Abstract: Methods of analyzing and capacity planning for multi-core, multi-chip, multi-threaded computer system environments by analyzing the scalability of a fourth layer complexity, the processor boards, and incorporating this factor into the calculation of the expected throughput of a system constructed with multiple processor boards. In particular, the method may comprise identifying a system for which system performance prediction is desired, specifying a simulation model, and determining configuration parameters for the system, the system with at least one processor board, at least one chip per board, at least one core per chip, and at least one thread per core. The method may further comprise obtaining scalability factors based on the configuration data for the system, executing a simulation process for the simulation model for a deterministic simulation time, calculating a throughput of the system as a prediction of the performance of the system, and storing the results in a storage device.

Abstract: Methods of analyzing and capacity planning for multi-core, multi-chip, multi-threaded computer system environments by analyzing the scalability of a fourth layer complexity, the processor boards, and incorporating this factor into the calculation of the expected throughput of a system constructed with multiple processor boards. In particular, the method may comprise identifying a system for which system performance prediction is desired, specifying a simulation model, and determining configuration parameters for the system, the system with at least one processor board, at least one chip per board, at least one core per chip, and at least one thread per core. The method may further comprise obtaining scalability factors based on the configuration data for the system, executing a simulation process for the simulation model for a deterministic simulation time, calculating a throughput of the system as a prediction of the performance of the system, and storing the results in a storage device.

Abstract: A method includes determining historical resource metrics for a host, and determining a workload forecast for the host based on the historical resource metrics. The method also includes determining a first series of virtual resource configurations based on the workload forecast. Each virtual resource configuration corresponds to a respective virtual machine of a plurality of virtual machines from the host. Each virtual resource configuration includes a time interval of the workload forecast, a processing specification of the corresponding virtual machine, and a host assignment indicative of a corresponding target host of the plurality of hosts on which to run the corresponding virtual machine. The method further includes configuring each respective virtual machine according to each corresponding virtual resource configuration in the first series of virtual resource configurations.

Abstract: A computing workload is allocated amongst servers based on energy usage considerations. An energy consumption model is created for different server configurations. Based on the energy consumption models for the respective server configurations, different energy consumptions are predicted for executing a workload in corresponding different allocations of the workload on the servers. One of the allocations is selected based on the predicted energy consumptions. The selected allocation could minimize total energy use, reduce peak energy use, spread out energy use, etc.

Abstract: A system and method for capacity planning for enterprise networks, such as identifying bottlenecks and removing or replacing the bottleneck device are provided. The device utilization for one or more network devices are measured or read from measured data. A relative load is calculated from the device utilization data and device utilization is compared to a device threshold to determine the bottleneck device. A method is also provided for determining network utilizations, network populations and a relative response times based on only limited measurable device usage data.