comfort hook is a powerful utensil that allows CAM to Tell something to carry out something. For example, using comfort hooks, CAM can plug-in onto an IPAM to request and fetch IP addresses that can live used as parameters (more on that later). comfort hooks could likewise register the newly created service or template to a Change Management Database (CMDB).

    Parameters and Shared Parameters: One of the powerful features of CAM are Parameters and Shared Parameters.

    Parameters are data that can live fed onto CAM templates. Things fancy CPU cores, RAM size, OS could live parameterized and not difficult coded within the CAM templates. This is significant as it brings modularity and flexibility. CAM templates authors could likewise elect to prompt users for these parameters or pre-set them during the creation of the templates. One wonderful example of parameters are OS passwords and private ssh keys, no one wants to discern those difficult coded in templates.

    The image above depicts how Terraform consumes parameters so they are not difficult coded onto the CAM templates.

    For example, $(var.folder), $(, $(var.cpu), and $(var.ipv4_prefix_length) are parameters that can live prompted and populated during VM creation or centrally stored within CAM for modularity and do-it-once purposes.

    Parameters are bound to separate templates. So what happens when CAM templates need to participate certain parameters? Enter Shared Parameters. Shared Parameters allow administrators to parameterize certain data that iterate amongst CAM templates. For example, in an Azure, IBM Cloud or AWS scenario, CAM templates might require the name/ID of the cloud region, which normally don’t change often. Cloud regions such as: us-east-1, eu-central-1, eu-west-1 could live shared parameters and used across multiple templates.

    Another example of Shared Parameters usage in VMware Clusters: Imagine having multiple VMware Clusters within an organization and each clusters Have different preset settings such as port groups, datastores, or IP gateways. Shared parameters bring sanity and organization to deployments.

    Parameters and Shared Parameters are even more powerful when combined with Decisions as each decision could point to different parameters and shared parameters.

    The concept of Shared Parameters promotes role separation, a cloud admin can live in suffuse of managing these and elect what parameters should live preset and which can live fiddled or selected by the cloud user/consumer.

    Spend some time watching this video titled, IBM Cloud Private to deploy IBM Cloud Kubernetes Service Clusters, which shows how almost each and every the features I covered in this article arrive together.

    A day in the life

    We’ve covered the basics of IBM Cloud Automation Manager and explained the different pitiable parts that do up CAM. Below is a brief description of what CAM administrators will mug day in day out managing CAM.

    A day in the life of a CAM Administrator

    Normally, CAM administrators need to worry about the following:

  • IBM Cloud Private and IBM Cloud Automation Manager alignment (namespaces, persistent volumes, node selectors, NFS shares, LDAP and RBAC etc..).
  • Cloud providers, whether these are VMware vSphere clusters, OpenStack environments or public clouds such as AWS, Azure, GCP, IBM Cloud or even Bring-Your-Own-Provider.
  • Centralized repository on Git to store, manage, fork and version Terraform and Chef templates.
  • Content publishing, which involves managing, building and then publishing CAM content. CAM content are simply CAM templates (Terraform or Terraform+Chef).
  • Service publishing of CAM templates or CAM templates+IBM Cloud Private Helm charts by composing services that consume parameters, request decisions, prompt users for information and integrate with 3rd party tools and platform using comfort hooks.
  • In the case of private clouds (VMware and OpenStack) Preparation of OS templates. CAM administrators should live responsible for building and maintaining OS templates that will live used during provisioning of workloads. For example, in the case of Linux, configuration of cloud-init, logical Volume Manager, apt-get/yum repos etc.. for Windows, things fancy sysprep, Windows Updates, Windows Features and vigorous Directory should live accommodated for and thought through.
  • In the case of private clouds (VMware and OpenStack) Preparation of Networking and IP subnets. CAM administrators should Have worked out a list of valid IP subnets, vSwitches, port groups, floating IPs, DNS and gateways that can live used in CAM.
  • In the case of private clouds (VMware and OpenStack) Preparation of Storage. CAM administrators should know about storage tiers and datastores available for VMs.
  • A day in the life of a CAM User/Consumer

    CAM users will eventually consume the templates and services built by CAM Administrators, this means:

  • CAM users will live greeted with a template library and a service library where they can browse versions of templates and services already provisioned for them by the CAM administrators.
  • Template libraries are where users will execute several provisioning.
  • Service libraries are where users will execute services made out of multiple templates.
  • CAM users will likewise live able to manage the workloads that they Have provisioned. Examples of such actions are (Start, Stop, Pause, Destroy, Delete resources).
  • If the CAM user is a developer or section of the DevOps team, then such executions of templates and services can live easily integrated to their DevOps utensil chain. By using the CAM API, developers can convoke the provisioning of templates and services from within Jenkins or any other deployment tool.
  • Conclusion

    Of course, there are far more features that I haven’t covered such as the talent to integrate CAM workloads with IBM Cloud Private’s ELK, as seen here, how to consume CAM’s template designer to author Terraform templates without learning Terraform syntax as seen here or even how to consume CAM’s API to convoke the service composer from a third party orchestration utensil as seen here.

    In conclusion, IBM Cloud Automation Manager is meant for infrastructure departments who crave the agility and precipitate experienced when using Docker and Kubernetes (such as with IBM Cloud Private) and want to bring these capabilities to infrastructure and VMs.

    For more information delight consult the following links:

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