Abstract: A platform-as-a-service infrastructure and application lifecycle manager is configured to implement a common services model to deploy selected services from a common set of services to service domains hosted on multiple different cloud platforms by abstracting dependence on availability of various additional supporting services, such as services that are platform-specific. The platform-as-a-service infrastructure and application lifecycle manager may also manage a lifecycle of available services, such as managing upgrades and/or patches to services.

Abstract: Non-transitory computer readable media are encoded with instructions which, when executed by one or more processors of a central computing system, cause the central computing system to connect with multiple service domains and determine that at least one service domain of the multiple service domains is eligible for upgrade. The instructions further cause the central computing system to store an upgrade package for the at least one service domain at a storage service separate from the central computing system and communicate access credentials to the storage service to the at least one service domain. The central computing system receives a communication from the at least one service domain indicative of upgrade status responsive to the at least one service domain having accessed the upgrade package at the storage service and updates a state management database based on the communication.

Abstract: A platform-as-a-service infrastructure and application lifecycle manager is configured to implement a common services model to deploy selected services from a common set of services to service domains hosted on multiple different cloud platforms by abstracting dependence on availability of various additional supporting services, such as services that are platform-specific. The platform-as-a-service infrastructure and application lifecycle manager may also manage a lifecycle of available services, such as managing upgrades and/or patches to services.

Abstract: A platform-as-a-service infrastructure and application lifecycle manager is configured to implement a common services model to deploy selected services from a common set of services to service domains hosted on multiple different cloud platforms by abstracting dependence on availability of various additional supporting services, such as services that are platform-specific. The platform-as-a-service infrastructure and application lifecycle manager may also manage a lifecycle of available services, such as managing upgrades and/or patches to services.

Abstract: A platform-as-a-service infrastructure and application lifecycle manager is configured to implement a common services model to deploy selected services from a common set of services to service domains hosted on multiple different cloud platforms by abstracting dependence on availability of various additional supporting services, such as services that are platform-specific. The platform-as-a-service infrastructure and application lifecycle manager may also manage a lifecycle of available services, such as managing upgrades and/or patches to services.

Abstract: Non-transitory computer readable media are encoded with instructions which, when executed by one or more processors of a central computing system, cause the central computing system to connect with multiple service domains and determine that at least one service domain of the multiple service domains is eligible for upgrade. The instructions further cause the central computing system to store an upgrade package for the at least one service domain at a storage service separate from the central computing system and communicate access credentials to the storage service to the at least one service domain. The central computing system receives a communication from the at least one service domain indicative of upgrade status responsive to the at least one service domain having accessed the upgrade package at the storage service and updates a state management database based on the communication.

Abstract: A containerized clustered computing system may be used to provide a platform as a service (PaaS) environment. The clustered system may be configured by initiating a bootstrap node including a first cluster infrastructure orchestrator and first container orchestrator and migrating cluster metadata to a key value store of the first container orchestrator. A second node in the cluster may be initiated by configuring a second cluster infrastructure orchestrator of the second node with a dependency on the first container orchestrator of the bootstrap node.

Abstract: A system and method include classifying and assigning virtual disks accessed from compute only nodes. The method determines, by a management processor of a virtual computing system, characteristics for a plurality of virtual disks hosted on a plurality of hyper converged nodes in a cluster of nodes in the virtual computing system. The method further classifies, by the management processor, each of the plurality of virtual disks based on the determined characteristics and identifies, by the management processor, one of the plurality of virtual disks to host data for a virtual machine on a compute only node based on the classification to spread out input-output demand in the cluster, reducing probability of input-output bottlenecks and increasing cluster-wide storage throughput. The method also assigns, by the management processor, the identified virtual disk to host data for the virtual machine located on the compute only node.

Abstract: Systems and methods described herein generally relate to compute node resource scheduling. AI inference services described herein may receive a request to execute a machine learning model in a clustered edge system. To determine which hardware resource comprising computing nodes of the clustered edge system on which to execute the machine learning model, AI inference services may compare the computational workload of the machine learning model, with the computational abilities and functions of the hardware resources. In examples, the comparison is based on a scheduling algorithm, including an identification stage to identify candidate hardware resources capable of executing the machine learning model, and a scoring stage to select the best candidate hardware resource for executing the machine learning model. A scheduler may assign the machine learning model to the selected hardware resource for execution by the AI inference services.

Abstract: A system and method include classifying and assigning virtual disks accessed from compute only nodes. The method determines, by a management processor of a virtual computing system, characteristics for a plurality of virtual disks hosted on a plurality of hyper converged nodes in a cluster of nodes in the virtual computing system. The method further classifies, by the management processor, each of the plurality of virtual disks based on the determined characteristics and identifies, by the management processor, one of the plurality of virtual disks to host data for a virtual machine on a compute only node based on the classification to spread out input-output demand in the cluster, reducing probability of input-output bottlenecks and increasing cluster-wide storage throughput. The method also assigns, by the management processor, the identified virtual disk to host data for the virtual machine located on the compute only node.

Abstract: A system and method for dynamically adjusting the amount of memory allocated to a virtual machine includes generating, by a memory resizing system, a current memory usage profile for the virtual machine. The memory resizing system and the virtual machine are part of a virtual computing system and the current memory usage profile is generated by mapping, as a function of time, memory usage information from the virtual machine. The system and method also include computing an upper baseline based upon a peak memory usage in the current memory profile, updating an initial memory allocation of the virtual machine based upon the upper baseline and a predetermined threshold for obtaining an initial revised memory allocation, determining a moving average of memory usage from a historical memory usage profile, and updating the initial revised memory allocation based upon the moving average of memory usage for obtaining a final revised memory allocation.

Abstract: A system and method include migrating virtual machines (VMs) between compute only (CO) and hyper converged (HC) nodes. The method includes identifying, by a management processor of a virtual computing system, a plurality of virtual machines hosted on compute only (CO) nodes in the virtual computing system. The management processor then identifies hyper converged (HC) nodes having virtual disks hosting data for the plurality of virtual machines hosted on CO nodes. When a virtual machine (VM) in the plurality of virtual machines is migrating, the management processor biases the VM to migrate to a first HC node hosting a virtual disk assigned to host data for the VM.

Abstract: A system and method include assigning virtual machines (VMs) to any one of compute only (CO) and hyper converged (HC) nodes. The method includes identifying, by a management processor of a virtual computing system, input-output patterns for a plurality of virtual disks in the virtual computing system; ranking, by the management processor, the plurality of virtual machines according to the identified input-output patterns; assigning, by the management processor, the plurality of virtual machines to any one of CO and HC nodes that optimize performance of the virtual computing system; monitoring, by the management processor, performance of the plurality of virtual machines; and determining, by the management processor, a change in behavior of one of the plurality of virtual machines.

Abstract: In one embodiment, a system for managing communication connections in a virtualization environment includes (1) a first host machine implementing a virtualization environment based on a first platform, wherein the first host machine includes a first hypervisor, at least one virtual machine, and one or more virtual infrastructure elements and (2) a virtual disk including a plurality of storage devices. A management module for the system may perform steps to convert the first host machine to a second platform by installing (on the first host machine) a second hypervisor associated with the second platform, disabling the first hypervisor, capturing a configuration describing elements of a virtual infrastructure associated with the first hypervisor, registering the captured configuration with the second hypervisor, creating elements of the captured configuration in the context of the second platform and in association with the second hypervisor, and then enabling the second hypervisor.

Abstract: A system and method include classifying and assigning virtual disks accessed from compute only nodes. The method determines, by a management processor of a virtual computing system, characteristics for a plurality of virtual disks hosted on a plurality of hyper converged nodes in a cluster of nodes in the virtual computing system. The method further classifies, by the management processor, each of the plurality of virtual disks based on the determined characteristics and identifies, by the management processor, one of the plurality of virtual disks to host data for a virtual machine on a compute only node based on the classification to spread out input-output demand in the cluster, reducing probability of input-output bottlenecks and increasing cluster-wide storage throughput. The method also assigns, by the management processor, the identified virtual disk to host data for the virtual machine located on the compute only node.

Abstract: A system and method include determining underprovisioning of a guest physical memory of a virtual machine running on a computing node. The node includes hardware resources that are mapped the guest physical memory by a hypervisor. The hypervisor receives page fault information from the virtual machine based on page faults in the virtual machine. The hypervisor generates a table that includes virtual memory address-process indicator pair entries and corresponding page fault numbers. The hypervisor removes those entries that have a corresponding page fault number that is less than a first threshold value. The hypervisor determines a size of a revolving memory based on the number of remaining entries and a page size of the guest physical memory. If the revolving memory size is less than a second threshold value in relation to the allocated size of the guest physical memory, the hypervisor indicates underprovisioning of the guest physical memory.

Abstract: A system and method include assigning virtual machines (VMs) to any one of compute only (CO) and hyper converged (HC) nodes. The method includes identifying, by a management processor of a virtual computing system, input-output patterns for a plurality of virtual disks in the virtual computing system; ranking, by the management processor, the plurality of virtual machines according to the identified input-output patterns; assigning, by the management processor, the plurality of virtual machines to any one of CO and HC nodes that optimize performance of the virtual computing system; monitoring, by the management processor, performance of the plurality of virtual machines; and determining, by the management processor, a change in behavior of one of the plurality of virtual machines.

Abstract: A system and method include migrating virtual machines (VMs) between compute only (CO) and hyper converged (HC) nodes. The method includes identifying, by a management processor of a virtual computing system, a plurality of virtual machines hosted on compute only (CO) nodes in the virtual computing system. The management processor then identifies hyper converged (HC) nodes having virtual disks hosting data for the plurality of virtual machines hosted on CO nodes. When a virtual machine (VM) in the plurality of virtual machines is migrating, the management processor biases the VM to migrate to a first HC node hosting a virtual disk assigned to host data for the VM.

Abstract: A system and method for dynamically adjusting the amount of memory allocated to a virtual machine includes generating, by a memory resizing system, a current memory usage profile for the virtual machine. The memory resizing system and the virtual machine are part of a virtual computing system and the current memory usage profile is generated by mapping, as a function of time, memory usage information from the virtual machine. The system and method also include computing an upper baseline based upon a peak memory usage in the current memory profile, updating an initial memory allocation of the virtual machine based upon the upper baseline and a predetermined threshold for obtaining an initial revised memory allocation, determining a moving average of memory usage from a historical memory usage profile, and updating the initial revised memory allocation based upon the moving average of memory usage for obtaining a final revised memory allocation.

Abstract: A system and method include determining underprovisioning of a guest physical memory of a virtual machine running on a computing node. The node includes hardware resources that are mapped the guest physical memory by a hypervisor. The hypervisor receives page fault information from the virtual machine based on page faults in the virtual machine. The hypervisor generates a table that includes virtual memory address-process indicator pair entries and corresponding page fault numbers. The hypervisor removes those entries that have a corresponding page fault number that is less than a first threshold value. The hypervisor determines a size of a revolving memory based on the number of remaining entries and a page size of the guest physical memory. If the revolving memory size is less than a second threshold value in relation to the allocated size of the guest physical memory, the hypervisor indicates underprovisioning of the guest physical memory.