Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.