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<main>
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<h1 id="adr-018-swarm-load-balanced-task-assignment"><a class="header" href="#adr-018-swarm-load-balanced-task-assignment">ADR-018: Swarm Load-Balanced Task Assignment</a></h1>
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<p><strong>Status</strong>: Accepted | Implemented
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<strong>Date</strong>: 2024-11-01
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<strong>Deciders</strong>: Swarm Coordination Team
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<strong>Technical Story</strong>: Distributing tasks across agents considering both capability and current load</p>
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<hr />
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<h2 id="decision"><a class="header" href="#decision">Decision</a></h2>
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<p>Implementar <strong>load-balanced task assignment</strong> con fórmula <code>assignment_score = success_rate / (1 + load)</code>.</p>
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<hr />
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<h2 id="rationale"><a class="header" href="#rationale">Rationale</a></h2>
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<ol>
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<li><strong>Success Rate</strong>: Seleccionar agentes que han tenido éxito en tareas similares</li>
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<li><strong>Load Factor</strong>: Balancear entre expertise y disponibilidad (no sobrecargar)</li>
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<li><strong>Single Formula</strong>: Combina ambas dimensiones en una métrica comparable</li>
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<li><strong>Prevents Concentration</strong>: Evitar que todos los tasks vayan a un solo agent</li>
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</ol>
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<hr />
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<h2 id="alternatives-considered"><a class="header" href="#alternatives-considered">Alternatives Considered</a></h2>
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<h3 id="-success-rate-only"><a class="header" href="#-success-rate-only">❌ Success Rate Only</a></h3>
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<ul>
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<li><strong>Pros</strong>: Selecciona best performer</li>
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<li><strong>Cons</strong>: Concentra todas las tasks, agent se sobrecarga</li>
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</ul>
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<h3 id="-round-robin-equal-distribution"><a class="header" href="#-round-robin-equal-distribution">❌ Round-Robin (Equal Distribution)</a></h3>
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<ul>
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<li><strong>Pros</strong>: Simple, fair distribution</li>
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<li><strong>Cons</strong>: No considera capability, bad agents get same load</li>
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</ul>
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<h3 id="-success-rate--1--load-chosen"><a class="header" href="#-success-rate--1--load-chosen">✅ Success Rate / (1 + Load) (CHOSEN)</a></h3>
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<ul>
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<li>Balancea expertise con availability</li>
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</ul>
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<hr />
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<h2 id="trade-offs"><a class="header" href="#trade-offs">Trade-offs</a></h2>
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<p><strong>Pros</strong>:</p>
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<ul>
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<li>✅ Considers both capability and availability</li>
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<li>✅ Simple, single metric for comparison</li>
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<li>✅ Prevents overloading high-performing agents</li>
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<li>✅ Encourages fair distribution</li>
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</ul>
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<p><strong>Cons</strong>:</p>
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<ul>
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<li>⚠️ Formula is simplified (linear load penalty)</li>
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<li>⚠️ May sacrifice quality for load balance</li>
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<li>⚠️ Requires real-time load tracking</li>
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</ul>
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<hr />
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<h2 id="implementation"><a class="header" href="#implementation">Implementation</a></h2>
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<p><strong>Agent Load Tracking</strong>:</p>
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<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
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</span><span class="boring">fn main() {
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</span>// crates/vapora-swarm/src/coordinator.rs
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pub struct AgentState {
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pub id: String,
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pub role: AgentRole,
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pub status: AgentStatus, // Ready, Busy, Offline
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pub in_flight_tasks: u32,
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pub max_concurrent: u32,
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pub success_rate: f32, // [0.0, 1.0]
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pub avg_latency_ms: u32,
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}
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impl AgentState {
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/// Current load (0.0 = idle, 1.0 = at capacity)
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pub fn current_load(&self) -> f32 {
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(self.in_flight_tasks as f32) / (self.max_concurrent as f32)
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}
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/// Assignment score: success_rate / (1 + load)
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/// Higher = better candidate for task
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pub fn assignment_score(&self) -> f32 {
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self.success_rate / (1.0 + self.current_load())
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}
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}
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<span class="boring">}</span></code></pre></pre>
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<p><strong>Task Assignment Logic</strong>:</p>
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<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
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</span><span class="boring">fn main() {
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</span>pub async fn assign_task_to_best_agent(
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task: &Task,
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agents: &[AgentState],
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) -> Result<String> {
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// Filter eligible agents (matching role, online)
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let eligible: Vec<_> = agents
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.iter()
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.filter(|a| {
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a.status == AgentStatus::Ready || a.status == AgentStatus::Busy
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})
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.collect();
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if eligible.is_empty() {
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return Err(Error::NoAgentsAvailable);
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}
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// Score each agent
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let mut scored: Vec<_> = eligible
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.iter()
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.map(|agent| {
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let score = agent.assignment_score();
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(agent.id.clone(), score)
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})
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.collect();
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// Sort by score descending
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scored.sort_by(|a, b| {
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b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal)
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});
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// Assign to highest scoring agent
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let selected_agent_id = scored[0].0.clone();
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// Increment in-flight counter
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if let Some(agent) = agents.iter_mut().find(|a| a.id == selected_agent_id) {
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agent.in_flight_tasks += 1;
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}
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Ok(selected_agent_id)
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}
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<span class="boring">}</span></code></pre></pre>
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<p><strong>Load Calculation Examples</strong>:</p>
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<pre><code>Agent A: success_rate = 0.95, in_flight = 2, max_concurrent = 5
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load = 2/5 = 0.4
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score = 0.95 / (1 + 0.4) = 0.95 / 1.4 = 0.68
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Agent B: success_rate = 0.85, in_flight = 0, max_concurrent = 5
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load = 0/5 = 0.0
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score = 0.85 / (1 + 0.0) = 0.85 / 1.0 = 0.85 ← Selected
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Agent C: success_rate = 0.90, in_flight = 5, max_concurrent = 5
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load = 5/5 = 1.0
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score = 0.90 / (1 + 1.0) = 0.90 / 2.0 = 0.45
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</code></pre>
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<p><strong>Real-Time Metrics</strong>:</p>
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<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
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</span><span class="boring">fn main() {
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</span>pub async fn collect_swarm_metrics(
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agents: &[AgentState],
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) -> SwarmMetrics {
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SwarmMetrics {
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total_agents: agents.len(),
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idle_agents: agents.iter().filter(|a| a.in_flight_tasks == 0).count(),
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|
|
busy_agents: agents.iter().filter(|a| a.in_flight_tasks > 0).count(),
|
||
|
|
offline_agents: agents.iter().filter(|a| a.status == AgentStatus::Offline).count(),
|
||
|
|
total_in_flight: agents.iter().map(|a| a.in_flight_tasks).sum::<u32>(),
|
||
|
|
avg_success_rate: agents.iter().map(|a| a.success_rate).sum::<f32>() / agents.len() as f32,
|
||
|
|
avg_load: agents.iter().map(|a| a.current_load()).sum::<f32>() / agents.len() as f32,
|
||
|
|
}
|
||
|
|
}
|
||
|
|
<span class="boring">}</span></code></pre></pre>
|
||
|
|
<p><strong>Prometheus Metrics</strong>:</p>
|
||
|
|
<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
|
||
|
|
</span><span class="boring">fn main() {
|
||
|
|
</span>// Register metrics
|
||
|
|
lazy_static::lazy_static! {
|
||
|
|
static ref TASK_ASSIGNMENTS: Counter = Counter::new(
|
||
|
|
"vapora_task_assignments_total",
|
||
|
|
"Total task assignments"
|
||
|
|
).unwrap();
|
||
|
|
|
||
|
|
static ref AGENT_LOAD: Gauge = Gauge::new(
|
||
|
|
"vapora_agent_current_load",
|
||
|
|
"Current agent load (0-1)"
|
||
|
|
).unwrap();
|
||
|
|
|
||
|
|
static ref ASSIGNMENT_SCORE: Histogram = Histogram::new(
|
||
|
|
"vapora_assignment_score",
|
||
|
|
"Assignment score distribution"
|
||
|
|
).unwrap();
|
||
|
|
}
|
||
|
|
|
||
|
|
// Record metrics
|
||
|
|
TASK_ASSIGNMENTS.inc();
|
||
|
|
AGENT_LOAD.set(best_agent.current_load());
|
||
|
|
ASSIGNMENT_SCORE.observe(best_agent.assignment_score());
|
||
|
|
<span class="boring">}</span></code></pre></pre>
|
||
|
|
<p><strong>Key Files</strong>:</p>
|
||
|
|
<ul>
|
||
|
|
<li><code>/crates/vapora-swarm/src/coordinator.rs</code> (assignment logic)</li>
|
||
|
|
<li><code>/crates/vapora-swarm/src/metrics.rs</code> (Prometheus metrics)</li>
|
||
|
|
<li><code>/crates/vapora-backend/src/api/</code> (task creation triggers assignment)</li>
|
||
|
|
</ul>
|
||
|
|
<hr />
|
||
|
|
<h2 id="verification"><a class="header" href="#verification">Verification</a></h2>
|
||
|
|
<pre><code class="language-bash"># Test assignment score calculation
|
||
|
|
cargo test -p vapora-swarm test_assignment_score_calculation
|
||
|
|
|
||
|
|
# Test load factor impact
|
||
|
|
cargo test -p vapora-swarm test_load_factor_impact
|
||
|
|
|
||
|
|
# Test best agent selection
|
||
|
|
cargo test -p vapora-swarm test_select_best_agent
|
||
|
|
|
||
|
|
# Test fair distribution (no concentration)
|
||
|
|
cargo test -p vapora-swarm test_fair_distribution
|
||
|
|
|
||
|
|
# Integration: assign multiple tasks sequentially
|
||
|
|
cargo test -p vapora-swarm test_assignment_sequence
|
||
|
|
|
||
|
|
# Load balancing under stress
|
||
|
|
cargo test -p vapora-swarm test_load_balancing_stress
|
||
|
|
</code></pre>
|
||
|
|
<p><strong>Expected Output</strong>:</p>
|
||
|
|
<ul>
|
||
|
|
<li>Agents with high success_rate + low load selected first</li>
|
||
|
|
<li>Load increases after each assignment</li>
|
||
|
|
<li>Fair distribution across agents</li>
|
||
|
|
<li>No single agent receiving all tasks</li>
|
||
|
|
<li>Metrics tracked accurately</li>
|
||
|
|
<li>Scores properly reflect trade-off</li>
|
||
|
|
</ul>
|
||
|
|
<hr />
|
||
|
|
<h2 id="consequences"><a class="header" href="#consequences">Consequences</a></h2>
|
||
|
|
<h3 id="fairness"><a class="header" href="#fairness">Fairness</a></h3>
|
||
|
|
<ul>
|
||
|
|
<li>High-performing agents get more tasks (deserved)</li>
|
||
|
|
<li>Overloaded agents get fewer tasks (protection)</li>
|
||
|
|
<li>Fair distribution emerges automatically</li>
|
||
|
|
</ul>
|
||
|
|
<h3 id="performance"><a class="header" href="#performance">Performance</a></h3>
|
||
|
|
<ul>
|
||
|
|
<li>Task latency depends on agent load (may queue)</li>
|
||
|
|
<li>Peak throughput = sum of all agent max_concurrent</li>
|
||
|
|
<li>SLA contracts respect per-agent limits</li>
|
||
|
|
</ul>
|
||
|
|
<h3 id="scaling"><a class="header" href="#scaling">Scaling</a></h3>
|
||
|
|
<ul>
|
||
|
|
<li>Adding agents increases total capacity</li>
|
||
|
|
<li>Load automatically redistributes</li>
|
||
|
|
<li>Horizontal scaling works naturally</li>
|
||
|
|
</ul>
|
||
|
|
<h3 id="monitoring"><a class="header" href="#monitoring">Monitoring</a></h3>
|
||
|
|
<ul>
|
||
|
|
<li>Track assignment distribution</li>
|
||
|
|
<li>Alert if concentration detected</li>
|
||
|
|
<li>Identify bottleneck agents</li>
|
||
|
|
</ul>
|
||
|
|
<hr />
|
||
|
|
<h2 id="references"><a class="header" href="#references">References</a></h2>
|
||
|
|
<ul>
|
||
|
|
<li><code>/crates/vapora-swarm/src/coordinator.rs</code> (implementation)</li>
|
||
|
|
<li><code>/crates/vapora-swarm/src/metrics.rs</code> (metrics collection)</li>
|
||
|
|
<li>ADR-014 (Learning Profiles)</li>
|
||
|
|
<li>ADR-018 (This ADR)</li>
|
||
|
|
</ul>
|
||
|
|
<hr />
|
||
|
|
<p><strong>Related ADRs</strong>: ADR-014 (Learning Profiles), ADR-020 (Audit Trail)</p>
|
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