Techniques described herein relate to systems and methods of data storage, and more particularly to providing layering of file system functionality on an object interface. In certain embodiments, file system functionality may be layered on cloud object interfaces to provide cloud-based storage while allowing for functionality expected from a legacy applications. For instance, POSIX interfaces and semantics may be layered on cloud-based storage, while providing access to data in a manner consistent with file-based access with data organization in name hierarchies. Various embodiments also may provide for memory mapping of data so that memory map changes are reflected in persistent storage while ensuring consistency between memory map changes and writes.

A data acquisition unit acquires data regarding an activity from a plurality of activity servers. A request receiving unit receives an acquisition request for activity information from a client device of a user. A data element acquisition unit acquires a plurality of data elements according to the acquisition request from among pieces of data regarding an activity. An activity information creating unit creates activity information in which identification information is added to a set of the plurality of data elements acquired. A providing unit provides the activity information to a client device of the user.

Described are computing systems and methods configured to detect a small, but meaningful, anomaly within one or more metrics associated with a platform. The system displays visuals of the metrics so that a user monitoring the platform can effectively notice a problem associated with the anomaly and take appropriate action to remediate the problem. An operational visual includes a radar-based visual with a heatmap arranging metrics, and a node representing a state of the metrics. Moreover, the system uses an ensemble of unsupervised machine learning algorithms for multi-dimensional clustering of hundreds of thousands of monitored metrics. Via the visuals and the implementation of the machine learning algorithms, the described techniques provide an improved way of representing and simulating many metrics being monitored for a platform. Moreover, the techniques are configured to expose actionable and useful information associated with the platform in a manner that can be effectively interpreted.

Hypervisor-independent reference copies of virtual machine payload data based on block-level pseudo-mount infrastructure and techniques are generated and stored in an illustrative data storage management system. An illustrative hypervisor-independent reference copy comprises one or more virtual-machine payload data files that originated from a first virtual machine. The hypervisor-independent virtual-machine-payload reference copy is governed by a distinct reference copy policy that controls retention, storage, tiering, scheduling, etc. for the reference copy, independently of how the illustrative system treats other virtual machine payload data files originating from the same virtual machine.

During operation, the system obtains hypervisor data for a set of virtual machines, wherein the hypervisor data was received from one or more hypervisors while the set of virtual machines was running on the hypervisors. The system also obtains operating system data for the set of virtual machines, wherein the operating system data was received from a set of operating systems while the set of operating systems was running on the set of virtual machines. Next, the system correlates hypervisor data for a virtual machine with corresponding operating system data for the virtual machine. Finally, the system presents the correlated hypervisor data and operating system data for the virtual machine to a user.

A method and system for communicating with IoT devices to gather information related to device failure or error(s) is disclosed. The system makes a copy of at least a portion of the device's non-volatile memory and/or receives IoT device data (e.g., sensor data and/or log files etc.) from an IoT device that recently failed. The system determines which log files and/or sensor data, for example, the IoT device created before and/or after a failure. After gathering this information, the system stores the information in a database, sends it to the IoT device manufacturer, for further analysis and diagnostics to troubleshoot the failure and send a fix or software update to the IoT device.

A device control system remotely controls one or more devices. The device control system includes the one or more devices, and a management apparatus connected to the one or more devices via a network. The management apparatus is capable of executing, in a plurality of execution modes having different processing performances, a program in order to control the one or more devices. The management apparatus accepts input of the program, executes the accepted program in a first execution mode, which is one of the plurality of execution modes, and determines, based on an execution result, an execution mode in which the program is to be executed.

In various embodiments, an electronic device includes an electrical and physical interface operable to be coupled to a port of a computer system that includes a processor. The electronic device also includes a casing housing the interface. In addition, the electronic device includes a circuit board including circuitry housed within the casing. The circuitry receives control signals from the computer system over the interface and controls an illumination element. The illumination element is disposed on the casing and is operable to produce a color that corresponds to a real-time status associated with the computer system. The real-time status is determined by the processor and information read by the processor from the computer system. Moreover, the electronic device includes a beacon technology system housed within the casing. The beacon technology system is operable to communicate with a beacon technology system externally located to the electronic device. The beacon technology system is operable to trigger an automatic process when detected.

Described are computing systems and methods configured to detect a small, but meaningful, anomaly within one or more metrics associated with a platform. The system displays visuals of the metrics so that a user monitoring the platform can effectively notice a problem associated with the anomaly and take appropriate action to remediate the problem. An operational visual includes a radar-based visual with a heatmap arranging metrics, and a node representing a state of the metrics. Moreover, the system uses an ensemble of unsupervised machine learning algorithms for multi-dimensional clustering of hundreds of thousands of monitored metrics. Via the visuals and the implementation of the machine learning algorithms, the described techniques provide an improved way of representing and simulating many metrics being monitored for a platform. Moreover, the techniques are configured to expose actionable and useful information associated with the platform in a manner that can be effectively interpreted.

Notifications can be managed across ecosystems. A centralized hub can implement a learning engine that uses an algorithm to evaluate incoming notifications that are intended for a user that uses multiple computing devices having different ecosystems. The algorithm can be configured to determine on which of the user's computing devices the notifications should be presented given a particular context. Agents executing on the user's computing devices can monitor how the user interacts with the notifications and provide indications of such interactions to the learning engine. The learning engine can then update its algorithm based on the user's interactions to cause future notifications to be delivered to the user via the ecosystem that is most appropriate for a given context in which each notification is received.