Dell Products Inc.

Emc IP Holding Company LLC

Durai S Singh

Parminder Singh Sethi

Lakshmi Saroja Nalam

Suren Kumar

Nithish Kote

C Narasimhaiah

Elanjelian V

Mohith Shanthigodu

Vivekananda Hegde

Smita Kapil

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.

Intelligent management of workloads in heterogeneous computing environment

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.

Workload management between edge computing sites

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.

Virtual machine migration based on power utilization

In one aspect, an example methodology implementing the disclosed techniques includes receiving, by a systems management console, a network address of a device in a virtual environment, and determining a network address associated with a virtual environment management console based on the received network address of the device in the virtual environment. The method also includes sending, by the systems management console via a systems management agent to the virtual environment management console using the determined network address associated with the virtual environment management console, a request for network addresses of virtual machine (VM) host servers and VMs in the virtual environment. The method also includes receiving, by the systems management console via the systems management agent from the virtual environment management console, the network addresses of the VM host servers and the VMs in the virtual environment and providing a notification of the discovered VM host servers and VMs.

Device discovery in a virtualized environment

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.

Framework for preventing software piracy in virtual machines (VMs) by using virtual hardware encryption verification

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.

Detection of modification to system configuration

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.

Device modification analysis framework

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.

Using machine learning to dynamically determine a protocol for collecting system state information from enterprise devices

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.

Determining compression levels to apply for different logical chunks of collected system state information

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.

Protection of data from malicious user in computing device from pre-boot state

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.

Method and system for user induced password scrambling

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.

Selection and configuration of one-hand keyboards for touch screen devices

In some examples, after a client system encounters a problem, a technical support specialist may connect to the client system via a console. The console may display a graphical representation of a client system that includes a plurality of components. The console may execute a machine learning module to determine one or more potential solutions to the particular problem. Each solution of the one or more solutions may correspond to a previously resolved problem that is similar to the particular problem and may have an associated confidence level determined based on: a similarity of the particular problem to the previously resolved problem, a similarity of the plurality of components to a second plurality of components included in a second client system associated with the previously resolved problem, a similarity of a network topology of the client system to a second network topology of the second client system.

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Chennai, India

Durai S Singh