Abstract: Quantifying power usage for a service. An example method may include identifying a dependency model for the service, the dependency model based in part on infrastructure providing the service. The method may also include determining power usage for the service using the dependency model.

Abstract: A system management command is stored in a management partition of a global memory by a first node of a multi-node computing system. The global memory is shared by each node of the multi-node computing system. In response to an indication to access the management partition, the system management command is accessed from the management partition by a second node of the multi-node computing system. The system management command is executed by the second node. Executing the system management command includes managing the second node.

Abstract: In response to determining that an event matches a condition of a rule, a given one of a plurality of computing nodes is selected to send the event, based on one or both of an attribute of the event and an identifier of the rule. Information of the event is sent to the given computing node to perform correlation of the event with another event.

Abstract: Quantifying power usage for a service. An example method may include identifying a dependency model for the service, the dependency model based in part on infrastructure providing the service. The method may also include determining power usage for the service using the dependency model.

Abstract: Quantifying power usage for a service. An example method may include identifying a dependency model for the service, the dependency model based in part on infrastructure providing the service. The method may also include determining power usage for the service using the dependency model.

Abstract: Examples relate to providing hardware assisted software versioning for clustered applications. In one example, virtualized global memory is accessible to application servers that provide a clustered application, where the clustered application includes multiple versions of a common data structure. After one of the application servers stores an element that is compatible with one version of the common data structure, other versions of the common data structure are located in the virtualized global memory. The element is then invalidated in the other versions of the common data structure to prevent access and translated directly in the virtualized global memory to the other versions of the common data structure. At this stage, the element can be validated in the other versions of the common data structure for access.

Abstract: In a method for generating a capacity schedule for a facility to meet a predefined operational goal, in which the facility is to receive resources from a first resource supply, a prediction of a supply of resources available from the first resource supply for a predetermined period of time is accessed. In addition, a prediction of a demand for resources in the facility during the predetermined period of time is accessed. The prediction of the supply of resources and the prediction of the demand for resources are inputted into a constrained optimization problem and the capacity schedule for the facility is generated by finding a solution to the constrained optimization problem.

Abstract: Examples relate to providing hardware assisted software versioning for clustered applications. In one example, virtualized global memory is accessible to application servers that provide a clustered application, where the clustered application includes multiple versions of a common data structure. After one of the application servers stores an element that is compatible with one version of the common data structure, other versions of the common data structure are located in the virtualized global memory. The element is then invalidated in the other versions of the common data structure to prevent access and translated directly in the virtualized global memory to the other versions of the common data structure. At this stage, the element can be validated in the other versions of the common data structure for access.

Abstract: In response to determining that an event matches a condition of a rule, a given one of a plurality of computing nodes is selected to send the event, based on one or both of an attribute of the event and an identifier of the rule. Information of the event is sent to the given computing node to perform correlation of the event with another event.

Abstract: A system management command is stored in a management partition of a global memory by a first node of a multi-node computing system. The global memory is shared by each node of the multi-node computing system. In response to an indication to access the management partition, the system management command is accessed from the management partition by a second node of the multi-node computing system. The system management command is executed by the second node. Executing the system management command includes managing the second node.

Abstract: According to an example, an application performance measurement for an application for a current time interval, a performance specification for the application, and a resource consumption metric for a resource of a plurality of resources that are to process the application for the current time interval may be accessed. In addition, the application performance measurement, the performance specification, and the resource consumption metric may be used to determine a resource specification for a next time interval for the resource of the plurality of resources. Moreover, the resource specification may be used to determine, by a processor, a resource weight for the resource of the plurality of resources for the next time interval.

Abstract: A method for power management may include collecting data related to resource utilization and process information from a server. The method may further include using the collected data to generate use and state models respectively based on use and state categories of the server. The method may also include determining a state condition of the server through use of the generated use and state models to manage power utilization of the server.

Abstract: A method of maximizing server utilization within a datacenter comprising collocating a number of workloads to a subset of a number of physical servers within a datacenter, adjusting access to resources by critical and non-critical workloads, isolating a number of critical workloads that share resources to the subset of resource instances, and controlling resource utilization of the collocated workloads. A system for collocating workloads in a datacenter, the system comprising a number of servers communicatively coupled to each other and a collocation module that receives information from each server and decides whether to leave a workload running on one of any of the servers on that server, or migrate a workload running on one of the number of servers to another server.

Abstract: A method of maximizing server utilization within a datacenter comprising collocating a number of workloads to a subset of a number of physical servers within a datacenter, adjusting access to resources by critical and non-critical workloads, isolating a number of critical workloads that share resources to the subset of resource instances, and controlling resource utilization of the collocated workloads. A system for collocating workloads in a datacenter, the system comprising a number of servers communicatively coupled to each other and a collocation module that receives information from each server and decides whether to leave a workload running on one of any of the servers on that server, or migrate a workload running on one of the number of servers to another server.

Abstract: Managing computer resources. Workload state information associated with one or more servers in a data center is accessed by a computer, the workload state information comprises workload placement on one or more servers, cooling conditions of the one or more servers, and performance requirements associated with a data center. Cooling state information of cooling resources associated with the one or more servers are accessed by the computer, the cooling state information comprises cooling conditions of the cooling resources. The workload state information and the cooling state information are compared by the computer. Based on the comparing, the computer generates workload repositioning instructions that instruct repositioning of workload on the one or more servers to meet the performance requirements.

Abstract: At least one embodiment is for a method for estimating resource costs required to process an workload to be completed using at least two different cloud computing models. Historical trace data of at least one completed workload that is similar to the workload to be completed is received by the computer. The processing of the completed workload is simulated using a t-shirt cloud computing model and a time-sharing model. The t-shirt and time-sharing resource costs are estimated based on their respective simulations. The t-shirt and resource costs are then compared.

Abstract: Quantifying power usage for a service. An example method may include identifying a dependency model for the service, the dependency model based in part on infrastructure providing the service. The method may also include determining power usage for the service using the dependency model.

Abstract: In a method for managing a facility that is to receive resources from a first resource supply, a supply of resources available from the first resource supply is predicted for a predetermined period of time. In addition, a demand for resources in the facility during the predetermined period of time is predicted. A capacity schedule for the facility is planned to meet a predefined operational goal, in which the plan of the capacity schedule uses as inputs, the predicted supply of resources available from the first resource supply and the predicted demand for resources in the facility during the predetermined period of time. Moreover, a determination as to whether the planned capacity schedule meets the predefined operational goal is made.

Abstract: According to an example, an application performance measurement for an application for a current time interval, a performance specification for the application, and a resource consumption metric for a resource of a plurality of resources that are to process the application for the current time interval may be accessed. In addition, the application performance measurement, the performance specification, and the resource consumption metric may be used to determine a resource specification for a next time interval for the resource of the plurality of resources. Moreover, the resource specification may be used to determine, by a processor, a resource weight for the resource of the plurality of resources for the next time interval.

Abstract: In a method for managing an infrastructure, a utilization of a plurality of infrastructure components in performing an objective is simulated. In addition, a resource demand for the infrastructure associated with the simulated utilization of the infrastructure components is determined and a supply of resources by a plurality of available sources to meet the determined resource demand is simulated. Moreover, at least one metric associated with the determined resource demand and the simulated supply of resources by the plurality of available sources is determined.