SSD Task Allocation via Dynamic CPU Core Assignment

The patent implements dynamic task allocation where the data manager adjusts the number of active CPU cores based on current workload conditions. When high bandwidth is needed, more CPU cores are activated; when quality of service is prioritized, fewer cores are used. This dynamic adjustment resolves the contradiction by matching resource consumption to actual processing needs rather than maintaining fixed high resource usage.

The system changes the parameter of active CPU core count based on service scenarios. By detecting whether the current operation requires high bandwidth or high quality of service, the system adjusts the number of participating CPU cores accordingly. This parameter change allows the system to achieve high productivity when needed while reducing hardware consumption when not required.

The patent segments data managers into sibling master and sibling slave groups, with master data managers handling critical timing-sensitive tasks and slave data managers handling bandwidth-intensive tasks. This segmentation allows the system to achieve high throughput by activating slave managers while maintaining low delay by ensuring master managers process urgent tasks independently, resolving the contradiction between throughput and delay.

Different data managers are assigned different functional qualities based on task requirements. Master data managers focus on quality-critical operations with strict timing requirements, while slave data managers handle bandwidth-optimized operations. This local differentiation of functional quality allows the system to simultaneously achieve both low delay for critical tasks and high throughput for non-critical tasks.

The system dynamically switches between quality-of-service-optimized and bandwidth-optimized task allocation modes based on detected service scenarios. When timeliness is critical, the system activates master data managers with dedicated CPU cores for low-latency processing. When bandwidth is the priority, the system activates slave data managers for parallel processing. This dynamic mode switching resolves the contradiction by selecting the appropriate optimization strategy based on current needs.

The system performs preliminary detection of service scenarios to determine the appropriate task allocation strategy before processing begins. By anticipating whether the current operation requires high quality of service or high bandwidth, the system pre-configures the appropriate data manager activation pattern, avoiding the need to switch modes mid-processing and ensuring optimal performance from the start.