Three-part series (linked as they go live):
A September 2018 Gartner report found that cloud computing would expand to $206.2 billion in 2019 at a 17.3 percent compound annual growth rate (CAGR). The bad news is that is a slight downturn in projected growth rate, with Gartner having forecast a 21% growth for 2018. Expansion is still rapid. Given that incredible general growth rate of cloud, the technology is a trend in and of itself. It is becoming so ubiquitous, it is increasingly worthwhile to consider how the field is changing and what that might mean it terms of opportunities for business and organizations.
Top trends in cloud computing for 2019 include the following:
One IT method that is becoming more prevalent is to sign up for a public cloud with a platform on it, with a fee paid to the cloud host for the platform – a tactic called serverless computing. This service, available through some hosting providers, allows you to use platform as a service (PaaS) via a container through a cloud host, which charges for the platform access. The host handles setup of the physical machines and configuration of the servers.
Serverless computing is attractive to organizations for the same reasons that cloud itself is – the ability to pay on-demand for services rather than having to make capital investments in costly machines and environments. Servers must be purchased, stored, and configured; all that can of course be avoided with serverless computing.
For multiclouds, the network management backplane that will be used will be service meshes such as LinkerD, Envoy, and Istio. The service meshes will allow companies to integrate their private and public cloud environments with on-premise containerized data. Hub-and-spoke and mesh systems will be increasingly used by cloud service providers to allow for easy integration and management of thousands of on-premise networks and virtual private clouds.
Artificially intelligent (AI) platforms are built to operate more smartly, and hence more optimally, than traditional systems. AI functionality is used within big data systems to develop stronger knowledge of how a business functions by enhancing ability to collect strong business data.
You can get work completed faster with AI installed, since it will ensure work is distributed evenly. When data governance standards are integrated, machine learning and AI engineers can be better managed to follow best practices through the platform.
An AI environment can also cut your expenses by helping you to automate some labor-expensive and/or simple tasks (e.g., data extraction and copying), as well as to avoid error duplication. Staff members and data scientists can work together to improve your efficiency and speed if your AI platform is well-designed.
Additional multicloud and hybrid cloud tools will become commercially available. In order to mitigate risk, control costs, and perform migrations quickly, organizations will increasingly want multicloud backplanes, migration tools, and professional services from their cloud providers – accelerating their development. As these functionalities becomes more widely available, transitioning to cloud-native backbones via lift-and-shift, whether for data, workloads, or applications, will grow, noted James Kobielus. More companies will be putting legacy workloads into containers, avoiding the need to rewrite the code. Doing that means that sophisticated migrations can occur without having to assume as much technical risk. Migration to IaaS and PaaS platforms from legacy, on-premise infrastructures will occur as it becomes increasingly affordable to do so.
Cloud-native development does not take care of the already developed on-premise apps, and lift-and-shift is not the only option for those existing systems. Refactoring will become a more broadly used practice too. Prior to designing their infrastructure for multicloud, organizations will often think about how to move workloads and refactor. In order to benefit from native cloud services, organizations will reprogram or refactor instead of using lift-and-shift as much in 2019, according to the analysis of Cloud Technology Partners technology evangelist Ed Featherston.
Shortage of cloud skills
The cloud carries with it the need for highly specialized skills that are costly, very much needed, and not easy to find. Since that’s the case, the transition to cloud could make the issue with staffing shortages that has been with IT for some time even worse.
According to a report featured in ITProToday, the cloud skills gap is so critical and so substantial, it costs the average large enterprise a quarter of a billion dollars ($258 million) annually. That amounts to 5% of their annual global revenue, on average.
Cloud is easy to adopt, but it can lead to waste. A recent report by RightScale found that 30% of cloud investment is wasted by the average cloud-using organization. People might spin up a cloud service and keep it running even if they do not use it. Cost optimization within cloud will continue to become a key point of focus, so that the wasted spending of orphaned resources can be avoided.
Cloud data lakes, databases, and warehouses
The greatest challenge for business intelligence and data warehousing has been answered by cloud data stores. Self-service platforms have typically been unreliable, noted an article in AI Business, while clunky schema configurations and slow relational methods in traditional architectures have damaged business access. The Internet of Things, artificial intelligence, and other technologies can benefit from the scalability of cloud data stores – as well as the fact that there is direct access to analytics tools.
Internet of Everything
Often we talk about the Internet of Things (IoT) in terms of the new world in which virtually everything around us becomes an endpoint of the Web. However, that discussion is often referring to a broader concept, the Internet of Everything (IoE), which goes beyond connected things to also include data, process, and people – as indicated by Angela Karl. “IoE works to provide an end-to-end ecosystem of connectivity that consists of ‘technologies, processes, and concepts employed across all connectivity use-cases,’” wrote Karl, quoting Cisco.
The IoE utilizes data, processes, and machine-to-machine communication in order to learn about how people interact with their environments. A good example of its use is hospitality robots in Japan. The intelligent robots can blink, breathe, make hand gestures, and otherwise behave as humans do. They can speak in Japanese as well as fluent Chinese, Korean, and English. They say hi to guests, interact in real-time, and provide simple services.
Hybrid clouds combine the two models of cloud, public and private. Dataversity forecast that the benefits of hybrid cloud would eventually make it the chief model for cloud.
The obvious upside of hybrid cloud is that it increases your flexibility. The downside is how this model increases complexity. Per the NIST definition of hybrid cloud, it is the combination of various IaaS types. That will often mean blending public cloud and private cloud infrastructures. It can also mean combining public cloud, community cloud, and/or private cloud.
Your cloud partner for 2019
Are you creating a cloud environment so that your organization can benefit from this technology as effectively as possible? Cloud is not just about what you do yourself but about having the right partners you can trust to deliver secure and reliable services. Like the Internet of Everything, that means not forgetting people. At Total Server Solutions, we maintain an around-the-clock staff of experts. Our people make all the difference.