Abstract: One example method includes collecting telemetry data for each of a group of virtual machines (VM), and each of the VMs is associated with a user, collecting usage data for each of the VMs, creating a user profile definition for each user, and the user profile definition is created based on the telemetry data and usage data of the VMs associated with that user, creating, for each user, a user profile that is based on the user profile definition for that user, clustering the users based on similarity of their respective user profiles, and generating a recommended VM hardware configuration for a VM of one of the users.

Abstract: A disclosed information handling system includes an edge device communicatively coupled to a cloud computing resource. The edge device is configured to respond to receiving, from an internet of things (IoT) unit, a numeric value for a parameter of interest by determining a compressed encoding for the numeric value in accordance with a non-lossless compression algorithm. The edge device transmits the compressed encoding of the numeric value to the cloud computing resource. The cloud computing resource includes a decoder communicatively coupled to the encoder and configured to respond to receiving the compressed encoding by generating a surrogate for the numeric value. The surrogate may be generated in accordance with a probability distribution applicable to the parameter of interest. The compression algorithm may be a clustering algorithm such as a k-means clustering algorithm.

Abstract: An air-gapped computer receives, from a simplex communication input device, an encoded message communicated by a simplex communication output device. The air-gapped computer system can use a private key to decrypt an encrypted message which was encrypted by a corresponding public key. The air-gapped computer system decodes the encoded message. The air-gapped computer system verifies that the decoded message indicates that a required number of approvers have approved of a user executing a command. The air-gapped computer system extracts the approved command from the decoded message. The air-gapped computer system enables execution of the command by executing the command, or by providing the user with an access token which enables the user to physically access the air-gapped computer system and execute the command.

Abstract: A system receives a request from a user to execute a command on an air-gapped computer system. If a role-based access control system permits the user to execute the command, the system prompts a number of approvers to determine whether to approve of the user executing the command. If a required number of approvers have approved of the user executing the command, the system encodes the command and incorporates the encoded command in an encoded message. The system uses a simplex communication output device to communicate the encoded message to a simplex communication input device for the air-gapped computer system. The system enables execution of the command by requesting the air-gapped computer system to execute the command, or by providing the user with an access token, received from the air-gapped computer system, which enables the user to physically access the air-gapped computer system and execute the command.

Abstract: Co-clustering of at least some parameters is employed to reduce data transferred between edge and cloud resources. Single-parameter cluster information, including cluster counts, for each of two or more parameters of interest is accessed. Each parameter may represent a time series of numeric values sent from an IoT unit to an edge device. A co-clustering ratio is determined for each unique parameter pair. The co-clustering ratio indicates whether the number of clusters produced by a co-clustering algorithm applied to a group of parameters is less than the number of clusters required to represent the parameters without co-clustering. Co-cluster groups may be identified based on the cluster ratios. For each co-cluster group, the co-clustering algorithm may be invoked to produce compressed encodings of numeric value tuples. The compressed encoding is then transmitted to a cloud computing resource and decoded into a tuple of surrogate values.

Abstract: One example method includes reading, at an air-gapped system, a code provided by a control system, and the code includes a message containing instructions from the control system to the air-gapped system, checking, by the air-gapped system, the message to determine if the message includes a command executable by the air-gapped system, and when the message identifies a command executable by the air-gapped system, and the command is included in a list of authorized commands, executing, by the air-gapped system, the command.

Abstract: According to one embodiment, a secure storage unit replacement and locking system includes computer-executable instructions to receive a request to remove one of the storage units from the enclosure, and generate a key, wherein the key includes information for identifying the one storage unit to be removed. When the key is presented at the enclosure, the instructions receive information associated with the key when the key is located at the disk enclosure, determine which one of the plurality of storage units are to be unlocked by the key, and unlock the one storage unit according to the determination.

Abstract: Techniques are used for balancing load on a storage system according to multiple variables. The techniques may be used to provide, among other things, defining, across at least two variables, a balance constraint for a load on a storage system. Among a set of transfers of volumes from one node to another node in the storage system, a transfer of a volume that minimizes the distance between the load and an ideal balanced state of the storage system is identified. The identified transfer of a volume is added to a combination of transfers of volumes. Whether the combination of transfers of volumes meets the balance constraint is determined. If the combination meets the balance constraint, the combination is selected as a solution to balance the load.

Abstract: Techniques are provided for customer support ticket aggregation. One method comprises obtaining a customer support ticket; extracting a topic of the customer support ticket using a topic model based on natural language processing techniques; converting the customer support ticket to a topic vector representation that identifies the extracted topic and comprises a list of words describing the topic based on a collection of processed customer support tickets; extracting features from the customer support ticket; generating a fingerprint for the customer support ticket that comprises the topic vector representation and the extracted features; applying the fingerprint to a machine learning similarity model that compares the fingerprint to fingerprints of processed customer support tickets from the collection of processed customer support tickets; and identifying a processed customer support ticket from the collection of processed customer support tickets that is related to the customer support ticket.

Abstract: In performance testing a data storage system, operating parameters and performance data are recorded as the system executes performance tests over a test period, where the performance data includes measures of a performance characteristic (e.g., latency) across a range of I/O operation rates or I/O data rates for each performance test. Subsets of recorded operating parameters and performance data are selected and applied to a machine learning model to train and use the model, and the model provides a model output indicative for each performance test of a level of validity of the corresponding performance data. Based on the model output indicating at least a predetermined level of validity for a given performance test, the performance data for the performance test are incorporated into a record of validated performance data for the data storage system, usable for benchmarking, regression analysis, hardware qualification, etc.

Abstract: One example method includes monitoring performance of an element of a runtime environment, where the monitoring includes collecting performance information concerning the element, analyzing the collected information, detecting, based on the analysis of the collected information, an anomaly in the performance of the element and, in response to detection of the anomaly, automatically marking a snapshot of the runtime environment element, and the marking of the snapshot overrides a retention policy applicable to the snapshot.

Abstract: Deduplication of files or other data or objects in a manner that is aware of a format of the file and the application. A file may be chunked based on the format. The chunks are more consistent and lead to higher deduplication ratios. The file may be presented as a single file. However, the file is stored in chunks or subfiles and deduplication is performed with respect to the chunks. When the file is read, the file is rebuilt from its respective chunks. The files may also be compressed using differential compression, which leverages the content of similar files to compress a current file.

Abstract: A disclosed information handling system includes an edge device communicatively coupled to a cloud computing resource. The edge device is configured to respond to receiving, from an internet of things (IoT) unit, a numeric value for a parameter of interest by determining a compressed encoding for the numeric value in accordance with a non-lossless compression algorithm. The edge device transmits the compressed encoding of the numeric value to the cloud computing resource. The cloud computing resource includes a decoder communicatively coupled to the encoder and configured to respond to receiving the compressed encoding by generating a surrogate for the numeric value. The surrogate may be generated in accordance with a probability distribution applicable to the parameter of interest. The compression algorithm may be a clustering algorithm such as a k-means clustering algorithm.

Abstract: Co-clustering of at least some parameters is employed to reduce data transferred between edge and cloud resources. Single-parameter cluster information, including cluster counts, for each of two or more parameters of interest is accessed. Each parameter may represent a time series of numeric values sent from an IoT unit to an edge device. A co-clustering ratio is determined for each unique parameter pair. The co-clustering ratio indicates whether the number of clusters produced by a co-clustering algorithm applied to a group of parameters is less than the number of clusters required to represent the parameters without co-clustering. Co-cluster groups may be identified based on the cluster ratios. For each co-cluster group, the co-clustering algorithm may be invoked to produce compressed encodings of numeric value tuples. The compressed encoding is then transmitted to a cloud computing resource and decoded into a tuple of surrogate values.

Abstract: A method for alleviating data poisoning in an edge computing resource includes receiving a numeric value from an Internet of Things (IoT) unit and associating the numeric value with a cluster selected from a plurality of clusters in accordance with a suitable clustering algorithm such as a k-means clustering algorithm. In at least some embodiments, the numeric value comprises a poisoned numeric value including an adversarial component injected by an adversary to negatively impact a trained model of a cloud-based artificial intelligence engine. Rather than permitting the injected adversarial component to corrupt the AI engine, a cluster with which the numeric value is associated is sampled in accordance with a probability distribution of the cluster to obtain a surrogate for the poisoned numeric value. The surrogate may then be provided as an input to an inference module of the AI engine to generate a prediction.

Abstract: Techniques described herein relate to a method for identifying offline accounts for online adoption targeting, including obtaining an account group data set; generating, using the account group data set, an enhanced account group data set comprising the account group data set and a derived data item; obtaining, using a ML model and the enhanced account group data set, a key metric set; performing a clustering analysis using the key metric set and a portion of the enhanced account group data set to obtain account clusters; assigning a weight to a cluster of the account clusters; performing a similarity computation for an offline account of the account group using the key metric set and the portion of the enhanced account group data set to obtain a weighted similarity score; adding the weighted similarity score to a ranked list of weighted similarity scores; and providing the ranked list to an interested entity.

Abstract: A system can train a neural network model at a first edge device regarding respective amounts of time to process data at the first edge device compared to corresponding amounts of time to process the data at cloud computing equipment that is connected to the first edge device via a communications network, wherein the data is generated at the first edge device. The system can update the neural network model to produce an updated neural network model based on information received from a second edge device regarding a performance of the cloud computing equipment in processing the data, wherein the first edge device and the second edge device having respective different processing capabilities. The system can determine whether to process first data, generated at the first edge device, locally at the first edge device.

Abstract: A method, comprising: receiving a first search query that is associated with a video file; retrieving one or more search results in response to the first search query, each of the search results corresponding to a different section in the video file; and displaying the search results on a display device, wherein displaying any of the search results includes displaying a link that points to the section of the video file, which corresponds to the search result.

Abstract: One example method includes detecting grey market orders with a detection model. Data from historical orders, which include confirmed grey market orders, can be clustered based on engineered features of the data such that similar orders are clustered together. A new order can be assigned to one of the clusters and a similarity score of the new order to the orders in the assigned cluster can be generated. The score reflects the likelihood that the new order is a grey market order. Action can be taken on the new order based on the score.

Abstract: Techniques are provided for user identity verification using dynamic identification policies. One method comprises obtaining, by an identity management server, a validation request to evaluate an identity of a user, wherein the validation request is processed by the identity management server in connection with an access request of the user to access a protected resource provided by a service provider that is distinct from the identity management server. The validation request may comprise an identification policy, generated by the service provider in response to receiving the access request, that specifies authentication consensus constraints that apply to the access request.