Abstract: Systems and techniques that facilitate automated validation of power topology are provided. In various embodiments, a control component can transmit a transition command to a power-distribution node of a data center, wherein the transition command can cause an outlet of the power-distribution node to transition between power states. In various aspects, a verification component can verify that a power-consumption node of the data center is connected to the outlet by comparing a pre-transition power characteristic of the power-consumption node with a post-transition power characteristic of the power-consumption node.

Abstract: An embodiment includes extracting statistical data associated with invocation of an application programming interface (API) from a log and using the statistical data to calculate a performance value and generate an aggregate dataset that combines the performance value with performance values associated with other invocations of the API. The embodiment includes calculating metric values for performance values for respective time intervals of a time period and calculating mean and standard deviation values of the metric values for the time period. The embodiment includes selecting the API as a candidate API and detecting a Customer Impacting Event (CIE) by applying a machine learning algorithm using monitored values associated with the candidate API during a time frame defined by a rolling window. The embodiment also includes automatically initiating a selected alert from among a plurality of alert options based at least in part on the monitored values associated with the CIE.

Abstract: A system is configured to determine a dominant error causing a provisioning step to become stuck during provisioning of a machine in a cloud environment. The system includes memory for storing instructions, and a processor configured to execute said instructions to determine an inverse error frequency (IEF) value for pre-intervention errors in a set of intervention provisioning data; determine a dominant error for a provision during said provisioning step in said set of intervention provisioning data based on a pre-intervention error that has a maximum IEF value; determine a duration frequency (DuF) value for the provision at said provisioning step for provisions in a set of non-intervention provisioning data; and determine said dominant error for each provision during said provisioning step in said set of non-intervention provisioning data based on an error that resulted in DuF value.

Abstract: A system is configured to determine a dominant error causing a provisioning step to become stuck during provisioning of a machine in a cloud environment. The system includes memory for storing instructions, and a processor configured to execute said instructions to determine an inverse error frequency (IEF) value for pre-intervention errors in a set of intervention provisioning data; determine a dominant error for a provision during said provisioning step in said set of intervention provisioning data based on a pre-intervention error that has a maximum IEF value; determine a duration frequency (DuF) value for the provision at said provisioning step for provisions in a set of non-intervention provisioning data; and determine said dominant error for each provision during said provisioning step in said set of non-intervention provisioning data based on an error that resulted in DuF value.

Abstract: An embodiment includes extracting statistical data associated with invocation of an application programming interface (API) from a log and using the statistical data to calculate a performance value and generate an aggregate dataset that combines the performance value with performance values associated with other invocations of the API. The embodiment includes calculating metric values for performance values for respective time intervals of a time period and calculating mean and standard deviation values of the metric values for the time period. The embodiment includes selecting the API as a candidate API and detecting a Customer Impacting Event (CIE) by applying a machine learning algorithm using monitored values associated with the candidate API during a time frame defined by a rolling window. The embodiment also includes automatically initiating a selected alert from among a plurality of alert options based at least in part on the monitored values associated with the CIE.

Abstract: Systems and techniques that facilitate automated validation of power topology are provided. In various embodiments, a control component can transmit a transition command to a power-distribution node of a data center, wherein the transition command can cause an outlet of the power-distribution node to transition between power states. In various aspects, a verification component can verify that a power-consumption node of the data center is connected to the outlet by comparing a pre-transition power characteristic of the power-consumption node with a post-transition power characteristic of the power-consumption node.

Abstract: Technology for computing number of active servers needed over time in a cloud/compute cluster includes the following operations (not necessarily in the following order): (i) determining the capacity of each VCE provisioned on the cloud against the resource guaranteed to that VCE; (ii) forecasting the resource needs over time using historical requests for each VCE flavor; and (iii) using the forecasted resource needs to determine the required number of future servers at some future time. Some embodiments of the present invention use a formula that accounts for the interplay among various parameter values of the VCE flavors and also the mapping of the needs of VCEs of various flavors to the capabilities of physical resources.

Abstract: Technology for computing number of active servers needed over time in a cloud/compute cluster includes the following operations (not necessarily in the following order): (i) determining the capacity of each VCE provisioned on the cloud against the resource guaranteed to that VCE; (ii) forecasting the resource needs over time using historical requests for each VCE flavor; and (iii) using the forecasted resource needs to determine the required number of future servers at some future time. Some embodiments of the present invention use a formula that accounts for the interplay among various parameter values of the VCE flavors and also the mapping of the needs of VCEs of various flavors to the capabilities of physical resources.

Abstract: Technology for computing number of active servers needed over time in a cloud/compute cluster includes the following operations (not necessarily in the following order): (i) determining the capacity of each VCE provisioned on the cloud against the resource guaranteed to that VCE; (ii) forecasting the resource needs over time using historical requests for each VCE flavor; and (iii) using the forecasted resource needs to determine the required number of future servers at some future time. Some embodiments of the present invention use a formula that accounts for the interplay among various parameter values of the VCE flavors and also the mapping of the needs of VCEs of various flavors to the capabilities of physical resources.

Abstract: Technology for computing number of active servers needed over time in a cloud/compute cluster includes the following operations (not necessarily in the following order): (i) determining the capacity of each VCE provisioned on the cloud against the resource guaranteed to that VCE; (ii) forecasting the resource needs over time using historical requests for each VCE flavor; and (iii) using the forecasted resource needs to determine the required number of future servers at some future time. Some embodiments of the present invention use a formula that accounts for the interplay among various parameter values of the VCE flavors and also the mapping of the needs of VCEs of various flavors to the capabilities of physical resources.

Abstract: Methods and systems for mapping an event type to an activity in a business process model are disclosed. In accordance with one such method, the event type and the activity are tokenized by determining event tokens for event type labels in the event type and determining activity tokens for activity labels in the activity. In addition, a score matrix is generated for pairs of the event tokens and the activity tokens indicating a degree of similarity between the event token and the activity token in each of the pairs. The method also includes determining whether the event type and the activity are correlated by determining scores of the pairs of event tokens and activity tokens that are ranked highest in said score matrix. Further, a mapping report indicating whether the event type and the activity are correlated in the business process model is output.