Skip to main content

Real-Time System Monitor — C++ (Concept)

A planned native C++ telemetry engine designed to sample CPU, RAM, GPU, disk, temperature, and network metrics and stream them efficiently to mobile.

Visit website
  • Systems Design
  • C++ Architecture
  • Low-Level Performance
  • Telemetry Framework Design

The Problem

Most system monitoring tools are built for desktops, heavy dashboards, or cloud servers. Mobile devices never receive truly real-time hardware telemetry without massive overhead, battery drain, or high latency. There is no lightweight, native, low-level engine that streams compact hardware metrics efficiently to mobile.

Vision

The idea is simple:

A tiny, native C++ engine that samples hardware metrics with near-zero overhead and streams them as binary frames to mobile in real time.

This project is not about UI first — it’s about learning how to design a telemetry engine, understand OS-level APIs, and reason about performance at the system boundary.

What This Project Represents

This is not a shipped product — it is a systems engineering exploration. The goal is to understand:

  • How to read CPU, RAM, GPU, and network counters natively.
  • How to pack metrics into minimal binary formats.
  • How to build a low-latency WebSocket relay for mobile.
  • How to reason about performance, concurrency, and sampling intervals.
The learning outcome is more important than the final UI.

High-Level Architecture

The engine is designed around a simple pipeline:

Sensors → Native Poller → Binary Encoder → Async Relay → Mobile Viewer

Even as a concept, the design focuses on:

  • Native performance: C++20, zero-copy patterns.
  • Async sampling: epoll/libuv style loops.
  • Binary streaming: compact frames instead of JSON.
  • Cross-platform: Windows, Linux, macOS APIs.

Philosophy

The project explores what it means to build software close to the hardware — where memory layout, CPU cycles, and syscalls matter. It’s a foundational step toward deeper systems-level engineering.

Planned Technical Approach

Instead of embedding unfinished code, this section outlines what the engine will eventually include:

  • Polling modules for CPU, RAM, GPU, Disk, Network
  • Binary protocol for minimal transport overhead
  • WebSocket transport for mobile sync
  • Configurable sampling rate (10ms–1s)
  • Thread-safe queues for metric dispatch
This keeps the project honest while still demonstrating clear architectural intent.

Roadmap

Phase 1 — Foundations

  • Research OS-specific APIs for hardware telemetry.
  • Build polling prototype for CPU + RAM.
  • Create initial binary frame format.
Phase 2 — Engine
  • Add GPU + Disk modules.
  • Implement async event loop + buffering.
  • Build WebSocket relay server.
Phase 3 — Viewer (Mobile)
  • Mobile dashboard in React Native.
  • Visualization components (charts, gauges).
Long Term
  • Temperature sensors, fan speed, thermal envelopes.
  • Sampling optimizations + native dispatch.
  • Cross-platform packaging.

Closing Thought

This project is not a polished app — it is a systems-architecture exercise. A way to understand how native engines work, how data moves from hardware to user, and how low-level engineering shapes real-time experiences.