Microsoft Technology Licensing, LLC

Ricardo Gouvea Bianchini

Marcus Felipe Fontoura

Ioannis Manousakis

Eric Clarence Peterson

Christian L Belady

Husam Atallah Alissa

Jie Jerry Liu

Sean Michael James

Nicholas Andrew Keehn

Bharath Ramakrishnan

Gregory Joseph McKnight

Eli Cortez

Anand Bonde

Mark Edward Shaw

Di Wang

Bryan David Kelly

David A Maltz

Brian A Janous

Sriram Govindan

Winston Allen Saunders

John Joseph Siegler

Behnaz Arzani

Yunqi Zhang

Alok Gautam Kumbhare

Nithish Mahalingam

Reza Azimi

William Clausen

Inigo Goiri

Sameh Elnikety

Inigo Goiri Presa

Stanko Novakovic

Girish Bablani

Henry Lee

Felipe Vieira Frujeri

Íñigo Goiri Presa

Georgios Prekas

Jie Liu

Jiaqi Gao

Xiaohang Wang

Gregory Joseph Mcknight

Eric Peterson

A liquid-submersible thermal management system includes a cylindrical outer shell and an inner shell positioned in an interior volume of the outer shell. The cylindrical outer shell has a longitudinal axis oriented vertically relative to a direction of gravity, and the inner shell defines an immersion chamber. The liquid-submersible thermal management system a spine positioned inside the immersion chamber and oriented at least partially in a direction of the longitudinal axis with a heat-generating component located in the immersion chamber. A working fluid is positioned in the immersion chamber and at least partially surrounding the heat-generating component. The working fluid receives heat from the heat-generating component.

Systems and methods for immersion-cooled datacenters

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.

Machine learning-based power capping and virtual machine placement in cloud platforms

A liquid-submersible thermal management system includes a shell, a heat-generating component, a working fluid, and at least one heat-dispersing element. The shell defines an immersion chamber where the heat-generating component is located in the immersion chamber. The working fluid is positioned in the immersion chamber and at least partially surrounds the heat-generating component so the working fluid receives heat from the heat-generating component. The at least one heat-dispersing element is positioned on exterior surface of the shell to conduct heat from the shell into the heat-dispersing element.

Techniques of managing oversubscription of network resources are disclosed herein. In one embodiment, a method includes receiving resource utilization data of a virtual machine hosted on a server in a computing system. The virtual machine is configured to perform a task. The method also includes determining whether a temporal pattern of the resource utilization data associated with the virtual machine indicates one or more cycles of resource utilization as a function of time and in response to determining that the temporal pattern associated with the virtual machine indicates one or more cycles of resource utilization as a function of time, causing the virtual machine to migrate to another server that is not oversubscribed by virtual machines in the computing system.

Resource oversubscription based on utilization patterns in computing systems

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.

Disaggregated computer systems

A system receives a request to deploy a virtual machine (VM) on one of a plurality of nodes running a plurality of VMs in a cloud computing system. The system receives a predicted lifetime for the VM and an indication of an average lifetime of VMs running on each of the plurality of nodes. The system allocates the VM to a first node when a first policy of collocating VMs with similar lifetimes on a node is adopted and the predicted lifetime is within a predetermined range of the average lifetime of VMs running on the first node. The system allocates the VM to a second node when a second policy of collocating VMs with dissimilar lifetimes on a node is adopted and the predicted lifetime is not within the predetermined range of the average lifetime of VMs running on the second node.

Method for deploying virtual machines in cloud computing systems based on predicted lifetime

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.

Harvesting unused resources in a distributed computing system

This document relates to electricity management using modulated waveforms. One example modulates electricity to obtain modulated electricity having at least two different alternating current frequencies including a first alternating current frequency and a second alternating current frequency. The example delivers the modulated electrical power having the at least two different alternating current frequencies to multiple different electrical devices, including a first electrical device configured to utilize the first alternating current frequency and a second electrical device configured to utilize the second alternating current frequency. The modulated electricity can be delivered at least partly over an electrical line shared by the first electrical device and the second electrical device.

Electricity management using modulated waveforms

A fuel cell power controller tracks load current and fuel cell output voltage, and alerts on excessive fuel cell ramp rate, so another power source can supplement the fuel cell and/or the load can be reduced. A power engineering process makes efficient use of available fuel cell power by ramping up power flow rapidly when power is available, while respecting the ramp rate and other power limitations of the fuel cell and safety limitations of the load. Power flow decreases after an alert indicating an electrical output limitation of the fuel cell. Permitted power flow increases in response to a power demand increase (actual or requested) from the load in the absence of the alert. Power flow may increase or decrease in a fixed amount, a proportional amount, or per a sequence. A power controller relay may trip open on a low fuel cell output voltage or high load current.

Power modulation for fuel cell powered datacenters

A system receives a request to deploy a virtual machine on a node from a plurality of nodes running a plurality of virtual machines in a cloud computing system. The system selects one of the plurality of nodes having a hard disk drive (HDD) input output operations per second (IOPS) value less than an observed HDD IOPS value for the plurality of nodes running the plurality of virtual machines. The system receives a predicted HDD IOPS value for the virtual machine and determines a new HDD IOPS value for the selected node based on the HDD IOPS value for the selected node and the predicted HDD IOPS value for the virtual machine. The system instantiates the virtual machine on the selected node when the new HDD IOPS value for the selected node is less than or equal to the observed HDD IOPS value for the plurality of nodes.

Resource management for virtual machines in cloud computing systems

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Bellevue, WA, United States of America

Ricardo Gouvea Bianchini