Course Overview
Modern computing systems have evolved from isolated servers into massive, hyper-connected heterogeneous environments. This course, NT2204, provides a comprehensive exploration of the theoretical foundations and practical engineering challenges of building robust Distributed Systems at scale.
Our curriculum emphasizes the transition toward Cloud-native architectures, Serverless computing, and Distributed Intelligence. Students will deep-dive into Dataflow execution models, consensus mechanisms (Raft/Spanner), and the orchestration of AI workloads across edge-cloud continuums. We place a high priority on architectural resilience, cross-node scheduling, and rigorous privacy-preserving security models for future-proof systems engineering.
Curriculum Lead
Dr. Dang Van Huynh
Department of Computer Networks
Systems Mastery
Master cloud-native and serverless paradigms with focus on performance and availability.
Distributed AI
Implement federated learning and distributed inference across heterogeneous nodes.
Security & Privacy
Address BFT consensus, TEEs, and Differential Privacy in decentralized environments.
Advanced Scheduling
Evaluate Dataflow execution and orchestration for large-scale multi-cloud clusters.
12-Week Curriculum
Capstone Research Topics
Technical Pillar
Distributed Architecture & Full Implementation
Quality Pillar
Systematic Testing & Latency Benchmarks
Visual Pillar
Technical Report & Team Oral Defense
Academic Pillar
Optional Scholarly Research Submission
Grading Policy
Final Examination
Comprehensive 90-minute theoretical evaluation.
Capstone Project
Technical depth, implementation excellence, and report.
Attendance
Active participation in modules and presentation slots.
References
Core Literature
Designing Data-Intensive Applications
Martin Kleppmann (O'Reilly)
Distributed Systems (4th Ed)
Maarten van Steen & Tanenbaum
Canonical Papers
- CACM'08 "MapReduce: Simplified Data Processing on Large Clusters", Dean & Ghemawat.
- ACM-TOCS '13 "Spanner: Google's Globally-Distributed Database", Corbett et al.
- USENIX ATC '23 "On-demand Container Loading in AWS Lambda", M. Brooker et al.