The Year of Automation and Intelligence for Hyperconverged Systems

In 2018, the new IT paradigm will be based on agility, delivery and intelligence, made possible by human-machine orchestration with IT deeply embedded in the business.

Bruce Milne is Vice President and CMO at Pivot3.

Another year ends, another round of predictions begins. This year, it’s all about looking forward to the role of intelligence in the enterprise’s adoption and execution of cloud strategies. Looking back to the widespread adoption of virtualization and blade consolidation of the mid-2000s – which reduced and simplified IT environments and paved the way for converged infrastructure – it’s clear we’ve come a long way.

Hyperconverged infrastructure (HCI) hardware-based appliances came next, and have since evolved into more software-defined infrastructure platforms. This has transformed HCI into becoming the platform for supporting hybrid cloud mobility and autonomous workload acceleration.

In the next decade, we can expect to see more stateless, programmable infrastructure models. By 2020, new tech and deployment models will continue to reduce ownership costs for the software-defined datacenter through on premises and multi-cloud offerings, as well as artificial intelligence (AI) and deep machine learning (ML). Software-defined intelligence will gain equal importance to software-defined infrastructure, and perhaps eventually eclipse it as we continue to move toward intelligent edge computing models.

Hyperconverged Confidence Sparks Demand for Smarter Infrastructure Capabilities

We’re seeing a significant growth in the size of hyperconverged deployments as customers continue to gain confidence in HCI’s ability to support broader workload consolidation. While we’re currently familiar with HCI solutions deployed with 10 to 15 nodes, in 2018, we’ll see exponential growth, with deployments averaging 100s of nodes and a tighter focus on tier 1 applications.

As deployment sizes grow, performance must follow. When evaluating HCI platforms, customers will increasingly rely on performance benchmarks – with a heavy emphasis on response time and latency reduction – to ensure it can meet the changing needs of their business- and mission-critical application workloads in real time.

In addition to performance and automated workload management, intelligence capabilities will be among key criteria in customer evaluations. Ultimately, a smarter infrastructure leads to autonomous cloud computing – a programmable, intelligent system that can automate and accelerate workloads between on-premise datacenters to private and public clouds according to rapidly-changing business needs. 

Embedded IT and an Agility-First Strategy

As we approach a more cognitive infrastructure, systems will be able to learn from IT teams, automatically making recommendations, and supporting faster data access and decision making. These developments will go beyond merely aligning IT with business objectives, they will embed it, which will impact how IT leaders utilize their teams. For example, rather than having an individual continuously configure the datacenter, CIOs will be able to reassign that same individual as an IT consultant in business units.

As we approach a more programmable infrastructure – such as applying software development tools and best practices like versioning, APIs, immutability and automation to the management of IT infrastructure – new levels of agility and responsiveness could disrupt how IT works within the enterprise, potentially reducing head count in traditional IT towers.

Digital transformation is on everyone’s mind, and while DX initiatives can look different for each organization, CIOs and IT leaders must all address how these technology advances will shape and nurture an agility-first strategy.

AI and ML-based automation will help support increased customer expectations for performance and reliability in the data center, and the merging of the physical and the digital will continue to synthesize the labor of human and machine. Businesses that do not effectively employ agility-first and embedded IT approaches face the risk of their data centers not being operationally and economically viable by the end of the data center.

HCI as Composer of Clouds

It's no surprise that enterprises will continue to transition to the cloud in 2018. Forrester reports that 67 percent of global enterprise infrastructure decision makers identify the development of a comprehensive cloud strategy as high or critical priority. And according to Gartner, 80 percent of organizations using DevOps will deploy all new services in the public cloud by 2021.

Traditional infrastructures are not capable of adapting to the rapid pace of change in cloud computing – for legacy systems, it has evolved beyond silos or die. HCI must evolve for the cloud era as well – a good example being our advanced policy-based management and priority-aware capabilities in the most recent version of our HCI software platform that supports multiple application workloads and hybrid cloud mobility.

As more granular intelligence and automation capabilities are developed, end users will be able to choose what they push to the public cloud and what they push to traditional infrastructure or private cloud environments, whether low-priority workloads or power-hungry applications. In the short term, HCI solutions that can easily scale up or down, orchestrate multiple cloud environments, deliver breakthrough performance and integrate with existing IT investments will take on the role of the on-premise private cloud and remain essential to datacenter modernization and digital transformation initiatives.

The Race Toward Human-Machine Orchestration

Throughout the last decade, HCI has been in a race to reduce complexity. But as complexity continues to increase exponentially, this has shifted into a race toward intelligence and automation. Intelligence-based, self-organizing systems are becoming necessary to handle the demands of cloud, edge-computing and IoT complexity. HCI must evolve to become a system of intelligence that combines policy, inference, and orchestration on a cognitive infrastructure to accelerate workload mobility at scale, deliver lower CapEx and OpEx, advance performance optimization, and increase management and control without requiring specialized skills.

In 2018, the new IT paradigm will be based on agility, delivery and intelligence, made possible by human-machine orchestration with IT deeply embedded in the business.

Opinions expressed in the article above do not necessarily reflect the opinions of Data Center Knowledge and Informa.

Industry Perspectives is a content channel at Data Center Knowledge highlighting thought leadership in the data center arena. See our guidelines and submission process for information on participating. View previously published Industry Perspectives in our Knowledge Library.

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