The disclosed embodiments relate to securely booting firmware images. In one embodiment, a method is disclosed comprising receiving, by a memory device, a firmware update; validating, by the memory device, a signature associated with the firmware update; copying, by the memory device, an existing firmware image to an archive location, the archive location storing a plurality of firmware images sorted by version identifiers; booting, by the memory device, and executing the firmware update; and replacing, by the memory device, the firmware update with the existing firmware image stored in the archive location upon detecting an error while booting the firmware update.

The disclosed embodiments relate to securely booting firmware images. In one embodiment, a method is disclosed comprising receiving, by a memory device, a firmware update; validating, by the memory device, a signature associated with the firmware update; copying, by the memory device, an existing firmware image to an archive location, the archive location storing a plurality of firmware images sorted by version identifiers; booting, by the memory device, and executing the firmware update; and replacing, by the memory device, the firmware update with the existing firmware image stored in the archive location upon detecting an error while booting the firmware update.

There are provided systems and methods for a compute platform for machine leaning model roll-out. A service provider, such as an electronic transaction processor for digital transactions, may provide intelligent decision-making through decision services that execute machine learning models. When deploying or updating machine learning models in these engines and decision services, a model package may include multiple models, each of which may have an execution graph required for model execution. When models are tested from proper execution, the models may have non-performant compute items, such as model variables, that lead to improper execution and/or decision-making. A model deployer may determine and flag these compute items as non-performant and may cause these compute items to be skipped or excluded from execution. Further, the model deployer may utilize a pre-production computing environment to generate the execution graphs for the models prior to deployment or upgrading.

There are provided systems and methods for a compute platform for machine leaning model roll-out. A service provider, such as an electronic transaction processor for digital transactions, may provide intelligent decision-making through decision services that execute machine learning models. When deploying or updating machine learning models in these engines and decision services, a model package may include multiple models, each of which may have an execution graph required for model execution. When models are tested from proper execution, the models may have non-performant compute items, such as model variables, that lead to improper execution and/or decision-making. A model deployer may determine and flag these compute items as non-performant and may cause these compute items to be skipped or excluded from execution. Further, the model deployer may utilize a pre-production computing environment to generate the execution graphs for the models prior to deployment or upgrading.

A method includes: creating, by a computing device, a container image group; adding, by the computing device, container images which share file characteristics into the container image group; defining, by the computing device, a homogeneity of the container image group; and applying, by the computing device, a life cycle action on image layers of the container images within the container image group based on the homogeneity of the container image group.

A method includes: creating, by a computing device, a container image group; adding, by the computing device, container images which share file characteristics into the container image group; defining, by the computing device, a homogeneity of the container image group; and applying, by the computing device, a life cycle action on image layers of the container images within the container image group based on the homogeneity of the container image group.

Accelerated behavior change for upgrades in a distributed system is described herein. A method as described herein can include facilitating a file system upgrade of a first computing node of a computing cluster from a first file system version to a second file system version that is newer than the first file system version, wherein the file system upgrade comprises pre-restart operations and a system restart performed subsequent to the pre-restart operations; activating a supervisor system of the first computing node in response to the first computing node completing the file system upgrade; and causing, in response to the activating, the supervisor system of the first computing node to initiate concurrent performance of the pre-restart operations of the file system upgrade at second computing nodes of the computing cluster, distinct from the first computing node.

Accelerated behavior change for upgrades in a distributed system is described herein. A method as described herein can include facilitating a file system upgrade of a first computing node of a computing cluster from a first file system version to a second file system version that is newer than the first file system version, wherein the file system upgrade comprises pre-restart operations and a system restart performed subsequent to the pre-restart operations; activating a supervisor system of the first computing node in response to the first computing node completing the file system upgrade; and causing, in response to the activating, the supervisor system of the first computing node to initiate concurrent performance of the pre-restart operations of the file system upgrade at second computing nodes of the computing cluster, distinct from the first computing node.

In one embodiment, a system for managing a virtualization environment includes a set of host machines, each of which includes a hypervisor, virtual machines, and a virtual machine controller, and a data migration system configured to identify one or more existing storage items stored at one or more existing File Server Virtual Machines (FSVMs) of an existing virtualized file server (VFS). For each of the existing storage items, the data migration system is configured to identify a new FSVMs of a new VFS based on the existing FSVM, send a representation of the storage item from the existing FSVM to the new FSVM, such that representations of storage items are sent between different pairs of FSVMs in parallel, and store a new storage item at the new FSVM, such that the new storage item is based on the representation of the existing storage item received by the new FSVM.

In one embodiment, a system for managing a virtualization environment includes a set of host machines, each of which includes a hypervisor, virtual machines, and a virtual machine controller, and a data migration system configured to identify one or more existing storage items stored at one or more existing File Server Virtual Machines (FSVMs) of an existing virtualized file server (VFS). For each of the existing storage items, the data migration system is configured to identify a new FSVMs of a new VFS based on the existing FSVM, send a representation of the storage item from the existing FSVM to the new FSVM, such that representations of storage items are sent between different pairs of FSVMs in parallel, and store a new storage item at the new FSVM, such that the new storage item is based on the representation of the existing storage item received by the new FSVM.