An example method is provided for recommending VM configurations, including one or more servers upon which one or more VMs can run. A user wishing to run these VMs can request a recommendation for an appropriate server or set of servers. The user can indicate a category corresponding to the type of workload that pertains to the VMs. The system can receive the request and identify a pool of servers available to the user. Using industry specifications and benchmarks, the system can classify the available servers into multiple categories. Within those categories, similar servers can be clustered and then ranked based on their levels of optimization. The sorted results can be displayed to the user, who can select a particular server (or group of servers) and customize the deployment as needed. This process allows a user to identify and select an optimized setup quickly and accurately.

An example method is provided for recommending VM configurations, including one or more servers upon which one or more VMs can run. A user wishing to run these VMs can request a recommendation for an appropriate server or set of servers. The user can indicate a category corresponding to the type of workload that pertains to the VMs. The system can receive the request and identify a pool of servers available to the user. Using industry specifications and benchmarks, the system can classify the available servers into multiple categories. Within those categories, similar servers can be clustered and then ranked based on their levels of optimization. The sorted results can be displayed to the user, who can select a particular server (or group of servers) and customize the deployment as needed. This process allows a user to identify and select an optimized setup quickly and accurately.

A distributed computing system uses dynamically calculates a subset size for each of a plurality of load balancers. Each of a plurality of load balancers logs requests from client devices for connections to back-end servers and periodically sends a request report to a traffic aggregator, which aggregates the report requests from the load balancers in the corresponding zone. Each traffic aggregator sends the aggregated request data to a traffic controller, which aggregates the request data to determine a total number of requests received at the system. The total request data is transmitted through each traffic aggregator to each load balancer instance, which calculates a percentage of the total number of requests produced by the load balancer and determines a subset size based on the calculated percentage.

This application provides a method for adjusting a resource of an intelligent analysis device and an apparatus. The method includes: obtaining status information of an intelligent analysis device that accesses a surveillance platform and application information deployed on the intelligent analysis device, where the status information includes resource usage and a quantity of bound cameras; after a camera accesses the surveillance platform, selecting a to-be-bound intelligent analysis device for the camera based on the status information and the application information of the intelligent analysis device that accesses the surveillance platform; and sending, to the selected intelligent analysis device, a command for binding the camera. In this way, the resource of the intelligent analysis device may be automatically allocated. This improves processing efficiency and avoids low efficiency caused by manual processing.

A method for managing systems with interrelated microservices with self-assembling and self-configuring microservices includes receiving at a first micro service a service request from a client. A determination is the made whether the first micro service is capable of processing the service request. If the first micro service is capable of processing the service requests, then processing the service request; if the first micro service cannot process the service request then routing the service request to a first stem service. The first stem service determines whether there is a second micro service that can process the service request. If the second micro service that can process the service requests exists, then forwarding the service request to the second micro service for processing. If there is no second micro service that can service the service requests then morphing the first stem service into a micro service that can service the service request.

A method for managing systems with interrelated microservices with self-assembling and self-configuring microservices includes receiving at a first micro service a service request from a client. A determination is the made whether the first micro service is capable of processing the service request. If the first micro service is capable of processing the service requests, then processing the service request; if the first micro service cannot process the service request then routing the service request to a first stem service. The first stem service determines whether there is a second micro service that can process the service request. If the second micro service that can process the service requests exists, then forwarding the service request to the second micro service for processing. If there is no second micro service that can service the service requests then morphing the first stem service into a micro service that can service the service request.

An I/O server service interacts with multiple containerized controller services each implementing the same control routine to control the same portion of the same plant. The I/O server service may provide the same controller inputs to each of the containerized controller services (e.g., representing measurements obtained by field devices and transmitted by the field devices to the I/O server service). Each containerized controller service executes the same control routine to generate a set of controller outputs. The I/O server service receives each set of controller outputs and forwards an “active” set to the appropriate field devices. The I/O server service and other services, such as an orchestrator service, may continuously evaluate performance and resource utilization in the control system, and may dynamically activate and deactivate controller services as appropriate. The I/O server service may interact with other containerized services, such as containerized historian services or workstation services, to facilitate control in the plant.

Some embodiments of the invention provide a method for evaluating multiple candidate resource elements associated with different resource element types for deploying one tenant deployable element in a single public cloud. The method deploys a set of one or more agents in the public cloud to collect metrics evaluating performance of each of the multiple candidate resource elements. The method communicates with the set of deployed agents to collect metrics to quantify performance of each candidate resource element. The method aggregates the collected metrics in order to generate a report that quantifies performance of each type of candidate resource element for deploying the tenant deployable element in the single public cloud.

Some embodiments of the invention provide a method for evaluating multiple candidate resource elements associated with different resource element types for deploying one tenant deployable element in a single public cloud. The method deploys a set of one or more agents in the public cloud to collect metrics evaluating performance of each of the multiple candidate resource elements. The method communicates with the set of deployed agents to collect metrics to quantify performance of each candidate resource element. The method aggregates the collected metrics in order to generate a report that quantifies performance of each type of candidate resource element for deploying the tenant deployable element in the single public cloud.

A method and apparatus for differentially optimizing a quality of service (QoS) includes: establishing a system model of a multi-task unloading framework; acquiring a mode for users executing a computation task, executing, according to the mode for users executing the computation task, the system model of the multi-task unloading framework; and optimizing a quality of service (QoS) on the basis of a multi-objective optimization method for a multi-agent deep reinforcement learning. According to the present invention, an unloading policy is calculated on the basis of a multi-user differentiated QoS of a multi-agent deep reinforcement learning, and with the differentiated QoS requirements among different users in a system being considered, a global unloading decision is performed according to a task performance requirement and a network resource state, and differentiated performance optimization is performed on different user requirements, thereby effectively improving a system resource utilization rate and a user service quality.