![]() AWS “resource tags”, “Linked Accounts” and “Organizations”.In this context, metadata can be differentiated between “Resource Metadata” where an individual resource is tagged or labelled or “Hierarchy Metadata” where categorization is applied to some other construct that provides grouping of resources. The information used to categorize costs and is encapsulated within CSP constructs like resource tags (AWS Azure) or labels (GCP). Cloud Cost Management Allocation Metadata We have seen the Anomaly Detection working best on a timescale of a day or consecutive days when an anomaly persists. This does not leave any room to find inter-day variation. This is where the Anomaly Detection process separates itself from budgets.īudgets are usually created and monitored on a monthly, quarterly or annual basis. Usually it’s best if the business users have some control over what you call a low-medium-high-critical anomaly and also being able to set alerts only on the high/critical ones and leave the low-medium for offline analysis.Īlso note that in a day, an anomaly may start off with low severity but as it accumulates more costs, it may escalate to a high or critical level. Taking the discussion on variation further, once you have established a minimum threshold, you still need to identify a low-impact anomaly from a high-impact one. Knowing that you expect compute resource costs to increase 25% for a given month over the prior year is helpful, but not sufficient as spend patterns often have variability from day to day that can be seen in the historical data, but are lost in a monthly bucket. More specifically, forecasts are typically by month and the department level by resource type. ![]() Forecast data is often aggregated at a relatively high level that makes it unusable without understanding the historical patterns. These systems rely on a combination of historical data and future data to determine anomalies with greater accuracy than historical data alone. More sophisticated systems are future aware and include forecast (budget) and event data in their models. The downside to not being future aware is more false positives. The systems may range in sophistication from a simple percent increase in spend to machine learning based models that understand (historical) spend patterns but are still based on learnings from historical data and lack future awareness. Most anomaly detection systems utilize historical data as a basis for detecting anomalies. Typically, there are other systems in place to identify such issues so most organizations will focus strictly on cost increases. For example, a misconfigured autoscaling system may cause a cost increase, or decrease if it fails to upscale. A cost anomaly can be an indicator of an underlying technology or business issue. Run phase) FinOps organizations should investigate decreases as well. While organizations primarily prioritize cost increases, mature (i.e. It typically takes time to improve the signal to noise level to an acceptable level of false positives. Companies just launching an anomaly detection system and processes will need to fine tune the settings and prove out their alerting/notification process. Organizations in the crawl or walk phase of anomaly detection typically focus on cost increases only. Cost-driven anomaliesĬost anomaly detection focuses on identifying deviations from an expected rate of spend. The level of variation which is considered an anomaly can greatly differ based on the size and type of company, the scope of how much they use cloud, and other variables of their particular operation. It’s important to note that we are not simply talking about the “outliers” (one method of approaching anomalies), but we are actually looking to find what are the “expected” or “predicted” costs for a period, and then measure if the actual costs accumulated in that period. Cloud Cost AnomaliesĪnomalies in the context of FinOps are unpredicted variations resulting in increases in cloud spending that are larger than would be expected given historical spending patterns. Anomaly Management Anomaly ManagementĪnomaly Management is the ability to detect, identify, clarify, alert and manage unexpected or unforecasted cloud cost events in a timely manner, in order to minimize detrimental impact to the business, cost or otherwise. Use the Table of Contents to navigate to a specific set of terms. ![]() Terms are added to this page either by Working Group output or by asset. This resources also includes finance and business terminology and definitions to help readers better understand terms used across the FinOps Foundation website, educational and training content. A glossary of FinOps concepts and related terminology used by practitioners all over the world.
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