A method comprises collecting operational data for one or more devices and identifying one or more anomalies associated with the one or more devices based at least in part on the collected operational data. At least a portion of the collected operational data corresponding to the identified one or more anomalies is analyzed, and a probability of automatic resolution for respective ones of the identified one or more anomalies is determined based at least in part on the analysis. The identifying, the analyzing and the determining are performed using one or more machine learning models.

In one embodiment, an apparatus includes: a first trace source to generate a plurality of first trace messages and a first local platform description identifier to identify the first trace source; a second trace source to generate a plurality of second trace messages and a second local platform description identifier to identify the second trace source; and a trace aggregator coupled to the first and the second trace sources, the trace aggregator to generate a global platform description identifier for the apparatus and output a trace stream including the global platform destination identifier, the first and second local platform description identifiers, the plurality of first trace messages and the plurality of second trace messages. Other embodiments are described and claimed.

Application discovery from access patterns is disclosed. Access histories from multiple servers are collected and stored at a warehouse, which may be part of a data protection system. A time series analysis is performed on the access history to identify consistency groups and applications from the perspective of devices and storage arrays. Data protection operations such as backup operations can then be performed on the basis of devices or storage in storage arrays or other arrangements that pertain to specific consistency groups or to specific applications.

Disclosed is a system and a method for creating a layered and portable multi-tenant architecture. Initially, a cloud container for a platform is created. The cloud container includes a namespace for a tenant accessing the platform. Further, a logical isolation for the namespace of the tenant is created. Subsequently, a plurality of tenants in the namespace is generated based on the logical isolation. The tenant is further allowed to access a tenant database via Application Programming Interfaces (APIs), thereby creating a layered multi-tenant architecture.

A data quality assessment and monitoring tool addresses inconsistency in large data sets from differing sources, determining data quality attributes such as completeness, conformity, validity, and accuracy. Flexible taxonomies and rollup strategies accommodate diverse business unit needs across a complex enterprise, and provides insight into individual entities' performance. An exemplary tool comprises a data importer for importing data from a data lake; a rules manager for generating rules and rule sets; a scoring engine for generating data quality scores; a job manager; a data profiler for running data assessment tasks and collating the data quality scores for a plurality of hierarchical data entity units; a hierarchical scoring aggregator for aggregating sets of data quality scores into a plurality of first tier aggregate data quality scores and to further aggregate the first tier aggregate data quality scores into one or more second tier aggregate data quality scores; and a reporting component.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for improving distributed monitoring using different groups of remote devices. In some implementations, a system communicates with a set of remote devices involved in a first monitoring program that involves collection of data from the remote devices over a communication network. The system identifies a pattern or similarity among monitoring data collected from a subset of the remote devices involved in the first monitoring program. The system determines that the identified pattern or similarity satisfies one or more criteria for initiating additional monitoring. In response, the system determines parameters specifying second types of data to collect in a second monitoring program. The system configures one or more devices to perform monitoring for the second monitoring program including acquiring data for the second types of data and providing the acquired data to a server over the communication network.

Indexes having local features are automatically selected from sensor data of a plurality of sensors. Sensor data of the plurality of sensors, each associated with the plurality of indexes, is partitioned into a plurality of blocks. A principal component analysis is applied to the sensor data of each of the partitioned blocks and a plurality of principal components are extracted from each of the blocks. A migration distance evaluation unit extracts, from two different blocks, two principal components that form a principal component pair, and calculates a migration distance between each of the principal components regarding the extracted principal component pair. A migration factor index detection unit detects, as a migration factor index, an index among the plurality of indexes configuring the principal components having a large migration distance among the migration distances between each of the principal components calculated by the migration distance evaluation unit.

A method includes for each node of a plurality of nodes, determining a runtime value associated with an execution of a continuous integration and continuous delivery/deployment (CI/CD) pipeline, determining, for each node of the plurality of nodes, an aggregate performance statistic in view of the runtime value of the plurality of nodes, identifying, in view of the aggregate performance statistic, at least one node of the plurality of nodes that satisfies a performance threshold criterion, and responsive to identifying the at least one node of the plurality of nodes that satisfied the performance threshold criterion, marking the at least one node of the plurality of nodes as underperforming.

Embodiments for providing global inline name space verification for a distributed file system in a network of a metadata server coupled to a plurality of data servers by taking a global dataless snapshot of a namespace of the distributed file system; walking all of the files in the namespace for each data server and the metadata server to generate parsed information; combining, by an XOR operation, the parsed information into data blocks for each server; obtaining a checksum of each data block of the data blocks; comparing actual and expected checksums from the metadata server and all of the data servers; and generating an alert if a comparison of any actual and expected checksums do not match.

Application discovery from access patterns is disclosed. Access histories from multiple servers are collected and stored at a warehouse, which may be part of a data protection system. A time series analysis is performed on the access history to identify consistency groups and applications from the perspective of devices and storage arrays. Data protection operations such as backup operations can then be performed on the basis of devices or storage in storage arrays or other arrangements that pertain to specific consistency groups or to specific applications.