Abstract: One embodiment may estimate the processing time of tasks requested by an application by maintaining a state-model for the application. The state model may include states that represent the tasks requested by the application, with each state including the average run-time of each task. In another embodiment, a state model may estimate which task is likely to be requested for processing after the current task is completed by providing edges in the state model connecting the states. Each edge in the state model may track the number of times the application transitions from one task to the next. Over time, data may be gathered representing the percentage of time that each edge is from a state node. Given this information, the scheduler may estimate the CPU cost of the next task based on the current state, the most likely transition, and the cost of the predicted next task.

Abstract: One embodiment may estimate the processing time of tasks requested by an application by maintaining a state-model for the application. The state model may include states that represent the tasks requested by the application, with each state including the average run-time of each task. In another embodiment, a state model may estimate which task is likely to be requested for processing after the current task is completed by providing edges in the state model connecting the states. Each edge in the state model may track the number of times the application transitions from one task to the next. Over time, data may be gathered representing the percentage of time that each edge is from a state node. Given this information, the scheduler may estimate the CPU cost of the next task based on the current state, the most likely transition, and the cost of the predicted next task.