Auto Scaling in TuskLang - Bash Guide

📈 Revolutionary Auto Scaling Configuration

Auto scaling in TuskLang transforms your configuration files into intelligent, self-managing scaling systems. No more manual scaling or rigid thresholds—everything lives in your TuskLang configuration with dynamic resource monitoring, intelligent scaling decisions, and comprehensive scaling analytics.

> "We don't bow to any king" – TuskLang auto scaling breaks free from traditional scaling constraints and brings modern elasticity to your Bash applications.

🚀 Core Auto Scaling Directives

Basic Auto Scaling Setup

#auto-scaling: enabled                 # Enable auto scaling
#as-enabled: true                     # Alternative syntax
#as-min-instances: 2                  # Minimum instances
#as-max-instances: 10                 # Maximum instances
#as-target-cpu: 70                    # Target CPU utilization
#as-scale-up-cooldown: 300           # Scale up cooldown (seconds)
#as-scale-down-cooldown: 600         # Scale down cooldown (seconds)

Advanced Auto Scaling Configuration

#as-metrics: cpu,memory,network       # Scaling metrics
#as-predictive: true                  # Enable predictive scaling
#as-scheduled: true                   # Enable scheduled scaling
#as-mixed-instances: true             # Enable mixed instance types
#as-monitoring: true                  # Enable scaling monitoring
#as-notifications: true               # Enable scaling notifications

🔧 Bash Auto Scaling Implementation

Basic Auto Scaler

#!/bin/bash
Load auto scaling configuration
source <(tsk load auto-scaling.tsk)
Auto scaling configuration
AS_ENABLED="${as_enabled:-true}"
AS_MIN_INSTANCES="${as_min_instances:-2}"
AS_MAX_INSTANCES="${as_max_instances:-10}"
AS_TARGET_CPU="${as_target_cpu:-70}"
AS_SCALE_UP_COOLDOWN="${as_scale_up_cooldown:-300}"
AS_SCALE_DOWN_COOLDOWN="${as_scale_down_cooldown:-600}"
Auto scaler
class AutoScaler {
    constructor() {
        this.enabled = AS_ENABLED
        this.minInstances = AS_MIN_INSTANCES
        this.maxInstances = AS_MAX_INSTANCES
        this.targetCPU = AS_TARGET_CPU
        this.scaleUpCooldown = AS_SCALE_UP_COOLDOWN
        this.scaleDownCooldown = AS_SCALE_DOWN_COOLDOWN
        this.instances = []
        this.lastScaleUp = 0
        this.lastScaleDown = 0
        this.stats = {
            scale_ups: 0,
            scale_downs: 0,
            instances_created: 0,
            instances_terminated: 0
        }
    }
    
    checkScaling() {
        if (!this.enabled) return
        
        const currentInstances = this.instances.length
        const currentCPU = this.getAverageCPU()
        
        // Check if scaling is needed
        if (currentCPU > this.targetCPU && currentInstances < this.maxInstances) {
            this.scaleUp()
        } else if (currentCPU < this.targetCPU * 0.5 && currentInstances > this.minInstances) {
            this.scaleDown()
        }
    }
    
    scaleUp() {
        const now = Date.now()
        if (now - this.lastScaleUp < this.scaleUpCooldown * 1000) return
        
        const newInstance = this.createInstance()
        if (newInstance) {
            this.instances.push(newInstance)
            this.lastScaleUp = now
            this.stats.scale_ups++
            this.stats.instances_created++
            this.sendNotification('scale_up', newInstance)
        }
    }
    
    scaleDown() {
        const now = Date.now()
        if (now - this.lastScaleDown < this.scaleDownCooldown * 1000) return
        
        const instanceToTerminate = this.selectInstanceToTerminate()
        if (instanceToTerminate) {
            this.terminateInstance(instanceToTerminate)
            this.instances = this.instances.filter(inst => inst.id !== instanceToTerminate.id)
            this.lastScaleDown = now
            this.stats.scale_downs++
            this.stats.instances_terminated++
            this.sendNotification('scale_down', instanceToTerminate)
        }
    }
    
    getAverageCPU() {
        if (this.instances.length === 0) return 0
        
        const totalCPU = this.instances.reduce((sum, instance) => sum + instance.cpu, 0)
        return totalCPU / this.instances.length
    }
    
    createInstance() {
        // Implementation for instance creation
        return { id: Date.now(), cpu: 0 }
    }
    
    terminateInstance(instance) {
        // Implementation for instance termination
    }
    
    selectInstanceToTerminate() {
        // Select instance with lowest load
        return this.instances.reduce((min, instance) => 
            instance.cpu < min.cpu ? instance : min
        )
    }
    
    sendNotification(type, instance) {
        // Implementation for scaling notification
    }
    
    getStats() {
        return { ...this.stats }
    }
}
Initialize auto scaler
const autoScaler = new AutoScaler()

Dynamic Resource Monitoring

#!/bin/bash
Dynamic resource monitoring
monitor_resources() {
    local monitoring_file="/var/log/auto-scaling/resources.json"
    
    # Collect resource metrics
    local cpu_usage=$(get_cpu_usage)
    local memory_usage=$(get_memory_usage)
    local disk_usage=$(get_disk_usage)
    local network_usage=$(get_network_usage)
    local active_instances=$(get_active_instances)
    
    # Generate resource report
    cat > "$monitoring_file" << EOF
{
    "timestamp": "$(date -Iseconds)",
    "cpu_usage_percent": $cpu_usage,
    "memory_usage_percent": $memory_usage,
    "disk_usage_percent": $disk_usage,
    "network_usage_mbps": $network_usage,
    "active_instances": $active_instances,
    "scaling_recommendation": "$(get_scaling_recommendation $cpu_usage $memory_usage $active_instances)"
}
EOF
    
    echo "✓ Resource monitoring completed"
}
get_cpu_usage() {
    # Get CPU usage percentage
    top -bn1 | grep "Cpu(s)" | awk '{print $2}' | cut -d'%' -f1
}
get_memory_usage() {
    # Get memory usage percentage
    free | awk 'NR==2{printf "%.2f", $3*100/$2}'
}
get_disk_usage() {
    # Get disk usage percentage
    df / | awk 'NR==2{print $5}' | sed 's/%//'
}
get_network_usage() {
    # Get network usage in Mbps
    local rx_bytes=$(cat /proc/net/dev | grep eth0 | awk '{print $2}')
    local tx_bytes=$(cat /proc/net/dev | grep eth0 | awk '{print $10}')
    local total_bytes=$((rx_bytes + tx_bytes))
    echo "scale=2; $total_bytes / 1024 / 1024" | bc -l
}
get_active_instances() {
    # Count active instances
    local instance_count=0
    
    # Check for running containers
    if command -v docker >/dev/null 2>&1; then
        instance_count=$(docker ps --format "table {{.Names}}" | wc -l)
        instance_count=$((instance_count - 1)) # Subtract header
    fi
    
    # Check for running processes
    local process_count=$(pgrep -c -f "application")
    instance_count=$((instance_count + process_count))
    
    echo "$instance_count"
}
get_scaling_recommendation() {
    local cpu_usage="$1"
    local memory_usage="$2"
    local active_instances="$3"
    local target_cpu="${as_target_cpu:-70}"
    local min_instances="${as_min_instances:-2}"
    local max_instances="${as_max_instances:-10}"
    
    if [[ $(echo "$cpu_usage > $target_cpu" | bc -l) -eq 1 ]] && [[ $active_instances -lt $max_instances ]]; then
        echo "scale_up"
    elif [[ $(echo "$cpu_usage < $((target_cpu / 2))" | bc -l) -eq 1 ]] && [[ $active_instances -gt $min_instances ]]; then
        echo "scale_down"
    else
        echo "maintain"
    fi
}

Intelligent Scaling Decisions

#!/bin/bash
Intelligent scaling decisions
make_scaling_decision() {
    local recommendation="$1"
    local current_instances="$2"
    local min_instances="${as_min_instances:-2}"
    local max_instances="${as_max_instances:-10}"
    local scale_up_cooldown="${as_scale_up_cooldown:-300}"
    local scale_down_cooldown="${as_scale_down_cooldown:-600}"
    
    case "$recommendation" in
        "scale_up")
            if can_scale_up "$current_instances" "$max_instances" "$scale_up_cooldown"; then
                scale_up_instance
                return 0
            fi
            ;;
        "scale_down")
            if can_scale_down "$current_instances" "$min_instances" "$scale_down_cooldown"; then
                scale_down_instance
                return 0
            fi
            ;;
        "maintain")
            echo "✓ Maintaining current instance count: $current_instances"
            return 0
            ;;
    esac
    
    return 1
}
can_scale_up() {
    local current_instances="$1"
    local max_instances="$2"
    local cooldown="$3"
    local last_scale_up_file="/tmp/last_scale_up"
    
    # Check instance limit
    if [[ $current_instances -ge $max_instances ]]; then
        echo "Cannot scale up: at maximum instances ($max_instances)"
        return 1
    fi
    
    # Check cooldown period
    if [[ -f "$last_scale_up_file" ]]; then
        local last_scale_up=$(cat "$last_scale_up_file")
        local current_time=$(date +%s)
        local time_since_scale_up=$((current_time - last_scale_up))
        
        if [[ $time_since_scale_up -lt $cooldown ]]; then
            echo "Cannot scale up: in cooldown period ($((cooldown - time_since_scale_up))s remaining)"
            return 1
        fi
    fi
    
    return 0
}
can_scale_down() {
    local current_instances="$1"
    local min_instances="$2"
    local cooldown="$3"
    local last_scale_down_file="/tmp/last_scale_down"
    
    # Check instance limit
    if [[ $current_instances -le $min_instances ]]; then
        echo "Cannot scale down: at minimum instances ($min_instances)"
        return 1
    fi
    
    # Check cooldown period
    if [[ -f "$last_scale_down_file" ]]; then
        local last_scale_down=$(cat "$last_scale_down_file")
        local current_time=$(date +%s)
        local time_since_scale_down=$((current_time - last_scale_down))
        
        if [[ $time_since_scale_down -lt $cooldown ]]; then
            echo "Cannot scale down: in cooldown period ($((cooldown - time_since_scale_down))s remaining)"
            return 1
        fi
    fi
    
    return 0
}
scale_up_instance() {
    local instance_type="${as_instance_type:-t3.micro}"
    local instance_name="app-instance-$(date +%s)"
    
    echo "Scaling up: Creating new instance ($instance_type)"
    
    # Create new instance (example with Docker)
    if command -v docker >/dev/null 2>&1; then
        docker run -d --name "$instance_name" \
            -p 8080:8080 \
            --restart unless-stopped \
            your-application-image
        
        if [[ $? -eq 0 ]]; then
            echo "$(date +%s)" > "/tmp/last_scale_up"
            log_scaling_event "scale_up" "$instance_name" "success"
            echo "✓ Instance created successfully: $instance_name"
        else
            log_scaling_event "scale_up" "$instance_name" "failed"
            echo "✗ Failed to create instance: $instance_name"
        fi
    else
        echo "Docker not available for instance creation"
        return 1
    fi
}
scale_down_instance() {
    echo "Scaling down: Terminating instance"
    
    # Find instance to terminate (lowest load)
    local instance_to_terminate=$(select_instance_to_terminate)
    
    if [[ -n "$instance_to_terminate" ]]; then
        # Terminate instance (example with Docker)
        if command -v docker >/dev/null 2>&1; then
            docker stop "$instance_to_terminate"
            docker rm "$instance_to_terminate"
            
            if [[ $? -eq 0 ]]; then
                echo "$(date +%s)" > "/tmp/last_scale_down"
                log_scaling_event "scale_down" "$instance_to_terminate" "success"
                echo "✓ Instance terminated successfully: $instance_to_terminate"
            else
                log_scaling_event "scale_down" "$instance_to_terminate" "failed"
                echo "✗ Failed to terminate instance: $instance_to_terminate"
            fi
        else
            echo "Docker not available for instance termination"
            return 1
        fi
    else
        echo "No suitable instance found for termination"
        return 1
    fi
}
select_instance_to_terminate() {
    # Select instance with lowest load
    if command -v docker >/dev/null 2>&1; then
        docker ps --format "table {{.Names}}" | grep "app-instance" | head -n 1
    fi
}

Predictive Scaling

#!/bin/bash
Predictive scaling
predictive_scaling() {
    local prediction_window="${as_prediction_window:-3600}" # 1 hour
    local historical_data_file="/var/log/auto-scaling/historical.json"
    
    # Analyze historical patterns
    local predicted_load=$(analyze_historical_patterns "$historical_data_file" "$prediction_window")
    local current_instances=$(get_active_instances)
    local target_instances=$(calculate_target_instances "$predicted_load")
    
    echo "Predicted load: $predicted_load"
    echo "Current instances: $current_instances"
    echo "Target instances: $target_instances"
    
    # Scale based on prediction
    if [[ $target_instances -gt $current_instances ]]; then
        local instances_to_add=$((target_instances - current_instances))
        echo "Predictive scaling: Adding $instances_to_add instances"
        
        for ((i=1; i<=instances_to_add; i++)); do
            scale_up_instance
        done
    elif [[ $target_instances -lt $current_instances ]]; then
        local instances_to_remove=$((current_instances - target_instances))
        echo "Predictive scaling: Removing $instances_to_remove instances"
        
        for ((i=1; i<=instances_to_remove; i++)); do
            scale_down_instance
        done
    fi
}
analyze_historical_patterns() {
    local data_file="$1"
    local window="$2"
    
    if [[ -f "$data_file" ]]; then
        # Analyze historical data for patterns
        local average_load=$(jq -r '.average_load' "$data_file" 2>/dev/null || echo "50")
        echo "$average_load"
    else
        echo "50" # Default prediction
    fi
}
calculate_target_instances() {
    local predicted_load="$1"
    local base_instances="${as_min_instances:-2}"
    local max_instances="${as_max_instances:-10}"
    
    # Calculate target instances based on predicted load
    local target_instances=$((base_instances + (predicted_load / 20)))
    
    # Ensure within bounds
    if [[ $target_instances -lt $base_instances ]]; then
        target_instances=$base_instances
    elif [[ $target_instances -gt $max_instances ]]; then
        target_instances=$max_instances
    fi
    
    echo "$target_instances"
}

Scheduled Scaling

#!/bin/bash
Scheduled scaling
scheduled_scaling() {
    local schedule_file="${as_schedule_file:-/etc/auto-scaling/schedule.conf}"
    
    if [[ -f "$schedule_file" ]]; then
        local current_time=$(date '+%H:%M')
        local current_day=$(date '+%A')
        
        while IFS= read -r schedule_line; do
            local day="${schedule_line%:*}"
            local time_config="${schedule_line#*:}"
            
            if [[ "$day" == "$current_day" ]] || [[ "$day" == "daily" ]]; then
                IFS=',' read -r time instances <<< "$time_config"
                
                if [[ "$time" == "$current_time" ]]; then
                    scale_to_instances "$instances"
                    break
                fi
            fi
        done < "$schedule_file"
    fi
}
scale_to_instances() {
    local target_instances="$1"
    local current_instances=$(get_active_instances)
    
    echo "Scheduled scaling: Target $target_instances instances (current: $current_instances)"
    
    if [[ $target_instances -gt $current_instances ]]; then
        local instances_to_add=$((target_instances - current_instances))
        
        for ((i=1; i<=instances_to_add; i++)); do
            scale_up_instance
        done
    elif [[ $target_instances -lt $current_instances ]]; then
        local instances_to_remove=$((current_instances - target_instances))
        
        for ((i=1; i<=instances_to_remove; i++)); do
            scale_down_instance
        done
    fi
}

Scaling Analytics

#!/bin/bash
Scaling analytics
scaling_analytics() {
    local analytics_file="/var/log/auto-scaling/analytics.json"
    local scaling_log="/var/log/auto-scaling/scaling.log"
    
    # Collect scaling statistics
    local total_scale_ups=$(grep -c "scale_up.*success" "$scaling_log" 2>/dev/null || echo 0)
    local total_scale_downs=$(grep -c "scale_down.*success" "$scaling_log" 2>/dev/null || echo 0)
    local total_failures=$(grep -c "failed" "$scaling_log" 2>/dev/null || echo 0)
    local current_instances=$(get_active_instances)
    local average_response_time=$(calculate_average_response_time)
    
    # Generate analytics report
    cat > "$analytics_file" << EOF
{
    "timestamp": "$(date -Iseconds)",
    "total_scale_ups": $total_scale_ups,
    "total_scale_downs": $total_scale_downs,
    "total_failures": $total_failures,
    "current_instances": $current_instances,
    "average_response_time_ms": $average_response_time,
    "scaling_efficiency": "$(calculate_scaling_efficiency $total_scale_ups $total_failures)"
}
EOF
    
    echo "✓ Scaling analytics generated"
}
calculate_average_response_time() {
    # Calculate average response time across instances
    local total_response_time=0
    local response_count=0
    
    if command -v docker >/dev/null 2>&1; then
        while IFS= read -r container; do
            if [[ -n "$container" ]]; then
                local response_time=$(measure_container_response_time "$container")
                total_response_time=$((total_response_time + response_time))
                response_count=$((response_count + 1))
            fi
        done < <(docker ps --format "{{.Names}}" | grep "app-instance")
    fi
    
    if [[ $response_count -gt 0 ]]; then
        echo $((total_response_time / response_count))
    else
        echo "0"
    fi
}
measure_container_response_time() {
    local container="$1"
    
    # Measure response time using curl
    local start_time=$(date +%s%N)
    if docker exec "$container" curl -s --max-time 5 http://localhost:8080/health >/dev/null 2>&1; then
        local end_time=$(date +%s%N)
        local response_time=$(((end_time - start_time) / 1000000)) # Convert to milliseconds
        echo "$response_time"
    else
        echo "999999" # High penalty for failed requests
    fi
}
calculate_scaling_efficiency() {
    local scale_ups="$1"
    local failures="$2"
    
    local total_operations=$((scale_ups + failures))
    if [[ $total_operations -gt 0 ]]; then
        local success_rate=$((scale_ups * 100 / total_operations))
        
        if [[ $success_rate -ge 90 ]]; then
            echo "excellent"
        elif [[ $success_rate -ge 75 ]]; then
            echo "good"
        elif [[ $success_rate -ge 50 ]]; then
            echo "fair"
        else
            echo "poor"
        fi
    else
        echo "unknown"
    fi
}
log_scaling_event() {
    local event_type="$1"
    local instance_name="$2"
    local status="$3"
    local scaling_log="/var/log/auto-scaling/scaling.log"
    
    echo "$(date '+%Y-%m-%d %H:%M:%S') | $event_type | $instance_name | $status" >> "$scaling_log"
}

🎯 Real-World Configuration Examples

Complete Auto Scaling Configuration

auto-scaling-config.tsk
auto_scaling_config:
  enabled: true
  min_instances: 2
  max_instances: 10
  target_cpu: 70
  scale_up_cooldown: 300
  scale_down_cooldown: 600
#auto-scaling: enabled
#as-enabled: true
#as-min-instances: 2
#as-max-instances: 10
#as-target-cpu: 70
#as-scale-up-cooldown: 300
#as-scale-down-cooldown: 600
#as-metrics: cpu,memory,network
#as-predictive: true
#as-scheduled: true
#as-mixed-instances: true
#as-monitoring: true
#as-notifications: true
#as-config:
 general:
   min_instances: 2
   max_instances: 10
   target_cpu: 70
   scale_up_cooldown: 300
   scale_down_cooldown: 600
 metrics:
   cpu:
     enabled: true
     threshold: 70
   memory:
     enabled: true
     threshold: 80
   network:
     enabled: true
     threshold: 1000
 predictive:
   enabled: true
   prediction_window: 3600
   historical_data: "/var/log/auto-scaling/historical.json"
 scheduled:
   enabled: true
   schedule_file: "/etc/auto-scaling/schedule.conf"
 mixed_instances:
   enabled: true
   types:
     - "t3.micro"
     - "t3.small"
     - "t3.medium"
 monitoring:
   enabled: true
   interval: 60
   metrics:
     - "instance_count"
     - "cpu_usage"
     - "response_time"
 notifications:
   enabled: true
   channels:
     slack:
       webhook: "${SLACK_WEBHOOK}"
       channel: "#auto-scaling"
     email:
       recipients: ["ops@example.com"]
       smtp_server: "smtp.example.com"

Multi-Tier Auto Scaling

multi-tier-auto-scaling.tsk
multi_tier_auto_scaling:
  tiers:
    - name: web
      min_instances: 2
      max_instances: 10
      target_cpu: 70
    - name: api
      min_instances: 3
      max_instances: 15
      target_cpu: 60
    - name: worker
      min_instances: 1
      max_instances: 5
      target_cpu: 80
#as-web: 2:10:70
#as-api: 3:15:60
#as-worker: 1:5:80
#as-config:
 tiers:
   web:
     min_instances: 2
     max_instances: 10
     target_cpu: 70
     instance_type: "t3.micro"
   api:
     min_instances: 3
     max_instances: 15
     target_cpu: 60
     instance_type: "t3.small"
   worker:
     min_instances: 1
     max_instances: 5
     target_cpu: 80
     instance_type: "t3.medium"

🚨 Troubleshooting Auto Scaling

Common Issues and Solutions

1. Auto Scaling Issues

Debug auto scaling
debug_auto_scaling() {
    echo "Debugging auto scaling..."
    monitor_resources
    scaling_analytics
    echo "Auto scaling debug completed"
}

2. Scaling Decision Issues

Debug scaling decisions
debug_scaling_decisions() {
    local cpu_usage=$(get_cpu_usage)
    local memory_usage=$(get_memory_usage)
    local active_instances=$(get_active_instances)
    
    echo "Current metrics:"
    echo "  CPU: ${cpu_usage}%"
    echo "  Memory: ${memory_usage}%"
    echo "  Instances: $active_instances"
    
    local recommendation=$(get_scaling_recommendation "$cpu_usage" "$memory_usage" "$active_instances")
    echo "Scaling recommendation: $recommendation"
}

🔒 Security Best Practices

Auto Scaling Security Checklist

Security validation
validate_auto_scaling_security() {
    echo "Validating auto scaling security configuration..."
    # Check instance security groups
    if [[ "${as_security_groups}" == "true" ]]; then
        echo "✓ Instance security groups enabled"
    else
        echo "⚠ Instance security groups not enabled"
    fi
    # Check instance encryption
    if [[ "${as_instance_encryption}" == "true" ]]; then
        echo "✓ Instance encryption enabled"
    else
        echo "⚠ Instance encryption not enabled"
    fi
    # Check access controls
    if [[ "${as_access_controls}" == "true" ]]; then
        echo "✓ Auto scaling access controls enabled"
    else
        echo "⚠ Auto scaling access controls not enabled"
    fi
}

📈 Performance Optimization Tips

Auto Scaling Performance Checklist

Performance validation
validate_auto_scaling_performance() {
    echo "Validating auto scaling performance configuration..."
    # Check scaling cooldowns
    local scale_up_cooldown="${as_scale_up_cooldown:-300}" # seconds
    if [[ "$scale_up_cooldown" -ge 60 ]]; then
        echo "✓ Reasonable scale up cooldown ($scale_up_cooldown s)"
    else
        echo "⚠ Short scale up cooldown may cause thrashing ($scale_up_cooldown s)"
    fi
    
    local scale_down_cooldown="${as_scale_down_cooldown:-600}" # seconds
    if [[ "$scale_down_cooldown" -ge 300 ]]; then
        echo "✓ Reasonable scale down cooldown ($scale_down_cooldown s)"
    else
        echo "⚠ Short scale down cooldown may cause thrashing ($scale_down_cooldown s)"
    fi
    
    # Check predictive scaling
    if [[ "${as_predictive}" == "true" ]]; then
        echo "✓ Predictive scaling enabled"
    else
        echo "⚠ Predictive scaling not enabled"
    fi
}

🎯 Next Steps

- Auto Scaling Optimization: Learn about advanced auto scaling optimization - Auto Scaling Visualization: Create auto scaling visualization dashboards - Auto Scaling Correlation: Implement auto scaling correlation and alerting - Auto Scaling Compliance: Set up auto scaling compliance and auditing

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Auto scaling transforms your TuskLang configuration into an intelligent, self-managing scaling system. It brings modern elasticity to your Bash applications with dynamic resource monitoring, intelligent scaling decisions, and comprehensive scaling analytics!