Abstract: A method comprises receiving data corresponding to operation of a plurality of edge devices from respective ones of a plurality of edge computing sites. The data comprises requests received by the edge devices to perform a plurality of tasks. The data is analyzed using a first machine learning algorithm to determine workloads of respective ones of the edge devices. The method further comprises predicting future workloads of the edge devices. The predicting is performed using a second machine learning algorithm and is based on the determined workloads of the edge devices. A determination is made whether to transfer at least a portion of one or more of the tasks from a first edge device to a second edge device. The first and second edge devices are located at first and second edge computing sites, respectively, and the determination is based on one or more of the predicted future workloads.

Abstract: One example method includes receiving, from a VM, a request for a software license, in response to the request, transmitting a UUID to the VM, receiving, from the VM, a response that comprises a first encrypted UUID corresponding to the request, and transmitting a client object that includes the first encrypted UUID, and the client object serves as a request for entry of the client object in a database of a cloud computing site.

Abstract: Techniques for failure prediction and remediation are disclosed. For example, a method comprises training one or more machine learning algorithms with a training dataset corresponding to a plurality of users, wherein the training dataset comprises at least one of product purchase data, product service data and product return data corresponding to the plurality of users. In the method, an input dataset corresponding to at least one user is received. The input dataset comprises at least one of product purchase data, product service data and product return data corresponding to the user. The input dataset is analyzed using the one or more machine learning algorithms. The method further comprises predicting, based at least in part on the analyzing, a likelihood of whether at least one product corresponding to the user will fail to be returned to a product providing entity when a return of the product has been requested.

Abstract: Intelligent workload management techniques in a heterogenous computing environment are disclosed. For example, a method comprises obtaining first identifying information associated with a set of workload types, and obtaining second identifying information associated with a set of server configurations associated with a computing environment, wherein one or more server configurations in the set of server configurations are different than one or more other server configurations in the set of server configurations. The method then maps individual ones of the set of workload types to individual appropriate ones of the set of server configurations based on the obtained first and second identifying information, and causes distribution of one or more workloads to one or more servers in the computing environment, in accordance with the mapping, for execution thereon. At least a portion of the obtaining and mapping steps are performed using one or more machine learning algorithms.

Abstract: Techniques for detecting a modification to a configuration of a system are disclosed. For example, a method comprises the step of collecting a first data set for a system at a first time instance, wherein the first data set comprises inventory data for a configuration of the system present at the first time instance. The method compares the first data set to a second data set, wherein the second data set comprises inventory data for a configuration of the system present at a second time instance. The method obtains a third data set based on the comparison of the first data set and the second data set, wherein the third data set comprises data indicative of any differences between the inventory data for the configuration of the system present at the first time instance and the inventory data for the configuration of the system present at the second time instance.

Abstract: Techniques for device modification analysis are disclosed. For example, a method comprises collecting operational data from one or more devices, and receiving one or more modifications to at least one of firmware and software for the one or more devices. In the method, one or more virtual instances of respective ones of the one or more devices are generated, and the one or more modifications are tested on the one or more virtual instances to determine if there are one or more issues with the one or more modifications.

Abstract: Techniques for device modification analysis are disclosed. For example, a method comprises collecting operational data from one or more devices, and receiving one or more modifications to at least one of firmware and software for the one or more devices. In the method, one or more virtual instances of respective ones of the one or more devices are generated, and the one or more modifications are tested on the one or more virtual instances to determine if there are one or more issues with the one or more modifications.

Abstract: A method comprises retrieving power utilization data of a plurality of host devices and identifying at least one virtual machine for migration from a source host device to a target host device of the plurality of host devices. In the method, power utilization of the at least one virtual machine is predicted. The target host device is determined based, at least in part, on the power utilization data of the plurality of host devices and the predicted power utilization of the at least one virtual machine. The method further comprises migrating at least one virtual machine from the source host device to the target host device.

Abstract: A method includes receiving data collected from a plurality of managed devices in a plurality of data collections. The data collections are performed using a plurality of collection protocols. A trigger that generated each of given ones of the data collections is determined. The method further includes identifying a collection protocol of the plurality of collection protocols used for each of the given ones of the data collections, and determining one or more attributes of a plurality of attributes of the plurality of managed devices that have been collected using given ones of the collection protocols. A mapping is generated between the triggers, the collection protocols and the attributes using one or more machine learning algorithms. The generated mapping is used to predict one or more collection protocols of the plurality of collection protocols to use to collect data from one or more of the managed devices.

Abstract: Predictive techniques for issue impact management in a data center or other computing environment comprising a plurality of devices are disclosed. For example, a method comprises predicting an impact to a data center comprising a plurality of devices based on an issue associated with a given device of the plurality of devices within the data center, wherein the prediction utilizes at least one machine learning model. The method then causes one or more actions to be taken based on a result of the prediction.

Abstract: Techniques for detecting a modification to a configuration of a system are disclosed. For example, a method comprises the step of collecting a first data set for a system at a first time instance, wherein the first data set comprises inventory data for a configuration of the system present at the first time instance. The method compares the first data set to a second data set, wherein the second data set comprises inventory data for a configuration of the system present at a second time instance. The method obtains a third data set based on the comparison of the first data set and the second data set, wherein the third data set comprises data indicative of any differences between the inventory data for the configuration of the system present at the first time instance and the inventory data for the configuration of the system present at the second time instance.

Abstract: An apparatus comprises a processing device configured to collect system state information from host devices, to split the collected system state information into logical chunks, and to determine, based at least in part on a plurality of factors, a compression level to be applied to each of the logical chunks. The plurality of factors comprise a first factor characterizing a time at which the collected system state information is needed at a destination device and at least a second factor characterizing resources available for at least one of performing compression of the collected system state information and transmitting the collected system state information over at least one network to the destination device. The processing device is further configured to apply the determined compression level to each of the logical chunks to generate compressed logical chunks, and to transmit the compressed logical chunks to the destination device.

Abstract: Data theft protection for a computing device is provided by flagging a suspicious user within the pre-boot environment by systematic evaluation to determine suspicious location and/or anomalous user behavior. Depending on a suspicion score and a risk evaluation, the system automatically triggers one or more actions with respect to the data on the computing device.

Abstract: An apparatus comprises a processing device configured to collect system state information from host devices, to split the collected system state information into logical chunks, and to determine, based at least in part on a plurality of factors, a compression level to be applied to each of the logical chunks. The plurality of factors comprise a first factor characterizing a time at which the collected system state information is needed at a destination device and at least a second factor characterizing resources available for at least one of performing compression of the collected system state information and transmitting the collected system state information over at least one network to the destination device. The processing device is further configured to apply the determined compression level to each of the logical chunks to generate compressed logical chunks, and to transmit the compressed logical chunks to the destination device.

Abstract: A system, method, and computer-readable medium are provided to protect passwords for computer applications by providing a user-triggered password scrambling mechanism. Scrambling is enabled with a user configurable character within the password to be a trigger. Once triggered, a user can enter several incorrect password strings without fear of being locked out of the system. Ultimately, a user can enter a correct password string or a modified version of the correct password string to gain access to the system. Through password scrambling, an observer will have a more difficult time determining the correct version of the password. This improves system security, thereby protecting valuable information, resources, and reputation of an enterprise provider.

Abstract: Data theft protection for a computing device is provided by flagging a suspicious user within the pre-boot environment by systematic evaluation to determine suspicious location and/or anomalous user behavior. Depending on a suspicion score and a risk evaluation, the system automatically triggers one or more actions with respect to the data on the computing device.

Abstract: One example method includes receiving, from a VM, a request for a software license, in response to the request, transmitting a UUID to the VM, receiving, from the VM, a response that comprises a first encrypted UUID corresponding to the request, and transmitting a client object that includes the first encrypted UUID, and the client object serves as a request for entry of the client object in a database of a cloud computing site.

Abstract: A method includes receiving data collected from a plurality of managed devices in a plurality of data collections. The data collections are performed using a plurality of collection protocols. A trigger that generated each of given ones of the data collections is determined. The method further includes identifying a collection protocol of the plurality of collection protocols used for each of the given ones of the data collections, and determining one or more attributes of a plurality of attributes of the plurality of managed devices that have been collected using given ones of the collection protocols. A mapping is generated between the triggers, the collection protocols and the attributes using one or more machine learning algorithms. The generated mapping is used to predict one or more collection protocols of the plurality of collection protocols to use to collect data from one or more of the managed devices.

Abstract: A system, method, and computer-readable medium are provided to protect passwords for computer applications by providing a user-triggered password scrambling mechanism. Scrambling is enabled with a user configurable character within the password to be a trigger. Once triggered, a user can enter several incorrect password strings without fear of being locked out of the system. Ultimately, a user can enter a correct password string or a modified version of the correct password string to gain access to the system. Through password scrambling, an observer will have a more difficult time determining the correct version of the password. This improves system security, thereby protecting valuable information, resources, and reputation of an enterprise provider.

Abstract: A mechanism for determining whether a user should be using a single-hand virtual keyboard for a large-format touch screen device is provided. Additionally, a mechanism is provided to determine whether the single-hand virtual keyboard should be configured for a left-handed or a right-handed user. Further, a mechanism is provided to determine an appropriate size for the single-hand virtual keyboard for a particular user. In this manner, embodiments provide an input mechanism that allows for efficient and comfortable typing on a large-format touch screen device.