Jacob Miller

Systems Topology & Problem Space

Enterprise card transaction intelligence requires real-time analytics streaming at high velocity. Incoming event payloads arrive with non-uniform burst distribution, requiring sub-15ms processing guarantees without risking message loss or downstream database saturation.

[Inbound Card Authorization Feeds]
                 │
                 ▼
     [Kafka Distributed Cluster]
     (Partitioned by Account Hash)
     ┌───────────┬───────────┐
     ▼           ▼           ▼
[Consumer 01] [Consumer 02] [Consumer 03]  (AWS ECS Fargate Cluster)
     │           │           │
     ├───────────┴───────────┤
     ▼                       ▼
[Analytics Store]    [Dead-Letter Queue & Isolation]

Key Engineering Challenges & Solutions

1. Zero-Loss Partition Rebalancing

• The Challenge: Standard Kafka consumer group rebalances historically resulted in “stop-the-world” pauses, producing processing spikes and latency breaches during container auto-scaling operations.
• The Solution: Implemented cooperative sticky partition assignment strategies, enabling consumers to continue draining active partitions while reassigned partitions migrate incrementally. Minimized rebalance downtime from several seconds to under 80 milliseconds.

2. Three-Tiered Failure Isolation & Dead-Letter Routing

• Transient Failures: Rapid retry with exponential backoff and jitter for transient downstream network hiccups.
• Persistent Failures: Non-blocking redirection to a secondary delayed retry topic, isolating intermittent downstream issues from the primary throughput pipeline.
• Poison Pill Containment: Schema-invalid or unparseable payloads are captured, enriched with diagnostic headers (timestamp, consumer ID, stack trace), and routed to a segregated Dead-Letter Queue (DLQ) for asynchronous inspection.

3. Deterministic Ordering by Key

• Enforced strict account-level hashing keys to guarantee serial execution for individual customer accounts while maintaining complete horizontal parallelism across the cluster.

Performance & Reliability Outcomes

• Peak Sustained Load: Successfully handled over 100,000 events/second during peak seasonal transaction surges without consumer group starvation.
• End-to-End Latency: Maintained p99 processing latency strictly under 15ms from Kafka ingestion to transformed analytical payload emission.
• Resiliency SLA: Achieved 99.99% availability with zero recorded message loss across major production operational cycles.

Systems Context & Resilience Objective

To validate service autoscaling and latency SLAs under non-linear traffic surges, engineering required an automated, distributed load testing engine capable of reproducing high-concurrency workloads across containerized microservices.

[CI/CD Release Pipeline] ──► [Distributed k6 Orchestrator]
                                      │
           ┌──────────────────────────┼──────────────────────────┐
           ▼                          ▼                          ▼
   [k6 Worker Pod 1]          [k6 Worker Pod 2]          [k6 Worker Pod N]
   (AWS Fargate Task)         (AWS Fargate Task)         (AWS Fargate Task)
           │                          │                          │
           └──────────────────────────┼──────────────────────────┘
                                      ▼
                        [Target Microservices API]
                                      │
                   [Grafana Dashboards & SLA Telemetry]

Technical Architecture & Stress Harness

1. Distributed Virtual User Coordination

• Developed modular TypeScript load scripts compiled for the k6 runtime.
• Orchestrated multi-container executor swarms on AWS Fargate, synchronizing ramp-up, sustained soak, and spike stages without saturating client-side network interfaces.
• Simulated multi-step user interaction scenarios: OAuth token exchange, real-time transaction query, balance check, and notification webhooks.

2. Root Cause Discovery: Upstream Connection Exhaustion

• During sustained 50k concurrent user simulations, p99 latency sharply escalated from 45ms to >1,800ms despite nominal CPU and memory utilization on backend services.
• Network profiling identified client-side socket pool exhaustion during upstream HTTP client pooling under high-rate TLS handshakes.
• Resolution: Optimized keep-alive connection reuse, tuned max socket pools per host, and introduced resilient connection timeouts.

3. Automated Performance Gates in CI/CD

• Integrated automated 15-minute smoke-and-spike test stages into GitHub Actions pre-production release pipelines.
• Configured hard failure thresholds: build fails if error rate > 0.05% or if p95 latency exceeds baseline by more than 15%.

Performance Outcomes

• Concurrency Scale: Validated stable operations at sustained 50,000+ concurrent virtual users.
• 24-Hour Soak Stability: Zero memory leaks or socket exhaustion observed over continuous 24-hour endurance test executions.
• Zero Production Regressions: Prevented multiple latency regressions from escaping to production environments during peak traffic cycles.

Research Context & Intelligence Objective

Conducted at the Sanghani Center for Artificial Intelligence and Data Analytics in partnership with the Savannah River National Lab. The project addressed the challenge of automatically discovering early-warning signals, emergent geopolitical events, and operational anomalies across millions of multi-lingual, unstructured text documents.

[Unstructured Raw Text Feeds]
               │
               ▼
[Preprocessing & Language Detection]
               │
               ▼
[Transformer NER & Entity Extraction] (RoBERTa / Fine-Tuned PyTorch)
               │
               ▼
[Dense Vector Embeddings] (768-dim Spatial-Temporal Vectors)
               │
               ▼
[HDBSCAN Dynamic Anomaly Clustering]
               │
               ▼
[Interactive Geospatial & Event Intelligence Dashboard]

Research Methodology & Pipeline Engineering

1. Transformer-Based Entity Extraction

• Fine-tuned transformer models (RoBERTa and domain-adapted BERT) to identify multi-word named entities, geolocational coordinates, temporal expressions, and organizational relationships.
• Handled noisy and malformed social and open-source intelligence text with robust tokenization fallback filters.

2. High-Dimensional Temporal Clustering

• Projected extracted documents into dense vector spaces (768 dimensions) capturing both semantic meaning and temporal context.
• Implemented hierarchical density-based clustering (HDBSCAN) coupled with sliding time windows to detect sudden topic density surges without requiring predefined keywords or supervised topic tags.

3. Low-Latency Inference Architecture

• Engineered a high-throughput microservice using Python and FastAPI with GPU tensor acceleration.
• Implemented dynamic batching and memory-mapped embedding caches, reducing single-document inference from 180ms to under 25ms, supporting continuous ingestion throughput exceeding 1,200 documents per second.

Collegiate Research Findings & Delivery

• Detection Precision: Achieved 94.2% F1 score in identifying real-world anomaly outbreaks verified against historical validation ground truth.
• Scalability: Successfully ingested and clustered corpora exceeding 10 million unstructured documents.
• National Lab Deliverable: Delivered fully functioning, containerized intelligence analytics dashboard and inference engine directly to Savannah River National Lab stakeholders.

Systems Topology & Architectural Vision

Traditional enterprise web stacks burden small teams and modern portfolios with heavyweight container clusters, continuous idle VM billing, and complex multi-region replication overhead. SiteSwarm re-engineers this paradigm by leveraging edge-native serverless primitives, monorepo capability governance, and autonomous parallel agent swarms.

[Developer / AI Agent Fleet]
             │ (Worktrunk wt Worktrees)
             ▼
[GitHub Actions CI / AST Linter]
             │
      ┌──────┴──────┐
      ▼             ▼
[Ephemeral PR] [Production Deploy]
 (Staging)     (Global Edge)
      │             │
      └──────┬──────┘
             ▼
[Cloudflare Global Edge Network]
   ├── Edge Static Assets (Cache-Control: immutable)
   ├── Turnstile Honeypot Recruiter API (/api/contact)
   └── Edge Health Probe (/api/health)

Key Engineering Implementations

1. Compile-Time Capability Governance

• Created @siteswarm/governance featuring custom TypeScript AST linters that verify capability contracts (e.g. dynamic-seo, lead-capture, health) directly during build and test runs.
• Prevents cross-tenant dependency leakage, enforces headless capability purity, and enforces sub-200 LOC modular component boundaries.

2. Parallel Agent Fleet Worktrees

• Integrated Worktrunk (wt) isolated git worktrees, enabling multiple autonomous AI agents to work on isolated feature branches simultaneously without dirtying working trees or causing merge conflicts.
• Enforced zero untracked “ghost work” with automated GitHub Issue linking, structured verification comments, and PR-gated issue closure.

3. Serverless Edge Architecture

• Leveraged Cloudflare Pages static compilation and edge Workers serverless functions to eliminate dedicated server maintenance overhead.
• Configured edge caching headers and immutable asset hashing for fast worldwide content delivery.