Abstract: A plurality of virtual machines executing on physical machines may be monitored, and performance data characterizing consumption of physical resources of the physical machines by the plurality of virtual machines during the observation time may be extracted. Each of the plurality of virtual machines may be classified as active or idle during each time division of a plurality of time divisions of the observation time, based on the performance data and on idleness criteria, to thereby generate an active-idle series for each of the plurality of virtual machines. For each active-idle series of each virtual machine of the plurality of virtual machines, at least one periodicity of recurring idle times within the observation time may be determined. Then, for each virtual machine with the at least one periodicity, an on-off schedule may be determined, and each of the virtual machines may be transitioned with the at least one periodicity between an on state and an off state in accordance with the on-off schedule.

Abstract: A plurality of virtual machines executing on physical machines may be monitored, and performance data characterizing consumption of physical resources of the physical machines by the plurality of virtual machines during the observation time may be extracted. Each of the plurality of virtual machines may be classified as active or idle during each time division of a plurality of time divisions of the observation time, based on the performance data and on idleness criteria, to thereby generate an active-idle series for each of the plurality of virtual machines. For each active-idle series of each virtual machine of the plurality of virtual machines, at least one periodicity of recurring idle times within the observation time may be determined. Then, for each virtual machine with the at least one periodicity, an on-off schedule may be determined, and each of the virtual machines may be transitioned with the at least one periodicity between an on state and an off state in accordance with the on-off schedule.

Abstract: Embodiments provide a method for performing an automatic execution of a Box and Jenkins method for forecasting the behavior of said dataset. The method may include pre-processing the dataset including providing one or more missing values to the dataset, removing level discontinuities and outliers, and removing one or more last samples from the dataset, obtaining a trend of the pre-processed dataset including identifying and filtering the trend out of the dataset based on a coefficient of determination methodology, detecting seasonality to obtain a resulting stationary series including computing an auto correlation function of the dataset, repeating the detecting step on an aggregate series of a previous dataset, and removing detected seasonality based on a seasonal differencing process, and modeling the resulting stationary series under an autoregressive-moving-average (ARMA) model.

Abstract: Embodiments provide a method for upgrading resources in a system including normalizing a collected dataset, scattering data from the normalized dataset, obtaining a plurality of clusters based on the scattered data, discarding one or more clusters from the plurality of clusters with less than a percentage of a total number of observations, in each cluster, performing clusterwise regression and obtaining linear sub-clusters in a defined number, reducing one or more sub-clusters including applying a refinement procedure, removing one or more sub-clusters that fit to outliers and merging pairs of clusters that fit an equivalent model, updating one or more clusters with the reduced sub-clusters, removing one or more globular clusters, reducing a number of clusters with the refinement procedure, and de-normalizing one or more results.

Abstract: Embodiments provide a method for performing an automatic execution of a Box and Jenkins method for forecasting the behavior of said dataset. The method may include pre-processing the dataset including providing one or more missing values to the dataset, removing level discontinuities and outliers, and removing one or more last samples from the dataset, obtaining a trend of the pre-processed dataset including identifying and filtering the trend out of the dataset based on a coefficient of determination methodology, detecting seasonality to obtain a resulting stationary series including computing an auto correlation function of the dataset, repeating the detecting step on an aggregate series of a previous dataset, and removing detected seasonality based on a seasonal differencing process, and modeling the resulting stationary series under an autoregressive-moving-average (ARMA) model.

Abstract: Embodiments provide a method for performing an automatic execution of a Box and Jenkins method for forecasting the behavior of said dataset. The method may include pre-processing the dataset including providing one or more missing values to the dataset, removing level discontinuities and outliers, and removing one or more last samples from the dataset, obtaining a trend of the pre-processed dataset including identifying and filtering the trend out of the dataset based on a coefficient of determination methodology, detecting seasonality to obtain a resulting stationary series including computing an auto correlation function of the dataset, repeating the detecting step on an aggregate series of a previous dataset, and removing detected seasonality based on a seasonal differencing process, and modeling the resulting stationary series under an autoregressive-moving-average (ARMA) model.

Abstract: According to one general aspect, a method may include monitoring, via a communications network, an actual system resource usage of each of a plurality of target computing devices configured to execute one or more respective workload tasks. The method may also include receiving a request for a suggestion for an assigned target computing device to be assigned a new workload task. The method may further include providing the suggestion regarding the assigned target computing device to be assigned a new workload task, wherein the suggestion suggests one or more target computing device(s) that is included in the plurality of target computing devices. The method may also include adjusting a system resource usage profile of the assigned target computing device to include an estimated system resource usage for the new workload task and an actual system resource usage of the assigned target computing device that was previously monitored.

Abstract: According to one general aspect, a method may include monitoring, via a communications network, an actual system resource usage of each of a plurality of target computing devices configured to execute one or more respective workload tasks. The method may also include receiving a request for a suggestion for an assigned target computing device to be assigned a new workload task. The method may further include providing the suggestion regarding the assigned target computing device to be assigned a new workload task, wherein the suggestion suggests one or more target computing device(s) that is included in the plurality of target computing devices. The method may also include adjusting a system resource usage profile of the assigned target computing device to include an estimated system resource usage for the new workload task and an actual system resource usage of the assigned target computing device that was previously monitored.