Abstract: A system, method, and computer-readable medium are disclosed for performing a data center monitoring and management operation. The data center monitoring and management operation includes: monitoring a plurality of data center assets contained within a data center; identifying a plurality of data center issues based upon the monitoring; performing a data center issue grooming operation, the data center issue grooming operation identifying a number of data center issues from the plurality of data center issues; and performing a data center issue prioritization operation, the data center issue prioritization operation prioritizing the number of data center issues from the plurality of data center issues, the prioritizing being for at least one of resolution and remediation.

Abstract: As an example, a software application executing on a computing device of a channel partner may access an application programming interface (API) hosted by a server to access a manufacturer's data. The computing device may provide criteria to the API to identify a subset of a set of configurations. The computing device may be used to select a particular configuration in the subset and to perform one or more modifications to the particular configuration to create a modified configuration. The computing device may use the API to access a verification module to verify whether the modified configuration can be built. If the modified configuration is unbuildable, the computing device may be used to make additional modifications to the particular configuration until the modified configuration is verified as buildable. The channel partner may place an order for a particular quantity of the modified configuration.

Abstract: Techniques are provided for dynamic snapshot scheduling. In an example, a dynamic snapshot scheduler can analyze historical data about storage system resources. The dynamic snapshot scheduler can use this historical data to predict how the storage system resources will be used in the future. Based on this prediction, the dynamic snapshot scheduler can schedule snapshot activities for one or more times that are relatively unlikely to experience system resource contention. The dynamic snapshot scheduler can then initiate snapshot activities at those scheduled times.

Abstract: As an example, a server may receive and/or retrieve news items and process the news items using natural language processing to identify news related to entities (e.g., people, locations, and organizations) extracted from an enterprise resource planning system. A term frequency-inverse document frequency algorithm may be used to identify critical news items that may impact one or more supply chains associated with at least one product that is to be manufactured. A long short-term memory artificial recurrent neural network may be used to determine a confidence score for each critical news item. The confidence scores of the critical news items may be used to adjust replenishment planning and a master build plan that includes a plan to build the at least one product. In this way, news items may be used to automatically (e.g., without human interaction) adjust the master build plan.

Abstract: Methods, apparatus, and processor-readable storage media for automatically generating conditional instructions for resolving predicted system issues using machine learning techniques are provided herein.

Abstract: Methods, apparatus, and processor-readable storage media for automatically determining storage system data breaches using machine learning techniques are provided herein. An example computer-implemented method includes configuring a storage system by designating at least one storage object within the storage system for storing data identified as to be protected from breach; generating at least one multivariate data breach probability function using historical performance data of the designated storage object(s) and/or historical capacity data of the designated storage object(s); calculating at least one data breach score using the at least one multivariate data breach probability function, one or more machine learning techniques, and additional performance data of the designated storage object(s) and/or additional capacity data of the designated storage object(s); and performing one or more automated actions based at least in part on the at least one data breach score.

Abstract: Techniques are provided for storage system backup and de-duplication. In an example, a file system stores a plurality of files. These files can be grouped into smaller sets through a series of iterations, where a set of files is similar to each other. Then, files of a set of files can be directly compared for duplicate information relative to each other, one copy of this duplicate information can be stored, and other copies can be discarded.

Abstract: Architectures and techniques are described that can monitor or track change to storage system configuration. Changes to the configuration that are determined to be statistically significant in potentially affecting and/or causing performance issues of the storage system can be specifically tracked. Such can be accomplished by generating a hash data of the configuration data and sorting that hash data to data buckets based on a similarity score to other hash data of other storage systems.

Abstract: Methods, apparatus, and processor-readable storage media for detecting supply chain issues in connection with inventory management using machine learning techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to inventory items in connection with a supply chain; training machine learning techniques using the obtained data, wherein training the machine learning techniques comprises determining upper bounds and lower bounds for parameters related to the supply chain; detecting anomalies in audit data pertaining to at least one inventory item within the supply chain by processing the audit data using the machine learning techniques; generating a graph representing the supply chain based on the obtained data and the audit data; identifying at least one issue within the at least one supply chain by processing the graph in connection with the detected anomalies using graph algorithms; and performing an automated action based on the identified issue(s).

Abstract: Techniques are provided for dynamic snapshot scheduling. In an example, a dynamic snapshot scheduler can analyze historical data about storage system resources. The dynamic snapshot scheduler can use this historical data to predict how the storage system resources will be used in the future. Based on this prediction, the dynamic snapshot scheduler can schedule snapshot activities for one or more times that are relatively unlikely to experience system resource contention. The dynamic snapshot scheduler can then initiate snapshot activities at those scheduled times.

Abstract: A denial-of-service detection system includes a denial-of-service detection subsystem coupled to a plurality of storage systems via a network. The denial-of-service detection subsystem receives current first storage system data for each of a plurality of different storage system operating metrics from a first storage system included in the plurality of storage systems.

Abstract: The disclosed systems and methods are directed to a computer-implemented method for use in designing a service. In at least one embodiment, a method includes designing a service with one or more generic virtual network functions (VNFs), where the generic VNFs are defined independent of vendor sourcing information. One or more trained machine learning (ML) models are used to identify VNFs available from VNF vendors that may source one or more VNFs similar to the generic VNFs. The service is implemented using VNFs provided by one or more VNF vendors, where the VNFs provided by the one or more VNF vendors have network functionality generating similar to the one or generic VNFs.

Abstract: Methods, apparatus, and processor-readable storage media for determining server issues related to software versions using artificial intelligence techniques are provided herein. An example computer-implemented method includes determining one or more server issues, among multiple reported server issues, as being related to one or more software version states; labeling, using at least one clustering algorithm, each of at least a subset of the servers as being associated with at least one of the determined server issues; generating, by processing data pertaining to at least a portion of the labeled servers using artificial intelligence techniques, at least one knowledge base identifying automated actions to be carried out in response to observed data related to the one or more server issues; monitoring metrics across at least a portion of the one or more servers; and performing one or more automated actions based on the monitoring and the at least one knowledge base.

Abstract: In some examples, before saving a file, a device analyzes contents (e.g., text and images) of the file to create a bag of words. The device performs Latent Dirichlet Allocation of the bag of words to determine topics included in the file and orders the topics in descending order of frequency. The device generates a particular name of the file based on the ordered topics and automatically save (or suggest saving) the file with the particular name. The device determines, based on the ordered topics, a folder name of a folder in which to store the file. If an existing folder has the folder name, then the device saves the file in the existing folder. If not, then the device creates a new folder with the folder name and saves the file in the new folder.

Abstract: As an example, a software application executing on a computing device of a channel partner may access an application programming interface (API) hosted by a server to access a manufacturer's data. The computing device may provide criteria to the API to identify a subset of a set of configurations. The computing device may be used to select a particular configuration in the subset and to perform one or more modifications to the particular configuration to create a modified configuration. The computing device may use the API to access a verification module to verify whether the modified configuration can be built. If the modified configuration is unbuildable, the computing device may be used to make additional modifications to the particular configuration until the modified configuration is verified as buildable. The channel partner may place an order for a particular quantity of the modified configuration.

Abstract: Methods, information handling systems and computer readable media are disclosed for determining a priority score for a pending support request document. According to one embodiment, a method includes receiving current support request information from within a pending support request document and accessing current additional information associated with the pending support request document. The method further includes associating a set of parameter values with the pending support request document, wherein the values within the set of parameter values are based on information within one or both of the current support request information or the current additional information.

Abstract: As an example, a server may host an intelligent parts catalog that uses multiple machine learning models to identify a similarity between each part and one or more other parts in the intelligent parts catalog based on attributes of each of the parts in the intelligent parts catalog. For example, the attributes may include size, form factor, electrical characteristics, power consumption, price, regulations complied with, reliability, taxonomy, and the like. If a particular part becomes unavailable (e.g., due to weather, labor strike, or manufacturing issues), the intelligent parts catalog may provide a similarity score with each similar part that quantifies how similar each similar part is to the particular part. In this way, the same bill of materials can be used to identify parts to create a first product for a quality-conscious market, a second product for a price-sensitive market, and a third product to comply with local regulations.

Abstract: As an example, a server may receive and/or retrieve news items and process the news items using natural language processing to identify news related to entities (e.g., people, locations, and organizations) extracted from an enterprise resource planning system. A term frequency-inverse document frequency algorithm may be used to identify critical news items that may impact one or more supply chains associated with at least one product that is to be manufactured. A long short-term memory artificial recurrent neural network may be used to determine a confidence score for each critical news item. The confidence scores of the critical news items may be used to adjust replenishment planning and a master build plan that includes a plan to build the at least one product. In this way, news items may be used to automatically (e.g., without human interaction) adjust the master build plan.

Abstract: A machine learning (ML) module that can continuously learn from the market data and historical orders to dynamically recommend an optimum supplier portfolio to the manufacturer for a specific product. Using artificial intelligence, this analytical tool automates the supplier selection process based on evaluation of each supplier against a number of business features. The limitations of a manual selection of suppliers are substantially alleviated when each supplier is rigorously and automatically evaluated against a well-designed set of business features. For each supplier, the ML module generates a set of feature-specific scores for the business features used in evaluating the supplier. All scores are then combined to generate a supplier-specific final score for each supplier.