Abstract: The discussion relates to disaggregated computing. One example can monitor multiple two-phase liquid immersion tanks. Individual two-phase liquid immersion tanks can contain multiple components of a single type of component type. The example can receive requests for virtual machines and allocate sets of components from individual two-phase liquid immersion tanks to work together to support the virtual machines requests.

Abstract: The discussion relates to disaggregated computing. One example can monitor multiple two-phase liquid immersion tanks. Individual two-phase liquid immersion tanks can contain multiple components of a single type of component type. The example can receive requests for virtual machines and allocate sets of components from individual two-phase liquid immersion tanks to work together to support the virtual machines requests.

Abstract: The discussion relates to disaggregated computing. One example can monitor multiple two-phase liquid immersion tanks. Individual two-phase liquid immersion tanks can contain multiple components of a single type of component type. The example can receive requests for virtual machines and allocate sets of components from individual two-phase liquid immersion tanks to work together to support the virtual machines requests.

Abstract: Systems and methods related to harvesting of the unused resources in a distributed computing system are described. An example method, in a system including a host server, having a first instruction set architecture (ISA), and an interface card, having a second ISA is provided. The method includes designating at least one type of resource, associated with the host server for harvesting by compute entities configured for execution using the processor having the second ISA, where the host server is configured to execute compute entities requiring execution by the processor having the first ISA. The method further includes in response to a request for accessing the at least one type of resource by a compute entity, executing on the processor having the second ISA, automatically allowing the compute entity to access the at least one type of resource associated with the host server.

Abstract: The discussion relates to disaggregated computing. One example can monitor multiple two-phase liquid immersion tanks. Individual two-phase liquid immersion tanks can contain multiple components of a single type of component type. The example can receive requests for virtual machines and allocate sets of components from individual two-phase liquid immersion tanks to work together to support the virtual machines requests.

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 discussion relates to disaggregated computing. One example can monitor multiple two-phase liquid immersion tanks. Individual two-phase liquid immersion tanks can contain multiple components of a single type of component type. The example can receive requests for virtual machines and allocate sets of components from individual two-phase liquid immersion tanks to work together to support the virtual machines requests.

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 related to harvesting of the unused resources in a distributed computing system are described. An example method, in a system including a host server, having a first instruction set architecture (ISA), and an interface card, having a second ISA is provided. The method includes designating at least one type of resource, associated with the host server for harvesting by compute entities configured for execution using the processor having the second ISA, where the host server is configured to execute compute entities requiring execution by the processor having the first ISA. The method further includes in response to a request for accessing the at least one type of resource by a compute entity, executing on the processor having the second ISA, automatically allowing the compute entity to access the at least one type of resource associated with the host server.

Abstract: Aspects of the present disclosure relate to incident routing in a cloud environment. In an example, cloud provider teams utilize a scout framework to build a team-specific scout based on that team's expertise. In examples, an incident is detected and a description is sent to each team-specific scout. Each team-specific scout uses the incident description and the scout specifications provided by the team to identify, access, and process monitoring data from cloud components relevant to the incident. Each team-specific scout utilizes one or more machine learning models to evaluate the monitoring data and generate an incident-classification prediction about whether the team is responsible for resolving the incident. In examples, a scout master receives predictions from each of the team-specific scouts and compares the predictions to determine to which team an incident should be routed.

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: Data is stored in a distributed data storage system comprising a plurality of disks. When a disk fails, system reliability is restored by executing a set of reconstructions according to a schedule. System reliability is characterized by a dynamic Normalcy Deviation Score. The schedule for executing the set of reconstructions is determined by a minimum intersection policy. A set of reconstructions is received and divided into a set of queues rank-ordered by redundancy level ranging from a lowest redundancy level to a highest redundancy level. For reconstructions in each queue, an intersection matrix is calculated. Diskscores for each disk are calculated. The schedule for the set of reconstructions is based at least in part on the intersection matrices, the Normal Deviation Scores, and the diskscores.

Abstract: Data is stored in a distributed data storage system comprising a plurality of disks. When a disk fails, system reliability is restored by executing a set of reconstructions according to a schedule. System reliability is characterized by a dynamic Normalcy Deviation Score. The schedule for executing the set of reconstructions is determined by a minimum intersection policy. A set of reconstructions is received and divided into a set of queues rank-ordered by redundancy level ranging from a lowest redundancy level to a highest redundancy level. For reconstructions in each queue, an intersection matrix is calculated. Diskscores for each disk are calculated. The schedule for the set of reconstructions is based at least in part on the intersection matrices, the Normal Deviation Scores, and the diskscores.