An apparatus and method for a power-efficient framework to maintain data synchronization of a mobile personal computer (MPC) are described. In one embodiment, the method includes the detection of a data synchronization wakeup event while the MPC is operating according to a sleep state. Subsequent to wakeup event, at least one system resource is disabled to provide a minimum number of system resources required to re-establish a network connection. In one embodiment, user data from a network server is synchronized on the MPC without user intervention; the mobile platform system resumes operation according to the sleep state. In one embodiment, a wakeup alarm is programmed according to a user history profile regarding received e-mails. In a further embodiment, data synchronizing involves disabling a display, and throttling the system processor to operate at a reduced frequency. Other embodiments are described and claimed.

In one example, a computer implemented method may include retrieving resource definition data corresponding to an endpoint. The resource definition data includes resource type information. Further, an API response may be obtained from the endpoint by querying the endpoint using an API call. Furthermore, the API response may be parsed and a resource model corresponding to the resource definition data may be populated using the parsed API response. The resource model may include resource information and associated metric information correspond to a resource type in the resource type information. Further, a resource and/or metric data associated with the resource may be determined using the populated resource model. The resource may be associated with an application being executed in the endpoint.

In one example, a computer implemented method may include retrieving resource definition data corresponding to an endpoint. The resource definition data includes resource type information. Further, an API response may be obtained from the endpoint by querying the endpoint using an API call. Furthermore, the API response may be parsed and a resource model corresponding to the resource definition data may be populated using the parsed API response. The resource model may include resource information and associated metric information correspond to a resource type in the resource type information. Further, a resource and/or metric data associated with the resource may be determined using the populated resource model. The resource may be associated with an application being executed in the endpoint.

In one example, a computer implemented method may include retrieving resource definition data corresponding to an endpoint. The resource definition data includes adapter information and resource type information. Further, an adapter instance may be generated using the adapter information to establish communication with the endpoint. Furthermore, an API response may be obtained, via the adapter instance, from the endpoint by querying the endpoint using an API call. Further, the API response may be parsed. Further, a resource model corresponding to the resource definition data may be populated using the parsed API response. The resource model may include resource information and associated metric information corresponding to a resource type in the resource type information. Furthermore, a resource and/or metric data associated with the resource may be determined using the populated resource model. The resource may be associated with an application being executed in the endpoint.

In one example, a computer implemented method may include retrieving resource definition data corresponding to an endpoint. The resource definition data includes adapter information and resource type information. Further, an adapter instance may be generated using the adapter information to establish communication with the endpoint. Furthermore, an API response may be obtained, via the adapter instance, from the endpoint by querying the endpoint using an API call. Further, the API response may be parsed. Further, a resource model corresponding to the resource definition data may be populated using the parsed API response. The resource model may include resource information and associated metric information corresponding to a resource type in the resource type information. Furthermore, a resource and/or metric data associated with the resource may be determined using the populated resource model. The resource may be associated with an application being executed in the endpoint.

Methods and apparatuses in a cloud-based database management system are described. Data in a database are stored in a plurality of pages in a page store of the database. A plurality of redo log records are received to be applied to the database. The redo log records within a predefined boundary are parsed to determine, for each given redo log record, a corresponding page to which the given log record is to be applied. The redo log records are reordered by corresponding page. The reordered redo log records are stored to be applied to the page store of the database.

Systems and methods are disclosed for scheduling threads on a processor that has at least two different core types, such as an asymmetric multiprocessing system. Each core type can run at a plurality of selectable voltage and frequency scaling (DVFS) states. Threads from a plurality of processes can be grouped into thread groups. Execution metrics are accumulated for threads of a thread group and fed into a plurality of tunable controllers for the thread group. A closed loop performance control (CLPC) system determines a control effort for the thread group and maps the control effort to a recommended core type and DVFS state. A closed loop thermal and power management system can limit the control effort determined by the CLPC for a thread group, and limit the power, core type, and DVFS states for the system. Deferred interrupts can be used to increase performance.

Systems and methods are disclosed for scheduling threads on a processor that has at least two different core types, such as an asymmetric multiprocessing system. Each core type can run at a plurality of selectable voltage and frequency scaling (DVFS) states. Threads from a plurality of processes can be grouped into thread groups. Execution metrics are accumulated for threads of a thread group and fed into a plurality of tunable controllers for the thread group. A closed loop performance control (CLPC) system determines a control effort for the thread group and maps the control effort to a recommended core type and DVFS state. A closed loop thermal and power management system can limit the control effort determined by the CLPC for a thread group, and limit the power, core type, and DVFS states for the system. Deferred interrupts can be used to increase performance.

Disclosed herein are system, method, and computer program product embodiments for configuring a dynamic reassignment of an application flow across different computation layers based on various conditions. An embodiment operates by assigning a first rule of an application flow to a first computation layer of a plurality of computation layers. The embodiment assigns a second rule of the application flow to a second computation layer of the plurality of computation layers. The embodiment assigns a transition rule of the application flow to the first computation layer. The transition rule includes an action that causes the first rule of the application flow to be executed in the second computation layer of the plurality of computation layers based on a condition. The embodiment then transmits the application flow to the plurality of computation layers thereby causing the application flow to be configured for execution.

A message-hold decision maker system used with an electronic mail processing system that processes electronic messages for a protected computer network improves the electronic mail processing system's performance by increasing the throughput performance of the system. The improvements are achieved by providing an electronic mail processing gateway with additional logic that makes fast and intelligent decisions on whether to hold, block, allow, or sandbox electronic messages in view of potential threats such as viruses or URL-based threats. A message hold decision maker uses current and stored information from a plurality of specialized classification engines to quickly make the decisions. In some examples, the message hold decision maker will instruct an email gateway to hold an electronic mail message while the classification engines perform further analysis.