Abstract:
A virtual-machine environment can include a parent component (e.g., a host cluster, a host or a set of virtual machines) that is a parent to a set of two or more child components. For example, a host cluster can be a parent to multiple hosts; a host can be a parent to multiple virtual machines; and a set of virtual machines can be a parent to multiple virtual machines. Performance metrics for the child components can be monitored. A child-component performance state can be determined for each child component in the set of two or more child components using a corresponding monitored performance metric and a child-component state criterion (e.g., that maps performance-metric values to states). A parent performance state can be determined for the parent component using the child-component performance state for each child component in the set and a parent-component state criterion.
Abstract:
A scheduler manages execution of a plurality of data-collection jobs, assigns individual jobs to specific forwarders in a set of forwarders, and generates and transmits tokens (e.g., pairs of data-collection tasks and target sources) to assigned forwarders. The forwarder uses the tokens, along with stored information applicable across jobs, to collect data from the target source and forward it onto an indexer for processing. For example, the indexer can then break a data stream into discrete events, extract a timestamp from each event and index (e.g., store) the event based on the timestamp. The scheduler can monitor forwarders' job performance, such that it can use the performance to influence subsequent job assignments. Thus, data-collection jobs can be efficiently assigned to and executed by a group of forwarders, where the group can potentially be diverse and dynamic in size.
Abstract:
Techniques promote monitoring of hypervisor systems by presenting dynamic representations of hypervisor architectures that include performance indicators. A reviewer can interact with the representation to progressively view select lower-level performance indicators. Higher level performance indicators can be determined based on tower level state assessments. A reviewer can also view historical performance metrics and indicators, which can aid in understanding which configuration changes or system usages may have led to sub-optimal performance.