In today's digital age, cloud computing has become an essential part of many businesses and organizations. As the demand for cloud services continues to grow, it is crucial to optimize resources to ensure efficient use of slots (units of measurement) to accommodate varying workload patterns.

To model slot performance, we can analyze the usage pattern over a 30-day period, excluding any changes made to the slot allocation. We will focus on understanding how different factors affect performance, including:

1. Slot count and peak vs. average usage periods
2. Bucket-wise workloads and their corresponding performance metrics

Using Google Cloud BigQuery

To model slot performance using Google Cloud BigQuery, follow these steps:

1. Log in to the Google Cloud Console and navigate to the BigQuery page.
2. Select a management project from the dropdown menu.
3. Click on the "Capacity" tab in the navigation panel.
4. Click on the "Slot prediction tool" tab.
5. Choose a reservation with an available idle slot count that can be borrowed at any time.
6. Select one or more models to predict slot performance, and click "Apply".
7. Review the results in the table below, which shows:

• Historical workload data for the past 30 days
• Expected performance changes due to maximum slot increase or decrease
• Workload percentages grouped by bucket (P10-P90, P95, P99, P100)

Understanding Slot Performance Data

The performance data is categorized into different buckets (P10-P90, P95, P99, P100) based on the time spent executing tasks. The top 1% of tasks in the P100 bucket represent the longest-running tasks.

Each bucket row shows:

• Workload percentage
• Average task duration
• Number of tasks
• Expected average task duration for each model

The table also displays "30-day variation" statistics, which show the expected changes in total time spent executing tasks over a 30-day period for each model.

Interpreting Slot Usage Data

When analyzing slot usage data, consider the following:

1. Fixed reservations with shared idle slots can borrow unused slots from other reservations, allowing workloads to exceed allocated slots.
2. Adjust reservation sizes based on consistent borrowing patterns or low utilization.
3. Automatic expansion reservations use the following priority order: standard slots, shared idle slots (if set), and automatic expansion slots.

Pricing

The slot prediction tool is free to use.

Modeling slot performance using Google Cloud BigQuery provides valuable insights into workload patterns and expected changes in resource allocation. By understanding how different factors affect performance, businesses can optimize their cloud resources to meet growing demands and improve overall efficiency.