Abstract: A virtual keyboard setting method includes: determining an operation region and a trigger region, the operation region being a region operated in a screen for using an input method, and the trigger region being a region covered by an external entity when touching the screen; determining a display region of a virtual keyboard according to the operation region; determining an area of a virtual key in the virtual keyboard according to the trigger region; and setting the virtual keyboard according to the display region of the virtual keyboard and the area of the virtual key.

Abstract: An adaptive idle detection method determines whether software defined data centers (SDDCs) in a hyperconverged infrastructure (HCl) environment are idle. Idleness may be quantified via a coefficient of variation (CV) against resource usage, so as to adapt the idle detection method to SDDCs with different hardware specifications and workloads. Management overhead may also be filtered out by the idle detection method, and the idle detection method may use idleness scores to further reduce overhead.

Abstract: The disclosure provides a method for exposing object storage as a service. The method generally includes receiving, by an object service proxy provisioned for providing the service, a request to access a first object in an object store, wherein the object store leverages a percentage of datastore capacity for a datastore, determining, by the object service proxy, both of: an identifier of a container in the object store comprising the first object and an internet protocol (IP) address associated with an object protocol service provisioned to access the container comprising the first object, re-directing, by the object service proxy, the request to access the first object to the object protocol service using the IP address associated with the object protocol service, wherein the request comprises the identifier of the container and in response to re-directing the request, receiving the first object from the object protocol service.

Abstract: One example method for a host in a virtual storage area network (vSAN) cluster to support vSAN Internet small computer system interface (iSCSI) target services in a distributed storage system of a virtualization system is disclosed. The method includes obtaining ownership information of a target and determining, from the ownership information, whether the host is an owner of the target. In response to determining that the host is the owner of the target, the method further includes determining whether the host commits to a policy provided by the vSAN to support the vSAN iSCSI target services. In response to determining that the host fails to commit to the policy, the method includes reporting a warning message.

Abstract: An adaptive idle detection method determines whether software defined data centers (SDDCs) in a hyperconverged infrastructure (HCI) environment are idle. Idleness may be quantified via a coefficient of variation (CV) against resource usage, so as to adapt the idle detection method to SDDCs with different hardware specifications and workloads. Management overhead may also be filtered out by the idle detection method, and the idle detection method may use idleness scores to further reduce overhead.

Abstract: Solutions for secure metering of hyperconverged infrastructures are disclosed. Examples include: receiving a security token; accessing a secondary storage (e.g., cold storage, backups) using the security token; determining usage data for the secondary storage; generating a first message digest for a combination of the usage data and the security token; and transmitting, to a metering server, the usage data and the first message digest. In some examples, the combination of the usage data and the security token comprises a concatenation of the usage data and the security token. In some examples, the metering server requests verification usage data from the secondary storage, generates a second message digest for a combination of the verification usage data and the security token, and compares the first message digest with the second message digest. Examples do not persist the security token on customer premises. Examples leverage the usage data to optimize the secondary storage.

Abstract: The disclosure provides a method for measuring network latency between hosts in a cluster. The method generally includes receiving, by a first host, a first ping list indicating the first host is to engage in a first ping round with a second host; executing the first ping round with the second host, wherein executing the first ping round comprises: transmitting first ping requests to the second host; calculating a network latency for each of the first ping requests; and determining a first average network latency between the first host and the second host based on each of the network latencies calculated; determining the first average network latency is above a threshold; determining a cause of the first average network latency being above the threshold; and selectively triggering or not triggering an alarm based on whether the cause is determined to be a hardware or software layer impact, or neither.

Abstract: For a distributed storage system that has an active-active configuration for hosts and which uses an Internet small computer system interface (iSCSI) protocol, techniques are provided to identify/select a plurality of paths to a target. An active optimized path is selected for a host that is an object owner, and an active non-optimized path is selected for a host that is a component owner. The selection of the optimized path for a host is further based on whether that host has sufficient processor and memory resources to service input/output for the target. A standby path is selected for any other host that is neither an object owner or a component owner. The selected paths are provided to an initiator so as to enable the initiator to choose at least one of the paths to access the target for the input/output.

Abstract: The invention discloses a center lock structure, comprises a chassis, rotary sliding sleeve and a center rod, the top of the rotary sliding sleeve is circumferentially and slidely connected with the chassis; and the center rod is extended into the rotary sliding sleeve along the top of the chassis, an elastic member is provided at one side on the bottom of the center rod, the elastic member is exposed outside the center rod when there is no external force, and the elastic member is concealed in the center rod when squeezed by an external force; in the invention, by the center rod, upper and lower rotary sliding sleeve, through holes and the accommodating grooves for pressing or releasing the elastic member, the structure can be unlocked by rotation and pull-down.

Abstract: A vacuum switching valve and a suction system having the same. The vacuum switching valve comprises: a valve body, comprising a first end and a second end, the second end being provided with an air inlet, an air outlet and a through hole; a valve element movably arranged in the valve body; a cylinder, the cylinder being connected to the first end and the valve element, the cylinder drives the valve element to move in the valve body, to close or open the air inlet; a stopper passing through the through hole, the stopper comprising a third end and a fourth end, the third end being connected to the valve element, the fourth end being located on the side of the through hole away from the valve element.

Abstract: Described herein are systems, methods, and software to manage multi-type storage in a cluster computing environment. In one example, a host can identify health and performance information at a first time for each local data store on the host and a hyperconverged data store available to the host. The host can further identify health and performance information associated with the data stores at a second time and can compare the health and performance information at the first time and the second time to identify differences in the information. The host then communicates the differences to a second host in the computing environment.

Abstract: The disclosure relates to processing application programming interface (API) requests. Embodiments include receiving, at an API wrapper, from a first caller, a first call to an API and sending the first call to the API. Embodiments include receiving, by the API wrapper, from one or more second callers, a second one or more calls to the API prior to receiving a response from the API to the first call. Embodiments include receiving, by the API wrapper, the response from the API to the first call and responding to the first call from the first caller with the response from the API to the first call. Embodiments include responding, by the API wrapper, to the second one or more calls from the one or more second callers with the response from the API to the first call without sending the second one or more calls to the API.

Abstract: The disclosure provides automated update notifications. Embodiments include receiving, by a cloud provider, a request to manage updates for a computing device communicating with the cloud provider via a network. Embodiments include registering an agent at the computing device with the cloud provider as a recipient for an automated update notification based on configuration details of the computing device and detection of an update item related to an aspect of the configuration details. Embodiments include identifying, by the agent, the configuration details of the computing device, the configuration details comprising details of a hardware configuration of the computing device and a software configuration of the computing device. Embodiments include monitoring one or more databases for the update item. Embodiments include transmitting the automated update notification to the agent based on detection of the update item in the one or more databases.

Abstract: The current document is directed to methods and subsystems that manage submitted code changes for processing by continuous-integration/continuous-delivery/deployment systems. In disclosed implementations, code changes are processed as quickly as possible, when the code changes are flagged as being urgent. Non-urgent code changes are evaluated for the possibility of merging the non-urgent code changes with additional, subsequently submitted code changes in order to more efficiently employ computational resources needed for processing the code changes. When there is a code change, waiting for processing, with which a submitted code change can be merged, the submitted code change is merged with the waiting code change so that the merged code changes can be together verified.

Abstract: Example methods and systems associated with dynamic configuration of a statistics endpoint in a virtualized computing environment have been disclosed. One example method includes in response to receiving a first request, by a host in the virtualized computing environment, accepting a configuration file specified in the first request; in response to receiving a second request, by the host, parsing a rule based on the configuration file and collecting statistics based on the rule; processing, by the host, the statistics collected based on the rule; and sending, by the host, the processed statistics to a monitoring terminal.

Abstract: Example methods and systems for request handling with automatic scheduling are described. In one example, a computer system may receive, from multiple client devices, respective multiple requests that are generated and sent according to a first set of control parameters. Based on the multiple requests, request characteristic(s) may be monitored to determine whether an automatic scheduling condition is satisfied. In response to determination that the automatic scheduling condition is satisfied, the computer system may assign a second set of control parameters to the respective client devices and instruct the client devices to generate and send respective multiple subsequent requests according to the second set of control parameters to cause a modification of the request characteristic(s).

Abstract: The health of a distributed storage system provided by a virtualized computing environment may be evaluated. The evaluation techniques categorize health issues based on at least three categories (e.g., storage data availability and accessibility, storage data performance, and storage space utilization and efficiency), and provide priority levels for the health issues within each category. In this manner, a more user-oriented approach is provided wherein in addition to identifying health issues, the priority/urgency level of the health issue(s) can be provided so as to guide the user (such as a system administrator) in determining an appropriate remedial action to perform and when such remedial action should be performed to address health issues.

Abstract: Intelligent capacity planning is provided for storage in a hyperconverged infrastructure environment. The storage may be a logical storage unit that is supported by storage space of a plurality of hardware disks in a virtualized computing environment. Failure predictions can be obtained for each individual hardware disk, and a failure prediction for a number of hardware disk in a hardware disk set can also be obtained. A failure prediction and/or a reduced availability prediction for the logical storage unit can be generated based at least on a configuration state of the logical storage unit, a prediction for one or more hardware disks of the logical storage unit, and a prediction time. Predictions based on what-if operations are also able to be generated.

Abstract: Intelligent capacity planning is provided for storage in a hyperconverged infrastructure environment. The storage may be a logical storage unit that is supported by storage space of a plurality of hardware disks in a virtualized computing environment. Failure predictions can be obtained for each individual hardware disk, and a failure prediction for a number of hardware disk in a hardware disk set can also be obtained. A failure prediction and/or a reduced availability prediction for the logical storage unit can be generated based at least on a configuration state of the logical storage unit, a prediction for one or more hardware disks of the logical storage unit, and a prediction time. Predictions based on what-if operations are also able to be generated.

Abstract: An analytics portal, having a machine learning model, is deployed at an edge device in a virtualized computing environment. The machine learning model may be trained internally in the virtualized computing environment or via trained models received via an external network such as a cloud. The analytics portal is in an active mode, while another analytics portal at another host or edge device in the virtualized computing environment is in a passive mode. An election process may be used to change an analytics portal from the active mode to the passive mode. A failover process is also available to transition the passive analytics portal to the active mode, in response to a failure of the current active analytics portal.