Projecting Disk Usage Based on Historical Trends in a Cloud Environment
Abstract
Provisioning scarce resources among competing users and jobs remains one of the primary challenges of operating large-scale, distributed computing environments. Distributed storage systems, in particular, typically rely on hard operator-set quotas to control disk allocation and enforce isolation for space and I/O bandwidth among disparate
users. However, users and operators are very poor at predicting future requirements and, as a result, tend to over-provision grossly.
For three years, we collected detailed usage information for data stored in distributed filesystems in a large private cloud spanning dozens of clusters on multiple continents. Specifically, we measured the disk space usage, I/O rate, and age of stored data for thousands of different engineering users and teams. We find that although the
individual timeseries often have non-stable usage trends, regional aggregations, user classification, and ensemble forecasting methods can be combined to provide a more accurate prediction of future use for the majority of users.
We applied this methodology for the storage users in one
geographic region and back-tested these techniques over the past three years to compare our forecasts against actual usage. We find that by classifying a small subset of users with unforecastable trend changes due to known product launches, we can generate three-month out forecasts with mean absolute errors of less than ~12%. This compares favorably to the amount of allocated but unused quota that is generally wasted with manual operator-set quotas.
users. However, users and operators are very poor at predicting future requirements and, as a result, tend to over-provision grossly.
For three years, we collected detailed usage information for data stored in distributed filesystems in a large private cloud spanning dozens of clusters on multiple continents. Specifically, we measured the disk space usage, I/O rate, and age of stored data for thousands of different engineering users and teams. We find that although the
individual timeseries often have non-stable usage trends, regional aggregations, user classification, and ensemble forecasting methods can be combined to provide a more accurate prediction of future use for the majority of users.
We applied this methodology for the storage users in one
geographic region and back-tested these techniques over the past three years to compare our forecasts against actual usage. We find that by classifying a small subset of users with unforecastable trend changes due to known product launches, we can generate three-month out forecasts with mean absolute errors of less than ~12%. This compares favorably to the amount of allocated but unused quota that is generally wasted with manual operator-set quotas.
Research Areas
