Abstract: Mechanisms are provided to implement a neural flow attestation engine and perform computer model execution integrity verification based on neural flows. Input data is input to a trained computer model that includes a plurality of layers of neurons. The neural flow attestation engine records, for a set of input data instances in the input data, an output class generated by the trained computer model and a neural flow through the plurality of layers of neurons to thereby generate recorded neural flows. The trained computer model is deployed to a computing platform, and the neural flow attestation engine verifies the execution integrity of the deployed trained computer model based on a runtime neural flow of the deployed trained computer model and the recorded neural flows.

Abstract: Mechanisms are provided to implement a neural flow attestation engine and perform computer model execution integrity verification based on neural flows. Input data is input to a trained computer model that includes a plurality of layers of neurons. The neural flow attestation engine records, for a set of input data instances in the input data, an output class generated by the trained computer model and a neural flow through the plurality of layers of neurons to thereby generate recorded neural flows. The trained computer model is deployed to a computing platform, and the neural flow attestation engine verifies the execution integrity of the deployed trained computer model based on a runtime neural flow of the deployed trained computer model and the recorded neural flows.

Abstract: Mechanisms are provided to implement a neural flow attestation engine and perform computer model execution integrity verification based on neural flows. Input data is input to a trained computer model that includes a plurality of layers of neurons. The neural flow attestation engine records, for a set of input data instances in the input data, an output class generated by the trained computer model and a neural flow through the plurality of layers of neurons to thereby generate recorded neural flows. The trained computer model is deployed to a computing platform, and the neural flow attestation engine verifies the execution integrity of the deployed trained computer model based on a runtime neural flow of the deployed trained computer model and the recorded neural flows.

Abstract: From a hardware sensor at a first time, sensor data comprising a first sample and a second sample is received. The sensor data is modified to create new data, wherein a first data point of the new data corresponds to the first sample, a second data point of the new data corresponds to the second sample, and wherein the first data point and the second data point are arranged differently from the first sample and the second sample. The hardware sensor is emulated by sending, to a consumer of hardware sensor data at a second time, the new data instead of the sensor data.

Abstract: A database comprises historical information of a user's response to previous notifications. The database is accessed to determine a time at which to provide a (new) notification to the user, utilizing at least: a) current user activity status (e.g., determined from measurement information collected from one or more personal devices and/or user calendar events; b) time/day; and c) context information about the notification (e.g., geo-location, indoors/outdoors) including notification type (e.g., calendar entry, email, IM). The user gets the notification via a portable device at the determined time. A machine learning model can select the determined time by discriminating features of the previous notifications for which the user immediately attended versus those that were deferred and/or ignored. Content of the notification can also be altered in view of such discriminating features so as to increase a likelihood the user will immediately attend to the provided notification.

Abstract: From a hardware sensor at a first time, sensor data comprising a first sample and a second sample is received. The sensor data is modified to create new data, wherein a first data point of the new data corresponds to the first sample, a second data point of the new data corresponds to the second sample, and wherein the first data point and the second data point are arranged differently from the first sample and the second sample. The hardware sensor is emulated by sending, to a consumer of hardware sensor data at a second time, the new data instead of the sensor data.

Abstract: A database comprises historical information of a user's response to previous notifications. The database is accessed to determine a time at which to provide a (new) notification to the user, utilizing at least: a) current user activity status (e.g., determined from measurement information collected from one or more personal devices and/or user calendar events; b) time/day; and c) context information about the notification (e.g., geo-location, indoors/outdoors) including notification type (e.g., calendar entry, email, IM). The user gets the notification via a portable device at the determined time. A machine learning model can select the determined time by discriminating features of the previous notifications for which the user immediately attended versus those that were deferred and/or ignored. Content of the notification can also be altered in view of such discriminating features so as to increase a likelihood the user will immediately attend to the provided notification.

Abstract: Systems and articles of manufacture for coordinating application migration processes include selecting at least one migration service for an application based on analysis of application information and information pertaining to multiple migration services, creating a migration plan to migrate the application to a target cloud based on the at least one selected migration service, and executing the migration plan, utilizing the at least one selected migration service, to migrate the application to the target cloud.

Abstract: Techniques for coordinating application migration processes. A method includes selecting at least one migration service for an application based on analysis of application information and information pertaining to multiple migration services, creating a migration plan to migrate the application to a target cloud based on the at least one selected migration service, and executing the migration plan, utilizing the at least one selected migration service, to migrate the application to the target cloud.

Abstract: A method including replicating a first virtual machine (VM) in a first cloud and putting the replicated VM in a second cloud. Activating the first VM and pausing the replicated VM. First processing, at the first VM, traffic from VMs in the first cloud, wherein the first processing occurs when the first VM is activated and the replicated VM is paused. Buffering, at a hypervisor of the replicated VM, traffic from VMs in the second cloud, wherein the buffering occurs when the first VM is activated and the replicated VM is paused. Activating the replicated VM in response to state information of the first VM and pausing the first VM. Second processing, at the replicated VM, the buffered traffic according to the state information of the first VM, wherein the second processing occurs when the replicated VM is activated and the first VM is paused.

Abstract: A method including replicating a first virtual machine (VM) in a first cloud and putting the replicated VM in a second cloud. Activating the first VM and pausing the replicated VM. First processing, at the first VM, traffic from VMs in the first cloud, wherein the first processing occurs when the first VM is activated and the replicated VM is paused. Buffering, at a hypervisor of the replicated VM, traffic from VMs in the second cloud, wherein the buffering occurs when the first VM is activated and the replicated VM is paused. Activating the replicated VM in response to state information of the first VM and pausing the first VM. Second processing, at the replicated VM, the buffered traffic according to the state information of the first VM, wherein the second processing occurs when the replicated VM is activated and the first VM is paused.

Abstract: Systems and articles of manufacture for coordinating application migration processes include selecting at least one migration service for an application based on analysis of application information and information pertaining to multiple migration services, creating a migration plan to migrate the application to a target cloud based on the at least one selected migration service, and executing the migration plan, utilizing the at least one selected migration service, to migrate the application to the target cloud.

Abstract: Techniques, systems, and articles of manufacture for coordinating application migration processes. A method includes selecting at least one migration service for an application based on analysis of application information and information pertaining to multiple migration services, creating a migration plan to migrate the application to a target cloud based on the at least one selected migration service, and executing the migration plan, utilizing the at least one selected migration service, to migrate the application to the target cloud.

Abstract: A method including replicating a first virtual machine (VM) in a first cloud and putting the replicated VM in a second cloud. Activating the first VM and pausing the replicated VM. First processing, at the first VM, traffic from VMs in the first cloud, wherein the first processing occurs when the first VM is activated and the replicated VM is paused. Buffering, at a hypervisor of the replicated VM, traffic from VMs in the second cloud, wherein the buffering occurs when the first VM is activated and the replicated VM is paused. Activating the replicated VM in response to state information of the first VM and pausing the first VM. Second processing, at the replicated VM, the buffered traffic according to the state information of the first VM, wherein the second processing occurs when the replicated VM is activated and the first VM is paused.

Abstract: A method including replicating a first virtual machine (VM) in a first cloud and putting the replicated VM in a second cloud. Activating the first VM and pausing the replicated VM. First processing, at the first VM, traffic from VMs in the first cloud, wherein the first processing occurs when the first VM is activated and the replicated VM is paused. Buffering, at a hypervisor of the replicated VM, traffic from VMs in the second cloud, wherein the buffering occurs when the first VM is activated and the replicated VM is paused. Activating the replicated VM in response to state information of the first VM and pausing the first VM. Second processing, at the replicated VM, the buffered traffic according to the state information of the first VM, wherein the second processing occurs when the replicated VM is activated and the first VM is paused.

Abstract: A method, information processing system, and computer program product manage virtual machine migration. A virtual machine is selected from a set of virtual machines. Each physical machine in a plurality of physical machines is analyzed with respect to a first set of migration constraints associated with the virtual machine that has been selected. A set of physical machines in the plurality of physical machines that satisfy the first set of migration constraints is identified. A migration impact factor is determined for each physical machine in the set of physical machines that has been identified based on a second set of migration constraints associated with the virtual machine that has been selected. A physical machine is selected from the set of physical machines with at least a lowest migration impact factor on which to migrate the virtual machine that has been selected in response to determining the migration impact factor.

Abstract: A method, information processing system, and computer program product manage virtual machine migration. A virtual machine is selected from a set of virtual machines. Each physical machine in a plurality of physical machines is analyzed with respect to a first set of migration constraints associated with the virtual machine that has been selected. A set of physical machines in the plurality of physical machines that satisfy the first set of migration constraints is identified. A migration impact factor is determined for each physical machine in the set of physical machines that has been identified based on a second set of migration constraints associated with the virtual machine that has been selected. A physical machine is selected from the set of physical machines with at least a lowest migration impact factor on which to migrate the virtual machine that has been selected in response to determining the migration impact factor.

Abstract: A method and system for controlling network traffic to a network computer such as a server is provided wherein such control is provided based on a measured capacity of the server to service the network traffic and rule data which represents different policies for servicing the network traffic. A load-controller of the system installs more or less restrictive packet or request filtering policies based on the capacity of the server to throttle the traffic to the server. The method and system are sensitive to the actual capacity of the server by adopting this adaptive traffic-shaping feature instead of using rigid policies to control resource usage on the server.