Abstract:
Embodiments perform adaptive throttling of tasks into a virtual datacenter having dynamically changing resources. Tasks are processed concurrently in batches. The rate of change in throughput at different batch sizes is calculated. With each iteration, the batch size is increased or decreased based on the rate of change to achieve a maximum throughput for given resources and load on the virtual datacenter.
Abstract:
Embodiments perform adaptive throttling of tasks into a virtual datacenter having dynamically changing resources. Tasks are processed concurrently in batches. The rate of change in throughput at different batch sizes is calculated. With each iteration, the batch size is increased or decreased based on the rate of change to achieve a maximum throughput for given resources and load on the virtual datacenter.
Abstract:
A technique for managing distributed computing resources in a virtual computing environment is disclosed. In an embodiment, a method includes receiving a recommended change to a virtual architecture of a virtual computing environment; determining an impact on current workload in the virtual computing environment if the recommended change is performed; determining an impact on future workload in the virtual computing environment if the recommended change is performed; calculating a combined impact on current and future workload; determining if the combined impact is above or below a threshold; if the combined impact on current and future workload is below the threshold, do not perform the recommended change; and if the combined impact on current and future workload is above the threshold, perform the recommended change.
Abstract:
A technique for managing distributed computing resources in a virtual computing environment is disclosed. In an embodiment, a method includes receiving a recommended change to a virtual architecture of a virtual computing environment; determining an impact on current workload in the virtual computing environment if the recommended change is performed; determining an impact on future workload in the virtual computing environment if the recommended change is performed; calculating a combined impact on current and future workload; determining if the combined impact is above or below a threshold; if the combined impact on current and future workload is below the threshold, do not perform the recommended change; and if the combined impact on current and future workload is above the threshold, perform the recommended change.
Abstract:
A technique for predictive distributed resource scheduling and distributed power management includes analyzing patterns in the workload, predicting future workloads, and making recommendations for changes to the virtual computing environment. In addition, a cost-benefit analysis can be performed to determine whether the recommended change would likely result in improved performance.
Abstract:
A technique for predictive distributed resource scheduling and distributed power management includes analyzing patterns in the workload, predicting future workloads, and making recommendations for changes to the virtual computing environment. In addition, a cost-benefit analysis can be performed to determine whether the recommended change would likely result in improved performance.