Abstract: Systems and methods for clustering metrics for reducing a search space of metrics used for service health analyses. Determining a root cause of an event includes performing an automated analysis of metrics associated with the service. To diagnose and resolve events quickly and efficiently, aspects correlate and cluster a plurality of metrics for a specific service based on historical data, where each cluster represents a root cause direction. After clustering metrics by similarity, metrics are scored and ranked to select representative metrics from each cluster, which reduces the dimensionality of the search space. The representative metrics may provide a saliant representation of each metrics cluster. The representative metrics are provided to a service health analyzer, which performs a root cause analysis of the representative metrics to diagnose and mitigate the event.

Abstract: The techniques disclosed herein enable a system to perform a robust evaluation of resource requirement recommendations through a simulated computing environment that closely resembles current conditions of a live computing environment. To achieve this, system characteristics such as CPU, RAM, and storage are extracted from currently available computing resources at the live computing environment. In addition, active software deployments at the live computing environment are randomly sampled to generate an activity dataset. The system characteristics and the activity dataset are then used to generate the simulated computing environment. Instances of a pending software deployment are then assigned to the simulated computing environment according to a resource requirement recommendation. The instances are then executed across various scenarios and analyzed to calculate a level of resource utilization.

Abstract: The techniques disclosed herein enable systems to efficiently allocate computing resources for various computing workloads through a shared peak resource usage prediction. To achieve this, a predictive model analyzes a historical dataset defining resource usage of a computing environment for a past timeframe, and calculates a peak environment resource usage for a future or current timeframe. In addition, the predictive model estimates a peak number of computing workloads for the computing environment. Using the peak resource usage and/or the peak number of computing workloads, the system derives resource requests for allocating computing resources to a plurality of computing workloads. The computing workloads are subsequently assigned to computing nodes within the computing environment for execution. Furthermore, computing workloads within a computing node are configured to share computing resources to accommodate sudden surges in demand.

Abstract: The techniques disclosed herein enable a system to perform a robust evaluation of resource requirement recommendations through a simulated computing environment that closely resembles current conditions of a live computing environment. To achieve this, system characteristics such as CPU, RAM, and storage are extracted from currently available computing resources at the live computing environment. In addition, active software deployments at the live computing environment are randomly sampled to generate an activity dataset. The system characteristics and the activity dataset are then used to generate the simulated computing environment. Instances of a pending software deployment are then assigned to the simulated computing environment according to a resource requirement recommendation. The instances are then executed across various scenarios and analyzed to calculate a level of resource utilization.

Abstract: A method, system and computer program product, the method comprising: sampling data from a computer network for training a monitoring system, comprising: obtaining information about the computer network to be monitored; obtaining indicators of available resources for collecting training data from the computer network; receiving mandatory objects to be monitored within the computer network; selecting at least one object to be monitored from under-monitored objects within the computer network, said selecting based upon monitoring resources remaining after reducing resources required for monitoring the mandatory objects, from the available resources; and sampling data in accordance with the selection.