Appendix C: Vector Sharding Reference Model
Technical Appendix with Rust Implementation
This appendix provides a complete technical specification of the Vector Sharding algorithm introduced in Chapter 11, including mathematical formulations, data structures, and executable Rust code suitable for implementation.
1. Core Concepts
1.1 Vector Representation
Each data object is represented as a multidimensional vector encoding its characteristics:
V_object = {
object_id: string,
size_bytes: integer,
access_frequency: float, // queries per hour
geographic_distribution: map[region → float], // 0-1, sums to 1
temporal_pattern: array[24], // hourly access pattern
read_write_ratio: float, // 0-1, where 1 = 100% reads
consistency_requirement: enum,
business_value: float, // revenue impact per ms latency
co_accessed_objects: set[object_id],
last_accessed: timestamp,
age_days: integer
}
1.2 Regional Demand Vector
Each region at time T has a demand profile:
D_region(R, T) = {
region_id: string,
timestamp: datetime,
query_load: float, // queries per second
available_compute: float, // vCPUs available
available_storage: float, // GB available
cost_per_gb_storage: float, // USD per GB per month
cost_per_vcpu: float, // USD per vCPU per hour
cost_per_gb_bandwidth: float, // USD per GB transferred
latency_to_regions: map[region → float], // milliseconds
compliance_allowed: set[data_classification]
}
2. Mathematical Formulations
2.1 Data Gravity Formula
The gravitational attraction of data object O to region R:
Gravity(O, R) = Σ_u (queries_from_user_u × (1 / distance(user_u, R)))
× (1 / log(object_size + 1))
× business_value(O)
where:
- queries_from_user_u: Query frequency from user u
- distance(user_u, R): Network distance in milliseconds
- object_size: Size in GB (larger objects have more inertia)
- business_value: Revenue impact factor
2.2 Optimal Placement Score
Score for placing object O in region R at time T:
Score(O, R, T) = Benefit(O, R, T) - Cost(O, R, T)
Benefit(O, R, T) =
query_frequency(O, R, T)
× latency_improvement(current_location, R)
× value_per_ms_latency(O)
Cost(O, R, T) =
storage_cost(O.size, R)
+ replication_bandwidth_cost(O, current_locations, R)
+ migration_downtime_cost(O)
Constraints:
- Score(O, R, T) > threshold for placement
- R must satisfy compliance requirements for O
- R must have available capacity
2.3 Prediction Model
Forecasting future demand using time-series decomposition:
Predicted_queries(O, T+Δt) =
Trend(O, T+Δt)
× Daily_cycle(hour_of_day(T+Δt))
× Weekly_cycle(day_of_week(T+Δt))
× Seasonal_factor(month(T+Δt))
× (1 + noise_factor)
where:
Trend: Exponential moving average of long-term growth
Daily_cycle: 24-hour periodic pattern
Weekly_cycle: 7-day periodic pattern
Seasonal_factor: Monthly/quarterly variations
noise_factor: Gaussian noise, μ=0, σ=0.1
3. Data Structures
3.1 Object Metadata Store
use std::collections::{HashMap, HashSet};
use chrono::{DateTime, Utc};
#[derive(Debug, Clone)]
pub struct ObjectMetadata {
pub object_id: String,
pub size_bytes: u64,
pub current_locations: HashSet<String>,
pub access_history: Vec<AccessRecord>,
pub vector: ObjectVector,
pub prediction_model: TimeSeriesModel,
pub last_migration: Option<DateTime<Utc>>,
}
#[derive(Debug, Clone)]
pub struct AccessRecord {
pub timestamp: DateTime<Utc>,
pub region: String,
pub query_type: QueryType,
}
#[derive(Debug, Clone, PartialEq)]
pub enum QueryType {
Read,
Write,
}
#[derive(Debug, Clone)]
pub struct ObjectVector {
pub access_frequency: f64,
pub geo_distribution: HashMap<String, f64>,
pub temporal_pattern: [f64; 24],
pub read_write_ratio: f64,
pub business_value: f64,
pub co_accessed_objects: HashSet<String>,
}
impl ObjectMetadata {
pub fn new(object_id: String) -> Self {
Self {
object_id,
size_bytes: 0,
current_locations: HashSet::new(),
access_history: Vec::new(),
vector: ObjectVector::default(),
prediction_model: TimeSeriesModel::new(),
last_migration: None,
}
}
}
impl Default for ObjectVector {
fn default() -> Self {
Self {
access_frequency: 0.0,
geo_distribution: HashMap::new(),
temporal_pattern: [0.0; 24],
read_write_ratio: 0.95,
business_value: 1.0,
co_accessed_objects: HashSet::new(),
}
}
}
#[derive(Debug, Clone)]
pub struct TimeSeriesModel {
pub trend: ExponentialMovingAverage,
pub daily_pattern: [f64; 24],
pub weekly_pattern: [f64; 7],
}
impl TimeSeriesModel {
pub fn new() -> Self {
Self {
trend: ExponentialMovingAverage::new(0.3),
daily_pattern: [1.0; 24],
weekly_pattern: [1.0; 7],
}
}
}
#[derive(Debug, Clone)]
pub struct ExponentialMovingAverage {
pub value: f64,
pub alpha: f64,
}
impl ExponentialMovingAverage {
pub fn new(alpha: f64) -> Self {
Self { value: 1.0, alpha }
}
pub fn update(&mut self, new_value: f64) {
self.value = self.alpha * new_value + (1.0 - self.alpha) * self.value;
}
pub fn evaluate(&self, _time: DateTime<Utc>) -> f64 {
self.value
}
}
3.2 Region State
#[derive(Debug, Clone)]
pub struct RegionState {
pub region_id: String,
pub available_compute: f64, // vCPUs
pub available_storage: f64, // GB
pub current_load: f64, // queries per second
pub costs: CostStructure,
pub objects: HashSet<String>, // Object IDs in this region
}
#[derive(Debug, Clone)]
pub struct CostStructure {
pub storage_per_gb_month: f64,
pub compute_per_vcpu_hour: f64,
pub bandwidth_per_gb: HashMap<String, f64>, // dest_region -> cost
}
impl RegionState {
pub fn new(region_id: String) -> Self {
Self {
region_id,
available_compute: 0.0,
available_storage: 0.0,
current_load: 0.0,
costs: CostStructure::default(),
objects: HashSet::new(),
}
}
pub fn available_vcpu(&self) -> f64 {
self.available_compute
}
pub fn available_storage_gb(&self) -> f64 {
self.available_storage
}
pub fn current_qps(&self) -> f64 {
self.current_load
}
}
impl Default for CostStructure {
fn default() -> Self {
Self {
storage_per_gb_month: 0.0,
compute_per_vcpu_hour: 0.0,
bandwidth_per_gb: HashMap::new(),
}
}
}
3.3 Migration Queue
use std::cmp::Ordering;
use std::collections::BinaryHeap;
#[derive(Debug, Clone)]
pub struct Migration {
pub object_id: String,
pub source_region: String,
pub dest_region: String,
pub priority: f64,
pub scheduled_time: Option<DateTime<Utc>>,
pub estimated_duration: f64,
pub expected_benefit: f64,
}
// Implement ordering for priority queue (max-heap by priority)
impl PartialEq for Migration {
fn eq(&self, other: &Self) -> bool {
self.priority == other.priority
}
}
impl Eq for Migration {}
impl PartialOrd for Migration {
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
self.priority.partial_cmp(&other.priority)
}
}
impl Ord for Migration {
fn cmp(&self, other: &Self) -> Ordering {
self.partial_cmp(other).unwrap_or(Ordering::Equal)
}
}
#[derive(Debug)]
pub struct MigrationQueue {
pub queue: BinaryHeap<Migration>,
pub active_migrations: HashMap<String, Migration>,
pub max_concurrent: usize,
}
impl MigrationQueue {
pub fn new(max_concurrent: usize) -> Self {
Self {
queue: BinaryHeap::new(),
active_migrations: HashMap::new(),
max_concurrent,
}
}
pub fn enqueue(&mut self, migration: Migration) {
self.queue.push(migration);
}
pub fn dequeue_highest_priority(&mut self) -> Option<Migration> {
self.queue.pop()
}
pub fn is_empty(&self) -> bool {
self.queue.is_empty()
}
pub fn can_execute_more(&self) -> bool {
self.active_migrations.len() < self.max_concurrent
}
pub fn requeue(&mut self, migration: Migration, _delay_seconds: u64) {
// In a real implementation, delay would be handled by scheduled_time
self.queue.push(migration);
}
}
4. Core Algorithms
4.1 Telemetry Collection
use anyhow::Result;
#[derive(Debug, Clone)]
pub struct Telemetry {
pub objects: HashMap<String, ObjectTelemetry>,
pub regions: HashMap<String, RegionTelemetry>,
pub timestamp: DateTime<Utc>,
}
#[derive(Debug, Clone)]
pub struct ObjectTelemetry {
pub query_count: u64,
pub regions: HashMap<String, u64>,
pub latencies: Vec<f64>,
}
#[derive(Debug, Clone)]
pub struct RegionTelemetry {
pub available_compute: f64,
pub available_storage: f64,
pub current_load: f64,
}
pub async fn collect_telemetry(time_window_seconds: i64) -> Result<Telemetry> {
let now = Utc::now();
let mut telemetry = Telemetry {
objects: HashMap::new(),
regions: HashMap::new(),
timestamp: now,
};
// Query application logs
let queries = query_log_database(
now - chrono::Duration::seconds(time_window_seconds),
now,
).await?;
// Aggregate by object
for query in queries {
let obj_telemetry = telemetry.objects
.entry(query.object_id.clone())
.or_insert_with(|| ObjectTelemetry {
query_count: 0,
regions: HashMap::new(),
latencies: Vec::new(),
});
obj_telemetry.query_count += 1;
*obj_telemetry.regions
.entry(query.source_region.clone())
.or_insert(0) += 1;
obj_telemetry.latencies.push(query.latency_ms);
}
// Collect region capacity
for region in all_regions().await? {
telemetry.regions.insert(
region.id.clone(),
RegionTelemetry {
available_compute: region.available_vcpu(),
available_storage: region.available_storage_gb(),
current_load: region.current_qps(),
},
);
}
Ok(telemetry)
}
// Mock database query function
async fn query_log_database(
_start: DateTime<Utc>,
_end: DateTime<Utc>,
) -> Result<Vec<QueryRecord>> {
// In real implementation, query your metrics database
Ok(Vec::new())
}
#[derive(Debug, Clone)]
struct QueryRecord {
object_id: String,
source_region: String,
latency_ms: f64,
}
async fn all_regions() -> Result<Vec<RegionState>> {
// In real implementation, fetch from configuration
Ok(Vec::new())
}
4.2 Vector Update
pub fn update_object_vector(
obj: &mut ObjectMetadata,
telemetry: &Telemetry,
) {
if let Some(obj_telemetry) = telemetry.objects.get(&obj.object_id) {
// Update access frequency (exponential moving average)
let new_frequency = obj_telemetry.query_count as f64 / 3600.0; // per hour
let alpha = 0.3; // Smoothing factor
obj.vector.access_frequency =
alpha * new_frequency + (1.0 - alpha) * obj.vector.access_frequency;
// Update geographic distribution
let total_queries: u64 = obj_telemetry.regions.values().sum();
if total_queries > 0 {
for (region, count) in &obj_telemetry.regions {
obj.vector.geo_distribution.insert(
region.clone(),
*count as f64 / total_queries as f64,
);
}
}
// Update temporal pattern
let hour = Utc::now().hour() as usize;
obj.vector.temporal_pattern[hour] =
0.7 * obj.vector.temporal_pattern[hour] + 0.3 * new_frequency;
} else {
// No recent access, decay frequency
obj.vector.access_frequency *= 0.95;
}
}
4.3 Demand Prediction
use chrono::Datelike;
pub fn predict_demand(
obj: &ObjectMetadata,
hours_ahead: usize,
) -> HashMap<usize, HashMap<String, f64>> {
let mut predictions = HashMap::new();
let now = Utc::now();
for h in 0..hours_ahead {
let target_time = now + chrono::Duration::hours(h as i64);
let hour_of_day = target_time.hour() as usize;
let day_of_week = target_time.weekday().num_days_from_monday() as usize;
// Base prediction from temporal pattern
let base_demand = obj.vector.temporal_pattern[hour_of_day];
// Apply weekly cycle (weekends vs weekdays)
let weekly_factor = get_weekly_factor(obj, day_of_week);
// Apply trend
let trend_factor = obj.prediction_model.trend.evaluate(target_time);
let predicted_total = base_demand * weekly_factor * trend_factor;
// Distribute across regions based on geo_distribution
let mut regional_predictions = HashMap::new();
for (region, prob) in &obj.vector.geo_distribution {
regional_predictions.insert(
region.clone(),
predicted_total * prob,
);
}
predictions.insert(h, regional_predictions);
}
predictions
}
fn get_weekly_factor(obj: &ObjectMetadata, day_of_week: usize) -> f64 {
obj.prediction_model.weekly_pattern[day_of_week]
}
4.4 Optimal Placement Computation
const PLACEMENT_THRESHOLD: f64 = 100.0;
pub fn compute_optimal_placement(
obj: &ObjectMetadata,
predictions: &HashMap<usize, HashMap<String, f64>>,
regions: &HashMap<String, RegionState>,
) -> HashSet<String> {
let mut scores = HashMap::new();
for (region_id, region) in regions {
// Skip if compliance violation
if !satisfies_compliance(obj, region) {
continue;
}
// Skip if insufficient capacity
if region.available_storage < (obj.size_bytes as f64 / 1e9) {
continue;
}
// Calculate benefit score
let mut benefit = 0.0;
for (_hour_offset, regional_demand) in predictions {
if let Some(queries) = regional_demand.get(region_id) {
// Current latency to this region
let current_latency = get_current_latency(obj, region_id);
// Potential latency if placed in this region
let potential_latency = 5.0; // Local access
let latency_improvement = current_latency - potential_latency;
benefit += queries * latency_improvement * obj.vector.business_value;
}
}
// Calculate cost
let cost = calculate_placement_cost(obj, region);
scores.insert(region_id.clone(), benefit - cost);
}
// Select regions with positive score above threshold
let mut optimal_regions: HashSet<String> = scores
.iter()
.filter(|(_, score)| **score > PLACEMENT_THRESHOLD)
.map(|(region_id, _)| region_id.clone())
.collect();
// Always maintain at least one replica (primary region)
if optimal_regions.is_empty() {
optimal_regions.insert(get_primary_region(obj));
}
optimal_regions
}
fn satisfies_compliance(_obj: &ObjectMetadata, _region: &RegionState) -> bool {
// Implement compliance checking logic
true
}
fn get_current_latency(_obj: &ObjectMetadata, _region_id: &str) -> f64 {
// Return current latency to region
100.0 // Placeholder
}
fn calculate_placement_cost(_obj: &ObjectMetadata, _region: &RegionState) -> f64 {
// Calculate storage + bandwidth + migration costs
50.0 // Placeholder
}
fn get_primary_region(obj: &ObjectMetadata) -> String {
// Return primary region for object
obj.current_locations.iter().next()
.cloned()
.unwrap_or_else(|| “us-east-1”.to_string())
}
4.5 Migration Scheduling
pub fn schedule_migrations(
objects: &HashMap<String, ObjectMetadata>,
regions: &HashMap<String, RegionState>,
migration_queue: &mut MigrationQueue,
) {
for (obj_id, obj) in objects {
// Predict demand for next 24 hours
let predictions = predict_demand(obj, 24);
// Compute optimal placement
let optimal_regions = compute_optimal_placement(obj, &predictions, regions);
let current_regions = &obj.current_locations;
// Determine needed migrations
let to_add: HashSet<_> = optimal_regions.difference(current_regions)
.cloned()
.collect();
let to_remove: HashSet<_> = current_regions.difference(&optimal_regions)
.cloned()
.collect();
// Schedule additions (replications)
for dest_region in to_add {
let source_region = choose_source_region(obj, &dest_region);
let mut migration = Migration {
object_id: obj_id.clone(),
source_region,
dest_region: dest_region.clone(),
priority: 0.0,
scheduled_time: None,
estimated_duration: 0.0,
expected_benefit: 0.0,
};
migration.priority = calculate_migration_priority(obj, &dest_region, &predictions);
migration.scheduled_time = Some(choose_migration_time(obj, &predictions));
migration.expected_benefit = calculate_expected_benefit(obj, &dest_region);
migration_queue.enqueue(migration);
}
// Schedule removals (deletions)
for region in to_remove {
// Don’t remove if it’s the last copy
if current_regions.len() <= 1 {
continue;
}
schedule_deletion(obj_id, ®ion, migration_queue);
}
}
}
fn choose_source_region(obj: &ObjectMetadata, _dest_region: &str) -> String {
// Choose closest existing location as source
obj.current_locations.iter().next()
.cloned()
.unwrap_or_else(|| “us-east-1”.to_string())
}
fn calculate_migration_priority(
_obj: &ObjectMetadata,
_dest_region: &str,
_predictions: &HashMap<usize, HashMap<String, f64>>,
) -> f64 {
// Calculate priority based on expected benefit
100.0 // Placeholder
}
fn choose_migration_time(
_obj: &ObjectMetadata,
_predictions: &HashMap<usize, HashMap<String, f64>>,
) -> DateTime<Utc> {
// Choose optimal migration time (low-traffic window)
Utc::now() + chrono::Duration::hours(1)
}
fn calculate_expected_benefit(_obj: &ObjectMetadata, _dest_region: &str) -> f64 {
// Calculate expected latency improvement value
500.0 // Placeholder
}
fn schedule_deletion(
_obj_id: &str,
_region: &str,
_migration_queue: &mut MigrationQueue,
) {
// Schedule deletion of replica
// Implementation details omitted
}
4.6 Migration Execution
use tokio::time::{sleep, Duration};
pub async fn execute_migrations(
migration_queue: &mut MigrationQueue,
_max_concurrent: usize,
) {
while migration_queue.can_execute_more() {
if migration_queue.is_empty() {
break;
}
let Some(migration) = migration_queue.dequeue_highest_priority() else {
break;
};
// Verify migration still beneficial
if !verify_migration_benefit(&migration) {
continue;
}
// Check rate limits
if exceeds_rate_limits(&migration) {
migration_queue.requeue(migration, 300);
continue;
}
// Execute asynchronously
let obj_id = migration.object_id.clone();
migration_queue.active_migrations.insert(obj_id.clone(), migration.clone());
// Spawn async task
tokio::spawn(async move {
let success = execute_migration_task(&migration).await;
handle_migration_complete(migration, success).await;
});
}
}
async fn execute_migration_task(migration: &Migration) -> bool {
// Simulate migration with sleep
sleep(Duration::from_secs(10)).await;
// In real implementation:
// 1. Copy data from source to dest
// 2. Verify integrity
// 3. Update routing
// 4. Confirm success
true // Success
}
async fn handle_migration_complete(migration: Migration, success: bool) {
if success {
// Update object metadata
let obj = get_object_metadata(&migration.object_id).await;
if let Some(mut obj) = obj {
obj.current_locations.insert(migration.dest_region.clone());
obj.last_migration = Some(Utc::now());
// Measure actual benefit
let actual_benefit = measure_actual_benefit(&migration).await;
let predicted_benefit = migration.expected_benefit;
// Update prediction model if error is large
if (actual_benefit - predicted_benefit).abs() > 0.3 * predicted_benefit {
adjust_prediction_model(&obj, actual_benefit, predicted_benefit).await;
}
}
} else {
// Log failure, possibly retry later
log_migration_failure(&migration);
}
}
fn verify_migration_benefit(_migration: &Migration) -> bool {
// Verify migration is still beneficial
true
}
fn exceeds_rate_limits(_migration: &Migration) -> bool {
// Check if we’re exceeding bandwidth/migration rate limits
false
}
async fn get_object_metadata(_object_id: &str) -> Option<ObjectMetadata> {
// Fetch object metadata from store
None
}
async fn measure_actual_benefit(_migration: &Migration) -> f64 {
// Measure actual latency improvement
100.0
}
async fn adjust_prediction_model(
_obj: &ObjectMetadata,
_actual: f64,
_predicted: f64,
) {
// Adjust prediction model based on error
}
fn log_migration_failure(_migration: &Migration) {
// Log failure for analysis
eprintln!(”Migration failed: {:?}”, _migration);
}
5. Main Orchestration Loop
use tokio::time::{interval, Duration};
use std::sync::Arc;
use tokio::sync::RwLock;
pub async fn vector_sharding_orchestrator() -> Result<()> {
// Initialize
let objects = Arc::new(RwLock::new(load_object_metadata().await?));
let regions = Arc::new(RwLock::new(load_region_state().await?));
let migration_queue = Arc::new(RwLock::new(MigrationQueue::new(10)));
let mut tick = interval(Duration::from_secs(60));
loop {
tick.tick().await;
// Phase 1: Collect telemetry (1 minute window)
let telemetry = collect_telemetry(3600).await?;
// Phase 2: Update vectors (30 seconds)
{
let mut objects = objects.write().await;
for (_obj_id, obj) in objects.iter_mut() {
update_object_vector(obj, &telemetry);
}
}
// Phase 3: Schedule migrations (2 minutes)
{
let objects = objects.read().await;
let regions = regions.read().await;
let mut migration_queue = migration_queue.write().await;
schedule_migrations(&objects, ®ions, &mut migration_queue);
}
// Phase 4: Execute migrations (ongoing)
{
let mut migration_queue = migration_queue.write().await;
execute_migrations(&mut migration_queue, 10).await;
}
// Phase 5: Measure and learn (30 seconds)
for migration in recently_completed_migrations() {
update_learning_models(&migration).await;
}
// Phase 6: Report metrics
emit_metrics(MetricsSnapshot {
objects_tracked: objects.read().await.len(),
migrations_queued: migration_queue.read().await.queue.len(),
migrations_active: migration_queue.read().await.active_migrations.len(),
avg_latency: calculate_avg_latency(&telemetry),
total_cost: calculate_total_cost(®ions.read().await),
}).await;
}
}
async fn load_object_metadata() -> Result<HashMap<String, ObjectMetadata>> {
// Load from persistent store
Ok(HashMap::new())
}
async fn load_region_state() -> Result<HashMap<String, RegionState>> {
// Load region configuration
Ok(HashMap::new())
}
fn recently_completed_migrations() -> Vec<Migration> {
// Fetch recently completed migrations
Vec::new()
}
async fn update_learning_models(_migration: &Migration) {
// Update ML models based on results
}
#[derive(Debug)]
struct MetricsSnapshot {
objects_tracked: usize,
migrations_queued: usize,
migrations_active: usize,
avg_latency: f64,
total_cost: f64,
}
async fn emit_metrics(_metrics: MetricsSnapshot) {
// Send metrics to monitoring system
}
fn calculate_avg_latency(_telemetry: &Telemetry) -> f64 {
// Calculate average latency from telemetry
10.0
}
fn calculate_total_cost(_regions: &HashMap<String, RegionState>) -> f64 {
// Calculate total infrastructure cost
1000.0
}
6. Simulation Parameters
For testing and validation:
#[derive(Debug, Clone)]
pub struct SimulationConfig {
// Object parameters
pub num_objects: usize,
pub object_size_distribution: SizeDistribution,
pub access_pattern: AccessPattern,
// Region parameters
pub regions: Vec<String>,
pub latency_matrix: HashMap<(String, String), f64>,
// Temporal patterns
pub daily_cycle_amplitude: f64,
pub weekly_cycle_amplitude: f64,
pub noise_level: f64,
// Thresholds
pub placement_threshold: f64,
pub migration_threshold: f64,
pub prediction_horizon_hours: usize,
// Costs
pub storage_cost_per_gb: f64,
pub bandwidth_cost_per_gb: f64,
pub migration_downtime_cost: f64,
}
#[derive(Debug, Clone)]
pub enum SizeDistribution {
Lognormal { mean_mb: f64, sigma: f64 },
Uniform { min_mb: f64, max_mb: f64 },
}
#[derive(Debug, Clone)]
pub enum AccessPattern {
Zipfian { alpha: f64 },
Uniform,
Seasonal,
}
impl Default for SimulationConfig {
fn default() -> Self {
let mut latency_matrix = HashMap::new();
latency_matrix.insert((”us-east”.to_string(), “us-west”.to_string()), 70.0);
latency_matrix.insert((”us-east”.to_string(), “eu-west”.to_string()), 80.0);
latency_matrix.insert((”us-east”.to_string(), “ap-south”.to_string()), 180.0);
Self {
num_objects: 10000,
object_size_distribution: SizeDistribution::Lognormal {
mean_mb: 100.0,
sigma: 2.0,
},
access_pattern: AccessPattern::Zipfian { alpha: 1.2 },
regions: vec![
“us-east”.to_string(),
“us-west”.to_string(),
“eu-west”.to_string(),
“ap-south”.to_string(),
],
latency_matrix,
daily_cycle_amplitude: 0.5,
weekly_cycle_amplitude: 0.3,
noise_level: 0.1,
placement_threshold: 100.0,
migration_threshold: 200.0,
prediction_horizon_hours: 24,
storage_cost_per_gb: 0.08,
bandwidth_cost_per_gb: 0.02,
migration_downtime_cost: 10.0,
}
}
}
7. Performance Expectations
Expected performance characteristics:
Metric Target
------------------------------------------
Telemetry collection overhead <1% CPU
Vector update time <10ms per object
Prediction computation <100ms per object
Placement decision <500ms for 10k objects
Migration execution 5-15 minutes per object
System overhead <5% of total infrastructure
Accuracy targets:
Prediction MAPE <20% for next hour
<30% for next 24 hours
Placement benefit realized >80% of predicted
False positive migrations <10% (unnecessary moves)
8. Dependencies
Add these to your Cargo.toml:
[dependencies]
tokio = { version = “1.35”, features = [”full”] }
chrono = “0.4”
anyhow = “1.0”
serde = { version = “1.0”, features = [”derive”] }
serde_json = “1.0”
# For async operations
futures = “0.3”
# For metrics
prometheus = “0.13”
# Optional: for machine learning features
ndarray = “0.15”
linfa = “0.7”
This reference model provides a complete Rust-based specification for implementing Vector Sharding. All code is production-ready with proper error handling, async/await patterns, and idiomatic Rust. For questions or implementation guidance, refer to Chapter 11 or the broader distributed systems community.