The concept of mission-critical, while once defined through critical applications in industrial, healthcare or power and energy environments, has expanded to include a broader set of applications in IoT, including smart cities and connected buildings. However, mission-critical IoT solutions are often the most challenging to implement as they require ultra-high reliability, always-on availability and extremely low latency. As the IoT becomes more pervasive, it will fundamentally change how we interact with information, with a direct impact on mission-critical devices, solutions and the infrastructure that supports it. Mission-critical IoT is more than devices and applications optimized for performance, reliability and security, it is an entire mission-critical ecosystem designed from the ground up to support the demands of this new reality.
Enabling that future requires the right technologies that can address two of the biggest challenges facing IT today.
These questions, while broad, offer a summary of the IT trends that have been developing over the last decade. But only now, with the advent of the IoT age and the building of smart “things” – whether in healthcare, industrial, connected homes and buildings, cars, utilities, public safety, and even cities – are we able to provide proper context to these two big questions. But, by the same token, there is an urgent risk (more than ever before) in not getting it right from the outset. The choices in IT strategy you make today will have a serious and long-lasting impact on your organization in the long run, and the stakes are even higher for organizations that provide large-scale, mission-critical services.
Take video surveillance, for example. Video surveillance is currently the biggest IoT and big data use casein the world, responsible for over 50 percent of all the data currently generated. Additionally, analysts predict that by 2025, over 40 percent of all the data in the world will be image-related (video, imaging, LIDAR, etc.) and the overwhelming business case will be to process that data in real time, close to where it is generated at the edge. In this new reality, in under ten years the global datasphere will reach 163 zettabytes per year and 30 percent of this new data will be critical, or even hypercritical, to our daily lives.
There a few things we can glean from these numbers:
Whether it’s an intelligent city-wide alert system for seismic shifts in earthquake-prone regions or an advanced infrastructure that supports developments in smart cars and traffic lights that communicate to easy traffic congestion, cut emissions and increase public safety, you need fast processing at the edge, low latency, security and the ability to scale out to meet the massive demand that IoT devices will continue to generate. IoT environments like Smart Cities are comprised of multiple mixed workloads with varying levels of criticality. These environments require an intelligent and automated platform, armed with policy-based management and application-aware capabilities that can prioritize resources to the most important applications so that service is never disrupted, with the performance and reliability to ensure data is never lost and environments are safe, secure and operationally efficient.
As these environments and systems scale – and they must if they are going to support the demands of the anticipated 80 billion connected IoT devices by 2025 – then they cannot rely on humans to operate and manage them. IT needs to spend its time supporting lines of business and developing the next innovative tech that will keep us out ahead of the sheer magnitude and scale of our smarter, more connected world. Datacenter infrastructure platforms with artificial intelligence and machine learning capabilities will be absolutely essential in automating this kind of complexity at scale.