Abstract: One embodiment provides a method, the method including: obtaining patient information for at least one patient, wherein the patient information identifies an identifier of the at least one patient; identifying, based upon the patient information, at least one procedure to be performed for the patient at the healthcare facility; identifying a plurality of events corresponding to the at least one procedure and locations within the healthcare facility for at least a subset of the plurality of events; identifying at least one healthcare professional associated with at least one of the plurality of events; and automatically generating a schedule for the healthcare facility based upon the plurality of events and the at least one healthcare professional. Other aspects are described and claimed.

Abstract: An apparatus comprises a processing device configured to monitor development of a plurality of software applications, to identify a first one of the plurality of software applications that has a given version ready for deployment to a production environment of an information technology infrastructure, and to determine at least one dependency between the given version of the first one of the plurality of software applications and a given version of at least a second one of the plurality of software applications. The processing device is further configured to control deployment of the given version of the first one of the plurality of software applications in the production environment of the information technology infrastructure based at least in part on evaluating a deployment status of the given version of the second one of the plurality of software applications.

Abstract: An apparatus comprises a processing device configured to monitor development of a plurality of software applications, to identify a first one of the plurality of software applications that has a given version ready for deployment to a production environment of an information technology infrastructure, and to determine at least one dependency between the given version of the first one of the plurality of software applications and a given version of at least a second one of the plurality of software applications. The processing device is further configured to control deployment of the given version of the first one of the plurality of software applications in the production environment of the information technology infrastructure based at least in part on evaluating a deployment status of the given version of the second one of the plurality of software applications.

Abstract: Performance of a virtual machine system is improved by avoiding and/or eliminating bottlenecks in read and write operations. The system analyzes current virtualized workloads and provides working set estimates for individual VMs, hosts, and clusters. The working set estimate data is then utilized to make specific recommendations for different types of backend storage technologies. After procuring a storage device, the system provides a variety of information to aid in the operation of the system. From this information, the system can detect various scenarios and proactively make recommendations to the user about ways in which to improve storage performance at a host level and at a per-VM level. In some embodiments, these recommendations may be implemented automatically without user involvement.

Abstract: Distributed storage resources having multiple storage units are managed based on data collected from online monitoring of workloads on the storage units and performance characteristics of the storage units. The collected data is sampled at discrete time intervals over a time period of interest, such as a congested time period. Normalized load metrics are computed for each storage unit based on time-correlated sums of the workloads running on the storage unit over the time period of interest and the performance characteristic of the storage unit. Workloads that are migration candidates and storage units that are migration destinations are determined from a representative value of the computed normalized load metrics, which may be the 90th percentile value or a weighted sum of two or more different percentile values.

Abstract: Distributed storage resources are managed based on data collected from online monitoring of workloads on the storage resources and performance characteristics of the storage resources. Load metrics are calculated from the collected data and used to identify workloads that are migration candidates and storage units that are migration destinations, so that load balancing across the distributed storage resources can be achieved.

Abstract: Distributed storage resources are managed based on data collected from online monitoring of workloads on the storage resources and performance characteristics of the storage resources. Load metrics are calculated from the collected data and used to identify workloads that are migration candidates and storage units that are migration destinations, so that load balancing across the distributed storage resources can be achieved.

Abstract: A software controlled mechanism causing a test equipment to place the edges of test signals accurately. The mechanism determines expected time of occurrence of an edge of a signal in relation to a tester cycle time. The mechanism sends commands to the test equipment to receive back the signal (of interest) in multiple cycles and provides the time points corresponding to the edge in the multiple cycles. The software controlled mechanism computes an error based on the time points and the expected time, and adjusts the timing of the edges of the signal according to the error. Such computation and adjustment are performed until the error is within an acceptable range.