The Seven Characteristics of Cloud Native Application Architectures..

We are in the middle of a series of blogs on Software Defined Datacenters (SDDC) @ http://www.vamsitalkstech.com/?p=1833. The key business imperative driving the SDDC architectures is their ability to natively support digital applications. Digital applications are “Cloud Native” (CN) in the sense that these platforms are originally being written for cloud frameworks – instead of being ported over to the Cloud as an afterthought. Thus, Cloud Native application development emerging as the most important trend in digital platforms. This blog post will define the seven key architectural characteristics of these CN applications.

Image Credit – Shutterstock

What is driving the need for Cloud Native Architectures… 

The previous post in the blog covered the monolithic architecture pattern. Monolithic architectures , which currently dominate the enterprise landscape, are coming under tremendous pressures in various ways and are increasingly being perceived to be brittle. Chief among these forces include – massive user volumes, DevOps style development processes, the need to open up business functionality locked within applications to partners and the heavy human requirement to deploy & manage monolithic architectures etc. Monolithic architectures also introduce technical debt into the datacenter – which makes it very difficult for the business lines to introduce changes as customer demands change – which is a key antipattern for digital deployments.

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

Applications that require a high release velocity presenting many complex moving parts, which are worked on by few or many development teams are an ideal fit for the CN pattern.

Introducing Cloud Native Applications…

There is no single and universally accepted definition of a Cloud Native application. I would like to define a CN Application as “an application built using a combination of technology paradigms that are native to cloud computing – including distributed software development, a need to adopt DevOps practices, microservices architectures based on containers, API based integration between the layers of the application, software automation from infrastructure to code, and finally orchestration & management of the overall application infrastructure.”

Further, Cloud Native applications need to be architected, designed, developed, packaged, delivered and managed based on a deep understanding of the frameworks of cloud computing (IaaS and PaaS).

Characteristic #1 CN Applications dynamically adapt to & support massive scale…

The first & foremost characteristic of a CN Architecture is the ability to dynamically support massive numbers of users, large development organizations & highly distributed operations teams. This requirement is even more critical when one considers that cloud computing is inherently multi-tenant in nature.

Within this area, the typical concerns need to be accommodated –

  1. the ability to grow the deployment footprint dynamically (Scale-up)  as well as to decrease the footprint (Scale-down)
  2. the ability to gracefully handle failures across tiers that can disrupt application availability
  3. the ability to accommodate large development teams by ensuring that components themselves provide loose coupling
  4. the ability to work with virtually any kind of infrastructure (compute, storage and network) implementation

Characteristic #2 CN applications need to support a range of devices and user interfaces…

The User Experience (UX) is the most important part of a human facing application. This is particularly true of Digital applications which are omnichannel in nature. End users could not care less about the backend engineering of these applications as they are focused on an engaging user experience.

Demystifying Digital – the importance of Customer Journey Mapping…(2/3)

Accordingly, CN applications need to natively support mobile applications. This includes the ability to support a range of mobile backend capabilities – ranging from authentication & authorization services for mobile devices, location services, customer identification, push notifications, cloud messaging, toolkits for iOS and Android development etc.

Characteristic #3 They are automated to the fullest extent they can be…

The CN application needs to be abstracted completely from the underlying infrastructure stack. This is key as development teams can focus on solely writing their software and does not need to worry about the maintenance of the underlying OS/Storage/Network. One of the key challenges with monolithic platforms (http://www.vamsitalkstech.com/?p=5617) is their inability to efficiently leverage the underlying infrastructure as they have a high degree of dependency to it. Further, the lifecycle of infrastructure provisioning, configuration, deployment, and scaling is mostly manual with lots of scripts and pockets of configuration management.

The CN application, on the other hand, has to be very light on manual asks given its scale. The provision-deploy-scale cycle is highly automated with the application automatically scaling to meet demand and resource constraints and seamlessly recovering from failures. We discussed Kubernetes in one of the previous blogs.

Kubernetes – Container Orchestration for the Software Defined Data Center (SDDC)..(5/7)

Frameworks like these support CN Applications in providing resiliency, fault tolerance and in generally supporting very low downtime.

Characteristic #4 They support Continuous Integration and Continuous Delivery…

The reduction of the vast amount of manual effort witnessed in monolithic applications is not just confined to their deployment as far as CN applications are concerned. From a CN development standpoint, the ability to quickly test and perform quality control on daily software updates is an important aspect. CN applications automate the application development and deployment processes using the paradigms of CI/CD (Continuous Integration and Continuous Delivery).

The goal of CI is that every time source code is added or modified, the build process kicks off & the tests are conducted instantly. This helps catch errors faster and improve quality of the application. Once the CI process is done, the CD process builds the application into an artifact suitable for deployment after combining it with suitable configuration. It then deploys it onto the execution environment with the appropriate identifiers for versioning in a manner that support rollback. CD ensures that the tested artifacts are instantly deployed with acceptance testing.

 Characteristic #5 They support multiple datastore paradigms…

The RDBMS has been a fixture of the monolithic application architecture. CN applications, however, need to work with data formats of the loosely structured kind as well as the regularly structured data. This implies the need to support data streams that are not just high speed but also are better suited to NoSQL/Hadoop storage. These systems provide Schema on Read (SOR) which is an innovative data handling technique. In this model, a format or schema is applied to data as it is accessed from a storage location as opposed to doing the same while it is ingested. As we will see later in the blog, individual microservices can have their own local data storage.

A Holistic New Age Technology Approach To Countering Payment Card Fraud (3/3)…

Characteristic #6 They support APIs as a key feature…

APIs have become the de facto model that provide developers and administrators with the ability to assemble Digital applications such as microservices using complicated componentry. Thus, there is a strong case to be made for adopting an API centric strategy when developing CN applications. CN applications use APIs in multiple ways – firstly as the way to interface loosely coupled microservices (which abstract out the internals of the underlying application components). Secondly, developers use well-defined APIs to interact with the overall cloud infrastructure services.Finally, APIs enable the provisioning, deployment, and management of platform services.

Why APIs Are a Day One Capability In Digital Platforms..

Characteristic #7 Software Architecture based on microservices…

As James Lewis and Martin Fowler define it – “..the microservice architectural style is an approach to developing a single application as a suite of small services, each running in its own process and communicating with lightweight mechanisms, often an HTTP resource API. These services are built around business capabilities and independently deployable by fully automated deployment machinery. There is a bare minimum of centralized management of these services, which may be written in different programming languages and use different data storage technologies.” [1]

Microservices are a natural evolution of the Service Oriented Architecture (SOA) architecture. The application is decomposed into loosely coupled business functions and mapped to microservices. Each microservice is built for a specific granular business function and can be worked on by an independent developer or team. As such it is a separate code artifact and is thus loosely coupled not just from a communication standpoint (typically communication using a RESTful API with data being passed around using a JSON/XML representation) but also from a build, deployment, upgrade and maintenance process perspective. Each microservice can optionally have its localized datastore. An important advantage of adopting this approach is that each microservice can be created using a separate technology stack from the other parts of the application. Docker containers are the right choice to run these microservices on. Microservices confer a range of advantages ranging from easier build, independent deployment and scaling.

A Note on Security…

It goes without saying that security is a critical part of CN applications and needs to be considered and designed for as a cross-cutting concern from the inception. Security concerns impact the design & lifecycle of CN applications ranging from deployment to updates to image portability across environments. A range of technology choices is available to cover various areas such as Application level security using Role-Based Access Control, Multifactor Authentication (MFA), A&A (Authentication & Authorization)  using protocols such as OAuth, OpenID, SSO etc. The topic of Container Security is very fundamental one to this topic and there are many vendors working on ensuring that once the application is built as part of a CI/CD process as described above, they are packaged into labeled (and signed) containers which can be made part of a verified and trusted registry. This ensures that container image provenance is well understood as well as protecting any users who download the containers for use across their environments.

Conclusion…

In this post, we have tried to look at some architecture drivers for Cloud-Native applications. It is a given that organizations moving from monolithic applications will need to take nimble , small steps to realize the ultimate vision of business agility and technology autonomy. The next post, however, will look at some of the critical foundational investments enterprises will have to make before choosing the Cloud Native route as a viable choice for their applications.

References..

[1] Martin Fowler – https://martinfowler.com/intro.html

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

The foundation of digital business is the boundary-free enterprise, which is made possible by an array of time- and location-independent computing capabilities – cloud, mobile, social and data analytics plus sensors and APIs. There are no shortcuts to the digital enterprise.”

— Mike West,Analyst,Saugatack Research 2015

At its core Digital is a fairly straightforward concept. It is essentially about offering customers more contextual and relevant experiences while creating internal teams that can turn on a dime to serve customers. It is clear that these kinds of consumer capabilities just cannot be offered using an existing technology stack. This blogpost seeks to answer what this next generation computing stack may look like.

What Digital has in Store for Enterprises…

Digital transformation is a daily fact of life at web scale shops like Google, Amazon, Apple, Facebook and Netflix. These mega shops have built not just intuitive and appealing applications but have gradually evolved them into platforms that offer discrete marketplaces that serve global audiences. They also provide robust support for mobile applications that deliver services such as content, video, e-commerce, gaming etc via such channels. In fact they have heralded the age of new media and in doing so have been transforming both internally (business models, internal teams & their offerings) as well as externally.

CXOs at established Fortune 1000 enterprises were unable to find resonance in these stories from the standpoint of their enterprise’s reinvention. This makes a lot of sense as these established companies have legacy investments and legacy stakeholders – both of which represent change inhibitors that the FANGs (Facebook Amazon Netflix and Google) did not have. Enterprise practitioners need to understand how Digital technology can impact both existing technology investments and the future landscape.

Where are most Enterprises at the moment…

Much of what exists in the datacenters across organizations are antiquated from a technology stack. These range from hardware platforms to network devices & switches to monolithic applications running on them. Connecting these applications are often proprietary or manual integraton architectures. There are inflexible, proprietary systems & data architectures, lots of manual processes, monolithic applications and tightly coupled integration. Rapid provisioning of IT resources is a huge bottleneck which frequently leads to lines of business adopting the public cloud to run their workloads.  According to Rakesh Kumar, managing vice president at Gartner – “For over 40 years, data centers have pretty much been a staple of the IT ecosystem,Despite changes in technology for power and cooling, and changes in the design and build of these structures, their basic function and core requirements have, by and large, remained constant. These are centered on high levels of availability and redundancy, strong, well-documented processes to manage change, traditional vendor management and segmented organizational structures. This approach, however, is no longer appropriate for the digital world.” [2]

On that note, the below blogpost had captured the three essential technology investments that make up Digital Transformation.

The Three Core Competencies of Digital – Cloud, Big Data & Intelligent Middleware

If Digital has to happen, IT is one of the largest stakeholders…

Digital applications present seamless expereinces across channels & devices, are tailored to individual customers needs, understand their preferences & need to be developed in an environment of constant product innovation.

So, which datacenter capabilities are required to deliver this?

Figuring out the best architectural foundation to support , leverage & monetize on digital experiences is complex.  The past few years have seen the rapid evolution of many transformational technologies—Big Data, Cognitive Computing, Cloud technology (Public clouds, OpenStack, PaaS, Containers, Software-defined networking & storage), the Blockchain – the list goes on and on. These are leading enterprises to a smarter way of developing enterprise applications and to a more modern, efficient, scalable, cloud-based architectures.

So, what capabilities do Datacenters need to innovate towards?

digital_datacenter

                                         The legacy Datacenter transitions to the Digital Datacenter

While, the illustration above is self explanatory. Enterprise IT will need to majorly embrace Cloud Computing – whatever forms the core offering may take – public, private or hybrid. The compute infrastructure ranging from a mix of open source virtualization to Linux containers. Containers essentially virtualize the operating system so that multiple workloads can run on a single host, instead of virtualizing a server to create multiple operating systems. These containers are easily ported across different servers without the need for reconfiguration and require less maintenance because there are fewer operating systems to manage. For instance, the OpenStack Cloud Project specifies Docker (a defacto standard), a Linux format for containers that’s designed to automate the deployment of applications as highly portable, self-sufficient containers.

Cloud computing will also enable the rapid scale up & scale down across the gamut of infrastructure (compute – VM/Baremetal/Containers, storage – SAN/NAS/DAS, network – switches/routers/Firewalls etc) in near real-time (NRT). Investments in SDN (Software Defined Networking) will be de riguer in order to improve software based provisioning, network, time to market and to drive network equipment costs down. The other vector that brings about datacenter innovation is around automation i.e vastly reducing manual efforts in network and application provisioning. These capabilities will be key as the vast majority of digital applications are deployed as Software as a service (SaaS).

An in depth discussion of these Software Defined capabilities can be found at the below blogpost.

Financial Services IT begins to converge towards Software Defined Datacenters..

Applications developed for a Digital infrastructure will be developed as small, nimble processes that communicate via APIs and over infrastructure like service mediation components (e.g Apache Camel). These microservices based applications will offer huge operational and development advantages over legacy applications. While one does not expect legacy but critical applications that still run on mainframes (e.g. Core Banking, Customer Order Processing etc) to move over to a microservices model anytime soon, customer facing applications that need responsive digital UIs will definitely move.

Which finally brings us to the most important capability of all – Data. The heart of any successful Digital implementation is Data. The definition of Data includes internal data (e.g. customer data, data about transactions, customer preferences data), external datasets & other relevant third party data (e.g. from retailers) etc.  While each source of data may not radically change an application’s view of its customers, the combination of all promises to do just that.

The significant increases in mobile devices and IoT (Internet of Things) capable endpoints will ensure exponential increases in data volumes will occur. Thus Digital applications will need to handle this data – not just to process it but also to be able to glean real time insights.  Some of the biggest technology investments in ensuring a unified customer journeys are in the areas of Big Data & Predictive Analytics. Enterprises should be able to leverage a common source of data that transcends silos (a data lake) to be able to drive customer decisions that drive system behavior in real time using advanced analytics such as Machine Learning techniques, Cognitive computing platforms etc which can provide accurate and personalized insights to drive the customer journey forward.

Can Datacenters incubate innovation ?

Finally, one of the key IT architectural foundation strategies companies need to invest in is modern application development. Gartner calls such a feasible approach “Bimodal IT”. According to Gartner, “infrastructure & operations leaders must ensure that their internal data centers are able to connect into a broader hybrid topology“.[2]  Let us consider Healthcare – a reasonably staid vertical as an example. In a report released by EY, “Order from Chaos – Where big data and analytics are heading, and how life sciences can prepare for the transformational tidal wave,” [1] the services firm noted that an agile environment can help organizations create opportunities to turn data into innovative insights. Typical software development life cycles that require lengthy validations and quality control testing prior to deployment can stifle innovation. Agile software development, which is adaptive and is rooted in evolutionary development and continuous improvement, can be combined with DevOps, which focuses on the the integration between the developers and the teams who deploy and run IT operations. Together, these can help life sciences organizations amp up their application development and delivery cycles. EY notes in its report that life sciences organizations can significantly accelerate project delivery, for example, “from three projects in 12 months to 12 projects in three months.”

Finally, Big Data has evolved to enable the processing of data in a batch, interactive, low latency manner depending on the business requirements – which is a massive gain for Digital projects. Big Data and DevOps will both go hand in hand to deliver new predictive capabilities.

Further, business can create digital models of client personas and integrate these with predictive analytic tiers in such a way that an API (Application Programming Interface) approach is provided to integrate these with the overall information architecture.

Conclusion..

More and more organizations are adopting a Digital first business strategy.  The current approach as in vogue – to treat these as one-off, tactical project investments – does not simply work or scale anymore. There are various organizational models that one could employ from the standpoint of developing analytical maturity. These ranging from a shared service to a line of business led approach. An approach that I have seen work very well is to build a Digital Center of Excellence (COE) to create contextual capabilities, best practices and rollout strategies across the larger organization.

References –

[1] E&Y – “Order From Chaos” http://www.ey.com/Publication/vwLUAssets/EY-OrderFromChaos/$FILE/EY-OrderFromChaos.pdf

[2] Gartner – ” Five Reasons Why a Modern Data Center Strategy Is Needed for the Digital World” – http://www.gartner.com/newsroom/id/3029231

Why Software Defined Infrastructure & why now..(1/6)

The ongoing digital transformation in key verticals like financial services, manufacturing, healthcare and telco has incumbent enterprises fending off a host of new market entrants. Enterprise IT’s best answer is to increase the pace of innovation as a way of driving increased differentiation in business processes. Though data analytics & automation remain the lynchpin of this approach – software defined infrastructure (SDI) built on the notions of cloud computing has emerged as the main infrastructure differentiator & that for a host of reasons which we will discuss in this two part blog.

Software Defined Infrastructure (SDI) is essentially an idea that brings together  advances in a host of complementary areas spanning both infrastructure software, data as well as development environments. It supports a new way of building business applications. The core idea in SDI is that massively scalable applications (in support of diverse customer needs) describe their behavior characteristics (via configuration & APIs) to underlying datacenter infrastructure which simply obeys those commands in an automated fashion while abstracting away the underlying complexities.

SDI as an architectural pattern was originally made popular by the web scale giants – the so-called FANG companies of tech — Facebook , Amazon , Netflix and Alphabet (the erstwhile Google) but has begun making it’s way into the enterprise world gradually.

Common Business IT Challenges prior to SDI – 
  1. Cost of hardware infrastructure is typically growing at a high percentage every year as compared to  growth in the total  IT budget. Cost pressures are driving an overall re look at the different tiers across the IT landscape.
  2. Infrastructure is not completely under the control of the IT-Application development teams as yet.  Business realities that dictate rapid app development to meet changing business requirements
  3. Even for small, departmental level applications, still needed to deploy expensive proprietary stacks which are not only cost and deployment footprint prohibitive but also take weeks to spin up in terms of provisioning cycles.
  4. Big box proprietary solutions leading to a hard look at Open Source technologies which are lean and easy to use with lightweight deployment footprint.Apps need to dictate footprint; not vendor provided containers.
  5. Concerns with acquiring developers who are tooled on cutting edge development frameworks & methodologies. You have zero developer mindshare with Big Box technologies.

Key characteristics of an SDI

  1. Applications built on a SDI can detect business events in realtime and respond dynamically by allocating additional resources in three key areas – compute, storage & network – based on the type of workloads being run.
  2. Using an SDI, application developers can seamlessly deploy apps while accessing higher level programming abstractions that allow for the rapid creation of business services (web, application, messaging, SOA/ Microservices tiers), user interfaces and a whole host of application elements.
  3. From a management standpoint, business application workloads are dynamically and automatically assigned to the available infrastructure (spanning public & private cloud resources) on the basis of the application requirements, required SLA in a way that provides continuous optimization across the life cycle of technology.
  4. The SDI itself optimizes the entire application deployment by both externally provisioned APIs & internal interfaces between the five essential pieces – Application, Compute, Storage, Network & Management.

The SDI automates the technology lifecycle –

Consider the typical tasks needed to create and deploy enterprise applications. This list includes but is not limited to –

  • onboarding hardware infrastructure,
  • setting up complicated network connectivity to firewalls, routers, switches etc,
  • making the hardware stack available for consumption by applications,
  • figure out storage requirements and provision those
  • guarantee multi-tenancy
  • application development
  • deployment,
  • monitoring
  • updates, failover & rollbacks
  • patching
  • security
  • compliance checking etc.
The promise of SDI is to automate all of this from a business, technology, developer & IT administrator standpoint.
 SDI Reference Architecture – 
 The SDI encompasses SDC (Software Defined Compute) , SDS (Software Defined Storage), SDN (Software Defined Networking), Software Defined Applications and Cloud Management Platforms (CMP) into one logical construct as can be seen from the below picture.
FS_SDDC

                      Illustration: The different tiers of Software Defined Infrastructure

The core of the software defined approach are APIs.  APIs control the lifecycle of resources (request, approval, provisioning,orchestration & billing) as well as the applications deployed on them. The SDI implies commodity hardware (x86) & a cloud based approach to architecting the datacenter.

The ten fundamental technology tenets of the SDI –

1. Highly elastic – scale up or scale down the gamut of infrastructure (compute – VM/Baremetal/Containers, storage – SAN/NAS/DAS, network – switches/routers/Firewalls etc) in near real time

2. Highly Automated – Given the scale & multi-tenancy requirements, automation at all levels of the stack (development, deployment, monitoring and maintenance)

3. Low Cost – Oddly enough, the SDI operates at a lower CapEx and OpEx compared to the traditional datacenter due to reliance on open source technology & high degree of automation. Further workload consolidation only helps increase hardware utilization.

4. Standardization –  The SDI enforces standardization and homogenization of deployment runtimes, application stacks and development methodologies based on lines of business requirements. This solves a significant IT challenge that has hobbled innovation at large financial institutions.

5. Microservice based applications –  Applications developed for a SDI enabled infrastructure are developed as small, nimble processes that communicate via APIs and over infrastructure like messaging & service mediation components (e.g Apache Kafka & Camel). This offers huge operational and development advantages over legacy applications. While one does not expect Core Banking applications to move over to a microservice model anytime soon, customer facing applications that need responsive digital UIs will need definitely consider such approaches.

6. ‘Kind-of-Cloud’ Agnostic –  The SDI does not enforce the concept of private cloud, or rather it encompasses a range of deployment options – public, private and hybrid.

7. DevOps friendly –  The SDI enforces not just standardization and homogenization of deployment runtimes, application stacks and development methodologies but also enables a culture of continuous collaboration among developers, operations teams and business stakeholders i.e cross departmental innovation. The SDI is a natural container for workloads that are experimental in nature and can be updated/rolled-back/rolled forward incrementally based on changing business requirements. The SDI enables rapid deployment capabilities across the stack leading to faster time to market of business capabilities.

8. Data, Data & Data –  The heart of any successful technology implementation is Data. This includes customer data, transaction data, reference data, risk data, compliance data etc etc. The SDI provides a variety of tools that enable applications to process data in a batch, interactive, low latency manner depending on what the business requirements are.

9. Security –  The SDI shall provide robust perimeter defense as well as application level security with a strong focus on a Defense In Depth strategy.

10. Governance –  The SDI enforces strong governance requirements for capabilities ranging from ITSM requirements – workload orchestration, business policy enabled deployment, autosizing of workloads to change management, provisioning, billing, chargeback & application deployments.

The next & second blog in this series will cover the challenges in running massive scale applications.

Financial Services IT begins to converge towards Software Defined Datacenters..

Previous posts in this blog have commented on the financial services industry as increasingly undergoing a gradual makeover if not outright transformation – both from a business and IT perspective.  This is being witnessed across the spectrum that makes up this crucial vertical –  Retail & Consumer Banking, Stock Exchanges, Wealth Management/ Private Banking & Cards etc.

The regulatory deluge (Basel III, Dodd Frank, CAT Reporting, AML & KYC etc) and the increasing sophistication of cybersecurity threats have completely changed the landscape that IT finds itself in – compared to even five years ago.

Brett King writes in his inimitable style about the age of the hyper-connected consumer i.e younger segments of the population who expect to be able to bank from anywhere, be it from a mobile device or via the Internet from their personal computers instead of just walking into a physical branch.

Further multiple Fintechs (like WealthFront, Kabbage, Square, LendingClub, Mint.com, Cyptocurrency based startups etc)  are leading the way in pioneering a better customer experience.  For an established institution that has huge early mover advantage, the ability to compete with innovative players by using fresh technology approaches is critical to engage customers.

All of these imperatives place a lot of pressure on Enterprise FS IT to move from an antiquated command and control model to being able to deliver on demand services with the speed of an Amazon Web Services.

These new services are composed of Applications that encompass paradigms ranging from Smart Middleware, Big Data, Realtime Analytics, Data Science, DevOps and Mobility. The common business thread to deploying all of these applications is to be able to react quickly and expeditiously to customer expectations and requirements.

Enter the Software Defined Datacenter (SDDC). Various definitions exist for this term but I wager that it means – “a highly automated & self-healing datacenter infrastructure that can quickly deliver on demand services to millions of end users, internal developers without  imposing significant headcount requirements on the enterprise“.

Let’s parse this below.

The SDDC encompasses SDC (Software Defined Compute) , SDS (Software Defined Storage), SDN (Software Defined Networking), Software Defined Applications and Cloud Management Platforms (CMP) into one logical construct as can be seen from the below picture.

FS_SDDC

The core of the software defined approach are APIs.  APIs control the lifecycle of resources (request, approval, provisioning,orchestration & billing) as well as the applications deployed on them. The SDDC implies commodity hardware (x86) & a cloud based approach to architecting the datacenter.

The ten fundamental technology differentiators of the SDDC –

1. Highly elastic – scale up or scale down the gamut of infrastructure (compute – VM/Baremetal/Containers, storage – SAN/NAS/DAS, network – switches/routers/Firewalls etc) in near real time

2. Highly Automated – Given the scale & multi-tenancy requirements, automation at all levels of the stack (development, deployment, monitoring and maintenance)

3. Low Cost – Oddly enough, the SDDC operates at a lower CapEx and OpEx compared to the traditional datacenter due to reliance on open source technology & high degree of automation. Further workload consolidation only helps increase hardware utilization.

4. Standardization –  The SDDC enforces standardization and homogenization of deployment runtimes, application stacks and development methodologies based on lines of business requirements. This solves a significant IT challenge that has hobbled innovation at large financial institutions.

5. Microservice based applications –  Applications developed for a SDDC enabled infrastructure are developed as small, nimble processes that communicate via APIs and over infrastructure like service mediation components (e.g Apache Camel). This offers huge operational and development advantages over legacy applications. While one does not expect Core Banking applications to move over to a microservice model anytime soon, customer facing applications that need responsive digital UIs will need definitely consider such approaches.

6. ‘Kind-of-Cloud’ Agnostic –  The SDDC does not enforce the concept of private cloud, or rather it encompasses a range of deployment options – public, private and hybrid.

7. DevOps friendly –  The SDDC enforces not just standardization and homogenization of deployment runtimes, application stacks and development methodologies but also enables a culture of continuous collaboration among developers, operations teams and business stakeholders i.e cross departmental innovation. The SDDC is a natural container for workloads that are experimental in nature and can be updated/rolled-back/rolled forward incrementally based on changing business requirements. The SDDC enables rapid deployment capabilities across the stack leading to faster time to market of business capabilities.

8. Data, Data & Data –  The heart of any successful technology implementation is Data. This includes customer data, transaction data, reference data, risk data, compliance data etc etc. The SDDC provides a variety of tools that enable applications to process data in a batch, interactive, low latency manner depending on what the business requirements are.

9. Security –  The SDDC shall provide robust perimeter defense as well as application level security with a strong focus on a Defense In Depth strategy. Further data at rest and in motion shall be

10. Governance –  The SDDC enforces strong governance requirements for capabilities ranging from ITSM requirements – workload orchestration, business policy enabled deployment, autosizing of workloads to change management, provisioning, billing, chargeback & application deployments.

So how is doing SDDC at the moment? Most major banks have initiatives in place to gradually evolve their infrastructures to an SDI paradigm. Bank of America (for one) have been vocal about their approach in using two stacks, one Open Source & OpenStack based and the other a proprietary stack[1].

To sum up the core benefit of the SDDC approach, it brings a large enterprise closer to web scale architectures and practices.

The business dividends of the latter include –

1. Digital Transformation – Every large Bank is under growing pressure to transform lines of business or their entire enterprise into a digital operation. I define digital in this context as being able to – “adapt high levels of automation while enabling the business to support multiple channels by which products and services can be delivered to customers. ”

Further the culture of digital encourages constant innovation and agility resulting high levels of customer & employee satisfaction.”

2. Smart Data & Analytics –  Techniques that ensure that the right data is in the hands of the right employee at the right time so that contextual services can be offered in real time to customers. This has the effect of optimizing existing workflows while also enabling the creation of new business models.

3. Cost Savings – Oddly enough, the move to web-scale only reduces business and IT costs. You not only end up doing more with less employees due to higher levels of automation but also are able to constantly cut costs due to adopting technologies like Cloud Computing which enable one to cut CapEx and OpEx. Almost all webscale IT is dominated by open source technologies & APIs, which are much more cost effective than proprietaty platforms.

4. A Culture of Collaboration – The most vibrant enterprises that have implemented web-scale practices not only offer “IT/Business As A Service” but also have instituted strong cultures of symbiotic relationships between customers (both current & prospective), employees , partners and developers etc.

5. Building for the Future – The core idea behind implementing web-scale architecture and data management practices is “Be disruptive in your business or be disrupted by competition”. Web-scale practices enable the building of business platforms around which ecosystems can be created and then sustained based on increasing revenue.

To quote wikipedia, a widespread transition to the SDDC will take years:

Enterprise IT will have to become truly business focused, automatically placing application workloads where they can be best processed. We anticipate that it will take about a decade until the SDDC becomes a reality. However, each step of the journey will lead to efficiency gains and make the IT organization more and more service oriented.

The virtuous loop encouraged by constant customer data & feedback enables business applications (and platforms) to behave like agile & growing organisms –  SDDC based architectures offer them the agility to get there.

References

1.http://blogs.wsj.com/cio/2015/06/26/bank-of-america-adding-workloads-to-software-defined-infrastructure/