Why Legacy Monolithic Architectures Won’t Work For Digital Platforms..

As times change, so do architectural paradigms in software development. For the more than fifteen years the industry has been developing large scale JEE/.NET applications, the three-tier architecture has been the dominant design pattern. However, as enterprises embark or continue on their Digital Journey, they are facing a new set of business challenges which demand fresh technology approaches. We have looked into transformative data architectures at a great degree of depth in this blog, now let us now consider a rethink in the Applications themselves. Applications that were earlier deemed to be sufficiently well-architected are now termed as being monolithic.  This post solely focuses on the underpinnings of why legacy architectures will not work in the new software-defined world. My intention is not to merely criticize a model (the three-tier monolith) that has worked well in the past but merely to reason why it may be time for a generally well accepted newer paradigm.

Traditional Software Platform Architectures… 

Digital applications support a wider variety of frontends & channels, they need to accommodate larger volumes of users, they need wider support for a range of business actors  – partners, suppliers et al via APIs. Finally, these new age applications need to work with unstructured data formats (as opposed to the strictly structured relational format). From an operations standpoint, there is a strong need for a higher degree of automation in the datacenter. All of these requirements call for agility as the most important construct in the enterprise architecture.

As we will discuss, legacy applications (typically defined as created more than 5+ years ago) are beginning to emerge as one of the key obstacles in doing Digital. The issue is not just in the underlying architectures themselves but also in the development culture involved building and maintaining such applications.

Consider the vast majority of applications deployed in enterprise data centers. These applications deliver collections of very specific business functions – e.g. onboarding new customers, provisioning services, processing payments etc. Whatever be the choice of vendor application platform, the vast majority of existing enterprise applications & platforms essentially follows a traditional three-tier software architecture with specific separation of concerns at each tier (as the vastly simplified illustration depicts below).

Traditional three-tier Monolithic Application Architecture

The first tier is the Presentation tier which is depicted at the top of the diagram. The job of the presentation tier is to present the user experience. This includes the user interface components that present various clients with the overall web application flow and also renders UI components. A variety of UI frameworks that provide both flow and UI rendering is typically used here. These include Spring MVC, Apache Struts, HTML5, AngularJS et al.

The middle tier is the Business logic tier where all the business logic for the application is centralized while separating it from the user interface layer. The business logic is usually a mix of objects and business rules written in Java using frameworks such EJB3, Spring etc. The business logic is housed in an application server such as JBoss AS or Oracle WebLogic AS or IBM WebSphere AS – which provides enterprise services (such as caching, resource pooling, naming and identity services et al) to the business components run on these servers. This layer also contains data access logic and also initiates transactions to a range of supporting systems – message queues, transaction monitors, rules and workflow engines, ESB (Enterprise Service Bus) based integration, accessing partner systems using web services, identity, and access management systems et al.

The Data tier is where traditional databases and enterprise integration systems logically reside. The RDBMS rules this area in three-tier architectures & the data access code is typically written using an ORM (Object Relational Mapping) framework such as Hibernate or iBatis or plain JDBC code.

Across all of these layers, common utilities & agents are provided to address cross-cutting concerns such as logging, monitoring, security, single sign-on etc.

The application is packaged as an enterprise archive (EAR) which can be composed of a single or multiple WAR/JAR files. While most enterprise-grade applications are neatly packaged, the total package is typically compiled as a single collection of various modules and then shipped as one single artifact. It should bear mentioning that dependency & version management can be a painstaking exercise for complex applications.

Let us consider the typical deployment process and setup for a thee tier application.

From a deployment standpoint, static content is typically served from an Apache webserver which fronts a Java-based webserver (mostly Tomcat) and then a cluster of backend Java-based application servers running multiple instances of the application for High Availability. The application is Stateful (and Stateless in some cases) in most implementations. The rest of the setup with firewalls and other supporting systems is fairly standard.

While the above architectural template is fairly standard across industry applications built on Java EE, there are some very valid reasons why it has begun to emerge as an anti-pattern when applied to digital applications.

Challenges involved in developing and maintaining Monolithic Applications …

Let us consider what Digital business usecases demand of application architecture and where the monolith is inadequate at satisfying.

  1. The entire application is typically packaged as a single enterprise archive (EAR file), which is a combination of various WAR and JAR files. While this certainly makes the deployment easier given that there is only one executable to copy over, it makes the development lifecycle a nightmare. The reason being that even a simple change in the user interface can cause a rebuild of the entire executable. This results in not just long cycles but makes it extremely hard on teams that span various disciplines from the business to QA.
  2. What follows from such long “code-test & deploy” cycles are that the architecture becomes change resistant, the code very complex over time and as a whole the system subsequently becomes not agile at all in responding to rapidly changing business requirements.
  3. Developers are constrained in multiple ways. Firstly the architecture becomes very complex over a period of time which inhibits quick new developer onboarding. Secondly,  the architecture force-fits developers from different teams into working in lockstep thus forgoing their autonomy in terms of their planning and release cycles. Services across tiers are not independently deployable which leads to big bang releases in short windows of time. Thus it is no surprise that failures and rollbacks happen at an alarming rate.
  4. From an infrastructure standpoint, the application is tightly coupled to the underlying hardware. From a software clustering standpoint, the application scales better vertically while also supporting limited horizontal scale-out. As volumes of customer traffic increase, performance across clusters can degrade.
  5. The Applications are neither designed nor tested to operate gracefully under failure conditions. This is a key point which does not really get that much attention during design time but causes performance headaches later on.
  6. An important point is that Digital applications & their parts are beginning to be created using different languages such as Java, Scala, and Groovy etc. The Monolith essentially limits such a choice of languages, frameworks, platforms and even databases.
  7. The Architecture does not natively support the notion of API externalization or Continuous Integration and Delivery (CI/CD).
  8. As highlighted above, the architecture primarily supports the relational model. If you need to accommodate alternative data approaches such as NoSQL or Hadoop, you are largely out of luck.

Operational challenges involved in running a Monolithic Application…

The difficulties in running a range of monolithic applications across an operational infrastructure have already been summed up in the other posts on this blog.

The primary issues include –

  1. The Monolithic architecture typically dictates a vertical scaling model which ensures limits on its scalability as users increase. The typical traditional approach to ameliorate this has been to invest in multiple sets of hardware (servers, storage arrays) to physically separate applications which results in increases in running cost, a higher personnel requirement and manual processes around system patch and maintenance etc.
  2. Capacity management tends to be a bit of challenge as there are many fine-grained resources competing for compute, network and storage resources (vCPU, vRAM, virtual Network etc) that are essentially running on a single JVM. Lots of JVM tuning is needed from a test and pre-production standpoint.
  3. A range of functions needed to be performed around monolithic Applications lack any kind of policy-driven workload and scheduling capability. This is because the Application does very little to drive the infrastructure.
  4. The vast majority of the work needed to be done to provision, schedule and patch these applications is done by system administrators and consequently, automation is minimal at best.
  5. The same is true in Operations Management. Functions like log administration, other housekeeping, monitoring, auditing, app deployment, and rollback are vastly manual with some scripting.

Conclusion…

It deserves mention that the above Monolithic design pattern will work well for Departmental (low user volume) applications which have limited business impact and for applications serving a well-defined user base with well delineated workstreams. The next blog post will consider the microservices way of building new age architectures. We will introduce and discuss Cloud Native Application development which has been popularized across web-scale enterprises esp Netflix. We will also discuss how this new paradigm overcomes many of the above-discussed limitations from both a development and operations standpoint.

Why Platform as a Service (PaaS) Adoption will take off in 2017..

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Since the time Steve Ballmer went ballistic professing his love for developers, it has been a virtual mantra in the technology industry that developer adoption is key to the success of a given platform. On the face of it – Platform as a Service(PaaS) is a boon to enterprise developers who are tired of the inefficiencies of old school application development environments & stacks. Further, a couple of years ago, PaaS seemed to be the flavor of the future given the focus on Cloud Computing. This blogpost focuses on the advantages of the generic PaaS approach while discussing its lagging slow rate of adoption in the cloud computing market – as compared with it’s cloud cousins – IaaS (Infrastructure as a Service) and SaaS (Software as a Service).

Platform as a Service (PaaS) as the foundation for developing Digital, Cloud Native Applications…

Call them Digital or Cloud Native or Modern. The nature of applications in the industry is slowly changing. So are the cultural underpinnings of the development process and culture themselves- from waterfall to agile to DevOps. At the same time, Cloud Computing and Big Data are enabling the creation of smart data applications. Leading business organizations are cognizant of the need to attract and retain the best possible talent – often competing with the FANGs (Facebook, Amazon, Netflix & Google).

Couple all this with the immense industry and venture capital interest around container oriented & cloud native technologies like Docker – you have a vendor arms race in the making. And the prize is to be chosen as the standard for building industry applications.

Thus, infrastructure is enabling but in the end- it is the applications that are Queen or King.

That is where PaaS comes in.

Why Digital Disruption is the Cure for the Common Data Center..

Enter Platform as a Service (PaaS)…

Platform as a Service (PaaS) is one of the three main cloud delivery models, the other two being IaaS (Infrastructure such as compute, network & storage services) and SaaS (Business applications delivered over a cloud). A collection of different cloud technologies, PaaS focuses exclusively on application development & delivery. PaaS advocates a new kind of development based on native support for concepts like agile development, unit testing, continuous integration, automatic scaling, while providing a range of middleware capabilities. Applications developed on these can be deployed out as services & managed across thousands of application instances.

In short, PaaS is the ideal platform for creating & hosting digital applications. What can PaaS provide that older application development toolchains and paradigms cannot?

While the overall design approach and features vary across every PaaS vendor – there are five generic advantages from a high level –

  1. PaaS enables a range of Application, Data & Middleware components to be delivered as API based services to developers on any given Infrastructure as a Service (IaaS).  These capabilities include-  Messaging as a service, Database as a service, Mobile capabilities as a service, Integration as a service, Workflow as a service, Analytics as a service for data driven applications etc. Some PaaS vendors also provide ability to automate & manage APIs for business applications deployment on them – API Management.
  2. PaaS provides easy & agile access to the entire suite of technologies used while creating complex business applications. These range from programming languages to application server (and lightweight) runtimes to programming languages to CI/CD toolchains to source control repositories.
  3. PaaS provides the services which enables a seamless & highly automated manner of building the complete life cycle of building and delivering web applications and services on the internet. Industry players are infusing software delivery processes with practices such as continuous delivery (CD) and continuous integration (CI). For large scale applications such as those built in web scale shops, financial services, manufacturing, telecom etc – PaaS abstracts away the complexities of building, deploying & orchestrating infrastructure thus enabling instantaneous developer productivity. This is a key point – with it’s focus on automation – PaaS can save application and system administrators precious time and resources in managing the lifecycle of elastic applications
  4. PaaS enables your application to be ‘kind of cloud’ agnostic & can enable applications to be run on any cloud platform whether public or private. This means that a PaaS application developed on Amazon AWS can easily be ported to Microsoft Azure to VMWare vSphere to Red Hat RHEV etc
  5. PaaS can help smoothen organizational Culture and Barriers – The adoption of a PaaS forces an agile culture in your organization – one that pushes cross pollination among different business, dev and ops teams. Most organizations are just now beginning to go bimodal for greenfield applications can benefit immensely from choosing a PaaS as a platform standard.

The Barriers to PaaS Adoption Will Continue to Fall In 2017..

In general, PaaS market growth rates do not seem to line up well when compared with the other broad sections of the cloud computing space, namely IaaS (Infrastructure as a Service) and SaaS (Software as a Service). 451 Research’s Market Monitor forecasts that the total market for cloud computing (including PaaS, IaaS and infrastructure software as a service (ITSM, backup, archiving) – will hit $21.9B in 2016 more than doubling to $44.2bB by 2020. Of that, some analyst estimates contend that PaaS will be a relatively small $8.1 billion.

451-research-paas_vs_saas_iaas

  (Source – 451 Research)

The advantages that PaaS confers have sadly also caused its relatively low rate of adoption as compared to IaaS and SaaS.

The reasons for this anemic rate of adoption include, in my opinion  –  

  1. Poor Conception of the Business Value of PaaS –  This is the biggest factor holding back explosive growth in this category. PaaS is a tremendously complicated technology & vendors have not helped by stressing on the complex technology underpinnings (containers, supported programming languages, developer workflow, orchestration, scheduling etc etc) as opposed to helping clients understand the tangible business drivers & value that enterprise CIOs can derive from this technology. Common drivers include increased time to market for digital capabilities, man hours saved in maintaining complex applications, ability to attract new talent etc. These factors will vary for every customer but it is up to frontline Sales teams to help deliver this message in a manner that is appropriate to the client.
  2. Yes, you can do DevOps without PaaS but PaaS helps a long way  – Many Fortune 500 organizations are drawing up DevOps strategies which do not include a PaaS & are based on a simplified CI/CD pipeline. This is to the detriment of both the customer organization & the industry as PaaS can vastly simplify a range of complex runtime & lifecycle services that would otherwise need to be cobbled together by the customer as the application moves from development to production. There is simply a lack of knowledge in the customer community about where a PaaS fits in a development & deployment toolchain.
  3. Smorgasbord of Complex Infrastructure Choices – The average leading PaaS includes a range of open source technologies ranging from containers to runtimes to datacenter orchestration to scheduling to cluster management tools. This makes it very complex from the perspective of Corporate IT – not just it terms of running POCs and initial deployments but also to manage a highly complex stack. It is incumbent on the open source projects to abstract away the complex inner workings to drive adoption  -whether by design or by technology alliances.
  4. You don’t need Cloud for PaaS but not enough Technology Leaders get that – This one is perception. The presence of an infrastructural cloud computing strategy is not a necessary condition for PaaS. 
  5. The false notion that PaaS is only fit for massively scalable, greenfield applications – Industry leading PaaS’s (like Red Hat’s OpenShift) support a range of technology approaches that can help cut technical debt. They donot limit deployment on an application server platform such as JBOSS EAP or WebSphere or WebLogic, or a lightweight framework like Spring.
  6. PaaS will help increase automation thus cutting costs – For developers of applications in Greenfield/ New Age spheres such as IoT, PaaS can enable the creation of thousands of instances in a “Serverless” fashion. PaaS based applications can be composed of microservices which are essentially self maintaining – i.e self healing and self scalable up or down; these microservices are delivered (typically) by IT as Docker containers using automated toolchains. The biggest requirement in large datacenters – human involvement – is drastically reduced if PaaS is used – while increasing agility, business responsiveness and efficiencies.

Conclusion…

My goal for this post was to share a few of my thoughts on the benefits of adopting a game changing technology. Done right, PaaS can provide a tremendous boost to building digital applications thus boosting the bottom line. Beginning 2017, we will witness PaaS satisfying critical industry use cases as leading organizations build end-to-end business solutions that covers many architectural layers.

References…

[1] http://www.forbes.com/sites/louiscolumbus/2016/03/13/roundup-of-cloud-computing-forecasts-and-market-estimates-2016/#3d75915274b0

A deep look into OpenShift v2

The PaaS (Platform As A Service) market is dominated by two worthy technologies – OpenShift from Red Hat and Pivotal’s CloudFoundry. It is amazing that a disruptive technology category like PaaS is overwhelmingly dominated by these two open source ecosystems , which results in great choice for consumers.

I have used OpenShift extensively and have worked with customers on large & successful deployments. While I do believe that CloudFoundry is robust technology as well, this post will focus on what I personally know better – OpenShift.

Platform as a Service (PaaS) is a cloud application delivery model that is typically between IaaS and SaaS.

The Three Versions of OpenShift

OpenShift is Red Hat’s PaaS technology for both private and public clouds. There are three different versions: OpenShift Origin,Online and Enterprise.

OpenShift Origin, the community ( and open source) version of OpenShift, is the upstream project for the other two versions. It is hosted on GitHub and released under an Apache 2 license.

OpenShift Online is the public PaaS as a Service, currently hosted on Amazon AWS.

OpenShift Enterprise is the hardened version of OpenShift with ISV & vendor certifications.

3_flavors_of_openshift

OpenShift Terminology

The Broker and the Node are two main server types in OSE. The Broker is the manager cum orchesterator of the overall infrastructure and the overall brains behind the operation. The Nodes are VMs or baremetal servers where end user applications live.

The Broker exposes a WebGUI (a charming throwback to the 1990s) but more importantly an enterprise class and robust REST API. Typically one or more brokers that manage multiple nodes. Multiple brokers can be clustered for HA purposes.

Application

This is the typical web application or BPM application or integration application that will run on OpenShift. OpenShift v2 was focused on webapp workloads but offered a variety of mature extension points as cartridges.

You can interact with the OpenShift platform via RHC client command-line tools
you install on your local machine, the OpenShift Web Console, or a plug-in you
install in Eclipse to interact with your application in the OpenShift cloud.

Gear

A gear is a server container with a set of resources that allows users to run their
applications.

Cartridge

Cartridges give gears a personality and make them containers for specialized applications. Cartridges are the plug-
ins that house the framework or components that can be used to create and run an
application. One or more cartridges run on each gear, and the same cartridge can
run on many gears for clustering or scaling. There are two kinds of cartridges:
Standalone & Embedded. You can  also create a custom cartridge like its running in OSE. EAP+Autoconfig for some F5 stuff. E.g. Create an F5 cartridge that can call out the F5 API every-time it detects a load situation.

Gears

Scalable application

Application scaling enables your application to react to changes in traffic and au‐
tomatically allocate the necessary resources to handle your increased demand. OpenShift is unique in that it can do application scaling both ways – scale & descale – dynamically. The OpenShift infrastructure monitors incoming web traffic and automatically brings
up new gears with the appropriate web cartridge online to handle more requests.
When traffic decreases, the platform retires the extra resources. There is a web page
dedicated to explaining how scaling works on OpenShift.

Foundational Blocks of OpenShift v2 

The OpenShift Enterprise multi-tenancy model is based on Red Hat Enterprise Linux, and it provides a secure isolated environment that incorporates the following three security mechanisms:
SELinux
SELinux is an implementation of a mandatory access control (MAC) mechanism in the Linux kernel. It checks for allowed operations at a level beyond what standard discretionary access controls (DAC) provide. SELinux can enforce rules on files and processes, and on their actions based on defined policy. SELinux provides a high level of isolation between applications running within OpenShift Enterprise because each gear and its contents are uniquely labeled.
Control Groups (cgroups)
Control Groups allow you to allocate processor, memory, and input and output (I/O) resources among applications. They provide control of resource utilization in terms of memory consumption, storage and networking I/O utilization, and process priority. This enables the establishment of policies for resource allocation, thus ensuring that no system resource consumes the entire system and affects other gears or services.
Kernel Namespaces
Kernel namespaces separate groups of processes so that they cannot see resources in other groups. From the perspective of a running OpenShift Enterprise application, for example, the application has access to a running Red Hat Enterprise Linux system, although it could be one of many applications running within a single instance of Red Hat Enterprise Linux.
How does the overall system flow work? 

1. As mentioned above, the Broker and the Node are two server types in OSE. The Broker is the manager and orchesterator of the overall infrastructure. The Nodes are where the applications live.

The MongoDB that sits behind the broker manages all the metadata about the application; the Broker also manages the DNS stuff with a BIND plugin; Auth piece manages credentials and stuff.

2. As mentioned, the gear is the resource container and is where the application lives. The gear is actually CGroups and SELinux config. Using CGroups and SE Linux enables one to create high density, multitenant apps.

3. The cartridge is the framework and stack definition. E.g. Create a Tomcat definition with spring, struts installed on it.

4. The overall flow is that the user uses the REST API to request the Broker to create a Tomcat application for instance and asks for a Scaled or a NonScaled application for a given gear size(this is all completely customizable).

The Broker communicates with the Nodes and enquires which of those have the capacity and “can you host this application?”. The Broker then gets back a simple health-check from the nodes and decides to place the app on the nodes that have been identified based on a health indicator. All this communication happens via MCollective (which is based on ActiveMQ).

5. A bunch of things happen at that point of time once the Broker decides where to place the workload.

a) A UNIX level user is created on the gear. Ever application gets a UUID and a user associated with that.So it creates a home directory for the user, put the SELinux policies associated with that user on that gear, so that they have very limited and scoped access to whatever is on that gear only. If you go and if you do a ps, you cannot see anything that is not yours. It is all controlled by SELinux and all that stuff gets setup when you create an application.

b) The gear is created with whatever cgroups config with memory,cpu and disk space etc based on what the user picked.

c) The next step is the actual cartridge install and puts all actual stack (tomcat in this instance) and the associated libraries and all that stuff. Then it will go startup Tomcat on that node, do all the DNS resolution so that the app is publicly addressable.

d) Base OpenShift ships with lots of default cartridges with all kinds of applications. Spec 2.0 made it a lot easy to create new cartridges.

You can write custom cartridges than expose more ports and services across gears.

The other thing is you can build hybrid cartridges so that the first gear spun up would spin up the first framework and the next gear would spin up the next framework. So that a webfront end and then a vertex cluster running on the backend and all the intergear communication is handled by OSE. It is all very customizable.

For instance, you can add EAP and JDG/Business Rules in one hybrid cartridge and have it all be autoscaled. Or you can build multiple cartridges and one will be the main cartridge and have all these embedded cartridges back that up.

The difference between the hybrid and the single framework is that when it receives a scale-up event, it has to decide what to install on a given gear, so the logic gets a little more complicated.

openshift-enterprise-workflow

e) Networking and OSE.

The first thing to keep in mind is that all of OpenShift Online (which a lot of people know and understand) is really designed around an Amazon EC2 infrastructure. The 5 ports per gear limitation is really around EC2 limitations and does not really apply in enterprise where you are typically running this on a fully controlled VM.

Intergear communication between the gears of an app is via IPTables.And it is all configurable as well as SELinux policies with the ports etc. It is all customizable in the Enterprise version.

It is important to understand how routing works on a node to better understand the security architecture of OpenShift Enterprise. An OpenShift Enterprise node includes several front ends to proxy traffic to the gears connected to its internal network.
The HTTPD Reverse Proxy front end routes standard HTTP ports 80 and 443, while the Node.js front end similarly routes WebSockets HTTP requests from ports 8000 and 8443. The port proxy routes inter-gear traffic using a range of high ports. Gears on the same host do not have direct access to each other.
In a scaled application, at least one gear runs HAProxy to load balance HTTP traffic across the gears in the application, using the inter-gear port proxy.
OpenShift_Networking

THE COMPLETE PICTURE –
————————————

All OpenShift applications are built around a Git source control workflow – you code locally, then push your changes to the server. The server then runs a number of hooks to build and configure your application, and finally restarts your application. Optionally, applications can elect to be built using Jenkins, or run using “hot deployment” which speeds up the deployment of code to OpenShift.

As a developer on OpenShift, you make code changes on your local machine, check those changes in locally, and then “push” those changes to OpenShift.

Following a workflow –

The git push is really the trigger of a build/deploy/a start and stop. All that happens via a git push. GIT here is not really for source control but as a communication mechanism for an application push to the node.

So if one follows the workflow from the developer side, they typically use the client tools that speak REST/CLI/Eclipse to the broker i.e. create the app,or,start and stop the application. Once that application is created; everything you do from changing Tomcat config, changing your source, deploying a new war etc is all going to go through GIT.

The process is that I get to whatever client tool I use, REST/CLI/Eclipse etc to communicate to the broker asking for an application creation. The broker,via MCollective, goes to the nodes and picks the ones it intends to create the gears on.

Once the gears are created along with a Unix user/ Cgroups/SELinux/home directory/cartridge config etc based on whatever you pickup.

There it does routing configuration on the gear as all the applications bind to a loopback address on the nodes so they’re not externally routable. But on every node, there is also an Apache instance running so it multiplexes all that traffic so that all nodes are externally routable.

There is a callback to the Broker to go and handle all that DNS and the application is now accessible from the browser. At this point, all you have is a simple hello world templated app.

Also check out the below link on customizing  OSE autoscale behavior via HAProxy

https://www.openshift.com/blogs/customizing-autoscale-functionality-in-openshift

GIT and OSE

Every OpenShift application you create has its own Git repository that only you can access. If you create your application from the command line, rhc will automatically download a copy of that repository (Git calls this ‘cloning’) to your local system. If you create an application from the web console, you’ll need to tell Git to clone the repository. Find the Git URL from the application page, and then run:

$ git clone

Once you make changes, you’ll need to ‘add’ and ‘commit’ those changes – ‘add’ tells Git that a file or set of files will become part of a larger check in, and ‘commit’ completes the check in. Git requires that each commit have a message to describe it.

$ git add .
$ git commit -m “A checkin to my application”

Finally, you’re ready to send your changes to your application – you’ll ‘push’ these changes with:

$ git push

And thats it!

OpenShift v3, which was just launched this week at Red Hat Summit 2015, introduces big changes with a move to Docker and Kubernetes in lieu of vanilla linux containers & barebones orchestration. Subsequent posts will focus on v3 as it matures.