In the realm of ‘Modeling Infrastructure’, enhancing software reuse and interoperability within complex systems is essential. Consequently, this infrastructure includes software libraries that various organizations share. Initially, significant strides were made in climate and weather-related projects, where software components for different physical domains like the ocean or atmosphere were developed by experts from multiple companies.
Developers must link these components to construct complete applications, such as using a generic circulation model to transport data across various components.The fact that these models often require supercomputers to execute, account for the acquired data, and perform data analytics presents an additional difficulty. Due to the variety of workloads and communication routes, it was crucial to create an effective massively parallel computer system as well as the processing hardware and software.
After adapting to existing IT infrastructure for years, many companies are reassessing major purchases. This trend reflects the evolving ‘Modeling Infrastructure’. This shift has brought about a stark contrast in the purchasing approach compared to the past. George Lewandowski, capacity planning supervisor at Metavante Corp, reminisces about the ’90s: ‘It was almost like having a free checkbook to just go out and buy what you need.’ Presently, organizations are focusing on strategic investments, prioritizing ROI (Return on Investment) and TCO (Total Cost of Ownership) over mere resource utilization rates”.
Key Aspects and Benefits of Modeling Infrastructure:
- Enhanced Interoperability: Facilitates smoother interaction between different software components, leading to more efficient system integration.
- Software Reusability: Encourages the use of existing software libraries, reducing development time and costs.
- Scalability: Supports building systems that can easily adapt to increasing workloads or expanding operational needs.
- Advanced Data Analytics: Enables the handling and analysis of large datasets, essential in domains like climate and weather modeling.
- Cost-Effective IT Investments: Promotes strategic spending with an emphasis on long-term ROI and reduced TCO.
- Future-Proofing: Prepares organizations for upcoming technological advancements and changing industry demands.