Techniques are described for enabling real-time database performance measurement and tuning in a service provider network. To efficiently test one or more proposed configuration changes to a database in a service provider network, a database service is able to create a replicated copy of the database in an environment that mirrors that of the primary database. The database service then automatically causes database traffic destined for the primary database to be routed to both the primary database and the test database. Once the test database is created and traffic is routed to both databases, the database service obtains performance data by monitoring performance of both the primary database and the test database over a period of time. Based on the obtained performance database, the database service can automatically determine which of the primary database and the test database is exhibiting better performance.

A software defined (SD) process control system (SDCS) implements controller and other process control-related business logic as logical abstractions (e.g., application layer services executing in containers, VMs, etc.) decoupled from hardware and software computing platform resources. An SD networking layer of the SDCS utilizes process control-specific operating system support services to manage the usage of the computing platform resources and the creation, deletion, modifications, and networking of application layer services with devices disposed in the field environment and with other services, responsive to the requirements and needs of the business logic and dynamically changing conditions of SDCS hardware and/or software assets during run-time of the process plant (such as performance, faults, addition/deletion of hardware and/or software assets, etc.). Thus, dynamic (re-)allocation of hardware/software resources is primarily, if not entirely, and continually governed in real-time by present requirements and needs of application layer services as well as dynamically changing SDCS conditions.

A software defined (SD) process control system (SDCS) implements controller and other process control-related business logic as logical abstractions (e.g., application layer services executing in containers, VMs, etc.) decoupled from hardware and software computing platform resources. An SD networking layer of the SDCS utilizes process control-specific operating system support services to manage the usage of the computing platform resources and the creation, deletion, modifications, and networking of application layer services with devices disposed in the field environment and with other services, responsive to the requirements and needs of the business logic and dynamically changing conditions of SDCS hardware and/or software assets during run-time of the process plant (such as performance, faults, addition/deletion of hardware and/or software assets, etc.). Thus, dynamic (re-)allocation of hardware/software resources is primarily, if not entirely, and continually governed in real-time by present requirements and needs of application layer services as well as dynamically changing SDCS conditions.

A software defined (SD) process control system (SDCS) implements controller and other process control-related business logic as logical abstractions (e.g., application layer services executing in containers, VMs, etc.) decoupled from hardware and software computing platform resources. An SD networking layer of the SDCS utilizes process control-specific operating system support services to manage the usage of the computing platform resources and the creation, deletion, modifications, and networking of application layer services with devices disposed in the field environment and with other services, responsive to the requirements and needs of the business logic and dynamically changing conditions of SDCS hardware and/or software assets during run-time of the process plant (such as performance, faults, addition/deletion of hardware and/or software assets, etc.). A visualization system of the SDCS provides a user with a view as to the state of the SDCS as currently configured/running on the computing platform to enable a user to view currently configured interrelationships between logical elements of the control system and other logical and/or physical elements of the control system. The visualization system also provides performance metrics of the system as currently configured to enable a user to understand the operational health of the control system as currently configured.

A method and system for distributing a compute model and data to process to heterogeneous and distributed compute devices. The compute model and a portion of the data is processed on a benchmark system and the timing used to make a job execution speed estimate for each compute device. Compute devices are selected and assigned data chunks based on the estimate so distributed processing is completed within a predefined time period. The compute model and data chunks can be sent to the respective compute devices using separate processes, such as a payload manager configured to transfer compute jobs to remote devices and a messaging engine configured to transfer data messages, and where the payload manager and messaging engine communicate with corresponding software engines on the compute devices.

A computer-implemented method of managing tenants on a multi-tenant SIP server system has at least two multi-tenant enabled SIP server instances in an SIP server cloud or private datacenter environment. Each SIP server instance of the plurality of SIP server instances is configured as a virtual application. The method contains the steps of initially configuring a first tenant on a first SIP server instance, monitoring the capacity of the first SIP server instance, and monitoring the capacity of a second SIP server instance. The monitoring of the first and second SIP server instances is carried out by monitoring at least one predetermined capacity-relevant value. When the at least one predetermined capacity-relevant value exceeds a predetermined threshold value indicating that capacity resources are low on the first SIP server instance, then the first tenant is moved from the first SIP server instance to the second SIP server instance.

A data store system may include at least one storage device to store a plurality of data and at least one processor with access to the storage device. The at least one processor may receive a plurality of features associated with an environment. The at least one processor may further generate a state representation of the environment based on the plurality of features. The at least one processor may further generate a plurality of predicted future states of the environment based on the state representation. The at least one processor may further generate at least one action to be performed by the environment based on the plurality of predicted future states. The at least one processor may provide the at least one action to the environment to be performed. A method and computer-readable medium are also disclosed.

A method for adjusting the resource allocation ratio between microservices used to run an application. A microservice test sequence is defined which has an order that follows the traffic flow through the microservices. Each microservice is analyzed in order of the test sequence to classify whether or not it is acting as a bottleneck for the application. This is done by measuring whether or not decrementing the microservice's resource causes the application throughput to decrease. For each microservice classified as a bottleneck and in reverse order of the test sequence, its resource is successively incremented until the application throughput starts to increase, indicating it is no longer acting as a bottleneck. The resource allocation ratio can then be adjusted to reflect this procedure.

Failover is performed appropriately when a failure occurs in a physical server. In a plurality of physical servers of a storage system, one or more protocol VMs and one or more file system VMs are created. The protocol VMs perform processing related to a protocol for a file storage with a client via a front-end network. The file system VMs perform processing related to management of files in the file storage. A first physical server causes, when a failure occurs in a second physical server, a physical server other than the second physical server to operate a file system VM to be operated in place of the file system VM of the second physical server, and controls the protocol VM of the physical server other than the second physical server to perform processing to be performed by the protocol VM of the second physical server.

Examples described herein relate to policy-based logging using workload profiles. A workload profile of a first workload is identified. Based on the workload profile, a logging policy, which includes a predefined log pattern and a predefined log depth, is compiled. Workload log messages of a predetermined log level that are associated with the first workload are stored in a cache memory. On detecting the predefined log pattern in the workload log messages stored in the cache memory, the workload log messages are logged to a log file based on the predefined log depth.