Ca, Inc.

Other

Serguei Mankovskii

Steven Lloyd Greenspan

Maria C Velez-Rojas

Carrie Elaine Gates

Gabriel M Silberman

Maria Cecilia Velez Rojas

Douglas Neuse

Howard A Abrams

Victor Muntés-Mulero

Ramanjaneyulu Malisetti

Marc Solé Simó

Rajasekhar Gogula

Subhasis Khatua

Prashant Parikh

Preetdeep Kumar

Shweta Tiwari

Peter Brian Matthews

Colin Banger

Michael Godfrey

Taras Mankovskii

Paul C Lipton

John J Harrison, Jr

Riaz Ahmed Shaikh

Kamel Adi

George Watt

Luigi Logrippo

Jody Steele

Tomas Gray

Rashmi Gupta

Ian Davis

Systems and methods may include receiving, by an expert system, performance data for a monitored system. The systems and methods may include generating a prediction for the monitored system in response to determining that the performance data satisfies a condition. The prediction may identify an anomaly that is predicted to occur. The systems and methods may include receiving, by a filter system, the prediction, information identifying the condition, and user information. The user information may include user preference information and user feedback information. The systems and methods may include determining a filter criteria based on the user information. The filter criteria may be based on the preferences for predictions to be provided to the user and on the historical user feedback regarding the historical predictions. The systems and methods may include providing the prediction to the user in response to determining that the particular prediction satisfies the filter criteria.

Feedback and customization in expert systems for anomaly prediction

A multi-device data processing machine system includes a plurality of network-connected cliental servers including first and second production servers coupled to a dynamic load balancer. The machine system also includes an SaaS development server that is configured to pass under-development process requests to the load balancer in combination with a mix command such that the load balancer routes a mix of routine production traffic and the under-development process requests to at least one of the production servers that is instrumented for enabling remote debugging of code executing therein so that the under-development process requests can be debugged under the full or partial stresses of a live production environment.

Debugging in-cloud distributed code in live load environment

Determining the physical location of wirelessly connected devices within a network can provide a number of security benefits. However, manually determining and configuring the physical location of each device within a system can be burdensome. To ease this burden, devices within a network are equipped with a location detection sensor that is capable of automatically determining a device's location in relation to other devices within the network. A location detection sensor ('sensor') may include a light source, a light direction sensor, a rangefinder, and a radio or wireless network interface. Two location detection sensors can perform a location detection process to determine their relative locations to each other, such as the distance between them. As more sensors are added to a network, a sensor management system uses the relative locations determined by the sensors to map the sensors to a physical space layout.

Location detection sensors for physical devices

A method includes formatting for display, on a visual screen, an image comprising a coordinate system and a plurality of data points within the coordinate system, wherein the plurality of data points define historical performance data for a computer system. The method further includes receiving a user input defining a distinguishable area within the coordinate system and an action associated with the distinguishable area. The method additionally includes generating a formula representing the distinguishable area.

Visual generation of an anomaly detection image

Data processing workloads are selectively assigned within a data center and/or among data centers based on non-data processing overhead within the data center and/or among the data centers. Power consumption of a rack including servers is predicted based on data processing demands that are placed on the servers for a given data processing workload, and power consumed by the rack is measured when the servers are performing the given data processing workload. A metric of power consumed by the rack for non-data processing overhead is derived based on a difference between results of the predicting and the measuring. A future data processing workload is selectively assigned to the rack based on the metric of power of power consumed by the rack for the non-data processing overhead. Assignment may also take place at an aisle and/or data center level based on these metrics.

Assigning data processing workloads in data centers based on non-data processing overhead

A method includes identifying, using a machine learning algorithm, a recording device in a first image in a stream of images of a scene captured by a camera. The method also includes determining that the recording device is likely recording a protected resource based on a positioning of an aperture of the recording device and information associated with the recording device in the stream of images. The method additionally includes, in response to determining that the recording device is likely recording the protected resource, removing any content designated as protected content from a display screen.

Securing protected content by identifying recording devices

Techniques for managing distributed state for stateless transactions are disclosed herein. In some embodiments a distributed state manager detects a state-changing event that corresponds to a stateless transaction between a node and an application server. The stateless transaction is generated from a first instance of an application that is executing on the node and hosted by the application server. The distributed state manager records the event in a blockchain comprising blocks that each record a batch of one or more events associated with execution of the application. The distributed state manager detects an update to the blockchain associated with the recorded event and modifies a state of a second instance of the application executing on at least one other node based, at least in part, on the update to the blockchain.

Maintaining distributed state among stateless service clients

A method includes formatting for display, on a visual screen, an image comprising: (1) a coordinate system, (2) a plurality of distinguishable areas within the coordinate system, each distinguishable area graphically representing a respective formula, and (3) a plurality of data points. The method also includes receiving user input comprising a modification to a particular distinguishable area. In response to receiving the user input, the method includes modifying one or more respective formulas based on the modification to the distinguishable area. For each data point, the method further includes associating the data point with one of the distinguishable areas by determining which of the modified formulas the data point falls within. The method further includes formatting for display a graphical representation of each modified formula. The method additionally includes storing the graphical representation of each modified formula for use as a modified image in future operations.

Visual modification and training of an anomaly detection image

Operational event loggings and operational alarm productions within a running multiserver data processing system are automatically and repeatedly sampled and co-associated with one another so as to build annotated logs that can be used by post-process analytics for filling in mappings thereof into an anomalies versus parameters mapping space and for keeping track of unusual changes in the mappings or their rates where the unusual changes can be indicative of emerging new problems of significance within the system.

Anomaly classification, analytics and resolution based on annotated event logs

Mapping devices to a physical space

Detecting and mitigating wardriving

An enterprise wide data processing system includes at least one watchdog unit and/or software service that is configured to automatically detect an attempt to connect a dynamically connectable and disconnectable peripheral (DCP) such as a USB stick to a watchdog-watched Dynamic Connection-Making Mechanism (DCMM) of the system. The watchdog unit and/or software service is further configured to automatically determine if a type of the attempted connection is in accordance with at least one of a local list of connection permissions and connection rules, and if not to prevent an operatively effective connection to be actually made by way of the watchdog-watched DCMM. The system further includes a remotely modifiable storage storing the at least one of the local list of connection permissions and connection rules.

Discriminating dynamic connection of disconnectable peripherals

A system and method are disclosed for automatically updating a virtual model of a facility such as a datacenter, for example for use in maintaining an accurate thermal management plan. Photo images of the facility may periodically be captured. Thereafter, one or more image recognition algorithms may be run on the captured images to recognize positions and identities of equipment and structures in the facility. Using this information, the virtual model of the facility may be updated. In examples related to thermal management of a datacenter, once the virtual model of the facility is updated, the thermal management plan may be revised.

Method and system for automated datacenter floor volume calculation applied to datacenter thermal management

As a network increases in size and complexity, it becomes increasingly difficult to monitor and record relationships between components in the network. The lack of knowledge regarding component relationships can make it difficult to adequately and timely perform analysis of network issues or conditions. As a result, automated generation of a context graph that displays relationships among both hardware and software components in a network can help keep pace with a growing network and improve network analysis. The context graph may be generated based, for example, on event data (alternately referred to as event indications) generated by network components and/or event monitoring agents and network topology information. Additionally, the context graph may be augmented to display inter-component relationships based on multi-event correlations. The context graph can be used to assist in troubleshooting network issues or performing root cause analysis.

Growing community of inventors

San Ramon, CA, United States of America

Serguei Mankovskii