344 results about "Cloud computing systems" patented technology

The disclosure relates to utilizing an anomaly mitigation proposal system to determine root causes, summarize anomalous metrics, and report mitigation actions for service incidents in cloud computing systems. Based on receiving an incident report request, the anomaly mitigation proposal system utilizes a two-layer approach that implements large generative language models to generate incident reports that include clear and concise text narratives summarizing metric anomalies, root causes, and corresponding mitigation actions. For example, the anomaly mitigation proposal system initially utilizes an online generative language model to provide these incident reports and, when unavailable within a time threshold, a fallback model that references root cause datastores.

In a cross-security-region resource access method in a cloud computing system, a virtual block device, which is simulated by a virtio protocol-based BLK front-end driver in the cloud computing system and taken as an RPC calling client, receives an IO access request for data resources maintained by a resource service program triggered by a business application installed on each virtual node through a simulated RPC channel on the virtual block device; in response to the IO access request, encapsulates the IO access request into an RPC request for performing an RPC calling for target data resources, sends the RPC request to a RPC calling server program simulated by a virtio protocol-based BLK service through the RPC channel, so that the RPC calling server program submits the RPC request to a resource access agent program, which submits the RPC request to the resource service program to complete cross-security region resource access.

The invention discloses a temperature prediction method based on a graph neural network and a related device, and belongs to the technical field of cloud computing system data center thermal management, and the method comprises the steps: S1, collecting simulation environment basic information, and building a simulation model and simulation data of a data center machine room; s2, expanding the simulation data to obtain generated data, and merging the simulation data and the generated data to form a mixed data set; s3, constructing a graph topological structure of the data center machine room and a thermal prediction model of the data center machine room, and training the thermal prediction model of the data center machine room to obtain a trained thermal prediction model of the data center machine room; and S4, deploying the trained data center machine room heat prediction model to the data center, and inputting the real-time server power, the air conditioner set temperature and the indoor temperature into the trained data center machine room heat prediction model to obtain the server return air temperature. Not only can a complex heat transfer rule be captured, but also different space structures and dynamic changes can be adapted.

A distributed cloud computing system is disclosed that includes a controller configured to deploy a transit gateway and a first gateway in a security virtual private cloud (VPC) in a cloud computing network, wherein the first gateway is configured to connect to a first firewall instance deployed within the security VPC, and logic. The logic, upon execution by one or more processors, causes performance of operations including receiving network traffic at the transit gateway from an originating VPC deployed within the cloud computing network, routing the network traffic from the transit gateway to the first gateway, providing the network traffic to the first firewall instance for inspection, and routing the network traffic to a destination VPC deployed within the cloud computing network. In embodiments, the first gateway is connected to a plurality of firewall instances, where each instance of the plurality of firewall instances is an active firewall instance.

A distributed cloud computing system further includes logic, stored on non-transitory, computer-medium, that, upon execution by one or more processors, causes performance of operations including generating a first fingerprint for the first VPC being a statistical measure of a plurality of network metrics during a learning phase, generating a second fingerprint for the second VPC being a statistical measure of the plurality of network metrics during the learning phase, receiving, from the controller, metadata pertaining to each of the first gateway and the second gateway, receiving, from each of the first gateway and the second gateway, network data, wherein the metadata and the network data identify each of the plurality of constructs, the communication paths between each construct, and in which cloud computing network each construct is deployed, detecting an anomaly in one or more network traffic metrics of either the first VPC or the second VPC based on a comparison of received network traffic and a corresponding fingerprint, and generating an alert that the anomaly was detected.

A seine net position measurement system utilizes a plurality of sensor modules coupled to the seine net to determine the seine net position by recovering seine net position data, such as depth data, upon retraction of the seine net to the boat. The sensor modules may be configured proximal to the bottom of the net or the leadline. A depth profile of the net, as well as a rate of sinking of the net may be determined from the position data. The sensor modules have a sensor wireless signal transceiver that transmits a wireless signal to a wireless signal transceiver that sends the data to a computer for processing. The computer may be on the boat or part of a cloud computing system. This system is economical and enable a large number of sensor modules to be used to provide better accuracy of the net position.

A method for vehicle alerting includes receiving, at a cloud computing system, digital data that includes a location of a vehicle and a hazard lights on status of the vehicle, determining a functional classification of a road that corresponds to the location of the vehicle, wherein the digital data is received from the vehicle via a wireless connection, and determining whether to initiate an alert messaging operation in response to the functional classification of the road and the hazard lights on status, wherein determining whether to initiate the alert messaging operation involves selecting from available options of 1) initiating the alert messaging operation when the functional classification of the road is determined to be within a first set of functional classifications, and 2) not initiating the alert messaging operation when the functional classification of the road is determined to be within a second set of functional classifications.

A cloud computing system may be configured to test a cloud-native application to improve cloud-native application suitability. The system may include a processor and a computer readable medium operably coupled thereto, the computer readable medium including a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform cloud-based testing operations which may include adjusting one or more of: a network path of an interaction among the plurality of interconnected subcomponents, the automated testing routine, or an infrastructure component affecting the cloud-native application. The cloud-based testing operations may further include observing one or more of cloud-native application testing results and cloud-native application system telemetry, determining a cloud-native application flaw based on the observed one or more of cloud-native application testing results and cloud-native system telemetry, and outputting one or more configuration adjustments based on the determined cloud-native application flaw.

A distributed cloud computing system is disclosed that includes a controller configured to deploy network constructs including any of transit gateways, spoke gateways, subnets, or private networks and logic that, upon execution by one or more processors, causes performance of operations including: causing rendering of a graphical user interface that includes a display panel configured to display progress of a build process for a network topology graph, receiving first user input through the graphical user interface indicating selection of a first cloud service provider, a first access account, and a first cloud region, receiving second user input through the graphical user interface indicating selection of one or more of the network constructs to be deployed in the first cloud region, instructing the controller to deploy the one or more of the network constructs in the first cloud region according to the first user input and the second user input.

Described systems and methods may enable a cloud-computing system to use data model-based analysis to manage maintenance of an industrial automation system based on life stage predictions and real-time evaluations. Based on the data model-based analyses, the cloud-computing system may instruct an industrial control system of the industrial automation system to implement recommended adjustments and / or to confirm a part replacement occurred in situ. The recommended adjustment may include a part replacement. The cloud-computing system may generate instructions to cause shipment of the part to be installed as part of the part replacement to the industrial automation system.

The embodiments of the present invention provide a data communication method, a memory pooling switch device, a cloud computing system, and a storage medium. The data communication method comprises: converting verification preprocessed memory data from a receiver's communication protocol format to a sender's communication protocol format, wherein the receiver's communication protocol format and the sender's communication protocol format are used for asymmetric communication protocols on a communication bus between a server and a memory pooling switch device; performing verification processing on the verification preprocessed memory data to obtain a verification result; transmitting the verification result to the server via the communication bus in the sender's communication protocol format.

The invention relates to the technical field of cloud computing, and discloses a data processing method and system based on cloud computing, and the method comprises the steps: dividing a cloud computing system into a data storage unit, a computing resource scheduling unit and a task distribution unit, determining the parameters of each unit, carrying out the modeling, constructing a distributed mathematical simulation model, and solving to obtain an initial simulation result. By establishing a dynamic programming model and a genetic algorithm model, computing resource scheduling and task allocation are optimized respectively, and a data redundancy strategy and a fragmentation rule are determined at the same time. The minimum system total energy consumption, the minimum task average time delay or the maximum resource utilization rate serves as a global optimization objective function, and the convergence condition is judged to optimize system configuration. According to the method, the data storage reliability, resource scheduling reasonability and task allocation efficiency of the cloud computing system are effectively improved, global optimization of the system is realized, and the method has a good application prospect.

According to one aspect of the concepts and technologies disclosed herein, a cloud computing system can include a load adaptation architecture framework that performs operations for orchestrating and managing one or more services that may operate within at least one of layers 4 through 7 of the Open Systems Interconnection (“OSP”) communication model. The cloud computing system also can include a virtual resource layer. The virtual resource layer can include a virtual network function that provides, at least in part, a service. The cloud computing system also can include a hardware resource layer. The hardware resource layer can include a hardware resource that is controlled by a virtualization layer. The virtualization layer can cause the virtual network function to be instantiated on the hardware resource so that the virtual network function can be used to support the service.

A method, computer program product, and computing system for generating a plurality of trained machine learning models using a cloud computing system by training a plurality of machine learning models to forecast storage performance for one or more storage objects of a storage system, wherein the cloud computing system is separate from any storage system. A trained machine learning model is selected from the plurality of trained machine learning models to deploy to a target storage system. The trained machine learning model is deployed on the target storage system.

The current document is directed to an infrastructure-as-code (“IaC”) cloud-infrastructure-management service or system that allows users and upstream management systems to define and deploy infrastructure, such as virtual networks, virtual machines, load balancers, and connection topologies, within cloud-computing systems. The IaC cloud-infrastructure-management service or system includes a service frontend, a task manager, an event-processing component, and multiple Idem-service workers. The task manager manages execution of commands and requests received from the service frontend, using multiple queues, provides for prioritization of command-and-request execution by the multiple Idem-service workers, and provides for preemption of long-running executing commands and requests. The IaC cloud-infrastructure-management service or system enforces specified states of the cloud infrastructure using enforced-state identifiers and enforced-state versions supplied in state commands and enforce requests.

In some implementations, a routing device may indicate, to a direct connect gateway in a cloud computing system, a default route, wherein the default route is propagated to a virtual network function (VNF) running in the cloud computing system. The routing device may create a generic routing encapsulation (GRE) tunnel between the VNF and the routing device based on the default route, wherein the GRE tunnel permits a direct peering between the VNF and a private network outside of the cloud computing system. The routing device may perform a routing between the VNF and the private network using the GRE tunnel.

The embodiment of the invention provides a virtual machine migration method and device, a cloud computing system and electronic equipment, and relates to the technical field of cloud computing, and the method comprises the steps that a first physical machine interrupts running of a first virtual machine in response to a migration instruction of the first virtual machine, notifying the CXL switch to remove a first mapping relation between the first physical machine and the first memory space, and sending state recovery data to the second physical machine; the CXL switch relieves the first mapping relation and establishes a second mapping relation between the second physical machine and the first memory space; the second physical machine recovers and operates the first virtual machine based on the state recovery data and the memory data in the first memory space, and sends a virtual machine recovery operation notification to the first physical machine; and the first physical machine destroys the state recovery data in response to the virtual machine operation recovery notification. By applying the scheme provided by the embodiment of the invention, the migration efficiency of the virtual machine can be improved.

A method of displaying an operational parameter of a surgical system is disclosed. The method includes receiving, by a cloud computing system of the surgical system, first usage data, from a first subset of surgical hubs of the surgical system; receiving, by the cloud computing system, second usage data, from a second subset of surgical hubs of the surgical system; analyzing, by the cloud computing system, the first and the second usage data to correlate the first and the second usage data with surgical outcome data; determining, by the cloud computing system, based on the correlation, a recommended medical resource usage configuration; and displaying, on respective displays on the first and the second subset of surgical hubs, indications of the recommended medical resource usage configuration.

The described technology pertains to a distributed computing environment, specifically implementing a streaming protocol on a scalable object storage service. The technical problem addressed is the high computing resource demand of distributed streaming platforms. The solution involves separating content from location metadata, allowing centralized, scalable storage of metadata. Brokers store content in object storage services based on specific metrics, such as fast read / write times or greater storage capacity. A background platform remaps metadata sequences based on logical timestamps, optimizing data retrieval. This system enhances the efficiency of computing resources by reducing the load on brokers and improving data access speeds. A use can be in cloud computing systems for efficient content streaming and storage management.

Disclosed techniques relate to security of backup data. In some embodiments, a method includes receiving, by data protection service running on a cloud computing system, a first encrypted copy of a backup of a first data store that is associated with a first account of an organization, where the first encrypted copy is encrypted using a first custodian cryptographic key that is shared between the organization and the data protection service that is different than a first production cryptographic key that is private and used by the organization to encrypt a non-backup version of the first data store. The method may include generating a second encrypted copy of the backup, including by encrypting the backup using a storage cryptographic key. The method may include storing the second encrypted copy of the backup in a second data store that is associated with the data protection service.

Provided in the present disclosure are a fault prediction method and apparatus, a fault prediction model training method and apparatus, and a computing device, a computer storage medium and a computer program product. The fault prediction method is applied to a cloud computing system, wherein the cloud computing system comprises a service processing unit. The fault prediction method comprises: acquiring anomaly log data of a service processing unit and a log acquisition time of the anomaly log data; on the basis of the anomaly log data and the log acquisition time, determining an anomaly event sequence and an anomaly timestamp sequence; on the basis of a time interval threshold and anomaly occurrence times corresponding to anomaly events in the anomaly event sequence, grouping the anomaly events to obtain an anomaly group sequence; and on the basis of the anomaly event sequence, the anomaly timestamp sequence, and the anomaly group sequence, using a fault prediction model to obtain a fault prediction result of the service processing unit. Anomaly log data is analyzed from multiple dimensions, thereby improving the prediction accuracy of a fault prediction model, and improving the stability of a cloud computing system.

A cloud computing system built in accordance with a repeatable network architecture is disclosed that includes a controller, a first set of spoke gateways and a first transit gateway. The controller is configured to deploy the first set of spoke gateways in a first cloud thereby forming an applications layer of the repeatable network architecture, deploy the first transit gateway in the first cloud thereby forming a global transit layer of the repeatable network architecture, and establish communicative couplings between each of the first set of spoke gateways and the first transit gateway. The controller is also configured to deploy a first set of spoke VPCs within the first cloud, wherein each of the spoke VPCs has deployed therein one of the first set of spoke gateways, and a first transit VPC within the first cloud, wherein the first transit gateway is deployed in the first transit VPC.

Disclosed are systems, methods, devices, and computer-readable media for offloading lattice-based cryptographic operations to hybrid cloud computing system. In one embodiment, a method is disclosed comprising receiving a first network request from a client device via a secure application programming interface (API), the request including unencrypted data; encrypting the unencrypted data using an algorithm that generates homomorphically encrypted data; issuing a second network request to a second API of a cloud platform, the second network request including the encrypted data; receiving a response from the cloud platform in response to the second network request; and transmitting, in response to the first network request, a result to the client device based on the response, the result obtained by decrypting an encrypted output returned by the cloud platform.

Methods, systems, and devices for providing firmware management using a firmware management engine of a cloud computing system are described. The firmware management engine supports using a hot-plugged memory (e.g., Compute Express Link (CXL) pooled memory) to temporarily migrate system memory (e.g., Central Processing Unit (CPU) local memory or CXL attached memory) of a server node, while performing firmware management operations (e.g., a firmware update) on the server node. The hot-plugged memory can be CXL pooled memory that is a shared at a rack level, the CXL pooled memory can be used to store system memory data of system memory while updating and activating new memory initialization firmware code. A virtual machine associated with the server node stays operational temporarily using the hot-plugged memory. Firmware management is performed on a server node while virtual machines associated with the server node and the system memory of the server node remain operational.

Methods, storage systems and computer program products implement embodiments of the present invention for protecting a cloud computing system. In these embodiments, security alerts pertaining to cloud-based resources of the system are received, and a plurality of attack paths traversing the cloud-based resources are identified. Respective impact scores for the cloud-based resources can then be computed based on respective counts of the identified attack paths traversing each of the cloud-based resources. Finally, the security alerts can be prioritized responsively to the respective impact scores of the cloud-based resources to which the security alerts pertain.

Certain aspects of the present disclosure provide techniques for managing electric vehicle charging sessions. In examples, techniques may include receiving, at an edge computing device associated with a charging site, a connection status of an electric vehicle from a charging station, wherein the edge computing device is located on-site at the charging site; initiating, by the edge computing device, a charging session based on the connection status by creating a charging session record to be stored at the edge computing device; determining optimized charging parameters for the electric vehicle; sending the optimized charging parameters from the edge computing device to the charging station; logging usage information locally at the edge computing device for the charging session; and maintaining operation of the charging station during an interruption in network connectivity between the edge computing device and a cloud computing system by utilizing data stored locally at the edge computing device.

The invention discloses a software development cloud computing system with a big data processing module, which relates to the technical field of computer systems and data processing, and comprises a development input acquisition module used for receiving source code data, construction script data, dependency declaration data, configuration file data and development environment description data, and respectively executing integrity verification, structure analysis, dependency analysis, parameter extraction and environmental element analysis processing to form a standardized development input data set. According to the method, unified feature abstracting and aggregation are carried out on source codes, construction scripts, dependency declarations, configuration parameters and development environment elements, version fingerprint data capable of stably representing the software version state is formed, the strict degree of analysis, verification and influence analysis is remarkably enhanced in a high-difference version scene, and the software version state can be stably represented. Therefore, the problems of misjudgment, missed judgment or resource waste caused by adoption of a fixed threshold value or manual parameter configuration in the prior art are avoided.

The invention relates to an energy-saving method and system based on multi-dimensional resource intelligent scheduling in a cloud computing system, and belongs to the technical field of cloud computing. The data acquisition module acquires data of a host CPU, a memory, a network and storage resources in real time; calculating a load mean value based on a dynamic sliding window, wherein the window size is adaptively adjusted along with resource fluctuation; carrying out weighting calculation on a comprehensive load through an information entropy algorithm, and predicting a future load in combination with an ARIMA model; classifying the host as overload, normal or low load according to the current and predicted load; selecting the virtual machine with the maximum descending gradient of the resource utilization rate for the overloaded host to migrate, and closing the low-load host after migrating all the virtual machines; and matching resource complementary target hosts based on the Pearson's correlation coefficient, and verifying and triggering secondary migration after migration. Multi-dimensional resource collaborative optimization is realized through the monitoring and scheduling module, invalid migration is reduced by utilizing dynamic load prediction, the utilization rate is improved by utilizing resource complementary matching, and the service continuity is ensured while the energy consumption is reduced.

There are provided systems and methods for an acknowledgement process for message transmissions to connectable processing terminals for point-of-sale (POS) devices. A user may engage in a transaction with a merchant at a POS device, such as a purchase of items from a merchant at a physical merchant location and using payment terminals with the POS device. An online transaction processor may provide an acknowledgement process to determine if the payment terminal is online and receiving communications so that the user may view transaction details and complete transaction processing. This may be done through lookup of a previous connection of the payment terminal over a cloud computing system with the transaction processor and transmission of a request for acknowledgement over that connection. If the payment terminal responds with an acknowledgement, the transaction processor may then notify the POS device that data processing may proceed.

Techniques are disclosed for allocating computing resources in a computing network of a cloud computing system, the computing resources comprising a plurality of computing devices hosting virtual machines. An expected usage profile is determined based on first data indicative of a current demand for computing resource usage in the computing network, second data indicative of an adjacent demand for computing resource usage in the computing network, third data indicative of a historical demand for computing resource usage in the computing network, and fourth data indicative of events that are external to the computing network.