International Business Machines Corporation

Rajesh Kumar Saxena

Harish Bharti

Sandeep Sukhija

Abhay Kumar Patra

Deepak Bajaj

Balakrishnan Sreenivasan

Vikram Yadav

Amol Ashok Dhondse

Anand Pikle

Rakesh Shinde

Anupam Gupta

Gopal Kumar Bhageria

Dinesh G Wadekar

Pinaki Bhattacharya

Deependra Kumar Singh

Sarbajit Kumar Rakshit

Mukundan Sundararajan

Stan Kevin Daley

Pranshu Tiwari

Shankar S Kalyana

Lucia Larise Stavarache

Zheng Xie

Rajeev Mittal

Anupama Ratha

Sanjib Choudhury

Ingo Averdunk

Rajib Das

Hemant Khadke

Jayadev J

Ankit Singhal

Jitendra Hairiya

Vishal Agrawal

Ravi Kumar V Mandalika

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

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

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

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

A process for service chain instance pool sizing receives service requests for processing in a 5G network by a service function chain, of the 5G network, providing network function(s) to process the service requests. The method determines an average number of service requests to be processed, and determines a number of service chain instances to exist in a pool of service chain instances, of the service function chain, as a composite of (i) a number of instances to process service requests having arrived and making an initial relay through the service function chain and (ii) a number of instances to process service requests that were re-introduced after having previously arrived. The method additionally indicates the number of service chain instances to exist in the pool to a component responsible for controlling the number of service chain instances in the pool.

Service chain instance pool sizing

A core set of nodes of a graph representing a network of devices is identified, a node of the graph representing a device in the network of devices, an edge of the graph representing an ability to transmit data between two devices represented by corresponding nodes of the graph. A device represented by a node more than one hop from any node in the core set of nodes of the graph is removed from the network of devices, the removing resulting in a validated network of devices. Using a number of devices in the validated network of devices, a number of security keys in use in the validated network is validated. A device in the validated network is caused to perform a task.

Security and task performance validation for a cooperative device network

A method, computer program product, and system include a processor(s) that obtains a request for a transaction to be processed by an application in a computing system. The processor(s) applies, to the request for the transaction, an outlier detection model, to determine whether the transaction comprises attributes matching transaction tuples of one or more historical transactions identified as triggering issues in the computing system when the application processed the historical transactions. The processor(s) classifies the transaction as being an outlier transaction or as being a standard transaction, based on applying the outlier detection model. Based on determining that the transaction is an outlier transaction, concurrently with the application processing the transaction, the processor(s) turn on the debug trace to debug trace the application processing of the transaction.

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Thane East, India

Rajesh Kumar Saxena