3results about How to "Improve schedulability" patented technology

The invention relates to a multi-core real-time system task sorting and partition scheduling optimization method based on a DQN, and the method comprises the following steps: obtaining a real-time task set to be scheduled, and extracting the execution time, period, deadline and other feature information of tasks; modeling a task sorting process as a Markov decision process, and performing adaptive optimization on a task processing sequence by using a DQN model to generate a task sorting sequence; on the premise that the structure of a partition scheduling heuristic algorithm is not changed, the task sorting sequence is input into the partition scheduling algorithm, and tasks are distributed; and generating scheduling feedback information according to a task allocation result, wherein the scheduling feedback information is used for updating the DQN model. According to the method, intelligent optimization is carried out on the task sorting stage, the problem that a traditional fixed sorting strategy is difficult to adapt to a complex task set structure is solved, a partition scheduling algorithm can find a feasible task allocation scheme under the high system load condition, and therefore the schedulability and the scheduling success rate of the isomorphic multi-core real-time system are improved.

This invention relates to the field of artificial intelligence and discloses a method and system for scheduling computing resources in heterogeneous computing systems based on intelligent prediction. This solution obtains task description information from a queue of tasks to be scheduled, analyzes task resource requirements, topological constraints, and portability attributes, and classifies tasks into high-resource-demand tasks, ordinary tasks, and elastic tasks. Based on task category, resource fragmentation level, and the arrival status of high-resource-demand tasks, ordinary tasks and elastic tasks are allocated to fragmented partitions, while high-resource-demand tasks are allocated to reserved partitions. Continuous resource blocks matching the resource requirements of high-resource-demand tasks are reserved in the reserved partitions, generating scheduling decisions. Based on the scheduling decisions and the updated reserved and fragmented partitions, task start, pause, migration, and termination operations are executed, and operational feedback information is collected and written to an operational data repository. This improves the schedulability of high-resource-demand tasks.

This invention discloses an online scalable configuration method and system for dynamically changing time-sensitive networks (TSNs), relating to the Internet of Things (IoT) field. The invention proposes an efficient inter-flow conflict detection mechanism based on multi-level flow grouping. By using a correlation analysis of flow period and offset, it avoids traditional conflict detection methods based on link maximal cliques and link hyperperiods, enabling rapid response to large-scale data flow conflict detection needs. It designs an online incremental method based on decoupling routing and scheduling, as well as offline pre-routing, accelerating the computation speed of routing and scheduling schemes while ensuring scheduling space. This invention reduces the computational complexity of data flow set hyperperiod sensitivity and the maximum link slot occupancy in TSNs, accelerating online scheduling and providing efficient conflict detection support for online scalable configuration.