Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing autoscaling of computing resources in a distributed computing system. One of the methods includes receiving, by an autoscaler of a cloud computing platform, a plurality of capacity parameters for a workload that is to be executed in one or more instances of the cloud computing platform or to consume some amount of computing resources. An autoscaling process is repeatedly performed that computes a capacity cost for a workload based on a current utilization measure for multiple workloads of the cloud computing platform. A target capacity level is computed that quantifies the amount of computing resources that should be allocated to the workload to achieve the target service level for the computed demand forecast, resource allocation is automatically adjusted for the workload according to the target capacity level.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing autoscaling of computing resources in a distributed computing system. One of the methods includes receiving, by an autoscaler of a cloud computing platform, a plurality of capacity parameters for a workload that is to be executed in one or more instances of the cloud computing platform or to consume some amount of computing resources. An autoscaling process is repeatedly performed that computes a capacity cost for a workload based on a current utilization measure for multiple workloads of the cloud computing platform. A target capacity level is computed that quantifies the amount of computing resources that should be allocated to the workload to achieve the target service level for the computed demand forecast, resource allocation is automatically adjusted for the workload according to the target capacity level.

Abstract: A method and system of data analytics provide for retrieving data from a plurality of different sources, normalizing the data, predicting one or more values based on the normalized data, analyzing the normalized data based on the prediction, and delivering an output to a user based on the analysis. One such method includes using a computer to extract point observation data from different electronic data sources; converting time-series data into a plurality of reference (time, value) pairs; normalizing the reference (time, value) pairs using a coherence operation to convert the data into interval observation data; performing estimated weighted moving average with a residual bands adjustment to provide a range of predicted values; comparing current (time, value) pairs to the predicted value to identify anomalies.