International Business Machines Corporation

Harish Bharti

Rajesh Kumar Saxena

Sandeep Sukhija

Abhay Kumar Patra

Balakrishnan Sreenivasan

Deepak Bajaj

Sarbajit Kumar Rakshit

Rakesh Shinde

Pranshu Tiwari

Amol Ashok Dhondse

Anand Pikle

William Anthony Brown

Abdul Allam

Dinesh G Wadekar

Subrata Adhikary

Pinaki Bhattacharya

Anupam Gupta

Saurabh Trehan

Vijay Ekambaram

Stan Kevin Daley

Deependra Kumar Singh

Mukundan Sundararajan

Lucia Larise Stavarache

Ram Ravishankar

Bryan Sacks

Zheng Xie

Swarnalata Patel

Rajeev Mittal

Anupama Ratha

Sanjib Choudhury

Grigorij Kaplan

Vijay Kumar Ananthapur Bache

Sreekanth Ramakrishna Iyer

Gopal Kumar Bhageria

Raman Harishankar

Siddhartha Sood

Vikram Yadav

Tao Liu

Shankar S Kalyana

Sreenath Raghunath

Binayak Dutta

Atul Kumar Thapliyal

Ingo Averdunk

Shweta Jain

Rajib Das

Jayadev J

Madhusmita Patil

Renny I John

Shweta Vohra

Sainath Bandhakavi

Ankit Singhal

Jayant Kumar Sinha

Joy Patra

Pradeep Dash

Santosh Patil

Vaibhav Mayuresh Shah

Rama Prasad Reddy Munagala

Manohar Chintala

Anshul Chetal

Jitendra Hairiya

Sreeranjini R Seetharam

Vishal Agrawal

Ram Prasad Reddy Munagala

Raghava Venkat

Ravi Kumar V Mandalika

Jurgis Mikucionis

Hemant Khadke

Joydeep Banerjee

Naveen Narayanaswamy

Alberto Eduardo De Queiroz Dias

Prioritizing a work request pertaining to a physical asset can include generating a data structure that encodes the work request as a multidimensional representation indicating at least one classification, each at least one classification including at least one sub-category. In response to identifying multiple work requests encoded as multidimensional representations with respect to the physical asset, each multidimensional representation can be reduced to a one-dimensional (1-D) representation that preserves a variance factor of each sub-category of each multidimensional representation. Each 1-D structure can be input to a machine learning model trained to prioritize each of the work requests. The work requests can be prioritized in accordance with the machine learning model based on the 1-D structures. The priorities of each of the work requests can be output.

Multi-objective work prioritization for common assets

Embodiments of the present invention provide computer-implemented methods, computer program products and computer systems. Embodiments of the present invention can identify an effective channel on a transmission stream framework comprising a plurality of channels and nodes using current capacity states and current transmission schedules. Embodiments of the present invention can then determine whether to continue marshalling on a current channel or to marshal to the effective channel based on transmission demands between nodes and channel providing a minimum latency between available options within the transmission stream framework

Dynamic channel allocation

In an approach to improve service chain node adequacy, embodiments utilize fifth-generation technology standard (5G) for broadband cellular networks architecture for creating relevant network slices for dedicated business processes, to ensure that each node within the network slice has stabilized operations. Embodiments employ an optimization technique, maximize a model of network adequacy to ensure there is a predetermined amount of adequacy to handle a request, and introduce the model for pre-processor without impacting existing functionality. Embodiments apply one or more constraints to one or more systems to balance the one or more queues; and implement a rapid packet delivery to a subsequent entity comprising decongested queues.

Computer-based service chain node adequacy framework

An embodiment establishes a network model based at least in part on network data received from a network, wherein the network data comprises device data and certificate data. The embodiment samples the network to receive a network data sample. The embodiment compares the network data sample to the network model to determine whether an anomalous amount of devices is present in the network. The embodiment compares the network data sample to the network model to determine whether an anomalous amount of certificates is present in the network. The embodiment identifies a device population anomaly upon a determination that an anomalous amount of devices and/or an anomalous amount of certificates is present in the network.

Device population anomaly detection

Setting a primary server within a business density cluster when the business density location changes. A new primary edge provisioning server replaces a current primary edge provisioning server when the business density cluster is determined not to include the current primary edge provisioning server. An edge provisioning framework handles the associations and dissociations of the edge network(s) to build the new primary server within the business density cluster. The former primary server is respawned as a new secondary node to the new primary server.

Provisioning edge resources based on changes in business density location

Electronic circuit assemblies are provided, with an electronic circuit assembly including a transducer circuit to generate electrical energy based on applied pressure, and a static-electricity capture circuit to capture electrical energy from static-electrical charge. Further, the electronic circuit assembly includes a power management circuit electrically coupled to the transducer circuit and the static-electricity capture circuit to receive, at least in part, the generated electrical energy based on applied pressure at the transducer circuit and the electrical energy from static-electrical charge captured by the static-electricity capture circuit.

Electronic circuit assemblies for capturing electrical energy

Reliability-based service chain instance selection in a 5G network includes evaluating, for a service chain provided in a 5G network, the service chain comprising a series of network functions with processing hand-offs and take-overs therebetween, reliability of each service chain instance of a plurality of available service chain instances of the service chain, obtaining a service request for processing in the 5G network, the service request to be serviced by the service chain, selecting a service chain instance of the plurality of available service chain instances to process the service request, the selecting being based on the evaluated reliabilities of the plurality of available service chain instances, and invoking processing of the selected service chain instance to process the service request.

Reliability-based service chain instance selection in a 5G network

Provisioning business functions is provided. A runtime binary activation code is sent to a nodal edge server that has a needed runtime binary for a set of edge devices to perform a business function. A secure shell protocol connection with root operating system access is established to the nodal edge server that has the needed runtime binary to execute the runtime binary activation code.

Provisioning business function on edge

Identifying an indistinct entity within an image can include generating by an image filter multiple gradients, each of which corresponds to one of a plurality of pixels of an image captured by an imager. The image can be searched for a likely repeating pattern. Responsive to detecting, based on the multiple gradients, a likely repeating pattern within the image, data structures can be generated, the data structures comprising a set of probabilistically weighted feature vectors corresponding to the likely repeating pattern. A machine learning model can classify each of the set of probabilistically weighted feature vectors. An identity of the likely repeating pattern can be output, the identity based on the machine learning model classifications of the probabilistically weighted feature vectors.

Pattern recognition for identifying indistinct entities

Managing data element caching is provided. Non-functional requirements of a system running an application are mapped to resource utilization and system performance metric values corresponding to each of a plurality of caching parameters for each of data elements corresponding to the application suitable for caching. A caching decision is generated for each of the data elements corresponding to the application suitable for caching by identifying certain ones of the data elements for the caching to improve at least one of performance and throughput of the system based on the mapping. A data element caching decision recommendation is generated for the application based on the caching decision. The data element caching decision recommendation corresponding to the application is output to a client device of a customer via a network.

Determining caching parameter metrics for caching data elements

Runtime binary migration is provided. A slice of a 5G network is provisioned based on time period and bandwidth requirements in accordance with a service level agreement corresponding to a customer requesting performance of a business function transaction. A runtime binary for invoking the slice of the 5G network is migrated to a nodal edge server for a set of edge devices associated with the nodal edge server to perform the business function transaction using the slice of the 5G network.

Sequential bidirectional migration in an edge environment

A method includes using, by one or more processors of a computer system, a cognitive model to estimate software development parameters for a software development project based on one or more similar past projects, and automatically assigning, by the one or more processors of the computer system, story points to sprints of the software development project based on the estimated software development parameters.

Cognitive model for software development

Hybrid edge computing that includes a nimble framework that identifies services for available in a marketplace. The nimble framework defines a location for computing the services selected from the group consisting of a center server, an edge provision server and an edge node. The hybrid edge computing further includes a third party provider making are request for a service to the nimble framework. The hybrid edge computing further includes a virtualized service being provided by the nimble framework to the third party provider including a matched service to the third party provider request for the service, and an optimal location for computing.

Hybrid edge computing

A computer-implemented method according to one embodiment includes establishing, for each hop in pathways of a network, a relation between a desired reliability and channel error. Based on the established relations, a maximum number of copies of packets to send from a source in the network to a target in the network is determined. An overhead associated with sending packets from the source to the target in the network is determined for each of the hops, and a first sum of such overheads is also determined. An overhead associated with sending an acknowledgement packet to the source is determined for each of the hops. A second sum is a sum of the overheads associated with sending the acknowledgement packet to the source. In response to a determination that a sum of the first sum and the second sum is less than a predetermined threshold value, the pathways are used.

Determination of operational overhead over a unidirectional network pathway

A system, method, and computer program product for determining 'impact quantify measure-based' service chains cross interferences. The method includes quantifying the impact of one service chain on another service chain and to what extent so as facilitate making an informed decision whether to garner more resources and to fine tune the computational services for the service chain. There is further provided beforehand a certainty of required computational resources and the providing the impact or interferences details of one service chain on another helps in minimization of service quality degradation failures. The framework further runs a method step to apply a mutual convexity method on service chains to aid in forecasting cross interferences between chains and includes a step wherein, interferences between both dependent and independent service chain is calculated and provided. The provided interferences calculated result set will ensure while provisioning of virtual network functions doesn't consume energy excessively.

Quantifying service chain functions of virtual machines for cross interferences

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Pune, India

Harish Bharti