International Business Machines Corporation

Kyndryl, Inc.

Gravitant, Inc.

Ilyas Mohamed Iyoob

Manish Mahesh Modh

Omar Odibat

Jean Xu Yu

Emrah Zarifoglu

Shikhar Kwatra

Aly Megahed

Eran Raichstein

David Breitgand

Michael Masin

Ofer Biran

Dean Lorenz

Mohammed Farooq

Kishor Grandhe

Krishna Teja Rekapalli

Keri Wheatley

Aaron Michael Yan

Zehra Yalcin

Avi Weit

According to one embodiment, a method, computer system, and computer program product for provisioning a tag schema. The embodiment may include determining data types for tag-keys within an existing ecosystem of tags. Each tag of the existing ecosystem comprises a tag-key and a tag-value. The embodiment may include performing pairwise clustering of the tags within the ecosystem. The embodiment may include identifying a main tag-key within each cluster of tags. The embodiment may include grouping the clusters into a broader category. The embodiment may include providing a tag schema recommendation for the ecosystem based on the grouped clusters.

Automatically provisioned tag schema for hybrid multicloud cost and chargeback analysis

An embodiment includes generating an input document that includes a plurality of text fields of attribute data. The embodiment also includes extracting a set of candidate features from the attribute data using a feature extraction module that evaluates the attribute data using first and second machine learning models, where the first machine learning model scores terms in the input document and the second machine learning model includes a deep learning model. The embodiment also includes calculating feature-selection values for respective features of the set of candidate features and generating tag information for a remote computing asset using a machine learning classifier that predicts the tag information based on the feature-selection values.

Methods and apparatus for feature generation using improved term frequency-inverse document frequency (TF-IDF) with deep learning for accurate cloud asset tagging

Computer systems, methods and program products for automating pseudonymization of personal identifying information (PII) using machine learning, metadata, and crowdsourcing patterns to identify and replace PII. Machine learning models are trained for classifying known column names or key names for processing, using metadata. Column or key names are classified to be unprocessed, anonymized or pseudonymized by a pseudonymizer without revealing PII or scrubbing data into a useless format. A library of crowdsourced patterns are utilized for matching PII to data values within column or key names and PII is mapped to replacement methods. Feedback from user annotations retrains the algorithms to improve classification accuracy and Deep Learning algorithms automate the identification of PII using regular expression generation to concisely articulate how pseudonymizers search for PII patterns within a data set. PII replacement is mapped consistently across entire data packages and the crowdsourced pattern library is updated with generated regular expressions.

Cognitive data pseudonymization

Feature generation for asset classification

A heterogeneous resource reservation (HRR) manager configured to classify historical application requests from a past time interval for a first workload to generate labeled historical application requests. The HRR manager further configured to generate a forecast based on the labeled historical application requests and for predicting future application requests for the first workload for a future time interval and calculate a joint plan based on the forecast. The joint plan including a set of virtual resources, a set of billing contracts, and a set of load balancer weights. The HRR manager further configured to implement the joint plan for a distributed computing workload during the future time interval.

Heterogeneous resource reservation

A computer-implemented method of implementing comparison of cloud service provider package offerings comprises a plurality of operations. An operation of creating a cloud services architecture specification defining a cloud infrastructure that supports deployment of an application thereto is performed. An operation of determining a cloud services offering price for each one of the plurality of cloud service offerings to characterize implementation of the cloud infrastructure using cloud services thereof on a basis of pricing of such cloud services is performed. Determining the cloud services offering price for a particular one of the cloud service offerings includes mapping the cloud services architecture specification to cloud service pricing information for the particular one of the cloud service offerings.

Implementing comparison of cloud service provider package offerings

A system configured to facilitate brokering hybrid cloud services selects information requests for characterizing an information technology application. The system responses to the information requests and determines a plurality of sets of the responses each used for deriving a value of a respective one of a plurality of factor functions. The system determines a plurality of assessment results each derived using values for of a respective set of the factor functions. Each one of the assessment results characterizes a respective aspect of deploying the information technology application within a cloud environment.

Assessment of best fit cloud deployment infrastructures

A method, system and computer program product for accurately predicting capacity requirements for Information Technology (IT) resources in physical, virtual, cloud and hybrid cloud environments. The business demand is translated into IT application demand, which is translated into IT architecture layer demand. Resource groups are created for each IT application, IT architecture layer and resource type combination. The utilization for each IT resource group within the IT architecture layers is computed using the created resource groups, IT application demand and IT architecture layer demand. Under or over-utilized IT resource groups may be identified using the computed utilization for each IT resource group. Upon identifying such IT resources, an optimal resource profile (e.g., number of servers) for each resource group is computed. In this manner, the utilization of IT resources can be more accurately predicted thereby allowing the IT resources to be used more effectively without over-utilizing or underutilizing these resources.

Accurately predicting capacity requirements for information technology resources in physical, virtual and hybrid cloud environments

Growing community of inventors

Pflugerville, TX, United States of America

Ilyas Mohamed Iyoob