Software deployment methodology layer




















Scale with open, flexible technology. Run on the cleanest cloud in the industry. Connect your teams with AI-powered apps. Resources Events. Browse upcoming Google Cloud events. Read our latest product news and stories. Read what industry analysts say about us. Reduce cost, increase operational agility, and capture new market opportunities. Analytics and collaboration tools for the retail value chain. Solutions for CPG digital transformation and brand growth.

Computing, data management, and analytics tools for financial services. Health-specific solutions to enhance the patient experience. Solutions for content production and distribution operations.

Hybrid and multi-cloud services to deploy and monetize 5G. AI-driven solutions to build and scale games faster. Migration and AI tools to optimize the manufacturing value chain. Digital supply chain solutions built in the cloud. Data storage, AI, and analytics solutions for government agencies.

Teaching tools to provide more engaging learning experiences. Develop and run applications anywhere, using cloud-native technologies like containers, serverless, and service mesh. Hybrid and Multi-cloud Application Platform.

Platform for modernizing legacy apps and building new apps. End-to-end solution for building, deploying, and managing apps. Accelerate application design and development with an API-first approach. Fully managed environment for developing, deploying and scaling apps. Processes and resources for implementing DevOps in your org.

End-to-end automation from source to production. Fast feedback on code changes at scale. Automated tools and prescriptive guidance for moving to the cloud. Program that uses DORA to improve your software delivery capabilities. Services and infrastructure for building web apps and websites. Tools and resources for adopting SRE in your org.

Add intelligence and efficiency to your business with AI and machine learning. Products to build and use artificial intelligence. AI model for speaking with customers and assisting human agents. AI-powered conversations with human agents. AI with job search and talent acquisition capabilities. Machine learning and AI to unlock insights from your documents. Mortgage document data capture at scale with machine learning. Procurement document data capture at scale with machine learning.

Create engaging product ownership experiences with AI. Put your data to work with Data Science on Google Cloud. Specialized AI for bettering contract understanding. AI-powered understanding to better customer experience. Speed up the pace of innovation without coding, using APIs, apps, and automation. Attract and empower an ecosystem of developers and partners.

Cloud services for extending and modernizing legacy apps. Simplify and accelerate secure delivery of open banking compliant APIs. Migrate and manage enterprise data with security, reliability, high availability, and fully managed data services.

Guides and tools to simplify your database migration life cycle. Upgrades to modernize your operational database infrastructure. Database services to migrate, manage, and modernize data.

Rehost, replatform, rewrite your Oracle workloads. Fully managed open source databases with enterprise-grade support. Unify data across your organization with an open and simplified approach to data-driven transformation that is unmatched for speed, scale, and security with AI built-in. Generate instant insights from data at any scale with a serverless, fully managed analytics platform that significantly simplifies analytics.

Digital Transformation Accelerate business recovery and ensure a better future with solutions that enable hybrid and multi-cloud, generate intelligent insights, and keep your workers connected. Business Continuity. Proactively plan and prioritize workloads. Reimagine your operations and unlock new opportunities. Prioritize investments and optimize costs. Get work done more safely and securely.

How Google is helping healthcare meet extraordinary challenges. Discovery and analysis tools for moving to the cloud. Compute, storage, and networking options to support any workload. Tools and partners for running Windows workloads. Migration solutions for VMs, apps, databases, and more. Automatic cloud resource optimization and increased security. End-to-end migration program to simplify your path to the cloud.

Ensure your business continuity needs are met. Change the way teams work with solutions designed for humans and built for impact. Collaboration and productivity tools for enterprises. Secure video meetings and modern collaboration for teams. Unified platform for IT admins to manage user devices and apps. Enterprise search for employees to quickly find company information.

Detect, investigate, and respond to online threats to help protect your business. Solution for analyzing petabytes of security telemetry.

Threat and fraud protection for your web applications and APIs. Solutions for each phase of the security and resilience life cycle. Solution to modernize your governance, risk, and compliance function with automation. Data warehouse to jumpstart your migration and unlock insights. Services for building and modernizing your data lake. Run and write Spark where you need it, serverless and integrated. Insights from ingesting, processing, and analyzing event streams. Solutions for modernizing your BI stack and creating rich data experiences.

Solutions for collecting, analyzing, and activating customer data. Solutions for building a more prosperous and sustainable business. Data from Google, public, and commercial providers to enrich your analytics and AI initiatives. Accelerate startup and SMB growth with tailored solutions and programs.

Get financial, business, and technical support to take your startup to the next level. Explore solutions for web hosting, app development, AI, and analytics. Build better SaaS products, scale efficiently, and grow your business. Command-line tools and libraries for Google Cloud. Managed environment for running containerized apps. Data warehouse for business agility and insights.

Content delivery network for delivering web and video. Streaming analytics for stream and batch processing. Monitoring, logging, and application performance suite. Fully managed environment for running containerized apps. Platform for modernizing existing apps and building new ones. Speech recognition and transcription supporting languages. Custom and pre-trained models to detect emotion, text, more. Language detection, translation, and glossary support.

Sentiment analysis and classification of unstructured text. Custom machine learning model training and development. Video classification and recognition using machine learning. Options for every business to train deep learning and machine learning models cost-effectively. Conversation applications and systems development suite for virtual agents. Service for training ML models with structured data.

API Management. Manage the full life cycle of APIs anywhere with visibility and control. API-first integration to connect existing data and applications. Solution to bridge existing care systems and apps on Google Cloud. No-code development platform to build and extend applications.

Develop, deploy, secure, and manage APIs with a fully managed gateway. Serverless application platform for apps and back ends.

Server and virtual machine migration to Compute Engine. Compute instances for batch jobs and fault-tolerant workloads. Reinforced virtual machines on Google Cloud. Dedicated hardware for compliance, licensing, and management. Infrastructure to run specialized workloads on Google Cloud. Usage recommendations for Google Cloud products and services.

Fully managed, native VMware Cloud Foundation software stack. Registry for storing, managing, and securing Docker images. Container environment security for each stage of the life cycle. Solution for running build steps in a Docker container. Containers with data science frameworks, libraries, and tools. Containerized apps with prebuilt deployment and unified billing.

Package manager for build artifacts and dependencies. Components to create Kubernetes-native cloud-based software. IDE support to write, run, and debug Kubernetes applications. Platform for BI, data applications, and embedded analytics. Messaging service for event ingestion and delivery. Service for running Apache Spark and Apache Hadoop clusters. Data integration for building and managing data pipelines.

Workflow orchestration service built on Apache Airflow. Service to prepare data for analysis and machine learning. Intelligent data fabric for unifying data management across silos. Metadata service for discovering, understanding, and managing data. Service for securely and efficiently exchanging data analytics assets. Cloud-native wide-column database for large scale, low-latency workloads. Cloud-native document database for building rich mobile, web, and IoT apps.

In-memory database for managed Redis and Memcached. Cloud-native relational database with unlimited scale and Serverless, minimal downtime migrations to Cloud SQL. Infrastructure to run specialized Oracle workloads on Google Cloud. NoSQL database for storing and syncing data in real time. Serverless change data capture and replication service. Universal package manager for build artifacts and dependencies.

This project should reference the Application Core project, and its types should interact with infrastructure strictly through interfaces defined in Application Core. No direct instantiation of or static calls to the Infrastructure layer types should be allowed in the UI layer.

The Startup class or Program. The place where this logic is performed is known as the app's composition root , and is what allows dependency injection to work properly at run time. In order to wire up dependency injection during app startup, the UI layer project may need to reference the Infrastructure project.

This dependency can be eliminated, most easily by using a custom DI container that has built-in support for loading types from assemblies. For the purposes of this sample, the simplest approach is to allow the UI project to reference the Infrastructure project but developers should limit actual references to types in the Infrastructure project to the app's composition root. You can build a single and monolithic-deployment based Web Application or Service and deploy it as a container.

Within the application, it might not be monolithic but organized into several libraries, components, or layers. Externally, it's a single container with a single process, single web application, or single service. To manage this model, you deploy a single container to represent the application. To scale, just add additional copies with a load balancer in front. The simplicity comes from managing a single deployment in a single container or VM. But, following the container principle of "a container does one thing, and does it in one process ", the monolithic pattern might be a conflict.

If the entire application scales, it's not really a problem. However, in most cases, a few parts of the application are the choke points requiring scaling, while other components are used less. Using the typical eCommerce example, what you likely need to scale is the product information component. Many more customers browse products than purchase them. More customers use their basket than use the payment pipeline.

Fewer customers add comments or view their purchase history. And you likely only have a handful of employees, in a single region, that need to manage the content and marketing campaigns. By scaling the monolithic design, all the code is deployed multiple times. In addition to the "scale everything" problem, changes to a single component require complete retesting of the entire application, and a complete redeployment of all the instances.

The monolithic approach is common, and many organizations are developing with this architectural approach. Many are having good enough results, while others are hitting limits.

Many designed their applications in this model, because the tools and infrastructure were too difficult to build service-oriented architectures SOA , and they didn't see the need until the app grew.

If you find you're hitting the limits of the monolithic approach, breaking up the app to enable it to better leverage containers and microservices may be the next logical step.

Deploying monolithic applications in Microsoft Azure can be achieved using dedicated VMs for each instance. Azure App Services can run monolithic applications and easily scale instances without having to manage the VMs. Azure App Services can run single instances of Docker containers as well, simplifying the deployment. Using Docker, you can deploy a single VM as a Docker host, and run multiple instances.

Using the Azure balancer, as shown in the Figure , you can manage scaling. The deployment to the various hosts can be managed with traditional deployment techniques. The Docker hosts can be managed with commands like docker run performed manually, or through automation such as Continuous Delivery CD pipelines.

There are benefits of using containers to manage monolithic application deployments. Scaling the instances of containers is far faster and easier than deploying additional VMs.

Even when using virtual machine scale sets to scale VMs, they take time to create. When deployed as app instances, the configuration of the app is managed as part of the VM. Deploying updates as Docker images is far faster and network efficient. Docker Images typically start in seconds, speeding rollouts.

Tearing down a Docker instance is as easy as issuing a docker stop command, typically completing in less than a second. As containers are inherently immutable by design, you never need to worry about corrupted VMs, whereas update scripts might forget to account for some specific configuration or file left on the disk. You can use Docker containers for a monolithic deployment of simpler web applications.

This approach improves continuous integration and continuous deployment pipelines and helps achieve deployment-to-production success. A microservices-based architecture has many benefits, but those benefits come at a cost of increased complexity. In some cases, the costs outweigh the benefits, so a monolithic deployment application running in a single container or in just a few containers is a better option. A monolithic application might not be easily decomposable into well-separated microservices.

Microservices should work independently of each other to provide a more resilient application. If you can't deliver independent feature slices of the application, separating it only adds complexity.

An application might not yet need to scale features independently. Many applications, when they need to scale beyond a single instance, can do so through the relatively simple process of cloning that entire instance. The additional work to separate the application into discrete services provides a minimal benefit when scaling full instances of the application is simple and cost-effective.

Early in the development of an application, you might not have a clear idea where the natural functional boundaries are. As you develop a minimum viable product, the natural separation might not yet have emerged. Some of these conditions might be temporary. You might start by creating a monolithic application, and later separate some features to be developed and deployed as microservices.

Other conditions might be essential to the application's problem space, meaning that the application might never be broken into multiple microservices. Separating an application into many discrete processes also introduces overhead. There's more complexity in separating features into different processes. The communication protocols become more complex. Instead of method calls, you must use asynchronous communications between services.

As you move to a microservices architecture, you need to add many of the building blocks implemented in the microservices version of the eShopOnContainers application: event bus handling, message resiliency and retries, eventual consistency, and more.

The much simpler eShopOnWeb reference application supports single-container monolithic container usage. Optionally, you can run the application's Blazor-based admin component, which requires a separate API project to run as well. The application can be launched from the solution root using the docker-compose build and docker-compose up commands. This command configures a container for the web instance, using the Dockerfile found in the web project's root, and runs the container on a specified port.

You can download the source for this application from GitHub and run it locally. Even this monolithic application benefits from being deployed in a container environment. For one, the containerized deployment means that every instance of the application runs in the same environment. This approach includes the developer environment where early testing and development take place.

The development team can run the application in a containerized environment that matches the production environment. In addition, containerized applications scale out at a lower cost. Using a container environment enables greater resource sharing than traditional VM environments.

Deploy elastic layers. Deploy user layers. Update OS, app, or platform layer. Export and import layers. Exclude files from layers. Create or clone image template. Publish layered images from template. Manage image template. System settings.

Appliance settings. App Layering services. Users and groups. Directory service. Firewall ports. Document History. Aviso legal. This article explains how to deploy each of the most commonly used antivirus products in a layer.

You can layer any antivirus software, unless listed below as unsupported. Check this topic to see if new versions of your antivirus software have been tested. Some antivirus installation procedures require that you modify the Windows Registry. Back up the registry before you edit it. Using Registry Editor incorrectly can cause serious problems that require you to reinstall the operating system.

Citrix cannot guarantee that problems resulting from incorrect use of Registry Editor can be solved. Use Registry Editor at your own risk, and always back up the Registry before you edit it. You create the exclusions in the layer, and they are processed in the image after it is published.

This section explains how to layer antivirus software and configure major updates based on how you deploy the images. This applies only to major updates. Daily updates for virus definitions are done no matter what type of image you deploy. In all cases, we recommend making a new version of the app layer when the antivirus software has a major update. Once the layer has been updated, update all of the templates that use that antivirus app, and redeploy new images to take advantage of the changes in the antivirus software.

If you are deploying images without elastic layering enabled, consider whether your images are non-persistent or persistent:. If you are deploying images with elastic layering but no user layers, clear auto updates, since the machines are non-persistent and would be reverted on the reboot. Also, assign the antivirus layer to be deployed in the image and not loaded as an elastic layer, since the antivirus drivers must be loaded at boot time to function correctly.

When layers are assigned as elastic layers, they are only loaded once a user logs on to the machine, and therefore the drivers would not be present at boot time. If you are deploying images with elastic layering and a full user layer or a User personalization layer , we recommend turning off auto updates. The machines are non-persistent desktops, so they revert when the user logs off.

There is also the extra consideration that if the users remain logged on to the machines for several days, the daily updates to the virus definition files may end up in user layers. For most antivirus software this is not a problem. But, if you find that the antivirus software is having some problems running, you may want to determine the directory where they store their definitions and consider adding a registry setting to force these files to reside on the non-persistent image, instead of in the user layer.

Make sure that these settings are done in a different layer than the antivirus app, because you would not want those settings to interfere with updates to the antivirus layer. On newly deployed desktops, enable the Caching option again, which can happen automatically through integration with AVG Remote Administrator.

This section explains how to deploy Kaspersky in a layer. See the Kaspersky documentation for more instructions about installing the software in a VDI environment. The following versions of Kaspersky antivirus software have been tested by Citrix and are verified to work with App Layering:. Encryption with Kaspersky Kaspersky Before you deploy Kaspersky If you use the Kaspersky Administration Server to manage the desktop, install Kaspersky antivirus for Workstations and NetAgent on the Packaging Machine or a gold image.

If you do not plan to use the Kaspersky Administration Server, install Kaspersky antivirus for Workstations only on the Packaging Machine or the gold image. When you install the Kaspersky NetAgent, clear the selection for the start application during install option. When you install the Kaspersky antivirus for Workstations in a stand-alone configuration, do not enable password protection for any of the administrative options.

The password you type on the Packaging Machine or gold image does not work on the desktop after you deploy the software. After you install the Kaspersky software on a PackagingMachine for App Layers or layer revisions , a system restart and desktop image rebuild is required. Add a value to the Unifltr service in the registry before you add Kaspersky Attempts to finalize Kaspersky for Virtualization Light Agent 3.

The failure occurs when layer integrity attempts to restart. Install the Kaspersky software on the Packaging Machine. When you mark the image, the Kaspersky Administration Server considers the clones of this image dynamic.

When a clone is disabled, its information is automatically deleted from the database. For details, see the section of this article on Dynamic VDI support. Restart the Packaging Machine.

The Packaging Machine restarts normally. No intervention is necessary. When you deploy this layer, the desktops restart normally and the STOP message does not appear. The Kaspersky NetAgent might not start when users log on to the desktop for the first time.

This issue occurs when you assign the App Layer with the Kaspersky software to a desktop. Restart the desktop to start the NetAgent software. The following interoperability issues can occur on App Layering desktops that have Kaspersky antivirus software installed. The Windows Event Viewer can show the following error:. Then the NetAgent software starts correctly. To fix this issue, restart the Kaspersky software. The Network Attack Blocker continues to run.

The following versions of McAfee software have been tested by Citrix and are verified to work with App Layering:. If you use the ePolicy Orchestrator 5. Installation requirements to install McAfee antivirus on a gold image or App Layer are the same. You can also find the requirements for including the agent on an image in the McAfee ePO product guide. This allows for the agent to deregister from ePO on shutdown, which in turn prevents duplicate host names from populating in the ePO console.

For more about this requirement, see the McAfee KB See the McAfee documentation for the version of the software you are using for more information. Allow the McAfee Agent Updater to complete the update. This step can take several minutes to complete. Use this procedure if you plan to use a layer to deploy the McAfee antivirus software on App Layering desktops.

After completing the installation, the Packaging Machine is visible in the ePolicy Orchestrator System Tree systems list. Otherwise, allow the McAfee Agent Updater to complete an update. Open the registry editor, go to the following key, and change the Start value from 0 to a 1 :.



0コメント

  • 1000 / 1000