International Business Machines Corporation

Pooja Malik

Vikram Yadav

Gopal Kumar Bhageria

Sathya Santhar

Vijay Ekambaram

Michael Bender

Sandeep Sukhija

Stan Kevin Daley

Michael P Shute

Siddhartha Sood

Mechanisms are provided to implement an efficient translating mechanism to efficiently translating social media posts. A source language to be used to translate the social media post is identified based on words within the social media post. A highest classification is identified and the social media post is translated from the source language to a target language using a translation level associated with the highest classification. In the translation, each word and its related meaning in the target language are identified from a multi-language data structure; each word is categorized into its associated part of speech; a sentence is generated in the target language; and natural language processing is performed on each sentence in the target language to identity the existence of ambiguous connotations. Responsive to each sentence failing have any ambiguous connotations, a social medial post is generated in the target language utilizing the generated sentences.

Efficient translating of social media posts

Methods, computer program products, and systems are presented. The methods include, for instance: preparation of a user profile for a user whose stress response is being managed to attain a goal response. A use mode is determined according to objectives for the goal response. An Augmented Reality (AR) environment customized for the user is generated according to a use mode configuration corresponding to the use mode, and delivered to the user via a user AR device. Responses by the user to the AR environment is monitored by IoT devices, cognitively analyzed, and a determination if the goal response has been attained would be made according to the monitored user response.

Cognitive stress management by augmented reality (AR) customization

A method and system for reassigning failed jobs. It is determined that a job queue of a virtual network is overloaded. Each job is set in the job queue to be processed in a scalable mode of operation as a function of the job queue being overloaded. A job is apportioned in the job queue to a virtual machine in the virtual network operating in the scalable mode of operation. The job queued by the virtual machine fails to be completed. A probability of failing to complete the job by the virtual machine is computed. It is determined, as a function of the probability of failing to complete the job, whether to complete the job queued by the virtual machine or transfer the job to a queue of a second virtual machine operating in a dynamic voltage and frequency scaling (DVFS) mode or an active mode.

Virtual machine consolidation

Method of allocating tasks in a computing environment including: receiving a software application having tasks for processing; splitting the software application into the tasks; selecting a task for processing in a first computing environment without encryption, a second computing environment with homomorphic encryption or a third computing environment without encryption based on the following algorithm: analyzing the tasks for the presence of a security marker indicating a security level of the tasks; when there is no security marker, selecting the task for processing in the least costly of first computing environment without encryption or the third computing environment without encryption; and when the security marker is present and the processing of the task involves any computation, selecting the task for processing in the least costly of the second computing environment with homomorphic encryption or the third computing environment.

Allocating tasks in a computing environment

A method and system for allocating data processing jobs between public and private cloud based on various SLA and cost factors associated to each job, and particularly, job allocation using minimal cost association by applying logistic regression. Jobs are analyzed based on various factors such as compute and operational intensity, kind of environment, I/O operations bandwidth, costs involved to deploy in private and public cloud and all these parameters are balanced to arrive at minimized cost. Methods are implemented for receiving input data representing a current request to run a job on a virtual machine, associated job characteristics, features associated with VMs running on a public networked or private networked host computing environment, and features associated with the host computing environment. The learned classifier model is run for selecting one of: the public network or private network as a host computing environment to run said current requested job on a VM resource, the learned model based on a minimized cost function.

Allocating jobs to virtual machines in a computing environment

Method of allocating tasks in a computing environment including: receiving a software application having tasks for processing; splitting the software application into the tasks; selecting a task for processing in a first computing environment without encryption, a second computing environment with homomorphic encryption or a third computing environment without encryption based on the following algorithm: analyzing the tasks for the presence of a security marker indicating a security level of the tasks; when there is no security marker, selecting the task for processing in the least costly of first computing environment without encryption or the third computing environment without encryption; and when the security marker is medium or high and the processing of the task involves any computation, selecting the task for processing in the least costly of the second computing environment with homomorphic encryption or the third computing environment.

Systems, methods and tools for managing the job queues of virtual machines, maintaining a low energy profile and a quality of service within the contractual service agreement. The systems migrate jobs to a new VM queue when a assigned VM has failed. The systems employ machine learning techniques to make decisions whether or not to reallocate the job to a VM running in an active mode (non-scalable mode) or a VM operating under a dynamic voltage and frequency scaling (DVFS) mode. The systems reconcile job failures, transfer and/or complete jobs using the network of VMs without degrading the service quality, maintaining a lower power consumption policy through scalable modes, including idle, busy, sleep, DVFS gradient and DVFS maximum modes, improving the overall reliability of the data center by switching the jobs to scalable nodes, increasing the recoverability of the systems in the virtualized environments.

Managing handoffs between a plurality of mobile devices in a phone hosted microservices architecture in a same vicinity, with each of the mobile devices connected to a cognitive reliability engine. The cognitive reliability engine, for each of the plurality of mobile devices hosting a hosted microservice, determining a reliability score for a time period through context and activity recognition of a user owning the mobile device. Depending on the reliability score, different beacons with data packets indicating that the microservice will end, the microservice may end, or the microservice will continue with surety for a specific time period.

Cognitive reliability engine for smooth handoff in phone-hosted microservices

One or more processors determine an energy consumption need of a computing network. One or more processors lower an energy consumption level of one or more nodes in the computing network based, at least in part, on the energy consumption need. One or more processors raise the energy consumption level of at least one of the one or more nodes in the computing network in a dynamic response to a service request.

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New Delhi, India

Pooja Malik