Abstract: Methods and systems for detecting systemwide service issues by using anomaly localization. In an example, a method includes receiving time-series monitoring data for multiple services, the time-series monitoring data including multiple dimensions and an error metric; for the monitoring data from each service, evaluating scopes within the monitoring data based on an objective function for a time-series of the error metric to identify at least one anomalous scope, each scope including at least one dimension and a value for the dimension; based on evaluating the scopes, generating a ranked list of scopes for each service based on objective function scores for the scopes; correlating the ranked lists of scopes across the multiple services to identify a cross-service anomaly; and generating an alert for the services based on the cross-service anomaly, the alert indicating at least one scope as a potential root cause for the cross-service anomaly.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for identifying anomalies of failure events on a cloud computing system and determining cross-component and cross-layer correlation between change events that occur on the cloud computing system and the failure events associated with the anomalies. In particular, this disclosure describes a system that receives telemetry related to change events and failure events across any number of computing layers of a distributed computing environment (e.g., a cloud computing system) and detects anomalies based on counts of failure events that are manifested over discrete periods of time. Based on these detected anomalies, the anomaly correlation system can determine cross-layer and cross-component correlations between selective change events and the detected anomalies of failure events.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for determining optimal index configurations for intelligently managing updates of virtual machines in an offline manner in a cloud computing system. For instance, a virtual machine (VM) update system can efficiently determine when to apply updates to virtual machines in an intelligent manner that prevents the updates from interfering with the deallocation of virtual machines. In addition, the VM update system can utilize the operating system (OS) disk image snapshots to automatically provide safeguards and ensure that updates do not degrade the performance of the virtual machines, or in the case of an update failure, that the virtual machines are restored to their previous state without the data loss.

Abstract: Techniques and systems for detecting leakage of computing resources in cloud computing architectures are described. In some implementations, first data may be obtained that indicates usage of a computing resource, such as non-volatile memory, volatile memory, processor cycles, or network resources, by a group of computing devices included in a cloud computing architecture. The first data may be used to determine reference data that may include a distribution of values of usage of the computing resource by the group of computing devices. Second data may also be collected that indicates usage of the computing resource by the group of computing devices during a subsequent time frame. The second data may be evaluated against the reference data to determine whether one or more conditions indicating a leak of the computing resource are satisfied.

Abstract: The present disclosure relates to systems, methods, and computer readable media for diagnosing and mitigating memory impact events, such as memory leaks, high memory usage, or other memory issues causing a host node from performing as expected on a cloud computing system. The systems described herein involve receiving locally generated memory usage data from a plurality of host nodes. The systems described herein may aggregate the memory usage data and determine a memory impact diagnosis based on a subset of the aggregated memory usage data. The systems described herein may further apply a mitigation model for mitigating the memory impact event. The systems described herein provide an end-to-end solution for diagnosing and mitigating a variety of memory issues using a dynamic and scalable system that reduces a negative impact of memory leaks and other memory issues on a cloud computing system.

Abstract: Techniques and systems for detecting leakage of computing resources in cloud computing architectures are described. In some implementations, first data may be obtained that indicates usage of a computing resource, such as non-volatile memory, volatile memory, processor cycles, or network resources, by a group of computing devices included in a cloud computing architecture. The first data may be used to determine reference data that may include a distribution of values of usage of the computing resource by the group of computing devices. Second data may also be collected that indicates usage of the computing resource by the group of computing devices during a subsequent time frame. The second data may be evaluated against the reference data to determine whether one or more conditions indicating a leak of the computing resource are satisfied.

Abstract: A system for frequent pattern mining uses two layers of processing: a plurality of computing nodes, and a plurality of processors within each computing node. Within each computing node, the data set against which the frequent pattern mining is to be performed is stored in shared memory, accessible concurrently by each of the processors. The search space is partitioned among the computing nodes, and sub-partitioned among the processors of each computing node. If a processor completes its sub-partition, it requests another sub-partition. The partitioning and sub-partitioning may be performed dynamically, and adjusted in real time.

Abstract: The present disclosure relates to systems, methods, and computer readable media for diagnosing and mitigating memory impact events, such as memory leaks, high memory usage, or other memory issues causing a host node from performing as expected on a cloud computing system. The systems described herein involve receiving locally generated memory usage data from a plurality of host nodes. The systems described herein may aggregate the memory usage data and determine a memory impact diagnosis based on a subset of the aggregated memory usage data. The systems described herein may further apply a mitigation model for mitigating the memory impact event. The systems described herein provide an end-to-end solution for diagnosing and mitigating a variety of memory issues using a dynamic and scalable system that reduces a negative impact of memory leaks and other memory issues on a cloud computing system.

Abstract: The techniques described herein provide tools that summarize a dataset by creating a final set of segments that, when visually presented via a histogram or other data presentation tool, show the distribution of at least a portion of the data. To create the final set of segments, the techniques described herein may collect or receive a dataset with distinct values, and divide the dataset into a number of segments that is less than or equal to a segment presentation threshold (e.g., ten segments). After creating the final set of segments, the techniques may configure and/or present data visualizations, such as histograms, for the created segments so that an observer is provided with a good viewing experience.

Abstract: Techniques and systems for detecting leakage of computing resources in cloud computing architectures are described. In some implementations, first data may be obtained that indicates usage of a computing resource, such as non-volatile memory, volatile memory, processor cycles, or network resources, by a group of computing devices included in a cloud computing architecture. The first data may be used to determine reference data that may include a distribution of values of usage of the computing resource by the group of computing devices. Second data may also be collected that indicates usage of the computing resource by the group of computing devices during a subsequent time frame. The second data may be evaluated against the reference data to determine whether one or more conditions indicating a leak of the computing resource are satisfied.

Abstract: The present disclosure relates to systems, methods, and computer readable media for diagnosing and mitigating memory impact events, such as memory leaks, high memory usage, or other memory issues causing a host node from performing as expected on a cloud computing system. The systems described herein involve receiving locally generated memory usage data from a plurality of host nodes. The systems described herein may aggregate the memory usage data and determine a memory impact diagnosis based on a subset of the aggregated memory usage data. The systems described herein may further apply a mitigation model for mitigating the memory impact event. The systems described herein provide an end-to-end solution for diagnosing and mitigating a variety of memory issues using a dynamic and scalable system that reduces a negative impact of memory leaks and other memory issues on a cloud computing system.

Abstract: Methods, systems and computer program products are described for obtaining deployment signals comprising information relating to deployments of software components to a plurality of computing devices, obtaining fault signals comprising information relating to faults encountered by the plurality of computing devices, and obtaining device type information that describes a device type of each of the plurality of computing devices. Based on the deployment signals, fault signals, and device type information, a correlation score for each combination of software component, fault, and device type is obtained. Based on the correlation scores, attribution level decisions, fault type level decisions and device type level decisions are made. Based on these decisions, an overall decision is rendered whether to proceed or not proceed with a future deployment of the software component.

Abstract: The present disclosure relates to systems, methods, and computer readable media for diagnosing and mitigating memory impact events, such as memory leaks, high memory usage, or other memory issues causing a host node from performing as expected on a cloud computing system. The systems described herein involve receiving locally generated memory usage data from a plurality of host nodes. The systems described herein may aggregate the memory usage data and determine a memory impact diagnosis based on a subset of the aggregated memory usage data. The systems described herein may further apply a mitigation model for mitigating the memory impact event. The systems described herein provide an end-to-end solution for diagnosing and mitigating a variety of memory issues using a dynamic and scalable system that reduces a negative impact of memory leaks and other memory issues on a cloud computing system.

Abstract: Techniques and systems for detecting leakage of computing resources in cloud computing architectures are described. In some implementations, first data may be obtained that indicates usage of a computing resource, such as non-volatile memory, volatile memory, processor cycles, or network resources, by a group of computing devices included in a cloud computing architecture. The first data may be used to determine reference data that may include a distribution of values of usage of the computing resource by the group of computing devices. Second data may also be collected that indicates usage of the computing resource by the group of computing devices during a subsequent time frame. The second data may be evaluated against the reference data to determine whether one or more conditions indicating a leak of the computing resource are satisfied.

Abstract: A system may include a node historical state data store having historical node state data, including a metric that represents a health status or an attribute of a node during a period of time prior to a node failure. A node failure prediction algorithm creation platform may generate a machine learning trained node failure prediction algorithm. An active node data store may contain information about computing nodes in a cloud computing environment, including, for each node, a metric that represents a health status or an attribute of that node over time. A virtual machine assignment platform may then execute the node failure prediction algorithm to calculate a node failure probability score for each computing node based on the information in the active node data store. As a result, a virtual machine may be assigned to a selected computing node based at least in part on node failure probability scores.

Abstract: Automatically identifying insights from a dataset and presenting the insights graphically and in natural language text ranked by importance is provided. Different data types and structures in the dataset are automatic recognized and matched with a corresponding specific analysis type. The data is analyzed according to the determined corresponding analysis types, and insights from the analysis are automatically identified. The insights within a given insight type and between insight types are ranked and presented in order of importance in automatically generate charts that visually describe each insight and in natural language text that describes each insight in such a way that it may be understandable to a general audience who may not have a familiarity with statistics.

Abstract: A system may include a node historical state data store having historical node state data, including a metric that represents a health status or an attribute of a node during a period of time prior to a node failure. A node failure prediction algorithm creation platform may generate a machine learning trained node failure prediction algorithm. An active node data store may contain information about computing nodes in a cloud computing environment, including, for each node, a metric that represents a health status or an attribute of that node over time. A virtual machine assignment platform may then execute the node failure prediction algorithm to calculate a node failure probability score for each computing node based on the information in the active node data store. As a result, a virtual machine may be assigned to a selected computing node based at least in part on node failure probability scores.

Abstract: Methods, systems and computer program products are described for obtaining deployment signals comprising information relating to deployments of software components to a plurality of computing devices, obtaining fault signals comprising information relating to faults encountered by the plurality of computing devices, and obtaining device type information that describes a device type of each of the plurality of computing devices. Based on the deployment signals, fault signals, and device type information, a correlation score for each combination of software component, fault, and device type is obtained. Based on the correlation scores, attribution level decisions, fault type level decisions and device type level decisions are made. Based on these decisions, an overall decision is rendered whether to proceed or not proceed with a future deployment of the software component.

Abstract: Methods, systems and computer program products are described for obtaining deployment signals comprising information relating to deployments of software components to a plurality of computing devices, obtaining fault signals comprising information relating to faults encountered by the plurality of computing devices, and obtaining device type information that describes a device type of each of the plurality of computing devices. Based on the deployment signals, fault signals, and device type information, a correlation score for each combination of software component, fault, and device type is obtained. Based on the correlation scores, attribution level decisions, fault type level decisions and device type level decisions are made. Based on these decisions, an overall decision is rendered whether to proceed or not proceed with a future deployment of the software component.

Abstract: In some examples, a time-series data set can be analyzed and grouped in a fast and efficient manner. For instance, fast grouping of multiple time-series into clusters can be implemented through data reduction, determining cluster population, and fast matching by locality sensitive hashing. In some situations, a user can select a level of granularity for grouping time-series into clusters, which can involve trade-offs between the number of clusters and the maximum distance between two time-series in a cluster.