"Anything that can go wrong will go wrong." Sales Engineers engaging in high stakes demos are familiar with this saying. One of the most stressful moments they experience during customer PoCs happens actually before the demo. Is everything ready? Until recently, preparing the infrastructure for a technical product demo involved reserving and shipping some hardware, connecting these servers or appliances to the network and configuring everything end to end. We're talking weeks or possibly months of planning ahead of the actual "D" day with delays pretty much a given, lost shipment and unresolved IT tickets highly probable.
Then came virtualization and public cloud infrastructure.
Public cloud was a turning point for many sales engineers in the tech world. With unlimited on-demand capacity, Infrastructure as a Service is as simple as doing online shopping: there is no need to be technically advanced to deploy a virtual machine needed for a demo in Azure, AWS or Google Cloud, to name a few. Sales engineers were finally blessed with all the ingredients for a winning PoC.
Not so fast. Complains coming from the various stakeholders involved in the process were quick to emerge:
Was this line: "just run your demos on public cloud" too good to be true? Seems like we were missing a cheerleader team after all.
Have you ever dreamt of a self-service platform that let pre-sales engineers dynamically deploy their demo environments on the public cloud, no matter how complex they are, and clean them up after the demo is complete? If only...
It turns out Quali's CloudShell has been designed to natively offers these features on the private or public cloud of your choice. It recently came in the limelight with a case study published by partner Microsoft on a joint customer win (Skybox security). Nothing better than a real customer story to illustrate the point.
CloudShell provided Skybox a few key ingredients to enable their sales team demo their solution on the Azure public cloud effectively:
Now that we're back on the right track, why stop here? Environment-as-Service have been used in many similar use cases such as a training platform for internal employees, cyber range, marketing team or even for the support team to reproduce bugs.
It's been just over a week since the end of the 2018 edition of AWS Re:Invent in Las Vegas. Barely enough to recover from various viruses and bugs accumulated from rubbing shoulders with 50,000 other attendees. To Amazon's credit, they managed the complicated logistics of this huge event very professionally. We also discovered they have a thriving cupcake business :)
From my prospective, this trade show was an opportunity to meet with a variety of our alliance partners, including AWS themselves. It also gave me a chance to attend many sessions and learn about state of the art use cases presented by various AWS customers (Airbnb, Sony, Expedia...). This led me to reflect on this fascinating question: how can you keep growing at a fast pace when you're already a giant - without falling under your own weight?
Given the size of the AWS business ($26b revenue in Q3 2018 alone, with 46% growth rate), Andy Jassy must be quite a magician to come up with ways to keep the pace of growth at this stage. Amazon retail started the AWS idea, back in 2006, based on unused infrastructure capacity. It has since provided AWS a playground for introducing or refining their new products. including the latest Machine Learning predictive and forecast services. Thankfully since these early days, AWS customer base has grown into the thousands. These contributors and builders are providing constant feedback to fuel the growth engine of new features.
The accelerated pace of technology has also kept the AWS on its edge. to remain the undisputed leader in the cloud space, AWS takes no chance and introduces new services often shortly after or at the top of the hype curve of a given trend. Blockchain is a good example among others. Staying at the top spot is a lot harder than reaching it.
Andy Jassy announced dozens of new services on top of the existing 90 services already in place, during his keynote address. These services cover every corner of IT. Among others:
-Hybrid cloud: in sort of a reversal course of action, and after dismissing hybrid cloud as a "passing trend", AWS announced AWS Outpost, to provide racks of hardware to customers in their own data center. This goes beyond the AWS-VMware partnership that extended vCenter in the AWS public cloud.
-Networking: AWS transit gateway : this service is closer to the nuts and bolts of creating a production grade distributed application in the cloud. Instead of painfully creating peer to peer connections between each network domain, transit gateways provide a hub that centrally manages network interconnections, making it easier for builders to interconnect VPCs, VPNs, and external connections.
- Databases: Not the sexiest of services, databases are still at the core of any workload deployed in AWS. In an on-going rebuke to Oracle, Andy Jassy re-emphasize this is all about choices. Beyond SQL and NoSQL, he announced several new specialized flavors of databases, such as a graph database (Netptune). These types of databases are optimized to compute highly interconnected datasets.
-Machine Learning/AI: major services such as SageMaker were introduced last year in the context of fierce competition among all cloud providers to gain the upper ground in this red hot field, and this year was no exception to this continuing trend. All the sessions offered during the event in the AI track had a waiting list. Leveraging from their experience with Amazon retail (including the success of Alexa), AWS again showed their leadership and made announcements covering all layers of the AI technology stack. That included services aimed at non-data scientist experts such as a forecasting service and personalizing service (preferences). Recognizing the need for talent in this field, Amazon also launched their own ML University. Anyone can sign up for free...as long as you use AWS for all your practice exercises. :)
Considering the breadth of these rich services, how can AWS afford to keep innovating?
Turns out there are 2 business-driven tenants that Amazon always keeps at the core of its principles:
By remaining solid on these core principles, AWS can keep investing in new cutting edge services while remaining very profitable. The coming year looks exciting as well for all Cloud Service Providers. Amazon has set the bar pretty high, and the runner ups (Microsoft, Google Cloud, Oracle and IBM) will want to continue chipping away at its market share, which in turn will also fuel more creativity and innovation. That would not change if Amazon decided to spin-off AWS as an independent company, although for now that topic will be best left to speculators, or even another blog.
You have the data, analytic algorithms and the cloud platform to conduct the computations necessary to garner augmented insights. These insights provide the information necessary to make business, cybersecurity and technology decisions. Your organization seems poised to enable strategies that harness your proprietary data with external data.
So, what’s the problem you ask? Well, my answer is that things don’t always go according to plan:
Daunting would be an understatement if you did not have the appropriate capabilities in place to address the aforementioned challenges. Well…let’s take a look at how augmented intelligent environments can contribute to addressing these challenges. This blog highlights an approach in a few steps that can get you started.
Identifying the boundaries will help to focus on the specific components that you want to address. In the following example, the functional blocks are simplified into foundational infrastructure and data analytics functions. The analytics sub-components can entail a combination of cloud provided intelligence or your own enterprise proprietary software. Data sources can be any combination of IoT devices and the output viewed on any supported interfaces.
Environments can be established to segment the functionality required within each functional block. A variety of test tools, custom scripts, and AI components can be introduced without impacting other functional blocks. The following example segments the underlying cloud Platform Service environment from the Intelligent Analytics environment. The benefit is that these environments can be self-service and automated for the authorized personnel.
The opportunity to introduce augmented intelligence into the end to end workflow can have significant implications for an organization. Disconnected workflows, security gaps, and inefficient processes can be identified and remediated before hindering business transactions and customer experience. Blueprints can be orchestrated to model the required functional blocks. Quali CloudShell shells can be introduced to integrate with augmented intelligence plug-ins. Organizations would introduce their AI software elements to enable augmented intelligence workflows.
The following is an example environment concept illustration. It depicts an architecture that combines multiple analytics and platform components.
The opportunity to orchestrate augmented intelligence environments has now become a reality. Organizations are now able to leverage insights from these environments which result in better decisions regarding business, security and technology investments. The insights derived from these environments provide an augmentation to traditional knowledge bases within the organization. Coupled with the advancement in artificial intelligence software, augmented intelligence environments can be applied to any number of use cases across all markets. Additional information and resources can be found at Quali.com
Cloud migration and application modernization strategies have become more critical and complex when attempting to keep pace with business requirements and adapting to technology shifts. In order to determine the right strategy, a collaborative effort across multiple teams (IT, CyberSecurity, Application Developers, DevOps, Business etc.,) is required.
A key component of the strategy is to figure out the cloud provider tools needed for application migration. AWS offers many of these tools such as AWS Cloud Formation templates, however, these will require additional stitching to work for end to end solutions. This undertaking is certainly overwhelming and time consuming for the deployment team.
This blog will provide a high-level overview of how Cloudshell native integration with AWS can easily support a re-platform strategy for legacy on-premise applications into the AWS cloud.
The initial step is to deploy Cloudshell in your AWS cloud. This is accomplished by using a AWS Cloud Formation template. The deployment process will create a new management VPC and deploy four EC2 instances that facilitate various functions for CloudShell. These functions enable the creation of VM’s, VPC, executing automation and accessing VM consoles. The Quali Server (QServer) can also be installed on-premise and the other three components within AWS.
The next step is to create a new cloud provider resource in Cloudshell that connects it to your AWS account. In this example, AWS EC2 is selected as the cloud provider resource and a descriptive name such as “AWS West Region” is provided.
Additional information is required to complete the connectivity requirements. The following resource detail form provides a simple way to enter the specific details pertaining to AWS connectivity and deployment
The Re-Platform strategy for migrating legacy applications requires a combination of native AWS application templates, services, and integration with 3rd party service components. This requirement is supported within CloudShell by providing a cataloging capability for easy selection of components. In order to build the catalog, you have a number of options on how you want to access the resources. Pre-packaged Amazon Machine Images (AMI’s) are available by referencing their AMI ID’s as one option of building your catalog. Another option is to define the API endpoint of the native cloud service. Either method can be used to define the object and associate a category as highlighted below for easy access.
Once the catalog objects are defined, they can be introduced onto the blueprint canvas with a simple drag, drop and connect activity to easily model your application environment. The beauty of this approach is that you don’t have to worry about the underlying infrastructure definition since a domain administrator has already established that in the previous step. This blueprint design process is illustrated below with a AWS cloud-native Aurora database, pre-packaged AMI Drupal CMS application, 3rd party Nginx load balancer and a Software-as-a-Service Blazemeter load generation tool.
Once the blueprint is completed, the designer tests it and publishes it into the self-service catalog. It is now ready for consumption by the test engineer. The test engineer will select the blueprint, fills in some input parameters if needed, and with a simple click deploys it. The built-in orchestration does the heavy lifting.
Once the blueprint is deployed, the sandbox is active and now you’re in a position to access individual components as warranted or start the value added services such as starting a Blazemeter load and performance test.
The rubber hits the road once you start to compare your on-premise baseline load and performance metrics with the AWS Re-Platformed solution. Your organization can tweak configuration parameters that align with your cost models, application response times and other SLA’s that will dictate how you migrate to a public cloud. In either case, key to success is how quickly you can stand up components that impact your application service. The modeling functionality of Cloudshell provides an easy way to incorporate network level, application layer and data structures to validate the effectiveness of your migration strategy.
Making a decision to utilize public clouds services for cost savings, scalability and agile deployments is a foregone conclusion for most organizations. Cloudshell provides an easy to model, simple to deploy orchestration solution to help you achieve your objectives. To learn more on this “How To” please visit the Quali resource center to access the documents and video resources.
The power of the public cloud is quite enticing. Once you've experienced the thrill of clicking a button and watching a machine magically spin up - ready for personal use in mere seconds or minutes - is quite intoxicating. But when you dive into deploying real-world environments on a public cloud platform, things get more challenging. Manually configuring multiple instances, networks, security groups, etc. directly from a cloud provider's portal is tedious, time-consuming, and error-prone. Alternatively, many cloud platforms like Azure, employ template languages to allow deploying complex cloud environments automatically through human-readable "infrastructure-as-code" template files. Azure's template files are called ARM Templates (Azure Resource Manager Templates). Though they allow deploying complex cloud environments, template files are not very easy to use, and have some other challenges which I'll cover in a separate blog.
So how do we get the best of both worlds?
In this how-to blog, I'll provide a basic overview of how CloudShell's native integration with Azure makes it easy to model and deploy complex application environments on Azure. This how-to assumes CloudShell is already installed on your Azure cloud, local data center, or local machine.
If CloudShell is not installed in Azure, you need to deploy a few CloudShell specific resources in your Azure cloud in a new resource group. The first is a CloudShell automation server so that CloudShell can drive orchestration directly from your Azure cloud (rather than via SSH over the internet) and the second is the QualiX server to allow secure SSH, RDP and other console access to your resources w/o requiring a VPN. We've made this pretty easy by providing a single ARM template that will deploy the resources and configure the networking for you. Follow the directions here. You'll just need to note the name of the CloudShell resource group you deployed the CloudShell ARM template in.
Once this is done, your next step is to create a new Cloud Provider Resource in CloudShell that points to your Amazon cloud account. In CloudShell just go to your inventory and click "Add New". From there select the "Microsoft Azure" resource. Give your Azure cloud provider a name that uniquely identifies this Azure deployment. For example, something like this might make sense: "QA Team - Azure - US West".
From here, you'll need to provide some specific information about your Azure account including your Azure Subscription ID, Tenant ID, Client ID, Secret key, and the name of the resource group you created in the above step. Detailed instructions are here. Once you've done this, CloudShell will connect to your Azure account and add your new Azure cloud to the list of clouds that you can deploy to when building application blueprints.
CloudShell allows you to build a catalog of apps, which are templates that define the infrastructure, deployment mechanism, and configuration mechanism for a deployed app. Watch this Chalk Talk session to learn more about CloudShell's app and Shells architecture.
So, you'll want to set up new or existing apps to deploy to your newly added Azure cloud. To do this select (or create a new) app, and click on Deployment Paths. From there select "New Deployment Path". Here you'll need to specify if this Azure app will be pulled from the Azure Marketplace or from a custom image. Once you've selected that, you'll then select what Azure cloud this deployment will target; select the Azure cloud instance you just created above. You'll have to provide additional app-specific information depending on whether you select a Marketplace or Custom Image.
This is one of the aspects of CloudShell's "app first" approach that is so powerful - each app can have multiple cloud deployment targets making it very easy to change an app's deployment from, say an on-prem vCenter deployment, to a public cloud Azure deployment.
Now we're set up to model a complex cloud environment without having to mess around with ARM templates!
Create a new Blueprint and simply drag in all the applications you want to deploy. For each app you add to the blueprint, you'll be prompted to select what cloud the resource should deploy to; select the Azure cloud you added. Each blueprint is managed as a whole. When you deploy a blueprint, the blueprint orchestration code manages the deployment of each Azure resource in that blueprint - deploying each resource to the specified cloud. CloudShell creates a separate Azure resource group for each deployed blueprint, as well as a separate, isolated, Azure VNET that all the resources will be deployed in.
CloudShell supports hybrid-cloud deployments. So, in theory, your blueprint could include Azure resources as well as, say, vCenter resources. CloudShell's orchestration code delegates the deployment to each resource as well as handling network and other services. In the case of Azure, CloudShell deploys via the Azure API using the Azure Resource Manager interface.
In CloudShell, deployed blueprints are called sandboxes. Once deployed, CloudShell allows you to directly RDP or SSH into Azure resources in your sandbox without having to jump into the Azure portal. You can even connect directly to resources that don't have public IPs without having to set up a VPN.
You can add more Azure resources to a live sandbox just by dragging them into the sandbox environment and running their "deploy" orchestration command.
When you select a blueprint to be deployed, you are (by default) required to specify a duration. At the end of a sandbox's specified duration, CloudShell's teardown orchestration will run, which - like the setup - will de-provision all your Azure resources. This is a great way to ensure your dev and test teams don't unintentionally consume too many Azure resources.
Furthermore, when CloudShell deploys resources in Azure it adds the following custom tags to each resource: Blueprint, CreatedBy, Domain, Name, Owner, and SandboxId. We also create a custom usage report under your Azure billing with these tags. This allows you to get better visibility into what users and teams are consuming what resources and how much they're consuming. This is especially helpful when Azure resources are consumed for pre-production purposes like development environments, test/QA environments, or even sales/marketing demos. CloudShell's InSight business intelligence tool also gives you direct usage dashboards for your Azure deployments.
We just touched the surface of how CloudShell makes it easier to deploy applications on Azure. There's a lot more we could dive into on how to customize and extend the out the box support for deploying resources to Azure. If you want to learn more you can request a hands-on technical demo.
As part of their digital transformation efforts, businesses are overwhelmingly moving towards a multi-cloud approach. Modernizing applications involves taking advantage of the scalability and rich set of services available in the public Cloud. The Oracle Cloud Infrastructure (OCI) provides many of these services such as Database, Load Balancing, Content Caching…
However, when it comes to validating these complex application architectures before releasing to production, there are many challenges the QA engineer/Release Manager faces.
- How do you guarantee that your application will perform as well once you migrate it into the Oracle public Cloud?
- How do you guarantee that your application and its data will meet all regulatory and security requirements once you migrate it into the OCI?
- How do you make sure that your application environment gets properly terminated after the test has been completed?
- How can you scale these efforts across your entire organization?
Let's consider a retail company, Acme inc., who needs to modernize their Sylus e-commerce web application as part of an overall business transformation initiative. Their current legacy workload is hosted on a vCenter private cloud and contains the typical component you would expect: namely a backend Oracle Database RAC cluster, an application server tier and a set of load balanced Apache web servers front-ended by HA Proxy. The business unit has decided to migrate it to the Oracle public cloud and validate its performance using Quali's CloudShell Platform. They want to make sure that not only it will perform as well or better, but also it will meet all the regulatory and security requirements before they release to production.
The first step is for the application designer to model all the architecture of the target application. To that end, the Cloudshell blueprint provides a simple way to describe precisely all the necessary components in a template format, with parameters that can be injected dynamically at runtime such as build number and dataset. For the sake of this example, the designer considers 2 blueprints: the first one represent the legacy application architecture with Jmeter as a tool to generate traffic, the second one represents the OCI architecture with Blazemeter as a traffic generator service and Oracle Load Balancing service in front of the web front end.
Once this step is complete, the blueprint is published to the self-service catalog and becomes available to specific teams of users defined by access policies (also called "user Domains"). This process removes the barriers between the application testers and IT Operations and enables the entire organization to scale their DevOps efforts.
From the Cloudshell portal, an authorized end user can now select the Sylus blueprint and deploy an application instance in the vCenter private cloud to validate its performance with Jmeter. This is a simple task: all the user needs is to select input parameters and duration.
Built-in set up orchestration handles the deployment of all the virtual machines, as well as their connectivity and configuration to meet the blueprint definition. In this case, the latest build of the Sylus application is loaded from a source repository on the application server, and the corresponding dataset is loaded on the database cluster.
Once the Sandbox is active, the tester can run a JMeter load test from the automation available through the JMeter resource and directly view the test results for post analysis.
Once the test is complete, the Sandbox is automatically terminated and all the VMs deleted. This built-in teardown process ensures that the resource consumption is under control.
Then the test user deploys the same application in the Oracle Cloud Infrastructure from the OCI blueprint, and validate its performance with Blazemeter using the same test as in the previous blueprint. In such case, a link is provided to the live reports and from a quick glance: thankfully, the results show that there is no performance degradation so the team can proceed with confidence to the next stage. Since all the VMs in the Sandbox are deleted at the end of each test case, there is no cost overrun from remaining "ghost" VMs.
The same process can be repeated for validating security and compliance, all the way to staging.
Note that this can also be 100% API driven triggered by a tool like Jenkins Pipeline or JetBrain's TeamCity using the Sandbox REST API.
Using the CloudShell platform and dynamic self-service environments, the Acme company can now release their modernized Sylus e-commerce application in production in the Oracle Cloud with confidence and repeat this process for their entire portfolio of applications for each new version.
This blog was also published on the Oracle Partner site.
The Financial Services Industry (FSI) is in the midst of an application transformation cycle. This transformation involves modernizing FSI applications into fully digital cloud services to provide bank customers a significantly better user experience. In turn, an improved customer experience opens the door for the FSI to offer tailored products and services. To enable this application modernization strategy, Financial Institutions are adopting a range of new technologies hosted on Cloud infrastructure.
The technologies that are introduced during a Financial Service application modernization effort may include:
Together, these technology components provide the capability for FSI’s to meet market demands by offering mobile-friendly, scalable applications to meet the demand and requirements within a specific geographic region. Each region may have stringent compliance laws which protect the customer privacy and transactional data. The challenge is to figure out how to release these next-generation FSI applications while ensuring that validation activities have been performed to meet regulatory requirements. The net result is that any certification process for a financial application and the associated modernization effort can take weeks, if not months.
The approach to overcoming the challenges mentioned in the previous section is to streamline the application validation and certification process. Quali Cloudshell solution is a self-service orchestration platform that enables FSI’s to design, test and deploy modernized application architectures. It provides the capabilities to manage application complexity with standard self-service blueprints and validate compliance with dynamic environments and automation.
This results in the following business benefits:
Using the CloudShell platform, the FSI application release manager can now quickly automate and streamline these workflows in order to achieve their required application updates.
Who said trade shows have to be boring? That was certainly not the case at the 2017 Jenkins World conference held in San Francisco last week and organized by CloudBees. Quali's booth was in a groovy mood and so was the crowd around us (not mentioning the excellent wine served for happy hours and the 70's band playing on the stage right next to us).
The colorful layout of the booth certainly didn't deter from very interesting conversations with show attendees around how to make DevOps real and solving real business challenges their companies are facing.
This was the third Jenkins conference we were sponsoring this summer (after Paris and Tel Aviv) and we could see many familiar faces from other DevOps leaders such as Atlassian, Jfrog and CA Blazemeter that have partnered with us to build end to end integrations to provide comprehensive CI/CD solutions for application release automation.
This really felt like a true community that collaborate together effectively to benefit a wide range of software developers, release manager and devOps engineers and empower them with choices to meet their business needs.
To illustrate these integrations, we showed a number of short demos around some the main use cases that we support (Feel free to browse these videos at your own pace):
As you would expect at a tech conference, there was the typical schwag, such as our popular TShirts (although we can't just claim the fame of the legendary Splunk outfit). In case you did not get your preferred size at the show, we apologize for that and invite you to sign up for a 14 day free trial of newly released CloudShell VE.
Quali is pleased to announce that we just released CloudShell version 8.1 in General availability.
This version provides several features that provide a better experience and performance for both the administrator , blueprint designers and end users many of them were contributed by our great community feedback and suggestions
Let's go over the main features delivered in CloudShell 8.1 and their benefits:
Orchestration is a first class citizen in CloudShell, so we've simplified and enhanced the orchestration capabilities for your blueprints.
We have created a standard approach for users to extend the setup and tear-down flows. By separating the orchestration into built in stages and events, the CloudShell user now has better control and visibility to the orchestration process.\
We've also separated the different functionality into packages to allow more simplified and better structured flows for the developer.
We have made various enhancements to Apps and CloudShell’s virtualization capabilities, such as allowing tracking the application setup process , passing dynamic attributes to the configuration management.
CloudShell 8.1 now supports vCenter 6.5 and Azure Managed disks and premium storage features
To enhance the visibility of what's going on during the lifespan of a Sandbox for all the users , CloudShell now allows a regular user to focus on a specific activity of any component in their sandbox and view detailed error information directly from the activity pane.
Administrator can now edit any resources from the inventory of the CloudShell web portal including Address, Attributes, Location, as well as the capability to exclude/include resources.
To allow uninterrupted automation process and prevent any error during the setup stage, the sandbox will be in a “read only” mode.
Blueprint editors using abstract resource can now select attribute values from a drop down list with existing values, this shortens and eases the creation process and reduces problems during abstract creation
A new view allows administrators to track the commands queued for execution.
The Sandbox list view now displays live status icons for sandbox components and allows remote connections to devices and virtual machines using QualiX.
Additional REST API functions have been added to allow better control over Sandbox consumption.
In addition, version 8.1 rolls out support for Ranorex 7.0 and HP ALM 12.x integration.
Providing more out-of-the-box Shells speeds up time to value with CloudShell. The 8.1 list includes Ixia Traffic Generators, OpenDayLight Lithium , Polatis L1, Breaking Point, Junos Firewall, and many more shells that were migrated to 2nd generation.
See you all in CloudShell 8.2 :)
The process of automating application and IT infrastructure deployment, also known as "Orchestration", has sometimes been compared to old fashioned manual stitching. In other words, as far as I am concerned, a tedious survival skill best left for the day you get stranded on a deserted island.
In this context, the term "Orchestration" really describes the process of gluing disparate pieces that never seem to fit quite perfectly together. Most often it ends up as a one-off task best left to the expert, system integrator and other professional service organization, who will eventually make it come together after throwing enough time, $$ and resources at the problem. Then the next "must have" cool technology comes around and you have to repeat the process all over again.
But it shouldn't have to be that way. What does it take for Orchestration to be sustainable and stand the test of time?
IT automation over the years has taken various names and acronyms. Back in the days (early 2000s - seems like pre-history) when I got first involved in this domain, it was referred to as Run Book Automation (RBA). RBA was mostly focused around troubleshooting automatically failure conditions and possibly take corrective action.
Cloud Orchestration became a hot topic when virtualization came of age with private and public cloud offerings pioneered by VMWare and Amazon. Its main goal was primarily to offer infrastructure as a service (IaaS) on top of the existing hypervisor technology (or public cloud) and provide VM deployment in a technology/cloud agnostic fashion. The primary intent of these platforms (such as CloudBolt) was initially to supply a set of ready to use catalog of predefined OS and Application images to organizations adopting virtualization technologies, and by extension create a "Platform as a Service" offering (PaaS).
Then in the early 2010s, came DevOps, popularized by Gene Kim's Phoenix Project. For Orchestration platforms, it meant putting application release front and center, and bridging developer automation and IT operation automation under a common continuous process. The wide spread adoption by developers of several open source automation frameworks, such as Puppet, Chef and Ansible, provided a source of community driven content that could finally be leveraged by others.
Integrating and creating an ecosystem of external components has long been one of the main value add of orchestration platforms. Once all the building blocks are available it is both easier and faster to develop even complex automation workflows. Front and center to these integrations has been the adoption of RESTful APIs as the de facto standard. For the most part, exposing these hooks has made the task of interacting with each component quite a bit faster. Important caveats: not all APIs are created equal, and there is a wide range of maturity level across platforms.
With the coming of age and adoption of container technologies, which provide a fast way to distribute and scale lightweight application processes, a new set of automation challenges naturally occurs: connecting these highly dynamic and ephemeral infrastructure components to networking, configuring security and linking these to stateful data stores.
Replacing each orchestration platform by a new one when the next technology (such as serverless computing) comes around is neither cost effective or practical. Let's take a look at what makes such framework(s) a sustainable solution that can be used for the long run.
What is clear from my experience interacting with this domain over the last 10 years is that there is no "one size fits all" solution, but rather a combination of orchestrations frameworks that depend on each others with a specific role and focus area. A report on the topic was recently published by SDx Central covering the plethora of tools available. Deciding what is the right platform for the job can be overwhelming at first, unless you know what to look for. Some vendors offer a one stop shop for all functions, often taking the burden of integrating different products from their portfolio, while some others provide part of the solution and the choices to integrate northbound and southbound to other tools and technologies.
To better understand how this would shape up, let's go through a typical example of continuous application testing and certification, using the Quali's CloudShell Platform to define and deploy the environment (also available in a video).
The first layer of automation will come with a workflow tool such as the Jenkins pipeline. This tool will be used to orchestrate the deployment of the infrastructure for the different stages of the pipeline as well as trigger the test/validation steps. It delegates the next layer down the task to deploy the application. then orchestration will set up the environment and deploy the infrastructure and configure the application on top. Finally the last layer, closest to the infrastructure will be responsible for deploying the Virtual Machines and Containers onto the hypervisor or physical hosts, such as Openstack and Kubernetes.
When it comes to scaling such a solution to multiple applications across different teams, there are 2 fundamental aspects to consider in any orchestration platform: standardization and extensibility.
Standardization should come from templates and modeling based on a common language. Aligning on an industry open standard such as TOSCA to model resources will provide a way to quickly on board new technologies into the platform as they mature without "reinventing the wheel".
Standardization of the content also means providing management and control to allow access by multiple teams concurrently. Once the application stack is modeled into a blueprint it is published to a self service catalog based on categories and permissions. Once ready for deployment, the user or API provides any required input and the orchestration creates a sandbox. This sandbox can then be used for completing some testing against its components. Once testing and validation is complete, another "teardown" orchestration kicks in and all the resources in the sandbox get reset to their initial state and are cleaned up (deleted).
Extensibility is what brings the community around a platform together. From a set of standard templates and clear rules, it should be possible to extend the orchestration and customize it to meet the needs of my equipment, application and technologies. That means the option, if you have the skill set, to not depend on professional services help from the vendor. The other aspect of it is what I would call vertical extensibility, or the ability to easily incorporate other tools as part of an end to end automation workflow. Typically that means having a stable and comprehensive northbound REST API and providing a rich set of plugins. For instance, using the Jenkins pipeline to trigger a Sandbox.
Another important consideration is the openness of the content. At Quali we've open sourced all the content on top of the CloudShell platform to make it easier for developers. On top of that a lot of out of the box content is already available so a new user will never have to start from scratch.
Want to learn more on how we implemented some of these best practices with Quali's Cloud Sandboxes? Watch a Demo!