Abstract: Placement decisions may be made to place data in a multi-tenant cache. Usage of multi-tenant cache nodes for performing access requests may be obtained. Usage prediction techniques may be applied to the usage to determine placement decisions for data amongst the multi-tenant cache nodes. Placement actions for the data amongst at the multi-tenant cache nodes may be performed according to the placement decisions.

Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving a request to monitor for anomalies from one or more data sources; analyzing time-series data from the one or more data sources; generating a recommendation for handling the determined anomaly, the recommendation generated by performing one or more of a root cause analysis, a heuristic analysis, and an incident similarity analysis; and reporting the anomaly and recommendation.

Abstract: Placement decisions may be made to place data in a multi-tenant cache. Usage of multi-tenant cache nodes for performing access requests may be obtained. Usage prediction techniques may be applied to the usage to determine placement decisions for data amongst the multi-tenant cache nodes. Placement actions for the data amongst at the multi-tenant cache nodes may be performed according to the placement decisions.

Abstract: Techniques for forecasting using deep factor models with random effects are described. A forecasting framework combines the strengths of both classical and neural forecasting methods in a global-local framework for forecasting multiple time series. A global model captures the common latent patterns shared by all time series, while a local model explains the variations at the individual level.

Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving a request to detect anomalies, using an anomaly detection service, in time series data using one or more detectors; configuring the anomaly detection service by: generating a configuration for the anomaly detection service based on at least in part on one or more of the request the time series data, and metadata, wherein the configuration identifies at least one particular detector of the one or more detectors, and configuring the anomaly detection service using the generated configuration; evaluating the time series data for an anomaly using the configured anomaly detection service by: observing potentially anomalous behavior using the identified at least one particular detector of the one or more detectors, and generating an anomaly indication.

Abstract: A composite time series forecasting model comprising a neural network sub-model and one or more state space sub-models corresponding to individual time series is trained. During training, output of the neural network sub-model is used to determine parameters of the state space sub-models, and a loss function is computed using the values of the time series and probabilistic values generated as output by the state space sub-models. A trained version of the composite model is stored.

Abstract: Techniques for anomaly detection are described.

Abstract: An input data set with a plurality of item descriptors comprising respective time series observations is identified. A routing directive indicating a predicate to be evaluated to determine whether a particular item descriptor is to be included in a training data set for a first learning algorithm is obtained. A plurality of learning algorithms are trained using training data sets derived from the input data set according to respective routing directives, and the trained algorithms are stored.

Abstract: A robust tenant placement and migration system, a process, and a respective controller for database-as-a-service environments are disclosed. Multiple tenants (T) are allocated on a set of in-memory database servers (S), such that workload changes depending on the tenants' behavior are considered, while maintaining strict service level objectives. The placement and migration are based on a migration command which, in turn, is based on an interleaved placement by interleaving tenants (T) across servers.

Abstract: A robust tenant placement and migration system, a process, and a respective controller for database-as-a-service environments are disclosed. Multiple tenants (T) are allocated on a set of in-memory database servers (S), such that workload changes depending on the tenants' behavior are considered, while maintaining strict service level objectives. The placement and migration are based on a migration command which, in turn, is based on an interleaved placement by interleaving tenants (T) across servers.