Introducing the Technologies Set to Redefine Cloud Computing

For many years, enterprise cloud computing has been a careful balance — and sometimes an epic battle — between what is possible and what is practical on the ground. This dichotomy has led to a lot of confusion, which in turn can hold back development. Here is introducing the technologies set to redefine Cloud computing.

Towards the end of the last decade, there was a sharpening of focus from both business owners and cloud service providers alike. Both parties understood that they needed each other, but neither side could determine exactly how that relationship should look.

In 2020, clarity has emerged related to what cloud computing is for the coming decade.

Understanding these patterns will be the key to success for service and product vendors when planning their business strategies.

A New Generation of Containers

Business owners are now beginning to understand the cloud cost reduction and performance benefits of containerization.

Just two years ago, barely half of the owners interviewed in Portworx and Aqua Security’s annual Container Adoption Study were looking at adopting containerized computing. As of 2019 — 87% of respondents in the study are now planning to use containers.

One thing that has been holding back enterprise adoption is the fear around security breaches and the complexity of isolating containers using Linux control groups, mandatory access controls, and the like.

To win the trust of businesses, a new breed of containers has been gaining traction over the last few years. If you haven’t already, you are going to start hearing a lot more about Kata Containers over the next few months and years.

Managed by the OpenStack Foundation, the Kata Containers project began back in 2017. The project’s overarching aim is to blend the benefits of containerization with that of virtualization – particularly workload isolation.

The Kata Containers project brings together the high performance of Intel’s Clear Containers with the adaptability of Hyper’s runV platform (RunV is a platform-agnostic cloud-based runtime based on super lightweight VMs).

With input from Google and Microsoft (among others), Kata Containers is clearly positioning itself as the containerization technology that will drive widespread enterprise adoption.

Kata Containers is designed to be compatible with all major network architectures and hypervisors, enabling workloads to run seamlessly in multi-cloud environments.

Improving Microservices at Scale

Twitter and Netflix have already proven that microservice architectures work well at scale, but there remains a lot of complexity behind the scenes. Communication between modular components can be problematic, leading to a lack of visibility and an ongoing challenge to maintain security and QoS.

To solve for this challenge, IBM, Google, and Lyft put their collective heads together to create a solution. The end result became Istio.

Istio is described as an open-source ‘service mesh’ designed to provide a common environment for connecting, securing, monitoring, and scaling distributed microservices. A key benefit to Istio is that it works across both hybrid and multi-cloud environments with no change in application code.

In terms of security, Istio creates a separate, secure communications channel between microservices and end-users (and between the microservices themselves). In terms of performance monitoring and troubleshooting, Istio provides an intuitive dashboard and system-wide view of the entire distributed environment.

The distributed environment enables operators to see not only how individual microservices are performing, but also how they are affecting one another. Problem areas can, therefore, be pinpointed and remediated very quickly.

Istio is likely to be welcomed with open arms by both developers and operators working with microservice architectures. By simplifying security and troubleshooting while also removing roadblocks to scaling, developers will be free to create new applications at their leisure. As a result, the microservice business model will become more attractive than ever.

The Race to Own the Hybrid Cloud Space

The developments detailed above are geared towards a hybrid and multi-cloud future. Any dreams that the major public cloud providers may have had of a public cloud-based ‘as-a-service’ monopoly have all but evaporated. Most important will be safety and security.

A recent Red Hat survey all but confirmed this new reality, revealing that only 4% of businesses see cloud-native as the best path forward. In contrast, 31% of respondents favored hybrid cloud deployments.

Predictably, the likes of Amazon, Microsoft, and Google have reacted by rolling out managed hybrid cloud services. These are likely to gain traction as they continue to blur the boundaries between on-premise and cloud computing.

Microsoft has a clear head start in this area due to its well-developed Azure Stack, and this is one reason why Azure has grown so quickly despite the dominance of AWS’s market share of public cloud services. Azure Stack works with a variety of partner vendors such as Dell EMC, Lenovo, and Cisco, but it uses the same pricing model as its public cloud.

Amazon’s most recent response came via a partnership with private cloud specialists Vmware to launch AWS Outposts. Outposts are marketed as a hybrid cloud solution for businesses needing low latency performance at the cloud’s edge. They include on-premise, single-vendor hardware deployments that are installed, configured, and managed by Amazon technicians. These are then connected, ideally via AWS Direct Connect, to a parent AWS Region.

Google’s approach, in its patented style, is slightly different. But as they claim, their innovative solution is one that truly solves the multi-cloud challenge.

Solving for the Multi-Cloud Challenge

While Microsoft and Amazon are clearly keen on expanding both their cloud environments and their service offerings to meet their clients’ needs, Google is placing itself as the company that will truly free businesses up to operate across any combination of private and public clouds. And, as usual, Google has an ace up its sleeve: Kubernetes.

Google’s hybrid and multi-cloud solution, Anthos, predictably runs on GKE, but it also includes an on-premise platform (GKE On-Prem), which runs on vSphere. Also included are Istio’s service mesh technology (described above), a configuration management platform to handle Kubernetes policies, and Stackdriver for monitoring.

With AWS and Azure both supporting Kubernetes, this gives Anthos users the ability to work with either or both public clouds in tandem with their own private clouds – i.e. a true, honest-to-goodness hybrid cloud.

Of course, Google also offers a cloud direct connect (Cloud Interconnect) to ensure high-speed, secure connectivity between on-premises networks and GCP.

But it doesn’t stop there. Google has also released Anthos Migrate, a free P2K (physical-to-Kubernetes) migration tool built from Velostrata technology. Anthos Migrate is designed to allow GCP users to easily modernize existing applications or, perhaps more interestingly, to migrate VMs over from other cloud services.

The Ultimate Machine Learning Hotbed

Cloud computing not only allows businesses to provide cheaper, faster, and more scalable services — but it also changes the nature and scope of what businesses can actually achieve. As cloud technologies become more widespread and ever-easier to use, the workloads predictably become more ambitious.

Speaking of ambition, many organizations have put artificial intelligence (both creating it and benefiting from it) at the top of their wish lists.

From diagnosing illnesses and identifying Earth-like planets to autonomous cars and language translation, the potential of machine learning outperforming humans on specific tasks will continue to develop and grow over the coming years.

That said, if you ask Google AI lead, Jeff Dean, the current method of starting from scratch on every project needs to change (June 07, 2020). Jeff envisions replacing the current atomic, unit-based models of ML with one multi-functional model. This model would be inactive most of the time but would build upon previous relevant learning whenever called upon to carry out a new task.

As Dean explained in a recent Keynote, this would more closely resemble adult human learning rather than the models of today, which he compares to the lengthy, inefficient process of infant learning.

There are sure to be a plethora of challenges on the road ahead, but as the cloud continues to expand in order to attract more businesses, the number of developers rising to meet those challenges will grow in kind.

Still, no one will really know what that future will look like until it’s actually here. Just be prepared to be awed, excited, and maybe even a little terrified by the sheer scope and scale of what can (and will) be achieved in the 2020s.

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