Abstract: Described herein are methods and a system for that monitors the performance, and provides reporting including replacement of IoT edge devices in an edge computing network. Data is received by an AI/ML model from multiple IoT edge devices which is process in time series. Analysis services and engines determine the performance of the multiple IoT edge devices. Severity and weightage are applied to the data, and algorithms determine performance and replacement recommendations.

Abstract: Described herein are methods and a system for that monitors the performance, and provides reporting including replacement of IoT edge devices in an edge computing network. Data is received by an AI/ML model from multiple IoT edge devices which is process in time series. Analysis services and engines determine the performance of the multiple IoT edge devices. Severity and weightage are applied to the data, and algorithms determine performance and replacement recommendations.

Abstract: Techniques are provided for automatically updating one or more firmware versions based on corresponding device driver versions of a server device. One method comprises, in response to an update of a device driver of a server device: obtaining, by the server device, a driver version of the device driver; identifying, using firmware mapping data structures, a firmware version of firmware of the server device associated with (e.g., compatible with) the obtained driver version; and updating the firmware to correspond to the identified firmware version from the firmware mapping data structures. The driver version may be obtained from: (i) a host agent associated with an operating system of the server device, and/or (ii) a memory of the server device, after being stored in the memory in response to: a loading of the device driver from the operating system and/or the update of the device driver.

Abstract: Methods, information handling systems and computer readable media are disclosed for prioritizing communications. In one embodiment, a method includes receiving, at a communications prioritization engine including a processor, communication data reflecting a communication. The communication data comprises source information representing a source of the communication. This method embodiment further includes identifying subject characteristic information within the communication data, and determining a subject characteristic score component using the subject characteristic information. The embodiment further includes determining a source score component using the source information and determining a response priority score using the subject characteristic score component and the source score component.

Abstract: A method and system for contraindicating firmware and driver updates. Specifically, the disclosed method and system entail discerning whether installation of a hardware device firmware and/or device driver update, targeting a hardware device on a host device, would succeed or fail given a set of features (or indicators) reflective of the current host device state and metadata respective to the hardware device update. Further, the determination may employ predictive machine learning techniques.

Abstract: A method and system for contraindicating firmware and driver updates. Specifically, the disclosed method and system entail discerning whether installation of a hardware device firmware and/or device driver update, targeting a hardware device on a host device, would succeed or fail given a set of features (or indicators) reflective of the current host device state and metadata respective to the hardware device update. Further, the determination may employ predictive machine learning techniques.

Abstract: In one or more embodiments, one or more systems, methods, and/or processes may receive a request for a data structure that includes information associated with a target physical information handling system (IHS) that complies with one or more criteria; may query each physical IHS of multiple physical information handling systems (IHSs) for information associated with multiple physical hardware components of the physical IHS; may receive, from at least one physical IHS of the multiple physical IHSs, the data structure that includes the information associated with the target physical IHS that complies with the one or more criteria; may determine that the data structure, from the at least one IHS of the multiple IHSs, includes the information associated with the target physical IHS that complies with the one or more criteria; and may provide, to a network, the data structure and a digital signature associated with the data structure.

Abstract: A web server receives a packet including a web request from a browser of a client. The request includes a session cookie comprising a client token and a session identifier. A secret session token is calculated based on the session identifier and header data that includes data from one or more packet header fields. The web request is processed if the secret session token matches the client token and blocked otherwise. Determining the secret session token may include hashing the session identifier, at least a portion of a user agent string included in a user agent header of the web request, and at least a portion of a source IP address included in an IP header of the packet. The secret session token may have been provided to the client as a session cookie included in a response to an initial web request from the client.

Abstract: Methods, information handling systems and computer readable media are disclosed for prioritizing communications. In one embodiment, a method includes receiving, at a communications prioritization engine including a processor, communication data reflecting a communication. The communication data comprises source information representing a source of the communication. This method embodiment further includes identifying subject characteristic information within the communication data, and determining a subject characteristic score component using the subject characteristic information. The embodiment further includes determining a source score component using the source information and determining a response priority score using the subject characteristic score component and the source score component.

Abstract: A web server receives a packet including a web request from a browser of a client. The request includes a session cookie comprising a client token and a session identifier. A secret session token is calculated based on the session identifier and header data that includes data from one or more packet header fields. The web request is processed if the secret session token matches the client token and blocked otherwise. Determining the secret session token may include hashing the session identifier, at least a portion of a user agent string included in a user agent header of the web request, and at least a portion of a source IP address included in an IP header of the packet. The secret session token may have been provided to the client as a session cookie included in a response to an initial web request from the client.