Containerisation, with its inherent portability, efficiency, scalability and consistency, has changed modern applications and IT infrastructure for the better. According to Red Hat’s State of Enterprise Open Source 2022 report, two-thirds of IT leaders are already running containers in their organisations. Furthermore, nearly one-third said they planned to increase their container usage in the 12 months after they were approached for the survey. These statistics reveal that containerisation is becoming the norm, and the momentum shows no signs of slowing any time soon.
So, what can we expect from containerisation in the coming years? In this article, we will explore the potential impact of containerisation on software development and deployment in the near future.
Containerisation: What is Driving Change?
It’s undeniable that containerisation has taken the IT world by storm. But no technology stands still for long. So, what will drive containerisation changes in the coming years? Here are our predictions:
Hybrid and multi-cloud deployments
Cloud services are expanding. Hybrid and multi-cloud deployments, which span on-premises, private cloud, and multiple public cloud environments, are driving change in the containerisation space. Organisations increasingly adopt multi-cloud strategies to reduce costs and avoid vendor lock-in to allow them to stay competitive. Containers have proven essential, ensuring applications remain consistent.
Containers allow for consistent deployment of applications regardless of the cloud provider. Consistency combined with the quick deployment of computing resources within cloud services allows organisations to quickly spin up applications across various cloud environments. Furthermore, it removes much of the complexities of migrating applications between different environments whilst ensuring the application remains consistent.
As cloud providers continue to expand their service offerings, containers have become closely integrated with the native cloud services. Cloud providers have recognised the importance of containerisation and are continually reducing the gap required, making the handling deployment and management of applications easier.
Edge computing
Edge computing is gaining prominence in the IT world, and containers—with their lightweight, modular nature—are well-suited to edge environments. The decentralised nature of edge computing, where data processing occurs closer to the data source, allows businesses to deliver better-performing applications to end-users and devices. This proximity means that edge containers offer several advantages over alternative solutions, including low latency, global load balancing, scalability and reduced bandwidth.
As the Internet of Things (IoT) expands, the need for real-time processing will become a priority. Edge containers will be instrumental in managing large quantities of data generated by IoT devices, ensuring rapid responses and actions. Additionally, by processing data locally, edge containers improve security by reducing the amount of data transmitted and processed in the cloud, keeping sensitive data on user devices. For IT teams, this results in more streamlined infrastructure and optimised resource allocation.
Machine learning and AI
The convergence of machine learning, AI and containerisation will be transformative. Machine learning models are resource-heavy—they require massive processing power to predict, validate and then recalibrate. Currently, GPUs are being used to handle the computing. However, containerisation will likely play a significant role in developing machine learning models.
Machine learning benefits from containerisation in the same ways as applications or microservices do—less server downtime, easier collaboration and consistent environments. Containerising machine learning models simplifies the lifecycle and enhances scalability, especially with tools like Kubernetes. Finally, they bolster container security by isolating the machine learning environment and safeguarding highly sensitive data.
By leveraging containers, developers can seamlessly integrate powerful machine learning models into applications, ensuring consistency across different environments. The development of machine learning models, dubbed ‘MLOps’, is heavily reliant on containerisation to keep environments consistent and ensure scalability and portability. For this reason, containerisation is only going to get more popular over time.
Final Thoughts: The Future of Containerisation Technologies
No matter how containerisation changes, one thing is certain: we will see more of it. The need for agile, scalable and consistent IT solutions isn't going anywhere, and containerisation allows organisations to meet these requirements. The convergence of containerisation with mature technologies, such as the cloud, and emerging technologies, including edge computing and machine learning, will benefit all sectors. As businesses continue to navigate the complexities of IT infrastructure, container platforms will reign supreme, providing a framework that promotes efficiency, agility and security.