Abstract: A rack in a datacenter is powered by a first power feed and a second power feed. The rack supports a plurality of servers which have a maximum combined power consumption which is greater than a maximum supplied power from either the first power feed or the second power feed. When power is lost from one of the power feeds, a rack manager reduces the total power consumption of the plurality of servers by throttling at least one of the servers and/or shutting off at least one of the plurality of servers.

Abstract: A power controller allocates input power to a datacenter between computing power for computing services, backup power, and overhead power for overhead systems. The power controller reallocates the overhead power and/or the backup power to the computing power. This may increase the overall utilization of the datacenter by allowing additional processing power of the servers to be used.

Abstract: A method of power management in a datacenter includes obtaining at least one workload status of at least one server rack, obtaining at least one infrastructure parameter, obtaining at least one utility telemetry, and comparing the at least one workload status to the at least one utility telemetry. The method further includes determining a workload demand based at least partially on a difference between the at least one workload status and the at least one utility telemetry and changing the at least one infrastructure parameter based on the workload demand and the at least one infrastructure parameter.

Abstract: The present disclosure relates to systems, methods, and computer readable media for enabling server devices to utilize a higher percentage of power resources while maintaining sufficient availability of power resources of a datacenter or other collection of server devices. For example, systems disclosed herein determine and implement power shaving actions based on virtual machine metadata and in accordance with a power shaving policy to facilitate a significantly higher utilization of power resources on a datacenter during normal operation as well as within periods of limited power capacity on various server devices. Systems described herein provide more efficient utilization of power resources while maintaining service availability guarantees for a variety of virtual machines hosted by servers of the datacenter.

Abstract: A rack in a datacenter is powered by a first power feed and a second power feed. The rack supports a plurality of servers which have a maximum combined power consumption which is greater than a maximum supplied power from either the first power feed or the second power feed. When power is lost from one of the power feeds, a rack manager reduces the total power consumption of the plurality of servers by throttling at least one of the servers and/or shutting off at least one of the plurality of servers.

Abstract: Techniques of virtual machine operation management are disclosed herein. In one embodiment, a technique includes determining an operating parameter to be set for executing any processes for a virtual machine with a CPU on a server upon detecting that a process corresponding to the virtual machine hosted on the server is assigned and scheduled to be executed by a processor of the CPU. The technique can then include programming the processor of the CPU assigned to execute the process according to the operating parameter in the accessed parameter record. Upon completion of programing the one of the multiple processors, the process corresponding to the virtual machine can be executed with the processor of the CPU to achieve a target performance level associated with the virtual machine.

Abstract: A rack in a datacenter is powered by a first power feed and a second power feed. The rack supports a plurality of servers which have a maximum combined power consumption which is greater than a maximum supplied power from either the first power feed or the second power feed. When power is lost from one of the power feeds, a rack manager reduces the total power consumption of the plurality of servers by throttling at least one of the servers and/or shutting off at least one of the plurality of servers.

Abstract: Techniques of virtual machine operation management are disclosed herein. In one embodiment, a technique includes determining an operating parameter to be set for executing any processes for a virtual machine with a CPU on a server upon detecting that a process corresponding to the virtual machine hosted on the server is assigned and scheduled to be executed by a processor of the CPU. The technique can then include programming the processor of the CPU assigned to execute the process according to the operating parameter in the accessed parameter record. Upon completion of programming the one of the multiple processors, the process corresponding to the virtual machine can be executed with the processor of the CPU to achieve a target performance level associated with the virtual machine.

Abstract: A rack in a datacenter is powered by a first power feed and a second power feed. The rack supports a plurality of servers which have a maximum combined power consumption which is greater than a maximum supplied power from either the first power feed or the second power feed. When power is lost from one of the power feeds, a rack manager reduces the total power consumption of the plurality of servers by throttling at least one of the servers and/or shutting off at least one of the plurality of servers.

Abstract: Systems and methods for machine learning-based power capping and virtual machine placement in cloud platforms are disclosed. A method includes applying a machine learning model to predict whether a request for deployment of a virtual machine corresponds to deployment of a user-facing (UF) virtual machine or a non-user-facing (NUF) virtual machine. The method further includes sorting a list of candidate servers based on both a chassis score and a server score for each server to determine a ranked list of the candidate servers, where the server score depends at least on whether the request for the deployment of the virtual machine is determined to be a request for a deployment of a UF virtual machine or a request for a deployment of an NUF virtual machine. The method further includes deploying the virtual machine to a server with highest rank among the ranked list of the candidate servers.

Abstract: Systems and methods for machine learning-based power capping and virtual machine placement in cloud platforms are disclosed. A method includes applying a machine learning model to predict whether a request for deployment of a virtual machine corresponds to deployment of a user-facing (UF) virtual machine or a non-user-facing (NUF) virtual machine. The method further includes sorting a list of candidate servers based on both a chassis score and a server score for each server to determine a ranked list of the candidate servers, where the server score depends at least on whether the request for the deployment of the virtual machine is determined to be a request for a deployment of a UF virtual machine or a request for a deployment of an NUF virtual machine. The method further includes deploying the virtual machine to a server with highest rank among the ranked list of the candidate servers.

Abstract: The present disclosure relates to systems, methods, and computer readable media for enabling server devices to utilize a higher percentage of power resources while maintaining sufficient availability of power resources of a datacenter or other collection of server devices. For example, systems disclosed herein determine and implement power shaving actions based on virtual machine metadata and in accordance with a power shaving policy to facilitate a significantly higher utilization of power resources on a datacenter during normal operation as well as within periods of limited power capacity on various server devices. Systems described herein provide more efficient utilization of power resources while maintaining service availability guarantees for a variety of virtual machines hosted by servers of the datacenter.

Abstract: Techniques of virtual machine operation management are disclosed herein. In one embodiment, a technique includes determining an operating parameter to be set for executing any processes for a virtual machine with a CPU on a server upon detecting that a process corresponding to the virtual machine hosted on the server is assigned and scheduled to be executed by a processor of the CPU. The technique can then include programming the processor of the CPU assigned to execute the process according to the operating parameter in the accessed parameter record. Upon completion of programming the one of the multiple processors, the process corresponding to the virtual machine can be executed with the processor of the CPU to achieve a target performance level associated with the virtual machine.

Abstract: Systems and methods for machine learning-based power capping and virtual machine placement in cloud platforms are disclosed. A method includes applying a machine learning model to predict whether a request for deployment of a virtual machine corresponds to deployment of a user-facing (UF) virtual machine or a non-user-facing (NUF) virtual machine. The method further includes sorting a list of candidate servers based on both a chassis score and a server score for each server to determine a ranked list of the candidate servers, where the server score depends at least on whether the request for the deployment of the virtual machine is determined to be a request for a deployment of a UF virtual machine or a request for a deployment of an NUF virtual machine. The method further includes deploying the virtual machine to a server with highest rank among the ranked list of the candidate servers.

Abstract: A method for facilitating increased utilization of a data center includes receiving information about availability of components in a data center's electrical infrastructure and about power consumption of servers in the data center. The method may also include detecting that the power consumption of the servers in the data center exceeds a reduced total capacity of the electrical infrastructure. The reduced total capacity may be caused by unavailability of at least one component in the data center's electrical infrastructure. The method may also include causing power management to be performed to reduce the power consumption of the servers so that the power consumption of the servers does not exceed the reduced total capacity of the electrical infrastructure of the data center.