Abstract: Described techniques determine performance metric values of a performance metric characterizing a performance of a system resource of an information technology (IT) system, and determine driver metric values of a driver metric characterizing an occurrence of an event that is at least partially external to the system resource. A correlation analysis may confirm a potential correlation between the performance metric values and the driver metric values as a correlation. A graph relating the performance metric to the driver metric may be generated. A plurality of extrapolation algorithms may be trained to obtain a plurality of trained extrapolation algorithms using a first subset of data points of the graph, and the plurality of trained extrapolation algorithms may be validated using a second subset of data points of the graph. A driver metric threshold corresponding to the performance metric threshold may be determined using a validated extrapolation algorithm.

Abstract: Described techniques determine performance metric values of a performance metric characterizing a performance of a system resource of an information technology (IT) system, and determine driver metric values of a driver metric characterizing an occurrence of an event that is at least partially external to the system resource. A correlation analysis may confirm a potential correlation between the performance metric values and the driver metric values as a correlation. A graph relating the performance metric to the driver metric may be generated. A plurality of extrapolation algorithms may be trained to obtain a plurality of trained extrapolation algorithms using a first subset of data points of the graph, and the plurality of trained extrapolation algorithms may be validated using a second subset of data points of the graph. A driver metric threshold corresponding to the performance metric threshold may be determined using a validated extrapolation algorithm.

Abstract: Described techniques determine performance metric values of a performance metric characterizing a performance of a system resource of an information technology (IT) system, and determine driver metric values of a driver metric characterizing an occurrence of an event that is at least partially external to the system resource. A correlation analysis may confirm a potential correlation between the performance metric values and the driver metric values as a correlation. A graph relating the performance metric to the driver metric may be generated. A plurality of extrapolation algorithms may be trained to obtain a plurality of trained extrapolation algorithms using a first subset of data points of the graph, and the plurality of trained extrapolation algorithms may be validated using a second subset of data points of the graph. A driver metric threshold corresponding to the performance metric threshold may be determined using a validated extrapolation algorithm.

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: 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.