IONET: Towards an Open Machine Learning Training Ground for I/O Performance Prediction

Low and stable latency is a critical key to the success of many services, but variable load and resource sharing in a modern cloud environment introduces resource contention that in turn increases the unpredictability of the systems which often cause a ”tail latency problem.” As one of the main buildingblocks of a complex request-chain, understanding the I/O request becomes an important topic to help parallel storage applications achieve performance predictability and to reduce the tail latency. This paper presents IONET, ML-based per-I/O latency predictor capable of achieving 80-97% inference accuracy and sub-10μs inference overhead for each I/O. IONET’s light-weight NN models demonstrate that this line of research is practical and incorporating the models inside operating systems for real-time decision-making is a feasible solution to achieve latency stable systems.