feat: update ideas list

Author: stdlib-bot

ideas.md

@@ -1872,71 +1872,4 @@ Knowledge of Node.js / JavaScript, experience with GitHub Actions and CI/CD, und
 
 ### Project length
 
-350
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-* * *
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-## Smart Adaptive Parallel Execution: Dynamic Concurrency Management for Efficient Async Processing
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-Linked issue: <https://github.com/stdlib-js/google-summer-of-code/issues/104>
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-### Idea
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-Managing parallel execution efficiently in large-scale applications is a challenge. Traditional async utilities like async.parallel and Promise.all execute a fixed number of tasks in parallel, which can lead to system overload or underutilization of available resources.
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-This project aims to introduce Smart Adaptive Parallel Execution, an asynchronous utility for stdlib that dynamically adjusts concurrency levels based on real-time CPU and memory usage. The goal is to ensure optimal performance by scaling the number of concurrent tasks up or down depending on system load.
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-This adaptive approach is particularly useful for large-scale data processing, high-performance computing, and server-side applications, where maintaining an optimal balance between performance and resource consumption is critical.
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-The proposed utility will monitor system resource usage (e.g., via os.loadavg() and os.freemem()) and adjust the execution of async tasks accordingly. Users will be able to define max/min concurrency limits, ensuring tasks run efficiently without overwhelming the system.
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-### Expected outcomes
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-✅ A new smartParallel API in stdlib that dynamically scales parallel execution.
-✅ Automatic load balancing based on system metrics like CPU and memory usage.
-✅ Customizable concurrency limits to prevent resource exhaustion.
-✅ Efficient task queueing to avoid blocking critical system resources.
-✅ Performance benchmarks and testing to measure efficiency improvements.
-✅ Comprehensive documentation and examples for easy adoption.
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-
-
-### Status
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-Currently, stdlib lacks an adaptive async execution model. Existing async utilities (e.g., async.parallel) run a fixed number of tasks, leading to potential overload or wasted resources. No mechanism exists to automatically scale execution based on system performance.
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-### Involved software
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-1. Node.js built-in OS module (for resource monitoring)
-2. Async utility libraries (for managing task execution)
-3. Benchmarking tools (to evaluate performance improvements)
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-### Technology
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-JavaScript, nodejs
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-### Other technology
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-1. Integration with Web Workers for more efficient async execution.
-2. Using worker threads in Node.js to further enhance performance.
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-### Difficulty
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-4
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-### Difficulty justification
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-1. Requires implementing a dynamic concurrency adjustment model.
-2. Needs efficient resource monitoring with minimal overhead.
-3. Must ensure correctness and robustness in various system conditions.
-4. Requires performance benchmarking to demonstrate effectiveness.
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-### Prerequisite knowledge
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-1. Strong understanding of Node.js / JavaScript.
-2. Experience with asynchronous programming & concurrency management.
-3. Familiarity with system resource monitoring in Node.js.
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-### Project length
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 350