Dynamic round-robin task scheduling to reduce cache misses for embedded systems

Abstract

Modern embedded CPU systems rely on a growing number of software features, but this growth increases the memory footprint and increases the need for efficient instruction and data caches. The embedded operating system will often juggle a changing set tasks in a round-robin fashion, which inevitably results in cache misses due to conflicts between different tasks. Our technique reduces cache misses by continuously monitoring CPU cache misses to grade the performance of running tasks. Through a series of step-wise refinements, our software system tunes the round-robin ordering to find a better temporal sequence for the tasks. This tuning is done dynamically during program execution and hence can adapt to changes in work load or external input stimulus. The benefits of this technique are illustrated using an ARM processor running application benchmarks with different cache organizations and round-robin scheduling techniques.

Semantics-Preserving Cosynthesis of Cyber-Physical Systems

“…For cost-sensitive systems, the question is can we achieve better QoC by exploiting certain characteristics of memory hierarchy and management. Along similar lines, the embedded systems community have exploited the cache reuse by code rearrangement during compile-time [115]- [117] or runtime [118]. However, this is not applicable for control applications because it is dificult to analyze the timing properties for such compile-time rearrangement in the design stage.…”

A PI-based control structure as an operating system scheduler

“…In this work a "scheduler" is realised as a feedback controller. This implies a perspective shift from the idea of complementing the original scheduler with a control mechanism able to tune its parameters, which is the classical adaptation approach, for example seen in Batcher and Walker (2008); Lu et al (2002); Xu et al (2006); Palopoli and Abeni (2009); Cucinotta et al (2010); Abeni et al (2002); Xia et al (2007). Fig.…”

Runtime adaptation

“…There has been significant work on runtime effects due to cache performance; however, most of this research focuses on minimizing cache misses [1,2,8,17,18,19]. By minimizing cache misses, energy spent in accessing memory is decreased, and the overall application runtime is improved.…”