Improving System Predictability and Performance via Hardware Accelerated Data Structures

In Dynamic Data-Driven Application Systems, applications must dynamically adapt their behavior in response to objectives and conditions that change while deployed. One approach to achieve dynamic adaptation is to offer middleware that facilitates component migration between modalities in response to such dynamic changes. The triggering, planning, and cost evaluation of adaptation takes place within a scheduler. Scheduling overhead is a major limiting factor for implementing dynamic scheduling algorithms with high frequency timer-tick resolution in real time systems. In this paper, we present a scalable hardware scheduler architecture for real time systems that reduces processing overhead and improves timing predictability of the scheduler. A new hardware priority queue design is presented, which supports insertions in constant time, and removals in O (log n )time. The hardware scheduler supports three (Rate Monotonic Scheduling (RMS), Earliest Deadline First (EDF), and priority based) scheduling algorithms, which can be configured during run-time. The interface to the scheduler is provided through a set of custom instructions as an extension to the processors instruction set architecture. We also report on our experience migrating between two implementations of an ordered-set implementation, with the goal of providing predictable performance for real-time applications.