When virtualizing legacy servers, the cooling cost calculation breaks down into three fundamental components:

// Pseudocode for cooling cost calculation
function calculateCoolingCost(serverWatts, coolingEfficiency, electricityCost) {
    const btusPerHour = serverWatts * 3.412; // Conversion factor
    const tonsOfCooling = btusPerHour / 12000;
    const hourlyCost = (tonsOfCooling / coolingEfficiency) * electricityCost;
    return hourlyCost * 24 * 30; // Monthly cost
}

From APC logs showing 880W draw:

• 880W × 3.412 = 3002.56 BTU/hr
• 3002.56 ÷ 12000 = 0.25 tons cooling required

Modern HVAC systems typically have efficiency ratings (EER) between 8-12. For calculations:

// Example with EdgeStar AP14000HS (EER 10.5)
const coolingEfficiency = 10.5;
const electricityRate = 0.12; // $/kWh
const monthlyCost = calculateCoolingCost(880, 10.5, 0.12);
console.log(Estimated monthly cooling cost: $${monthlyCost.toFixed(2)});

While ambient temperature affects absolute cooling requirements, the server-to-cooling ratio remains constant. What changes is the baseline HVAC workload.

function tempAdjustedCoolingCost(baseWatts, ambientTemp, targetTemp, ...params) {
    const deltaT = ambientTemp - targetTemp;
    const adjustmentFactor = 1 + (deltaT * 0.02); // 2% per °F above target
    return calculateCoolingCost(baseWatts * adjustmentFactor, ...params);
}

For data center planning, consider this Python class:

class ServerCoolingCalculator:
    def __init__(self, server_watts, eer=10.5, cost_per_kwh=0.12):
        self.server_watts = server_watts
        self.eer = eer
        self.cost_per_kwh = cost_per_kwh
    
    def calculate_btu(self):
        return self.server_watts * 3.412
    
    def monthly_cost(self, hours=24*30):
        cooling_power = self.calculate_btu() / self.eer
        return (cooling_power / 1000) * self.cost_per_kwh * hours

Cross-validate your calculations with:

1. UPS power draw metrics
2. HVAC system runtime logs
3. Utility bill analysis during peak loads

Modern virtualization reduces cooling needs through:

• Dynamic power management (DVFS)
• Thermal-aware workload scheduling
• Hot/cold aisle containment

When virtualizing legacy hardware, understanding cooling costs requires mastering three key conversions:

// Conversion formulas in Python
def watts_to_btu(watts):
    return watts * 3.412142

def btu_to_ton_hours(btu):
    return btu / 12000  # 1 ton = 12,000 BTU/hr

def kwh_to_cost(kwh, rate=0.12):
    return kwh * rate  # Default $0.12/kWh

Data center cooling isn't 1:1 efficient. Consider these variables:

• PUE (Power Usage Effectiveness) of your facility (typically 1.5-2.0)
• CRAC unit efficiency (modern units achieve 0.7-0.9 kW/ton)
• Ambient temperature differential (ΔT between intake and exhaust)

Let's calculate cooling costs for your 880W server:

# Sample calculation
server_watts = 880
cooling_pue = 1.8  # Average data center
electric_rate = 0.14  # $/kWh
crac_efficiency = 0.8  # kW/ton

btu = watts_to_btu(server_watts)
ton_hours = btu_to_ton_hours(btu) * 24  # Daily
cooling_kwh = ton_hours * crac_efficiency * cooling_pue
daily_cost = kwh_to_cost(cooling_kwh, electric_rate)

print(f"Daily cooling cost: ${daily_cost:.2f}")
# Output: Daily cooling cost: $8.11

For more accurate modeling:

# Temperature-adjusted calculation
def adjusted_cooling(base_btu, ambient_temp, target_temp=72):
    temp_factor = max(1, (ambient_temp - target_temp) / 10)
    return base_btu * temp_factor

Implement real-time monitoring with tools like:

# Pseudocode for cooling optimization
while True:
    current_temp = get_server_inlet_temp()
    if current_temp > threshold:
        increase_cooling()
    else:
        apply_aisle_containment()