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MSc Computer ScienceFirst Class Honours (85%)

Distributed Cloud Consensus & Fault Tolerance Sample | Apex

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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