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: 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 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 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: 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 of managing power in a computing platform may involve monitoring a runtime power consumption of two or more of a plurality of hardware components in the platform to obtain a plurality of runtime power determinations. The method can also include exposing one or more of the plurality of runtime power determinations to an operating system associated with the platform.

Abstract: Systems and methods of managing power in a computing platform may involve monitoring a runtime power consumption of two or more of a plurality of hardware components in the platform to obtain a plurality of runtime power determinations. The method can also include exposing one or more of the plurality of runtime power determinations to an operating system associated with the platform.

Abstract: A system and method of managing power may include determining a power state based on a first power management request from a first operating system executing on a mobile platform and a second power management request from a second operating system executing on the mobile platform. The first operating system and one or more components of the mobile platform can define a first virtual machine, and the second operating system and one or more components of the mobile platform can define a second virtual machine. In addition, the power state may be applied to the mobile platform.

Abstract: Systems and methods of managing power in a computing platform may involve monitoring a runtime power consumption of two or more of a plurality of hardware components in the platform to obtain a plurality of runtime power determinations. The method can also include exposing one or more of the plurality of runtime power determinations to an operating system associated with the platform.

Abstract: Systems and methods of managing power in a computing platform may involve monitoring a runtime power consumption of two or more of a plurality of hardware components in the platform to obtain a plurality of runtime power determinations. The method can also include exposing one or more of the plurality of runtime power determinations to an operating system associated with the platform.

Abstract: A system and method of managing power may include determining a power state based on a first power management request from a first operating system executing on a mobile platform and a second power management request from a second operating system executing on the mobile platform. The first operating system and one or more components of the mobile platform can define a first virtual machine, and the second operating system and one or more components of the mobile platform can define a second virtual machine. In addition, the power state may be applied to the mobile platform.

Abstract: A network device determines the presence of the pre-specified string in a message based on a sequence matching rule. A sequence represents non-contiguous portions of the message. A combination of content addressable memory, programmable processing units, and the programmable control unit may determine the presence of the pre-specified string in the message by comparing the non-contiguous portions of the message. Such an approach may reduce the computational resources required for searching the pre-specified string in the message.

Abstract: A network device determines the presence of the pre-specified string in a message based on a sequence matching rule. A sequence represents non-contiguous portions of the message. A combination of content addressable memory, programmable processing units, and the programmable control unit may determine the presence of the pre-specified string in the message by comparing the non-contiguous portions of the message. Such an approach may reduce the computational resources required for searching the pre-specified string in the message.