Next-generation data centers include software-defined environments, hybrid infrastructures, cognitive computing, and developing technology to meet the demands of data growth and management. They are software-defined, controlled by intelligent software rather than by IT workers who must manually run processes. They also provide physical and cloud environments, meeting the need for traditional data storage and developing cloud technologies. With data and infrastructure spread across different environments and geographical areas, one of the goals of next-generation data center technology is to unite these disparate environments and deliver data and networking and compute resources to meet demand. Virtual data center and software-defined data center are similar concepts.
Some of the biggest requirements for a next-generation data center are:
- Automation: the data centers must have processes that can run autonomously rather than requiring constant human management.
- Interconnection: data management must no longer be siloed, especially when companies now use both physical and cloud environments.
- Flexibility: the architecture of a data center must shift to manage applications, workloads, and machines and run processes most efficiently.
- Focus on business: data centers must provide real-time solutions and quick response to organizations that use them.
- Support: next-generation data centers must develop strong infrastructure that manages and secures data and provides a hybrid environment (physical, virtual, and cloud).
- Loose coupling: if the elements of a data center are too interdependent or closely connected, they can be susceptible to large-scale failure should one application or machine be compromised. Loose coupling allows for more flexibility and scalability.
Some of these characteristics seem to contradict each other. Not only must data centers keep up with the increasing demand for bigger and better data storage, but they must also manage data successfully – a challenging task as data is spread even further across geographic boundaries and into different cloud environments. Data center managers face the challenge of both consolidating data for easier management and security and scaling it across various environments: the dichotomy of organization and flexibility.
Intelligence in data centers
The only solid solution for autonomy, flexibility, and consolidation in data centers seems to be machine learning and intelligence. Human oversight simply can’t keep up with the increasing demand for data management and hybrid infrastructure. Intelligent workload management is a start: it deploys applications and workloads across environments that have available resources, whether in the cloud or on a physical server.
Cognitive computing, a popular term used in next-gen data center research, allows intelligent processes to make decisions, address and solve problems, and analyze and filter data. This form of artificial intelligence can learn from its previous work and develop accordingly. As data continues to increase and scatter across various locations and clouds, next-generation data centers will be required to use intelligence and cognitive computing to manage demands for security, flexibility, and consolidation.