Abstract: Intelligent memory brokering for multiple process instances, such as relational databases (e.g., SQL servers), reclaims memory based on value, thereby minimizing cost across instances. An exemplary solution includes: based at least on a trigger event, determining a memory profile for each of a plurality of process instances at a computing node; determining an aggregate memory profile, the aggregate memory profile indicating a memory unit cost for each of a plurality of memory units; determining a count of memory units to be reclaimed; identifying, based at least on the aggregate memory profile and the count of memory units to be reclaimed, a count of memory units to be reclaimed within each process instance so that a total cost is minimized to reclaim the determined count; and communicating, to each process instance having identified memory units to be reclaimed, a count of memory units to be reclaimed within the process instance.

Abstract: Intelligent memory brokering for multiple process instances, such as relational databases (e.g., SQL servers), reclaims memory based on value, thereby minimizing cost across instances. An exemplary solution includes: based at least on a trigger event, determining a memory profile for each of a plurality of process instances at a computing node; determining an aggregate memory profile, the aggregate memory profile indicating a memory unit cost for each of a plurality of memory units; determining a count of memory units to be reclaimed; identifying, based at least on the aggregate memory profile and the count of memory units to be reclaimed, a count of memory units to be reclaimed within each process instance so that a total cost is minimized to reclaim the determined count; and communicating, to each process instance having identified memory units to be reclaimed, a count of memory units to be reclaimed within the process instance.

Abstract: Techniques for differential overbooking on a cloud database. These techniques may include determining a reservation amount of a multi-tenant resource for a first service of a based upon an overbooking characteristic of the first service, and determining that a total usage value of the multi-tenant resource by a plurality of services is greater than a threshold value. In addition, the techniques may include determining a service usage value of the multi-tenant resource by the first service, determining a first overage value of the first service based on the service usage value and the reservation amount, and performing a resource reclamation process over the multi-tenant resource based on the first overage value of the first service.

Abstract: Methods for determining cache activity and for optimizing cache reclamation are performed by systems and devices. A cache entry access is determined at an access time, and a data object of the cache entry for a current time window is identified that includes a time stamp for a previous access and a counter index. A conditional counter operation is then performed on the counter associated with the index to increment the counter when the time stamp is outside the time window or to maintain the counter when the time stamp is within the time window. A counter index that identifies another counter for a previous time window where the other counter value was incremented for the previous cache entry access causes the other counter to be decremented. A cache configuration command to reclaim, or additionally allocate space to, the cache is generated based on the values of the counters.

Abstract: Techniques for differential overbooking on a cloud database. These techniques may include determining a reservation amount of a multi-tenant resource for a first service of a based upon an overbooking characteristic of the first service, and determining that a total usage value of the multi-tenant resource by a plurality of services is greater than a threshold value. In addition, the techniques may include determining a service usage value of the multi-tenant resource by the first service, determining a first overage value of the first service based on the service usage value and the reservation amount, and performing a resource reclamation process over the multi-tenant resource based on the first overage value of the first service.

Abstract: Methods for determining cache activity and for optimizing cache reclamation are performed by systems and devices. A cache entry access is determined at an access time, and a data object of the cache entry for a current time window is identified that includes a time stamp for a previous access and a counter index. A conditional counter operation is then performed on the counter associated with the index to increment the counter when the time stamp is outside the time window or to maintain the counter when the time stamp is within the time window. A counter index that identifies another counter for a previous time window where the other counter value was incremented for the previous cache entry access causes the other counter to be decremented. A cache configuration command to reclaim, or additionally allocate space to, the cache is generated based on the values of the counters.

Abstract: A solution is disclosed for memory management of serverless databases that includes: based at least on detecting a trigger event, determining whether memory is to be reclaimed; based at least on determining that memory is to be reclaimed, determining an amount of memory to be reclaimed; identifying memory to be reclaimed; and reclaiming the identified memory. Disclosed solutions are flexible, enabling customization of the aggressiveness and manner of memory reclamation. This permits users to specify a tailored balance point between performance and cost, for arrangements that bill users based on resource usage (e.g., memory consumed by a serverless database). In some examples, users specify a ramp-down parameter that is used to determine how much memory can be evicted in a particular reclamation event, time intervals (or another criteria) for triggering a reclamation event, and a definition for whether a cache is active.

Abstract: Intelligent memory brokering for multiple process instances, such as relational databases (e.g., SQL servers), reclaims memory based on value, thereby minimizing cost across instances. An exemplary solution includes: based at least on a trigger event, determining a memory profile for each of a plurality of process instances at a computing node; determining an aggregate memory profile, the aggregate memory profile indicating a memory unit cost for each of a plurality of memory units; determining a count of memory units to be reclaimed; identifying, based at least on the aggregate memory profile and the count of memory units to be reclaimed, a count of memory units to be reclaimed within each process instance so that a total cost is minimized to reclaim the determined count; and communicating, to each process instance having identified memory units to be reclaimed, a count of memory units to be reclaimed within the process instance.

Abstract: Intelligent memory brokering for multiple process instances, such as relational databases (e.g., SQL servers), reclaims memory based on value, thereby minimizing cost across instances. An exemplary solution includes: based at least on a trigger event, determining a memory profile for each of a plurality of process instances at a computing node; determining an aggregate memory profile, the aggregate memory profile indicating a memory unit cost for each of a plurality of memory units; determining a count of memory units to be reclaimed; identifying, based at least on the aggregate memory profile and the count of memory units to be reclaimed, a count of memory units to be reclaimed within each process instance so that a total cost is minimized to reclaim the determined count; and communicating, to each process instance having identified memory units to be reclaimed, a count of memory units to be reclaimed within the process instance.

Abstract: Intelligent memory brokering for multiple process instances, such as relational databases (e.g., SQL servers), reclaims memory based on value, thereby minimizing cost across instances. An exemplary solution includes: based at least on a trigger event, determining a memory profile for each of a plurality of process instances at a computing node; determining an aggregate memory profile, the aggregate memory profile indicating a memory unit cost for each of a plurality of memory units; determining a count of memory units to be reclaimed; identifying, based at least on the aggregate memory profile and the count of memory units to be reclaimed, a count of memory units to be reclaimed within each process instance so that a total cost is minimized to reclaim the determined count; and communicating, to each process instance having identified memory units to be reclaimed, a count of memory units to be reclaimed within the process instance.

Abstract: A solution is disclosed for memory management of serverless databases that includes: based at least on detecting a trigger event, determining whether memory is to be reclaimed; based at least on determining that memory is to be reclaimed, determining an amount of memory to be reclaimed; identifying memory to be reclaimed; and reclaiming the identified memory. Disclosed solutions are flexible, enabling customization of the aggressiveness and manner of memory reclamation. This permits users to specify a tailored balance point between performance and cost, for arrangements that bill users based on resource usage (e.g., memory consumed by a serverless database). In some examples, users specify a ramp-down parameter that is used to determine how much memory can be evicted in a particular reclamation event, time intervals (or another criteria) for triggering a reclamation event, and a definition for whether a cache is active.

Abstract: A data scrambling method and a scrambling apparatus, where the method includes a scrambling apparatus scrambling a data stream including a first data block and a second data block. The first data block and the second data block may belong to a same sub-data stream, or may belong to different sub-data streams. A specification of the data stream when the first data block and the second data block belong to a same sub-data stream is different from a specification of the data stream when the first data block and the second data block belong to different sub-data streams, and the scrambling apparatus can scramble data streams of different specifications.

Abstract: A data scrambling method and a scrambling apparatus, where the method includes a scrambling apparatus scrambling a data stream including a first data block and a second data block. The first data block and the second data block may belong to a same sub-data stream, or may belong to different sub-data streams. A specification of the data stream when the first data block and the second data block belong to a same sub-data stream is different from a specification of the data stream when the first data block and the second data block belong to different sub-data streams, and the scrambling apparatus can scramble data streams of different specifications.

Abstract: Embodiments of the present invention provide a data processing method and apparatus, including: receiving X data streams through X physical lanes of an Ethernet interface, converting the X data streams into M data streams transmitted on M virtual lanes, and performing bandwidth adjustment on each data stream of the M data streams to obtain M first data streams; receiving Y data streams through Y physical lanes of the Ethernet interface, converting the Y data streams into N data streams transmitted on N virtual lanes, and performing bandwidth adjustment on each data stream of the N data streams to obtain N second data streams. In addition, a corresponding apparatus is further provided. By using the foregoing technical solution, received data streams of different sources and different frequencies can be processed.

Abstract: In a scenario of a flexible Ethernet, the number of Ethernet frame streams received by an Ethernet interface of a receiver and a bit width of each Ethernet frame stream may need to be adjusted. A corresponding scrambling circuit is disposed for each data segment in the Ethernet frame streams, so as to separately perform scrambling on each data segment, where the number of bits of each data segment is M. Furthermore, X scrambling circuits corresponding to X data segments in a first Ethernet frame stream may separately calculate scrambling results of the X data segments. Therefore, if an adjusted bit width of each Ethernet frame stream is an integral multiple of M, a corresponding number of scrambling circuits may be reconfigured to perform scrambling on each adjusted Ethernet frame stream.

Abstract: In a scenario of a flexible Ethernet, the number of Ethernet frame streams received by an Ethernet interface of a receiver and a bit width of each Ethernet frame stream may need to be adjusted. A corresponding scrambling circuit is disposed for each data segment in the Ethernet frame streams, so as to separately perform scrambling on each data segment, where the number of bits of each data segment is M. Furthermore, X scrambling circuits corresponding to X data segments in a first Ethernet frame stream may separately calculate scrambling results of the X data segments. Therefore, if an adjusted bit width of each Ethernet frame stream is an integral multiple of M, a corresponding number of scrambling circuits may be reconfigured to perform scrambling on each adjusted Ethernet frame stream.

Abstract: Embodiments of the present invention provide a data processing method and apparatus, including: receiving X data streams through X physical lanes of an Ethernet interface, converting the X data streams into M data streams transmitted on M virtual lanes, and performing bandwidth adjustment on each data stream of the M data streams to obtain M first data streams; receiving Y data streams through Y physical lanes of the Ethernet interface, converting the Y data streams into N data streams transmitted on N virtual lanes, and performing bandwidth adjustment on each data stream of the N data streams to obtain N second data streams. In addition, a corresponding apparatus is further provided. By using the foregoing technical solution, received data streams of different sources and different frequencies can be processed.