Abstract: Methods and apparatus for hierarchical reinforcement learning (RL) algorithm for network function virtualization (NFV) server power management. A first RL model at a first layer is trained by adjusting a frequency of the core of processor while performing a workload to obtain a first trained RL model. The trained RL model is operated in an inference mode while training a second RL model at a second level in the RL hierarchy by adjusting a frequency of the core and a frequency of processor circuitry external to the core to obtain a second trained RL model. Training may be performed online or offline. The first and second RL models are operated in inference modes during online operations to adjust the frequency of the core and the frequency of the circuitry external to the core while executing software on the plurality of cores of to perform a workload, such as an NFV workload.

Abstract: Embodiments include a support member for an extendable trailer with one or more flange members and one or more web members. In an embodiment, the web members include tabs and the flange members define slots. Other embodiments are also included herein.

Abstract: Embodiments include a support member for an extendable trailer with one or more flange members and one or more web members. In an embodiment, the web members include tabs and the flange members define slots. Other embodiments are also included herein.

Abstract: Embodiments include a support member for an extendable trailer with one or more flange members and one or more web members. In an embodiment, the web members include tabs and the flange members define slots. Other embodiments are also included herein.

Abstract: Embodiments include a support member for an extendable trailer with one or more flange members and one or more web members. In an embodiment, the web members include tabs and the flange members define slots. Other embodiments are also included herein.