On the Design and Implementation of a Cache-aware Soft Real-time Scheduler for Multicore Platforms

JOHN MICHAEL CALANDRINO: On the Design and Implementation of a Cache-Aware Soft Real-Time Scheduler for Multicore Platforms (Under the direction of James H. Anderson) Real-time systems are those for which timing constraints must be satisfied. In this dissertation, research on multiprocessor real-time systems is extended to support multicore platforms, which contain multiple processing cores on a single chip. Specifically, this dissertation focuses on designing a cache-aware real-time scheduler to reduce shared cache miss rates, and increase the level of shared cache reuse, on multicore platforms when timing constraints must be satisfied. This scheduler, implemented in Linux, employs: (1) a scheduling method for real-time workloads that satisfies timing constraints while making scheduling choices that reduce shared cache miss rates; and (2) a profiler that quantitatively approximates the cache impact of every task during its execution. In experiments, it is shown that the proposed cache-aware scheduler can result in significantly reduced shared cache miss rates over other approaches. This is especially true when sufficient hardware support is provided, primarily in the form of cache-related performance monitoring features. It is also shown that scheduler-related overheads are comparable to other scheduling approaches, and therefore overheads would not be expected to offset any reduction in cache miss rate. Finally, in experiments involving a multimedia server workload, it was found that the use of the proposed cache-aware scheduler allowed the size of the workload to be increased. Prior work in the area of cache-aware scheduling for multicore platforms has not addressed support for real-time workloads, and prior work in the area of real-time scheduling has not addressed shared caches on multicore platforms. For real-time workloads running on multicore platforms, a decrease in shared cache miss rates can result in a corresponding decrease in execution times, which may allow a larger real-time workload to be supported, or hardware