Abstract: One example method includes encoding data as a polysaccharide structure, synthesizing the polysaccharide structure to create polysaccharide storage media that comprises the data, and storing the polysaccharide storage media. The example method may also include receiving a read request directed to the polysaccharide storage media, mapping the polysaccharide structure to create a map in response to the read request, traversing the map of the polysaccharide structure to determine an X-base number, and obtaining the data by converting the X-base number to a binary form.

Abstract: A system can determine to produce a storage device for a user identity indicative of a user. The system can determine a number of extra disks to include with the storage device as part of the production, the extra disks enabling further storage capacity for the storage device beyond a specified storage capacity, the determining of the number of extra disks being based on data from a group of data, the group of data comprising first cost data representative of a first cost associated with including the second number of extra disks, probability data representative of a probability that the further storage capacity beyond the specified storage capacity will be requested during a defined time period after the production, and second cost data representative of a second cost associated with installing the second number of extra disks after the storage device has been delivered to the user site.

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: 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: 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 system can establish a primary file system on a block array of computer storage, wherein the block array comprises a range of addresses, wherein the primary file system is configured to address the range of addresses. The system can establish a shadowed file system on the block array, wherein the shadowed file system is configured to access portions of the block array that are unused by the primary file system. The system can, in response to receiving a request to write data to the primary file system, and in response to determining that an amount of the block array utilized by the primary file system is full, transfer a first portion of the block array utilized by the shadowed file system to the primary file system.

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: 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: 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 extracting anomaly related rules from organizational data. One method comprises obtaining anomaly analysis data integrated from multiple data sources of an organization, wherein the multiple data sources comprise at least one set of labeled anomaly data related to anomalous transactions; extracting features from the integrated anomaly analysis data that correlate with an indication of an anomaly; training multiple machine learning models using the extracted features, where the machine learning models are trained using different combinations of the extracted features; evaluating a performance of the trained machine learning models; and extracting rules from the trained machine learning models based on the performance, wherein the extracted rules are used to classify transactions as anomalous. The trained machine learning models comprise a decision tree comprising paths to an anomaly classification. The extracted rules are optionally in a human-readable format.

Abstract: Systems and methods for providing data protection operations including cyber-threat protection operations. A sentiment analysis may be performed using language analysis to identify or determine a general or specific sentiment with or without intent to do harm. A score of the sentiment is then determined to assess risk. The data backup policy can be updated based on the assessed risk.

Abstract: Information technology resource forecasting based on time series analysis is described. A system creates multiple forecasts for an information technology resource by applying corresponding multiple time series models to first data associated with the information technology resource. The system selects a model of the multiple time series models by comparing the multiple forecasts for the information technology resource to second data associated with the information technology resource. The system outputs a forecast that is created by applying the selected model to third data associated with the information technology resource.

Abstract: A system can determine to produce a storage device for a user identity indicative of a user. The system can determine a number of extra disks to include with the storage device as part of the production, the extra disks enabling further storage capacity for the storage device beyond a specified storage capacity, the determining of the number of extra disks being based on data from a group of data, the group of data comprising first cost data representative of a first cost associated with including the second number of extra disks, probability data representative of a probability that the further storage capacity beyond the specified storage capacity will be requested during a defined time period after the production, and second cost data representative of a second cost associated with installing the second number of extra disks after the storage device has been delivered to the user site.

Abstract: A system, method, and computer-readable medium are disclosed for improved searching of contact information that includes receiving a request for a member's contact information. An initial list of candidates is returned. Based on organizational charts, distances between candidates and a point of reference are calculated, calculation is made as to a number of messages that are exchanged between candidates and the point of reference. Scores are determined based on the calculations. The scores are aggregated, and a refined list of candidates' contact information based on the aggregated scores is returned.

Abstract: Described are computer-implementable method, system and computer-readable storage medium for supervised graph-based model for prediction of program failure using program log files. Using log file from a running program application, a log file graph is created. Node-level labels are adding to the log file graph, where the labels include an indication of first failure. The node-level labeled log file graph is processed by a graph neural network (GNN) and predictions are provided as to program cause of failure or first failure indication of other log file graphs based on the GNN processed node-level labeled log file graph.

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: Identifying risky code in microservices is described. A system identifies a modification to code segments in an application. The system determines a segments similarity score based on comparing the code segments, which are in a microservice of the application, against other code segments, which are in another microservice of the application. The system determines whether the segments similarity score satisfies a segments similarity threshold. The system outputs a recommendation to make the modification, which was made to the code segments in the microservice of the application, to the other code segments in the other microservice in the application, in response to a determination that the segment similarity score satisfies the similarity threshold.

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 automatic discovery of data records. One method comprises obtaining data records each corresponding to a different item and comprising features extracted from a data source, wherein the data records identify related items identified using a collaborative filter that relates items based on user preferences; generating an item network comprising multiple nodes each corresponding to a different item, where two nodes are connected by an edge based on: (i) an item type of the two nodes, (ii) a ratio of numerical values associated with the two nodes, and/or (iii) a pair-wise configuration similarity score for the two nodes; clustering the nodes into node clusters based on topological properties of the item network; and identifying items related to a given item that (i) share an edge with the given item and (ii) are in a node cluster comprising a node of the given item.

Abstract: Techniques are provided for recommending hardware configuration changes using a user satisfaction rating. One method comprises obtaining usage data indicating user activity for users on computing devices; generating a user profile for each user; clustering the users into user clusters based on the user profiles; determining, for a given user cluster, a satisfaction score for each user in the given user cluster based on the obtained usage data for each user on the computing device; providing suggested hardware upgrades for the computing device of a given user in the given user cluster, wherein the given user is selected based on a lower corresponding satisfaction score relative to the satisfaction scores of other users in the given cluster, and wherein the one or more suggested hardware upgrades are based on hardware configurations of other users in the given cluster having a higher corresponding satisfaction score.