Abstract: Techniques for predicting anomalies in forecasted time-series data are disclosed. A system. A system predicts whether a monitored computing system will experience anomalies by comparing forecasted values associated with components in the monitored computing system to threshold values. The system utilizes time-series machine learning models to forecast workloads of computing resources in the monitored computing system. The system trains and tests multiple different versions of a time-series model and selects the most accurate version to generate forecasts for a particular workload in the computing system. The system compares the forecasts to threshold values to predict anomalies. Based on detecting anomalies, the system generates recommendations for remediating predicted anomalies.

Abstract: Techniques for selecting and training candidate time-series models to forecast computational workloads are disclosed. A system creates a candidate set of time-series models for forecasting computing workloads by filtering the sets of parameter values to a number that meets system performance specifications. The system selects different sets of parameter values for different candidate models based on analyzing correlogram data. The system identifies in the correlogram data a set of one or more correlation values that (a) meet or exceed a threshold value, and (b) meet a distance criteria from the threshold value. The system trains the candidate set of time-series models with a training data set. The system selects the best-performing time-series model to generate forecasts for a particular computing resource in a computing system.

Abstract: Systems, methods, and machine-readable media may place workloads of source systems in a migration of data and applications from the source systems to target systems. Data relating to a first number of source nodes and a second number of target nodes may be received and analyzed. A migration plan that specifies placement of workloads from source systems into target systems may be created. The placement may include packing a pluggable environment or a clustered environment. The packing the pluggable environment or the clustered environment may include: when the second number of target nodes is greater than the first number of source nodes, placing workload from the source nodes to the target nodes; and, when the second number of target nodes is less than the first number of source nodes, the workload from the source nodes is not placed to the target nodes.

Abstract: Techniques for multi-layer forecasting of computational workloads are disclosed. A system identifies a level of granularity associated with a request to forecast a computational workload for a particular entity. The system obtains attribute data of computational resources at the specified level of granularity. The system determines whether computational resources, not specified in the request, should be included in a workload forecast. The system applies a time-series forecast model to time-series data obtained from computational resources associated with the request. The system presents one or more workload forecasts for computational workloads associated with the request.

Abstract: Techniques for time-series analysis for forecasting computational workloads are disclosed. A resource management system monitors a computing system and obtains metrics data from the computing system. The metrics data is stored as a set of historical data points of a data set. A first portion of the set of historical data points comprise an outlier that does not correspond to a seasonality pattern associated with a second portion of the set of historical data points. The resource management system tests a first time-series model that incorporates a first exogenous variable corresponding to a first exogeneous factor to determine that the first time-series model fits both the first portion of the set of historical data points and the second portion of the set of historical data points within an error threshold. Then, the resource management system selects the first time-series model to predict future data points of the data set.

Abstract: A method of pre-allocating resources for database provisioning in a private cloud may include determining characteristics of a plurality of databases in a customer's database system. The method may also include grouping the characteristics of the plurality of databases to generate a plurality of database templates that are descriptive of the plurality of databases. The method may additionally include causing resources to be pre-allocated in the private cloud based on at least some of the plurality of database templates. The method may further include receiving a selection of a database template from the plurality of database templates as part of a provisioning request. The method may also include causing a database described at least in part by the database template to be provisioned in the private cloud.

Abstract: Techniques for time-series analysis for forecasting computational workloads are disclosed. A resource management system monitors a computing system and obtains metrics data from the computing system. The metrics data is stored as a set of historical data points of a data set. A first portion of the set of historical data points comprise an outlier that does not correspond to a seasonality pattern associated with a second portion of the set of historical data points. The resource management system tests a first time-series model that incorporates a first exogenous variable corresponding to a first exogeneous factor to determine that the first time-series model fits both the first portion of the set of historical data points and the second portion of the set of historical data points within an error threshold. Then, the resource management system selects the first time-series model to predict future data points of the data set.

Abstract: A method of pre-allocating resources for database provisioning in a private cloud may include determining characteristics of a plurality of databases in a customer's database system. The method may also include grouping the characteristics of the plurality of databases to generate a plurality of database templates that are descriptive of the plurality of databases. The method may additionally include causing resources to be pre-allocated in the private cloud based on at least some of the plurality of database templates. The method may further include receiving a selection of a database template from the plurality of database templates as part of a provisioning request. The method may also include causing a database described at least in part by the database template to be provisioned in the private cloud.