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Zorluk: OrtaAWS Cost Explorer and Cost Anomaly Detection

A company runs large-scale, ad-hoc data processing jobs using Amazon EMR. The run schedule and scale of these jobs vary greatly based on client data delivery, leading to highly variable but legitimate compute costs. A SysOps administrator needs to implement a solution that detects unexpected cost spikes due to configuration errors or orphaned resources, while minimizing false alarms caused by the variable EMR workloads. Which solution meets these requirements with the least operational overhead?

  1. A
    Configure an AWS Budget with a daily cost limit set to the average historical spend of the EMR clusters, and set up an Amazon SNS notification when costs exceed 110% of the threshold.
  2. B
    Enable detailed monitoring in CloudWatch for the EMR cluster instances, and create an Amazon EventBridge rule that triggers an AWS Systems Manager Automation runbook to terminate instances when billing metrics spike.
  3. Create an AWS Cost Anomaly Detection monitor using the AWS services monitor type, and create an alert subscription with an alerting threshold to notify the administrator of anomalies.Cevap
  4. D
    Enable cost allocation tags for EMR clusters in the Billing console, and schedule a daily Cost Explorer report to be sent to the administrator to manually review untagged resource costs.

Cevap

Create an AWS Cost Anomaly Detection monitor using the AWS services monitor type, and create an alert subscription with an alerting threshold to notify the administrator of anomalies.
AWS Cost Anomaly Detection uses machine learning to dynamically baseline and monitor cost patterns for individual AWS services. This approach accommodates the highly variable and ad-hoc nature of workloads like Amazon EMR by adapting to historical spend trends, thereby minimizing false positives and alerting the administrator only when an actual anomaly is detected.

Adım Adım Çözüm

1
Identify the nature of the workload's cost pattern.
The workload has highly variable, ad-hoc, but legitimate cost spikes.
Understanding the cost pattern helps in selecting a tool that can adapt to variance without generating excessive false alerts.
2
Evaluate the capabilities of AWS billing and monitoring tools.
AWS Cost Anomaly Detection uses machine learning to automatically establish baselines and detect anomalous deviations, whereas AWS Budgets use static thresholds.
Machine learning-based anomaly detection is required to distinguish true anomalies from legitimate variable usage.
3
Configure the monitor and alert subscription.
Select the AWS services monitor type to evaluate Amazon EMR spend individually and set up an email or SNS alert subscription.
A service-level monitor evaluates cost anomalies per service, and the alert subscription ensures timely notification with minimal operational overhead.

Anahtar Kavram

AWS Cost Anomaly Detection for variable workloads
Tahmini Süre:1m 30s
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AWS Cost Explorer and Cost Anomaly Detection

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