[RFC]: Smart Adaptive Parallel Execution: Dynamic Concurrency Management for Efficient Async Processing

[NA-VNEET]

Idea

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.

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.

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.

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.

Expected outcomes

✅ 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.

Status

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.

Involved software

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)

Technology

JavaScript, nodejs

Other technology

1. Integration with Web Workers for more efficient async execution.
2. Using worker threads in Node.js to further enhance performance.

Difficulty

4

Difficulty justification

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.

Prerequisite knowledge

1. Strong understanding of Node.js / JavaScript.
2. Experience with asynchronous programming & concurrency management.
3. Familiarity with system resource monitoring in Node.js.

Project length

350

Checklist

• I have read and understood the Code of Conduct.
• I have read and understood the application materials found in this repository.
• The issue name begins with [Idea]: and succinctly describes your idea.
• I understand that, in order to apply to be a GSoC contributor, I must submit my final application to https://summerofcode.withgoogle.com/ before the submission deadline.