Data Model Infrastructure as a Service for Resource Efficiency
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Solution Overview
Problem
Data model infrastructures that run as always-on servers waste memory and processing power for infrequently used applications, and face challenges in quick launch times and low overhead when implemented as a service.
Innovation Solution
Implementing data model infrastructure as a service using a system programming language like Rust, where the model is compiled prior to launch, and utilizing a function-as-a-service platform to launch and close applications only when needed, reducing memory and processing overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data model infrastructure is implemented as an always-on server, then service availability is improved, but memory and processing power consumption increase
Solution Approach 1:
The system dynamically transitions between static (compiled model stored) and active (service running) states based on demand. The compiled CDS model is persisted and can be rapidly instantiated when needed, rather than maintaining a continuously running server process. This dynamic approach allows the system to maintain service availability while reducing resource consumption during low-demand periods.
Solution Approach 2:
The CDS model is compiled in advance into a persistent format (e.g., JavaScript module or bytecode) before deployment. This preliminary compilation action eliminates the need for runtime compilation and enables rapid service instantiation. The pre-compiled model can be stored and reused, allowing quick startup when service is needed without wasting resources on continuous compilation processes.
2Use of energy by moving object
If data model infrastructure is implemented as a service that launches on demand, then resource efficiency is improved, but launch time increases
Solution Approach 1:
The CDS model is pre-compiled into an optimized executable format (such as a JavaScript module, bytecode, or native binary) before deployment. This preliminary compilation stores the processed model in a ready-to-execute state, eliminating runtime compilation overhead. When the service is triggered, it can immediately instantiate from the pre-compiled artifact, achieving both resource efficiency and fast launch times.
Solution Approach 2:
The system changes the state parameter of the model from source code to compiled binary form. By transforming the CDS model through compilation into a different representation (e.g., JavaScript module, WebAssembly, or native code), the system enables faster loading and execution. This parameter change in the model's physical state (from text to binary) directly reduces launch time while maintaining resource efficiency.
3Productivity
If system programming language like Rust is used, then execution speed and memory efficiency are improved, but development complexity increases
Solution Approach 1:
The system uses a compiled binary format (from Rust or other systems languages) that can be rapidly instantiated and discarded. The service runs in a containerized or sandboxed environment where the compiled executable is loaded, executed for the required operation, and then terminated. This approach leverages the memory efficiency and speed of systems languages while avoiding long-term resource consumption, making the development complexity worthwhile through superior runtime performance.
Data Source
AI summary
In an example embodiment, a data model infrastructure is implemented as a service rather than as an always-running server. Specifically, one of the technical issues with past implementations is that the data models are deployed onto a server that is intended to be “always running”, even if there are no requests to the server. This utilizes memory and processing power. While it may be useful to have an always running server for commonly used applications, for applications that are infrequently used (e.g., 10 times a month), it can mean that memory and processing power is wasted. Thus, by implementing the data model infrastructure as a service rather than an always-running server, the service can be launched only when actually needed, saving both memory and processing power.