An Efficient Knapsack-Based Approach for Calculating the Worst-Case Demand of AVR Tasks

Abstract

Engine-triggered tasks are real-time tasks that are released when the crankshaft in an engine completes a rotation, which depends on the angular speed and acceleration of the crankshaft itself. In addition, the execution time of an engine-triggered task depends on the speed of the crankshaft. Tasks whose execution times depend on a variable period are referred to as adaptive-variable rate (AVR) tasks. Existing techniques to calculate the worst-case demand of AVR tasks are either inexact or computationally intractable. In this paper, we transform the problem of finding the worst-case demand of AVR tasks over a given time interval into a variant of the knapsack problem to efficiently find the exact solution. We then propose a framework to systematically reduce the search space associated with finding the worst-case demand of AVR tasks. Experimental results reveal that our approach is at least 10 times faster, with an average runtime improvement of 146 times, for randomly generated tasksets when compared to the state-of-the-art technique.