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Difficulty: MediumCapacity Planning and Infrastructure Workload Scaling Optimization

A media streaming provider is preparing for a live sports event expected to cause a 10x traffic surge on its I/O-bound metadata API service, which is deployed on Compute Engine Managed Instance Groups (MIGs). During previous high-traffic events, the service experienced request timeouts because CPU-based autoscaling reacted too slowly to memory and network connection bottlenecks, and automated scaling requests were blocked by regional compute resource limits. Which TWO actions should you take to optimize capacity planning and ensure seamless workload scaling during the upcoming event?

  1. Configure the Managed Instance Group (MIG) autoscaling policy to scale based on Cloud Monitoring custom metrics such as active HTTP connections or queue depth, and proactively request regional Compute Engine quota increases ahead of the event.Answer
  2. Implement scheduled scaling policies on the Managed Instance Groups to pre-provision baseline instance capacity prior to the expected event start time.Answer
  3. C
    Migrate the stateless API microservice to a multi-region Google Kubernetes Engine (GKE) enterprise cluster to automatically bypass regional compute quota constraints.
  4. D
    Maintain MIG autoscaling based strictly on average CPU utilization while relying on automated system quota scaling during peak traffic hours.

Answer

To optimize capacity planning and scaling, you should configure MIG autoscaling based on custom I/O metrics while submitting proactive regional quota increase requests, and set up scheduled scaling policies to pre-provision capacity before the event starts.
The combination of using relevant custom metrics (active HTTP connections/queue depth) for I/O-bound workloads, proactively requesting regional compute quota increases, and using scheduled scaling policies to pre-warm instance groups effectively addresses both latency response delays and quota capacity exhaustion during planned peak events.

Step-by-Step Solution

1
Identify the scaling metric bottleneck for I/O-bound workloads.
Recognize that CPU utilization is an inadequate metric for I/O-bound services and replace it with custom metrics (e.g., active connection count or HTTP request queue depth).
I/O-bound workloads saturate network threads or database connection pools well before CPU limits are reached.
2
Evaluate regional project quota requirements.
Submit quota increase requests for Compute Engine resources in the target regions prior to the planned traffic event.
Autoscaling cannot exceed project regional quotas; quota increases require administrative approval time.
3
Address known temporal traffic spikes.
Configure scheduled autoscaling rules on the MIG to scale up instances ahead of the event start time.
Pre-provisioning baseline capacity eliminates warm-up latency and prevents request drops when traffic begins abruptly.

Key Concept

Capacity Planning and Workload Autoscaling Optimization
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Topics

Capacity Planning and Infrastructure Workload Scaling Optimization