International Business Machines Corporation

Herbert Daniel Pearthree

Brian M O'Connell

Thomas Benjamin Harrison

Aaron K Baughman

Kevin Edward Vaughan

Richard Elderkin Nesbitt

Richard J Bogdany

Mauro Marzorati

Stephen Carl Hammer

Brian James Snitzer

Paul Reed

Clay T Upton

Michael Shannon Nichols

Matthew Robert Ganis

Paul Roger Briscoe

Cameron Donald Ferstat

Shengzhi Sun

Gary Bob Kip Hansen

Sean Alan Harp

Andrew R Jones

Rosa M Bolger

Gregory A Porpora

Cameron Niall-Kerr McAvoy

Cameron Naill-Kerr McAvoy

In an approach to attesting control over network devices, responsive to receiving a first signal from a client, wherein the first signal initiates a network connection between the client and a server, a first certificate is sent to the client that contains a common name that is an internet protocol (IP) address. A second certificate is sent to the client that contains a common name that is a uniform resource locator (URL) of the server. Responsive to receiving a second signal from the client that the first certificate and the second certificate are trusted, the client is connected with the server.

Attesting control over network devices

For distributed processing using drift-based dynamic clustering of Internet of Things (IoT) devices, at a central device, a data source to be used for processing a workload is determined. A set is selected of devices operating within a threshold distance from the data source at a first time. A first subset of the set of devices is selected to form a cluster of devices. Each device in the first subset satisfies a clustering condition. A first device in the first subset is instructed to configure an application at the first device to participate in the cluster and process the workload. From a performance check on the first device, a change is discovered in a performance metric. In response to the change resulting from an increased demand for a computing resource at the first device, the first device is replaced with a second device from the first subset.

Distributed load processing using drift-based dynamic clusters of internet of things devices

Approaches are provided for answering an inquiry of a cognitive distributed network. An approach includes receiving the inquiry at the cognitive distributed network. The approach further includes determining a classification for the inquiry based on natural language of the inquiry. The approach further includes classifying the inquiry as a single question class. The approach further includes determining, by at least one computing device, a type of introspection to be used by the cognitive distributed network on the inquiry. The approach further includes generating an answer to the inquiry based on the determined type of introspection.

Cognitive distributed network

At a central device, a data source to be used for processing a workload is determined. A set is selected of devices operating within a threshold distance from the data source at a first time. A first subset of the set of devices is selected to form a cluster of devices. Each device in the first subset satisfies a clustering condition. A first device in the first subset is instructed to configure an application at the first device to participate in the cluster and process the workload. From a performance check on the first device, a change is discovered in a performance metric. In response to the change resulting from an increased demand for a computing resource at the first device, the first device is replaced with a second device from the first subset.

Distributed load processing using drift-based dynamic clusters of Internet of Things devices

Embodiments of the present invention provide an approach for allocating computing resources based on social networking/media trends in a networked computing environment (e.g., a cloud computing environment). In a typical embodiment, a baseline computing resource allocation will be determined for the networked computing environment based upon historical computing resource data (e.g., stored in at least one computer storage device). Social networking trend data corresponding to usage of a set of social networking websites may be received and analyzed to determine a forecasted computing resource allocation (e.g., based on social networking trends). The baseline computing resource allocation may be compared to the forecasted computing resource allocation to identify any difference therebetween. A computing resource allocation protocol/plan may then be determined based on the comparison (e.g., to address the difference).

Resource allocation based on social networking trends in a networked computing environment

In a method for authenticating a current user of a near field communication (NFC) device, a profile for an authorized user of the NFC device is established based on data received from one or more sensors of the NFC device over a first period of time. Responsive to a request for a payment transaction, a profile for the current user of the NFC device is established based on data received from the one or more sensors over a second period of time after the first period of time. The profile for the current user is compared with the profile for the authorized user. A determination is made as to whether one or more values in the profile for the current user are within a range of one or more values in the profile for the authorized user at a confidence level.

Authentication for transactions using near field communication

For distributed processing using forecasted location-based IoT device clusters, at a central IoT device, a data source that is to be used and a duration for processing a workload is determined. A set of IoT devices operating within a threshold distance from the data source at a first time is selected. A subset of the set is selected to form a sub-cluster of IoT devices where a forecasted travel path of a member IoT device in the subset keeps the member within the threshold distance from the data source for the duration. A lightweight application is configured at a first IoT device in the subset which enables the first IoT device to participate in the sub-cluster and process the workload.

Distributed load processing using forecasted location-based internet of things device clusters

For distributed processing using location-based IoT device clusters, using a processor and a memory at a central IoT device, a data source that is to be used for processing a workload is determined. A set of IoT devices that are operating within a threshold distance from the data source at a first time is selected. At the central IoT device, to form a cluster of IoT devices, a subset of the set of IoT devices is selected. Each IoT device in the subset satisfies a clustering condition. The processor at the central IoT device is instructed to configure a device application at a first IoT device in the subset of IoT devices, the device application enabling the first IoT device to participate in the cluster and process the workload.

Distributed load processing using location-based internet of things device clusters

For distributed processing using clustering of interdependent Internet of Things (IoT) devices, at a central device, a data source to be used for processing a workload is determined. A set is selected of devices operating within a threshold distance from the data source at a first time. A first subset of the set of devices is selected. Each device in the first subset satisfies a clustering condition. A first device in the subset is instructed to configure a lightweight application to participate in the cluster and process the workload. The processing of the workload is halted on a second device, where the first device has a processing dependency on the second device in processing the workload. A preserved current state of processing the workload is transferred from the first device to a third device. The processing of the workload is continued using the second device and the third device.

Distributed load processing using clusters of interdependent internet of things devices

For distributed processing using forecasted location-based IoT device clusters, at a central IoT device, a data source that is to be used and a duration for processing a workload is determined. A set of IoT devices operating within a threshold distance from the data source at a first time is selected. A first subset of the IoT devices is selected to form a cluster of IoT devices where each IoT device satisfies a clustering condition. A second subset of the first subset is selected to form a sub-cluster of IoT devices where a forecasted travel path of a member IoT device in the second subset keeps the member within the threshold distance from the data source for the duration. A lightweight application is configured at a first IoT device in the second subset which enables the first IoT device to participate in the sub-cluster and process the workload.

An approach is provided for allowing a network computing (e.g., cloud computing) infrastructure to modify its resource allocation plan (e.g., an instance count) by using a Kth derivative vector plot, which may be generated using historical logs. Among other things, this approach enables an infrastructure to project an allocation forecast for a specified duration and adapt to changes in network traffic.

Resource provisioning using predictive modeling in a networked computing environment

For distributed processing using sampled clusters of location-based Internet of Things (IoT) devices, at a central device, a data source to be used for processing a workload is determined. A set is selected of devices operating within a threshold distance from the data source at a first time. A first subset including a first sample number of devices is selected from the set. A ratio is determined of a first amount of a computing resource needed to process the workload and a second amount of the computing resource available in the first subset to process the workload. From the set, to form a cluster, a second subset is selected of a size at least equal to a multiple of the ratio and the first sample number. Each device in the second subset satisfies a clustering condition. A lightweight application is configured at the first device to process the workload.

Distributed load processing using sampled clusters of location-based internet of things devices

System, method and program for identifying a subset of a multiplicity of source networks. The subset including one or more source networks which have sent messages to one of a plurality of destination locations having a same IP address. For each of the multiplicity of source networks, a determination is made whether there are fewer intervening hops from the source network to the one destination location than from the source network to other of the plurality of destination locations. If so, the source network is included in the subset. If not, the source network is not included in the subset. One application of the present invention is to identify a source of a denial of service attack. After the subset is identified, filters can be sequentially applied to block messages from respective source networks in the subset to determine which source network in the subset is sending the messages.

Growing community of inventors

Cary, NC, United States of America

Herbert Daniel Pearthree