Generative and Multi-phase Learning for Computer Systems Optimization

Machine learning and artificial intelligence are invaluable for computer systems optimization: as computer systems expose more resources for management, ML/AI is necessary for modeling these resources' complex interactions. The standard way to incorporate ML/AI into a computer system is to first train a learner to accurately predict the system's behavior as a function of resource usage- e.g., to predict energy efficiency as a function of core usage-and then deploy the learned model as part of a system-e.g., a scheduler. In this paper, we show that (1) continued improvement of learning accuracy may not improve the systems result, but (2) incorporating knowledge of the systems problem into the learning process improves the systems results even though it may not improve overall accuracy. Specifically, we learn application performance and power as a function of resource usage with the systems goal of meeting latency constraints with minimal energy. We propose a novel generative model which improves learning accuracy given scarce data, and we propose a multi-phase sampling technique, which incorporates knowledge of the systems problem. Our results are both positive and negative. The generative model improves accuracy, even for state-of-the-art learning systems, but negatively impacts energy. Multi-phase sampling reduces energy consumption compared to the state-of-the-art, but does not improve accuracy. These results imply that learning for systems optimization may have reached a point of diminishing returns where accuracy improvements have little effect on the systems outcome. Thus we advocate that future work on learning for systems should de-emphasize accuracy and instead incorporate the system problem's structure into the learner.