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Difficulty: HardUnderstand concepts of cloud economics

A smart home IoT provider experiences significant daily fluctuations in data processing demands as users interact with their devices primarily during morning and evening hours. Additionally, the company runs a heavy data aggregation job once per month that lasts for 24 hours. The engineering team is transitioning this workload from an on-premises data center to AWS to optimize costs. Which of the following strategies represent correct applications of AWS cloud economics to minimize operational costs for these workloads? (Select TWO.)

  1. Utilizing EC2 Auto Scaling to automatically scale compute capacity in response to the daily morning and evening IoT traffic spikes, paying only for the resources consumed.Answer
  2. Using On-Demand or Spot Instances for the 24-hour monthly data aggregation job, rather than purchasing Reserved Instances that would remain idle for the rest of the month.Answer
  3. C
    Purchasing a 3-year All Upfront Reserved Instance contract specifically for the monthly data aggregation job to minimize the upfront capital expenses.
  4. D
    Overprovisioning the daily processing instances to continuously run at peak evening capacity to guarantee performance and eliminate variable operational costs.
  5. E
    Treating all monthly AWS usage bills as Capital Expenses (CapEx) to establish a predictable, long-term asset value on the balance sheet.

Answer

The correct strategies are utilizing EC2 Auto Scaling to dynamically adjust to daily traffic spikes, and using On-Demand or Spot Instances for the monthly 24-hour aggregation job.
The correct strategies align resource costs with usage patterns. Using Auto Scaling ensures the company only pays for active compute capacity during morning and evening IoT spikes. Similarly, using On-Demand or Spot Instances for the short 24-hour monthly workload avoids the continuous cost of Reserved Instances, which would otherwise sit idle for the vast majority of the month.

Step-by-Step Solution

1
Analyze the workload characteristics
Two distinct workloads exist: a daily fluctuating IoT workload with morning/evening peaks, and an intermittent 24-hour monthly workload.
Different workload patterns require different cost-optimization strategies in cloud economics.
2
Evaluate the daily fluctuating workload strategy
Elasticity is the best approach. Dynamic scaling matches resource supply with demand, avoiding overprovisioning costs.
Auto Scaling allows the company to transition from a fixed capacity model to a variable pay-as-you-go model.
3
Evaluate the monthly 24-hour workload strategy
On-Demand or Spot Instances are best because they only incur costs for the 24 hours of execution, whereas Reserved Instances would charge for unused hours.
Reserved Instances are designed for continuous, steady-state workloads (high utilization), not intermittent tasks.
4
Identify the correct choices
Selecting Auto Scaling for daily demand and On-Demand/Spot for the monthly job optimizes TCO.
These choices align with the pay-as-you-go pricing model and elastic resource provisioning.

Key Concept

Cloud economics principles emphasizing the shift from Capital Expenses (CapEx) to Operating Expenses (OpEx), leveraging elasticity to align resource supply with demand, and selecting correct pricing models based on workload utilization patterns.
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Understand concepts of cloud economics

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