Hewlett-packard Development Company, L.p.

Hewlett Packard Enterprise Development LP

Micro Focus Lcc

Mehmet Kivanc Ozonat

Claudio Bartolini

Donald E Young

Ludmila Cherkasova

Sven Graupner

Sujoy Basu

Julie Anna Symons

Tahir Cader

Sharad Singhal

Ira Cohen

Matthew Richard Slaby

Ningfang Mi

Dejan S Milojicic

Mehmet Oguz Sayal

Sergey Serebryakov

Brent Arthur Enck

Mahashweta Das

Natalia Vasileva

Ablimit Aji

In exemplary aspects of optimizing data centers, historical data corresponding to a data center is collected. The data center includes a plurality of systems. A data center representation is generated. The data center representation can be one or more of a schematic and a collection of data from among the historical data. The data center representation is encoded into a neural network model. The neural network model is trained using at least a portion of the historical data. The trained model is deployed using a first set of inputs, causing the model to generate one or more output values for managing or optimizing the data center with respect to design and risk aspects.

Machine-learning based optimization of data center designs and risks

Recommending analytic tasks based on similarity of datasets is disclosed. One example is a system including a data processor, a matching module, and a recommendation module. The data processor receives an incoming dataset via a processing system, and generates a feature vector for the incoming dataset. The matching module determines similarity measures between the generated feature vector and representative feature vectors for a plurality of datasets in a data repository, and selects at least one dataset of the plurality of datasets based on the similarity measures. The recommendation module identifies at least one analytic task associated with the selected dataset, and recommends, to a computing device via the processing system, the at least one analytic task to be performed on the incoming dataset.

Recommending analytic tasks based on similarity of datasets

Systems and methods are provided for optimizing parameters of a system across an entire stack, including algorithms layer, toolchain layer, execution or runtime layer, and hardware layer. Results from the layer-specific optimization functions of each domain can be consolidated using one or more consolidation optimization functions to consolidate the layer-specific optimization results, capturing the relationship between the different layers of the stack. Continuous monitoring of the programming model during execution may be implemented and can enable the programming model to self-adjust based on real-time performance metrics. In this way, programmers and system administrators are relieved of the need for domain knowledge and are offered a systematic way for continuous optimization (rather than an ad hoc approach).

Self-adjustable end-to-end stack programming

In exemplary aspects of optimizing data centers, historical data corresponding to a data center is collected. The data center includes a plurality of systems. A data center representation is generated. The data center representation can be one or more of a schematic and a collection of data from among the historical data. The data center representation is encoded into a neural network model. The neural network model is trained using at least a portion of the historical data. The trained model is deployed using a first set of inputs, causing the model to generate one or more output values for managing or optimizing the data center using supplemental indicators.

Machine-learning based optimization of data centers using supplemental indicators

One embodiment is a method that receives a description of services desired by a service requestor and then crawls web sites to extract information on services offered by service providers. The extracted information is used to transform the description of services desired by the service requestor into an improved description of services.

Transforming a description of services for web services

Systems and methods are provided for performing a fast simulation using test parameter vectors as inputs. The method includes retrieving precomputed samples from a sample repository stored in a non-volatile memory, the precomputed samples being precomputed using a simulated model, predetermined parameter vectors, and random inputs; storing respective subsets of the precomputed samples in local memories of a plurality of respective hardware processors; storing the test parameter vectors in the local memories of the hardware processors; at each of the hardware processors, selecting a subset of the precomputed samples stored in the local memory of the hardware processor based on the test parameter vectors, computing test samples by executing the simulated model using the test parameter vectors and the random inputs; and combining the subset of the precomputed samples and the test samples to produce a simulation result.

Systems and methods for performing a fast simulation

Identifying a subset of signifiers to analyze can include determining a set of distance metrics between a first signifier and each of a plurality of second signifiers, identifying a subset of the plurality of second signifiers to analyze based on the set of distance metrics using a computing device, and determining a relation between the subset of the plurality of second signifiers and the first signifier based a subset of the set of distance metrics.

Identifying subsets of signifiers to analyze

Clustering signifiers in a semantics graph can comprise coarsening a semantics graph associated with an enterprise communication network containing a plurality of nodes into a number of sub-graphs containing supernodes; partitioning each of the number of sub-graphs into a number of clusters; and iteratively refining the number of clusters to reduce an edge-cut of the semantics graph, based on the number of clusters.

Clustering signifiers in a semantics graph

To model a computing entity, information relating to transactions associated with the computing entity is received. The received information forms a collection of information. The collection is segmented into a plurality of segments, and at least one anomalous segment is identified. A model of the computing entity is built.

Modeling a computing entity

Building a semantics graph for an enterprise communication network can include extracting a first signifier and a second signifier from the enterprise communication network, determining a semantic proximity of the first signifier and the second signifier using an engine executing computer readable instructions, and building the semantics graph, wherein the first signifier and the second signifier are represented as nodes connected by an edge representing the semantic proximity of the first signifier and the second signifier.

Building a semantics graph for an enterprise communication network

One embodiment collects performance data for an application server that processes transactions received from a client computer to a database server. An application log is created from the performance data and used for capacity planning in a multi-tiered architecture.

Capacity planning of multi-tiered applications from application logs

One embodiment is a method that builds a standardized web form that includes information extracted from multiple web forms retrieved over a web from different service providers. The standardized web form is used to retrieve price quotes from the different service providers.

Building a standardized web form

A method for calculating a content subset can include crawling a number of webpages for content, determining a relevance to a particular domain of the content, determining a penalty value for each of the number of webpages; and calculating, utilizing a data tree-based model, a subset of the content to analyze based on the relevance and the penalty value.

Calculating a content subset

Building a semantics graph for an enterprise communication network can include calculating a distance metric between a first signifier and a second signifier associated with an enterprise communication network, wherein the distance metric includes a plurality of relationships defined based on a frequency of co-occurrences of the first signifier and the second signifier, and building a semantics graph for the enterprise communication network using the calculated distance metric.

Semantics graphs for enterprise communication networks

Systems, articles of manufacture, and associated computer-executed methods determine an optimum temporal segmentation for automated information technology (IT) management. A computer-executed method detects changes in a performance metric in an automated information technology (IT) management system comprising defining a plurality of temporal segments as sets of contiguous time samples wherein time samples within a segment are mutually more similar in terms of performance metric behavior than time samples in previous and subsequent segments, and discovering the segments using an information-theoretical approach. Detecting changes in the performance metric can further comprise associating cost with the segments that is lesser for homogeneous metric behavior and greater for heterogeneous metric behavior within a segment, and finding segmentation that minimizes the cost using dynamic programming.

Growing community of inventors

San Jose, CA, United States of America

Mehmet Kivanc Ozonat