Software configuration deployment techniques for disaggregated computing architectures, platforms, and systems are provided herein. In one example, a method includes presenting a user interface configured to receive instructions related to deployment of software to compute units, and receiving user selections of a software element for deployment to a compute unit comprising a processing element and a storage element. Responsive to the user selections, the method includes instructing a management processor of a communication fabric to deploy the software element for use by the compute unit by at least establishing a first partitioning in the communication fabric between the management processor and the storage element, deploying the software element to the storage element using the first partitioning, de-establishing the first partitioning, and establishing a second partitioning in the communication fabric between the processing element and the storage element comprising the software element, wherein the processing element operates using the software element.

Methods, systems, and computer storage media for providing a set of common flat files in a composite image that can be mounted as a container (i.e. composite container) to support isolation and interoperation of computing resources. Container management is provided for a container management system based on a composite image file system engine that executes composite operations. In particular, a remote sharing manager operates with a composite engine interface to support generating composite images configured for split layer memory sharing, split layer direct access memory sharing, and dynamic base images. In operation, a plurality of files and a selection of a remote sharing configuration for generating a composite image are accessed. The composite image for the plurality of files and the remoting sharing configuration is generated. The composite image is communicated to cause sharing of the composite image, sharing of the composite image is based on the remote sharing configuration.

One example method includes creating an image definition file, using the image definition file to create an image that is deployable to an edge device, copying an agent into the image, stripping any user passwords out of the image, and removing any unnecessary packages from the image, and after the stripping and the removing, the image becomes an immutable image. The immutable image may then be deployed to a group of edge devices, and one or more layers of the immutable image may be updated by the agent.

Disclosed are devices and methods for improving the initialization of devices housing memories. In one embodiment, a method is disclosed comprising writing a test program to a first region of a memory device during production of the memory device; executing a self-test program in response to detecting a first power up of the memory device, the self-test program stored within the test program; and retrieving and installing an image from a remote data source in response to detecting a subsequent power up of the memory device, the retrieving performed by the test program.

Implementations of the disclosure provide systems and methods for receiving, by a processing device, a request for an application image. A sequence of commands associated with the application image and a value of a parameter associated with the sequence of commands is received. Responsive to determining that the sequence of commands has been previously executed with the value of the parameter, the processing device retrieves, from a cache, a result of executing the sequence with the value of the parameter. The application image is built using the first result of executing the sequence.

Customized DL anomaly detection models and generated and deployed on disparate edge devices. Configuration-related information is fetched from the edge devices and, based on the configuration/capabilities of the edge device, at least one primary deep learning-based anomaly detection model is selected, which are customized based on the configuration/capabilities of the edge device. Customization involves limiting the volume of the predictors/variables and optimizing the iterations used to determine anomalies and/or make predictions. The customized models are subsequently packaged in edge device-specific formats, such as a customized set of binaries in C language or the like. The resulting customized DL anomaly detection application is subsequently deployed to the edge device where it is executable without the need for specialized hardware or communication with network entities, such as cloud nodes or servers.

A device can operate a processor, a primary platform, and a nonvolatile memory that includes a first boot firmware for the processor. The nonvolatile memory can comprise a (i) read-only memory for the processor and (ii) a read and write memory for the primary platform. Upon power up, the processor can load the first boot firmware with a first certificate and first set of cryptographic algorithms to verify a digital signature for a second boot firmware, where the second boot firmware is loaded by the processor after the first boot firmware. The primary platform can securely download a secondary platform bundle (SPB) with a boot update image and a second certificate and second set of cryptographic algorithms. The SPB can replace the first boot firmware with the updated first boot firmware. The processor verifies the second boot firmware with the second certificate and the second set of cryptographic algorithms.

An IoT electronic device executes services distributed by an IoT service orchestration device. A Lightweight Machine-to-Machine (LwM2M) request message is received. The LwM2M request message contains a LwM2M object identifying hardware resources of the IoT electronic device for which characteristics are requested. A LwM2M command is executed that accesses a LwM2M interface identified based on content of the LwM2M object to determine the characteristics of the hardware resources of the IoT electronic device which are identified by the LwM2M object. A response message contains information identifying the characteristics of the hardware resources of the IoT electronic device. The response message is communicated toward the IoT service orchestrator device. A service image is received for execution which is adapted by the IoT service orchestrator device, responsive to the information in the response message identifying the characteristics of the hardware resources of the IoT electronic device.

A network fabric deployment system includes a fabric deployment management system that is coupled to a DHCP server. The fabric deployment management system generates a cloud-based network fabric that is based on a network fabric topology file and that includes a plurality of cloud-based networking devices that are assigned a physical networking device identifier that identifies a corresponding physical networking device. The fabric deployment management system configures and validates each of the plurality of cloud-based networking devices causing each physical networking device identifier being mapped to an IP address at the DHCP server and then retrieves a deployment image file from each of the plurality of cloud-based networking devices that have been configured and validated, and stores each of the deployment image files in a database in association with the physical networking device identifier such that the corresponding physical networking device boots from that deployment image file.

Transformation of inputs including beacon inputs, Global Positioning System (GPS) inputs, captured panorama inputs, user-penned descriptive inputs, and payment-amount-specifying inputs, via components, into outputs including user device POS configuration setting outputs and/or payment-gateway-directed authorization request outputs. Further, transformation of inputs including POS scanner inputs, POS keyboard inputs, and/or POS printer-directed inputs, via components, into outputs including compliance check outputs, tagged omnibus record outputs, SKU-UPC mapping outputs, and/or convergence/correlation outputs. Additionally, transformation of inputs including older limited-capability POS software image inputs and/or newer limited-capability POS software image inputs, via components, into outputs including update directive outputs. Functionality set forth includes allowing user devices to directly communicate with payment gateways, capturing and making use of scanner-obtained data and printer-destined data in a way that does not require code alternation of already-installed POS software, and allowing software of limited-capability POS devices to be updated without sending large, full-overwrite software images.