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  • Changsha University of Science and Technology
  • China / Changsha

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yixin0724/README.md
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M.S. Student in Software Engineering · Joint Training @ National University of Defense Technology (NUDT)

Research focus: workflow scheduling algorithms based on Deep Reinforcement Learning

📍 Changsha, China

🔭 About Me

  • 🎓 M.S. student in Software Engineering at Changsha University of Science & Technology(CSUST), jointly trained at the National University of Defense Technology (NUDT), graduating in 2027.
  • 🧠 Researching workflow scheduling algorithms based on Deep Reinforcement Learning
  • 🛠️ AI full-stack engineer working on Agent application development — from LLM orchestration to production-grade backend services.
  • 🚀 Goal: becoming a software engineer at a leading Internet technology company.

🧰 Tech Stack

Languages

Languages   Python Java

AI / Deep Learning   PyTorch TensorFlow LangChain

Backend   FastAPI Flask Spring Boot Maven

Data & DevOps   MySQL Redis PostgreSQL Docker Kubernetes Git Linux

🚀 Featured Project

A high-fidelity discrete-event simulator for scientific workflow scheduling research, built on the WorkflowSim / CloudSim codebase.

Instead of the usual "fork and hope it runs" simulator maintenance, this project provides:

  • ✅ Strict input validation for DAX 2.1 XML, WfCommons WfFormat JSON, and WfInstances 1.5 JSON
  • 🧪 Explicit experiment configuration — every parameter declared in code, reproducible from a single right-click in IntelliJ IDEA
  • 📦 Auditable evidence artifacts — each run emits a verifiable manifest.json / metrics.json / events.jsonl bundle
  • 🔬 Semantically tested algorithm tracks — online ready-Job scheduling (FCFS, MINMIN, MAXMIN, MCT, ...), static DAG mapping (HEFT, CPOP, ETF, DLS, PEFT), and an RL scheduling track (RlEnvironment + RL_POLICY)

The RL track exposes a state/action/reward environment contract with a deterministic closed loop, enabling external DRL trainers to be bridged out-of-process — the foundation of my research on DRL-based workflow scheduling.

Also includes Fat-tree topology-aware link-contention modeling (fatTreeContentionV1) and a 360-run deterministic Fat-tree × scheduling campaign, demonstrating how network contention shifts per-DAG rankings even when average rankings hold.

Highlights: 446 tests passing under mvn verify · full HEFT/CPOP paper reproduction (Topcuoglu et al., IEEE TPDS 2002) · JDK 17 + Maven


📫 Connect with Me

Email GitHub

Profile Views

"Make the experiment reproducible, or it never happened."

Pinned Loading

  1. WorkflowGenerator WorkflowGenerator Public

    Optimized Pegasus Classic Scientific Workflow Application Example XML Generator

    Java 1

  2. wfcommons-generator wfcommons-generator Public

    Used to generate wfcommons workflow instances

    Python 1

  3. workflowsim-fat-tree workflowsim-fat-tree Public

    Java 1

  4. workflowsim-research workflowsim-research Public

    Auditable discrete-event simulator for scientific workflow scheduling — every published number is independently verifiable bit-by-bit by any third party with one Python command.

    Java 1