266 results about "Cloud computing systems" patented technology

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 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 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.

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.

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.

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.

A method, computer program product, and computing system for processing an incident request using a triage engine associated with a cloud computing system. A candidate triage group generative artificial intelligence (AI) model is identified by processing the incident request. An assignment recommendation is generated from the candidate triage group generative AI model by processing the incident request using the candidate triage group generative AI model using training data associated with the respective candidate triage group. A target triage group is selected for triaging the incident request by processing the assignment recommendation from the candidate triage group generative AI model using the triage engine.

The invention discloses a cloud fault positioning method and related equipment, which can be applied to the technical field of cloud computing. According to the method, after a plurality of types of historical data including topological data, log data or index data are acquired, different types of historical data are subjected to fusion processing to obtain a historical fusion feature vector, and then after a preset long-short-term memory network model with a modal perception input gate and an internal feature attention unit is constructed, the modal perception input gate and the internal feature attention unit are constructed; training a preset long-short-term memory network model through the historical fusion feature vector to obtain a target long-short-term memory network model, so that the target long-short-term memory network model does not need to excessively depend on the labeled data for training and can capture a dynamic dependency relationship; and inputting a plurality of types of real-time observation data in the cloud environment computing system into the target long short-term memory network model, and performing fault detection and positioning of the cloud environment computing system by using the target long short-term memory network model, thereby effectively improving the accuracy of fault positioning in the cloud computing system.

The invention relates to the field of intelligent scheduling allocation, in particular to an edge cloud computing resource allocation optimization method based on deep learning, which adopts a space-time prediction algorithm based on multi-head attention and gating mechanism optimization to design time coding and space coding. The spatial relationship and interaction between time sequence characteristics of the computing power load and edge server nodes are captured, and meanwhile, a multi-head attention mechanism and expansion causal convolution are combined, so that instantaneous computing power load fluctuation can be captured, and the long-term trend of the computing power load can be mined; therefore, a reliable basis is provided for subsequent computing power scheduling by predicting an accurate computing power load. The invention designs an alternating direction multiplier method based on genetic algorithm optimization, which is not only suitable for a nonlinear and multi-constraint optimization problem, but also can be expanded to a larger-scale distributed edge node cloud computing system, and meanwhile, a global optimal solution is quickly approached through the genetic algorithm, so that the quality of an initial solution is improved, and model convergence is accelerated; and the distributed collaborative allocation scheduling efficiency is improved.

The invention provides a data processing method and a related device. The embodiment of the invention can be applied to a resource virtualization scene in the field of cloud computing. The method is applied to the cloud computing system, the cloud computing system comprises a service layer, a management layer, a virtualization layer and a resource layer, and the management layer is used for processing an identity label of a target virtual machine carried in a virtual machine access request after the virtual machine access request is obtained, obtaining a first character string, generating a target uniform locator according to the first character string, and sending the target uniform locator to the cloud computing system; when the second character string is the same as a preset character string, the target virtual machine is accessed through the target uniform resource locator, the second character string is a character string obtained by decoding the first character string, and the preset character string is a character string stored in the target virtual machine. The preset character string used for authentication in the target virtual machine does not need to be persisted to the database, so that the data security problem possibly caused by attack or leakage of the database is avoided, and the security of the virtual machine data is improved.

The invention discloses a public network flow forwarding method, device and equipment for a cloud computing system and a medium, and relates to the technical field of cloud computing. The public network flow forwarding method for the cloud computing system comprises the steps that a virtual switch and a virtual machine are deployed on a host machine; a cloud computing system is built in a manner of deploying a virtual gateway on a cloud computing gateway, so that only a first message of public network flow in a cloud computing network is forwarded by a gateway node and a high-speed forwarding table is generated, and the gateway node does not need to participate in subsequent other public network flow messages which are not the first message. Public network traffic forwarding between hosts is directly carried out only by utilizing the generated high-speed forwarding table and the host information table, so that the technical problems of high load and high delay of public network traffic forwarding of the existing gateway node are solved, the forwarding efficiency of the public network traffic in the cloud computing network is greatly improved, and the forwarding efficiency of the public network traffic in the cloud computing network is improved. And meanwhile, the loss and the load pressure of the gateway node are greatly reduced.

One embodiment relates to a cloud computing system. The cloud computing system includes one or system devices configured to collect and transmit data. The cloud computing system also includes an internet-of-things (IoT) cloud configured to provide processing and memory storage capabilities to the system devices. The IoT cloud includes one or more device software applications configured to run the one or more system devices and a processing circuit comprising at least one processor and memory, the memory having instructions stored thereon, that when executed by the at least one processor cause the processing circuit to: communicably couple the one or more system devices to the IoT cloud through a network, receive data from the one or more system devices, determine a correct software application of the one or more device software applications to stream to a correct device of the one or more system devices based on the data received from the one or more system devices, and stream the correct software application to the correct device. The software applications are not stored locally on the one or more system devices in the cloud computing system.

A system and method of securing a Function as a Service (FaaS) cloud computing system without using access rights to operating system (OS) kernels of the cloud service system. The method includes receiving a request to invoke a user-function associated with a computing language. The method includes executing the user-function within an operating system that executes on a processing device of the cloud service system. The method includes monitoring, by the processing device, a real-time behavior of the user-function using a security sensor that executes within the operating system, wherein the security sensor is without access rights to a kernel of the operating system. The method includes acquiring behavioral data indicative of the real-time behavior of the user-function.

A computer-implemented method can be used for distributing workload in a cloud computing system that includes a number of data sources. Each data source includes one or more queues. A plurality of replicas of a microservice are instantiated. Each replica is configured to process data from the data sources. Each replica receives information regarding a total number of active replicas and an identifier specific to that replica. Each replica determines a starting queue based on the total number of active replicas and the specific identifier. Each replica processes data from its determined starting queue and subsequent queues in a predefined order.

This disclosure describes a policy validation system that determines the validity of RBAC policies. For example, the policy validation system determines when invalid RBAC policies implemented within a cloud computing system are granting resource and service requests that should be unauthorized. This way, the policy validation system ensures that RBAC policies adhere to a set of intended specifications across all conceivable inputs in user requests. In various implementations, the policy validation system utilizes symbolic abstraction models and satisfiability solver models to identify invalid policies based on determining valid counterexamples, which allow requests to be granted that should be blocked by the policy.

A computer-implemented method for secure policy distribution to a cloud system. The method includes defining an access policy for a set of resources on a cloud computing system, where the access polic

Techniques disclosed herein can include receiving an instruction to perform a stress test on one or more cloud computing resources of a cloud computing system. Worker nodes of the cloud computing system can be provisioned by a resource manager to perform the stress test on the cloud computing resources. The resource manager can instruct the one or more worker nodes of the cloud computing system to perform the stress test. Data generated by the worker nodes during the stress test can be received by the resource manager and used to train a projection framework comprising a trained machine learning model. The projection framework can generate a resource projection and the projection can be used to provision cloud computing resources to host the cloud service.