MSc Computer ScienceFirst Class Honours (85%)
Distributed Cloud Consensus and Fault Tolerance in Asynchronous Microservices
Implementation and performance benchmarking of a Byzantine-resilient Raft consensus cluster in Python and Go, evaluating network partition tolerance.
1.2% Similarity • Zero Generative AICitation: IEEE FormatLength: 3,800 Words + Source Code
Model Academic Paper Excerpt
“This study implements an asynchronous multi-node Raft consensus cluster featuring dynamic leader election and log replication over gRPC transport. Under simulated 300ms network partition conditions, the cluster maintains 99.98% state consistency across healthy follower nodes, executing leader reelection in an average of 184ms (±12ms). Formal Big-O message complexity is bounded at O(N) per committed log entry. Automated unit and integration testing via pytest achieved 98.4% code branch coverage across all failure modes...”
Senior University Marker Evaluation:Clean distributed systems architecture with complete pytest coverage and 99.98% cluster uptime simulation. Mathematical proofs and IEEE references are flawless.
Rubric Assessment Scores
Software Architecture & Code Quality88/100
Production-grade asynchronous Python implementation with modular gRPC layers.
Algorithmic Complexity Proofs86/100
Rigorous Big-O latency and message complexity proofs.
Empirical Benchmarking82/100
High-resolution latency histograms and simulated chaos engineering partition tests.
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