Overcoming Limitations of Off-the-Shelf Priority Schedulers in Dynamic Environments

It is common nowadays to architect and design scaled-out systems with off-the-shelf computing components operated and managed by off-the-shelf open-source tools. While web services represent the critical set of services offered at scale, big data analytics is emerging as a preferred service to be colocated with cloud web services at a lower priority raising the need for off-the-shelf priority scheduling. In this paper we report on the perils of Linux priority scheduling tools when used to differentiate between such complex services. We demonstrate that simple priority scheduling utilities such as nice and ionice can result in dramatically erratic behavior. We provide a remedy by proposing an autonomic priority scheduling algorithm that adjusts its execution parameters based on on-line measurements of the current resource usage of critical applications. Detailed experimentation with a user-space prototype of the algorithm on a Linux system using popular benchmarks such as SPEC and TPC-W illustrate the robustness and versatility of the proposed technique, as it provides consistency to the expected performance of a high-priority application when running simultaneously with multiple low priority jobs.