Abstract: Techniques for rebalancing computing workloads between a set of on-premises resources of an on-premises system and a set of cloud computing resources of a cloud computing system. A method embodiment commences upon detecting a rebalancing trigger event. Responsive to the trigger event, a set of resource data corresponding to observations pertaining to the on-premises resources and resource data corresponding to observations pertaining the cloud resources is accessed. Based at least in part on the resource data, a plurality of candidate workload rebalancing plans are generated. The candidate workload rebalancing plans are evaluated against the likelihood of achieving quantitative objectives resulting from the rebalancing. A portion of the scheduling commands to carry out the workload rebalancing plans are sent to the cloud computing system, and another portion of the scheduling commands are sent to the on-premises computing system. The receiving computing systems carry out the scheduling commands.

Abstract: Techniques for rebalancing computing workloads between a set of on-premises resources of an on-premises system and a set of cloud computing resources of a cloud computing system. A method embodiment commences upon detecting a rebalancing trigger event. Responsive to the trigger event, a set of resource data corresponding to observations pertaining to the on-premises resources and resource data corresponding to observations pertaining the cloud resources is accessed. Based at least in part on the resource data, a plurality of candidate workload rebalancing plans are generated. The candidate workload rebalancing plans are evaluated against the likelihood of achieving quantitative objectives resulting from the rebalancing. A portion of the scheduling commands to carry out the workload rebalancing plans are sent to the cloud computing system, and another portion of the scheduling commands are sent to the on-premises computing system. The receiving computing systems carry out the scheduling commands.

Abstract: Systems for distributed resource management. A method for deploying two or more computing workloads across heterogeneous computing systems commences upon establishing network communications between an on-premises computing system and a cloud computing system. Resource data corresponding to the on-premises resources and the cloud resources are collected continuously and saved as a time series history of observations. Upon a user request or other event, workload placement planning commences. A set of workload placement plans are generated, which workload placement plans are then evaluated in accordance with one or more quantitative objectives. Scheduling commands to carry out the workload placement plans are generated. A first portion of the scheduling commands is executed at the cloud computing system, and another portion of the scheduling commands is executed at the on-premises computing system.

Abstract: A method of discovering a status of entities associated with one or more data centers of a data center cluster. The method includes receiving, a request for entity data associated with one or more data centers of a data center cluster; identifying a host machine of a data center of the one or more data centers based on the request; and sending a discovery request to the host machine of the data center. The discovery request configured to cause operations comprising generating, by the host machine of the data center, entity data associated with the host machine of the data center and a second host machine of a second data center; generating display data based on the entity data; and sending the display data to the user device. The display data configured to cause the user device to display the entity data in a window of the application.

Abstract: Customers of shared resources or services can be informed of unusual variations in cost patterns over at least one determined period, in order to prevent those customers from being surprised upon receiving a significantly larger (or smaller) bill than expected. The daily cost (or portions of the daily cost) for a customer can be compared against the previous day's cost to determine a percentage cost variation. This cost variation can be compared against the mean of the percentage daily cost variations over a previous period, such as the last thirty days, to determine whether the variation falls outside a standard deviation (or other such threshold) from the mean. If so, the cost variation can be determined to be unusual, and the customer notified. The variation can be analyzed using daily, weekly, and monthly periods in order to identify unusual accumulations of small variations as well.