Storage Forecasting
Prevent business interruptions with Storage Forecasting powered by predictive analytics
SQL Sentry Storage Forecasting helps remove capacity-planning guesswork
SQL Sentry Storage Forecasting applies machine learning algorithms to forecast daily usage for storage volumes across servers, enabling data professionals to proactively manage compute resources. The algorithms analyze workloads, fine-tune disk capacity forecasts, and provide intelligent reporting to help prevent system downtime.
Daily usage forecasts
Storage Forecasting produces daily usage forecasts for all logical disks, customizes a forecast for each volume, and analyzes previous forecasts daily. Over time, the system learns the unique workload of each volume and fine-tunes its forecasts accordingly.
Windows Disk Forecast report
By default, the Windows Disk Forecast report shows all volumes in the monitored environment that are forecast to run out of space within 180 days. You can customize the report to generate forecasts for up to three years into the future. You can generate a report for all volumes on a particular server, and you can include a capacity planning forecast, so you can take measures to help ensure you’ll meet your future storage needs.
Disk Space tab
By clicking on a logical disk in the Disk Space tab, you’ll see a new chart with a history of available free disk space for the volume for the past 180 days. If a forecast is available, you’ll also get the next forecasted 180 days of consumption.
The chart displays historical data and forecasted free disk space remaining with a prediction interval. The further into the future you predict, the wider the prediction interval. The chart also shows the forecasted Resource Exhaustion Date.
Alerting for forecasted disk exhaustion
Beyond viewing daily reports of forecasted storage capacity, you can customize Advisory Conditions to receive alerts if any volumes are forecast to run out of space in the next 90 days, or if a forecasted exhaustion date changes by more than a month earlier than previously predicted. Knowing when the forecasted exhaustion date changes help you identify new database deployments or workload changes that could signal accelerated database growth.
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