An efficient adaptive search algorithm for scheduling real-time traffic

For many service disciplines that provide delay guarantees, the scheduler of a channel repeatedly searches for the smallest element in a set of priority values (or deadlines). It is required that each search finishes within a time bound. Furthermore, the search algorithm should be highly efficient. To meet these requirements, we have developed a search algorithm based upon a new data structure, called adaptive heap; it behaves like a heap most of the time, but adaptively changes its strategy when necessary to satisfy the time bound. We show that the algorithm has an optimal worst-case time complexity and a good average performance. To further improve the efficiency, the basic algorithm is extended to include the use of group scheduling. We present empirical results on the performance of adaptive heap search with and without group scheduling. We conclude that adoptive heap search performs as intended, and that group scheduling provides a substantial reduction in the scheduler's work when channel utilization is high.