data/aicl/examples/34_serverless_platform.aicl

# AICL Example: Serverless Platform
# Implements a serverless/function-as-a-service platform with function runtime management,
# cold start optimization, event triggers, auto-scaling, billing, and execution isolation.

# Level 1: Architecture
Goal: Provide an elastic serverless compute platform that executes user-defined functions in response to events with sub-second cold start times, automatic scaling from zero to thousands of concurrent invocations, per-invocation billing, and secure multi-tenant isolation.

Constraint: Cold start latency must not exceed 500ms for interpreted runtimes and 2s for compiled runtimes
Constraint: Function execution timeout must be strictly enforced at configured limit (max 15 minutes)
Constraint: Concurrent invocations per function must scale from 0 to 1000 within 60 seconds
Constraint: Billing must be metered per invocation with 1ms granularity
Constraint: Function isolation must prevent cross-tenant data access and resource interference

Risk: Cold start latency spikes causing SLA violations during traffic bursts
Recovery: Maintain warm execution pool with pre-initialized containers; predictive pre-warming based on traffic patterns; reserved concurrency for latency-sensitive functions; snapshot-based restore for sub-100ms cold starts

Risk: Noisy neighbor causing performance degradation for co-located functions
Recovery: Strict resource isolation via Firecracker microVMs; per-function CPU and memory limits enforced by cgroups; network bandwidth shaping; dedicated worker pools for premium functions

Risk: Event source failure causing missed function invocations
Recovery: At-least-once delivery with idempotent execution; dead-letter queue for failed invocations; event source retry with exponential backoff; checkpoint-based offset tracking for stream sources

Risk: Runaway function execution consuming excessive resources
Recovery: Hard execution timeout enforced at runtime level; memory limit with OOM kill; CPU throttling via cgroups; per-function concurrent invocation cap; automatic suspension after repeated failures

Risk: Billing metering inaccuracy from distributed counter drift
Recovery: Atomic timestamped execution records; dual-path metering (real-time estimate + reconciled billing); periodic reconciliation with audit trail; per-invocation billing records persisted before response

Risk: Security breach via function escape to host system
Recovery: Defense in depth: microVM isolation, seccomp, AppArmor, read-only filesystem, non-root execution, network policies, and regular CVE patching; immediate quarantine on detection

Layer: FunctionRuntime
    SubLayer: ContainerPool
    SubLayer: ColdStartOptimizer
    SubLayer: ExecutionEngine
    SubLayer: ResourceEnforcer
Layer: EventIngestion
    SubLayer: TriggerManager
    SubLayer: EventQueue
    SubLayer: DLQProcessor
Layer: ScalingController
    SubLayer: ConcurrencyManager
    SubLayer: WorkerProvisioner
    SubLayer: ScalePredictor
Layer: BillingMetering
    SubLayer: ExecutionRecorder
    SubLayer: UsageAggregator
    SubLayer: InvoiceGenerator

Validation: Function deployment package must not exceed 250MB uncompressed
Constraint: Each function invocation must have a unique invocation ID for tracing
Validation: Reserved concurrency must not exceed account-level concurrency limit
Validation: Event trigger configurations must be validated before activation
Validation: Execution timeout must be enforced even if function code ignores signals
Validation: Billing records must be immutable once written

# Level 2: Entities
Entity Function
    functionId: string
    functionName: string
    runtime: string
    handler: string
    codeSize: integer
    memorySize: integer
    timeout: integer
    reservedConcurrency: integer
    environment: dict
    layers: list
    lastModified: datetime
    version: string

Entity Invocation
    invocationId: string
    functionId: string
    triggerType: string
    startTime: datetime
    endTime: datetime
    duration: float
    memoryUsed: integer
    billedDuration: float
    status: string
    errorType: string
    requestId: string
    logOutput: string

Entity ExecutionEnvironment
    envId: string
    functionId: string
    workerNode: string
    state: string
    createdAt: datetime
    lastInvocation: datetime
    invocationCount: integer
    memoryAllocation: integer
    isActive: boolean
    snapshotId: string

Entity EventTrigger
    triggerId: string
    functionId: string
    triggerType: string
    sourceArn: string
    batchSize: integer
    maximumBatchingWindow: integer
    filterPattern: dict
    enabled: boolean
    lastTriggered: datetime
    failedInvocations: integer

Entity ConcurrencyState
    functionId: string
    reservedConcurrency: integer
    availableConcurrency: integer
    activeInvocations: integer
    pendingInvocations: integer
    throttleCount: integer
    scaleTargetConcurrency: integer
    lastScaleTime: datetime

Entity BillingRecord
    recordId: string
    accountId: string
    functionId: string
    invocationId: string
    startTime: datetime
    endTime: datetime
    durationMs: integer
    memoryMB: integer
    computeCharge: float
    requestCharge: float
    region: string
    writtenAt: datetime

# Level 3: Behaviors
Behavior InvokeFunction
    Input: functionId: string, payload: bytes, invocationType: string
    Output: response: bytes, invocationId: string, duration: float, billedDuration: float
    Action:
        Validate function exists and is in active state
        Check concurrency limits; throttle if exceeded
        Acquire execution environment from warm pool or create new
        Inject event payload and context into function runtime
        Start execution timer with configured timeout
        Execute function handler with resource limits enforced
        Capture response, logs, and metrics
        Return environment to warm pool if reusable
        Record billing with millisecond granularity
        Return response or error with invocation ID

Behavior PreWarmEnvironment
    Input: functionId: string, runtime: string, memorySize: integer, count: integer
    Output: envIds: list, warmTime: float
    Action:
        Predict upcoming demand based on historical patterns
        Provision container with function code and runtime
        Initialize runtime environment (load handler, set env vars)
        Optionally create snapshot for fast restore
        Add to warm pool with availability timestamp
        Track warm pool size against reserved concurrency
        Emit pre-warm metric with provisioning time

Behavior ProcessEventTrigger
    Input: triggerId: string, events: list
    Output: invocationIds: list, successCount: integer, failureCount: integer
    Action:
        Validate trigger is enabled and function is active
        Batch events according to trigger configuration
        Apply filter pattern to remove non-matching events
        For each batch, invoke function with event payload
        Handle partial batch failures with retry logic
        Send failed invocations to DLQ after max retries
        Update trigger metrics (success/failure counts)
        Acknowledge event source to advance checkpoint

Behavior ScaleConcurrency
    Input: functionId: string, currentLoad: float, targetUtilization: float
    Output: newConcurrency: integer, provisionedWorkers: integer
    Action:
        Measure current concurrent invocations and queue depth
        Calculate desired concurrency based on target utilization
        Respect reserved and account concurrency limits
        If scaling up, provision new execution environments
        If scaling down, allow warm pool to drain naturally
        Implement scale-up cooldown of 30s and scale-down of 300s
        Emit scaling decision metric with reasoning

Behavior EnforceTimeout
    Input: invocationId: string, timeoutSeconds: integer
    Output: terminated: boolean, terminationReason: string
    Action:
        Set hard deadline timer at invocation start
        If function completes before deadline, cancel timer
        If deadline reached, send SIGTERM to function process
        Wait 500ms graceful shutdown period
        If still running, send SIGKILL and destroy environment
        Record timeout error in invocation log
        Emit timeout metric with function ID

Behavior RecordBilling
    Input: functionId: string, invocationId: string, durationMs: integer, memoryMB: integer
    Output: recordId: string, computeCharge: float, requestCharge: float
    Action:
        Calculate billed duration (rounded up to nearest ms)
        Compute compute charge = durationMs * memoryMB * rate
        Compute request charge per invocation
        Create immutable billing record with timestamp
        Persist to billing store before returning invocation response
        Aggregate hourly usage for account-level reporting
        Emit billing metric with cost breakdown

# Level 4: Conditions
Condition: ConcurrencyLimitReached
    When active invocations equal reserved concurrency for a function
    Then throttle additional invocations with 429 TooManyRequests; queue invocations if async; emit throttle metric; trigger emergency scaling if account limit permits

Condition: ColdStartSpike
    When more than 10 cold starts occur within 5 seconds for a single function
    Then activate predictive pre-warming with increased pool size; check if traffic pattern matches known spike; emit cold-start-spike metric; increase warm pool target by 50%

Condition: FunctionErrorRateHigh
    When function error rate exceeds 10% over a 5-minute rolling window
    Then emit error-rate alert; if exceeding 50%, circuit break and return error immediately; send notification to function owner; capture error details in DLQ for analysis

Condition: WorkerPoolExhausted
    When no available workers in the execution pool for new invocations
    Then queue invocations with admission control; provision new workers urgently; if provision timeout, fail fast with 503; emit worker-pool-exhausted metric

Condition: DLQThresholdReached
    When dead-letter queue depth exceeds 10000 messages per function
    Then pause event trigger to prevent further failures; alert function owner with error summary; enable manual replay mechanism; emit DLQ-critical metric

# Level 5: Events
Event: OnFunctionDeployed
    On new function version deployed to the platform
    Action: Invalidate warm pool for previous version, begin pre-warming new version, update routing to canary or all-at-once, validate health check invocation succeeds, emit deployment metric

Event: OnColdStartExecuted
    On function invocation requires new execution environment
    Action: Record cold start duration metric, update warm pool sizing algorithm, check if pre-warming should have prevented, emit cold-start metric with runtime and memory configuration

Event: OnInvocationCompleted
    On function execution finishes (success or failure)
    Action: Record execution metrics, persist billing record, return environment to warm pool if healthy, update concurrency counters, emit completion metric with duration and status

Event: OnScaleDecision
    On scaling controller adjusts concurrency for a function
    Action: Provision or decommission execution environments, update concurrency state, log scaling reason and metrics, emit scale-decision metric, enforce cooldown periods

Event: OnDLQMessageAdded
    On failed invocation message added to dead-letter queue
    Action: Increment DLQ depth counter, emit DLQ metric, notify function owner if threshold reached, preserve full invocation context for replay, schedule DLQ retention cleanup

# Level 6: Concurrency
Parallel:
    Independent function invocations across worker nodes
    Event trigger processing from multiple sources
    Warm pool maintenance and pre-warming
    Billing record persistence and aggregation
    Concurrency scaling decisions per function

# Level 7: Optimization
Optimize: Cold start latency
    Priority: Snapshot-based environment restoration; pre-warmed pool with predictive sizing; lightweight microVM (Firecracker) for fast boot; runtime-specific initialization optimizations; lazy loading of function code

Optimize: Invocation throughput
    Priority: Connection pooling for downstream services; keep-alive between invocations; batch event processing for stream triggers; efficient serialization (MessagePack over JSON); lock-free concurrency tracking

Optimize: Cost efficiency
    Priority: Granular billing with 1ms resolution; automatic scale-to-zero; memory right-sizing recommendations; spot worker nodes for non-critical functions; tiered pricing for committed usage

# Level 8: Learning
Learn: Optimal warm pool size per function
    Goal: Minimize cold starts while minimizing idle resource cost
    Adapt: warmPoolTargetSize per function
    Based: Historical invocation patterns (time-of-day, day-of-week), cold start frequency, and traffic prediction models over 14-day windows

Learn: Optimal memory allocation per function
    Goal: Right-size memory allocation to minimize cost while meeting performance targets
    Adapt: recommendedMemorySize per function
    Based: Actual memory usage patterns, execution duration vs. memory correlation, and cost-performance trade-off analysis

Learn: Predictive scaling triggers
    Goal: Pre-scale before anticipated traffic bursts to eliminate cold starts
    Adapt: scalingTriggerThreshold and preWarmDuration per function
    Based: Recurring traffic patterns, event trigger schedules, and known traffic surge events from historical data

# Level 9: Security
Security:
    Encrypt: Function code at rest using AES-256 with per-tenant keys
    Encrypt: Invocation payloads in transit using TLS 1.3
    Encrypt: Environment variables and secrets using envelope encryption with KMS
    Protect: Function isolation via microVM boundaries with minimal attack surface
    Protect: Network access via per-function security groups and VPC configuration
    Protect: Against credential leakage via IAM roles with least-privilege execution roles
    Protect: Billing data integrity via append-only immutable log with cryptographic verification

# Level 10: Native
Native: Rust
{
use std::sync::Arc;
use tokio::sync::{Semaphore, RwLock};
use std::time::{Duration, Instant};

struct FunctionRuntime {
    pool: Arc<WarmPool>,
    concurrency_limiter: Arc<Semaphore>,
    billing: Arc<BillingRecorder>,
    metrics: Arc<MetricsCollector>,
    config: FunctionConfig,
}

struct WarmPool {
    environments: RwLock<Vec<ExecutionEnv>>,
    function_id: String,
    target_size: usize,
    max_size: usize,
}

struct ExecutionEnv {
    id: String,
    container_id: String,
    state: EnvState,
    created_at: Instant,
    last_used: Instant,
    invocation_count: u64,
}

#[derive(PartialEq)]
enum EnvState {
    Idle,
    InUse,
    PreWarming,
    Draining,
}

impl FunctionRuntime {
    async fn invoke(&self, payload: &[u8]) -> Result<InvocationResult, InvokeError> {
        let start = Instant::now();
        
        // Acquire concurrency permit
        let permit = self.concurrency_limiter.try_acquire()
            .map_err(|_| InvokeError::Throttled)?;
        
        // Get execution environment
        let (env, cold_start) = match self.pool.acquire().await {
            Some(env) => (env, false),
            None => {
                let env = self.pool.provision_new().await?;
                (env, true)
            }
        };
        
        if cold_start {
            self.metrics.record_cold_start(&self.config.function_id);
        }
        
        // Execute with timeout enforcement
        let timeout = Duration::from_secs(self.config.timeout as u64);
        let result = tokio::time::timeout(timeout, self.execute(&env, payload)).await;
        
        let duration = start.elapsed();
        let billed_ms = (duration.as_millis() as u64).max(1);
        
        // Record billing
        self.billing.record(BillingRecord {
            function_id: self.config.function_id.clone(),
            duration_ms: duration.as_millis() as u64,
            billed_ms,
            memory_mb: self.config.memory_size,
            compute_charge: self.calculate_charge(billed_ms),
            timestamp: chrono::Utc::now(),
        }).await;
        
        // Return env to pool
        self.pool.release(env).await;
        drop(permit);
        
        match result {
            Ok(Ok(response)) => Ok(InvocationResult {
                response,
                duration: duration.as_secs_f64(),
                cold_start,
            }),
            Ok(Err(e)) => Err(e),
            Err(_) => Err(InvokeError::Timeout),
        }
    }
    
    fn calculate_charge(&self, billed_ms: u64) -> f64 {
        let gb_seconds = (self.config.memory_size as f64 / 1024.0) 
            * (billed_ms as f64 / 1000.0);
        gb_seconds * 0.0000166667 // per GB-second rate
    }
}