Section: New Results

Adaptive Request Scheduling for the I/O Forwarding Layer using Reinforcement Learning

I/O optimization techniques such as request scheduling can improve performance mainly for the access patterns they target, or they depend on the precise tune of parameters. In this work [40], we propose an approach to adapt the I/O forwarding layer of HPC systems to the application access patterns by tuning a request scheduler. Our case study is the TWINS scheduling algorithm, where performance improvements depend on the time window parameter, which depends on the current workload. Our approach uses a reinforcement learning technique — contextual bandits — to make the system capable of learning the best parameter value to each access pattern during its execution, without a previous training phase. We evaluate our proposal and demonstrate it can achieve a precision of $88\%$ on the parameter selection in the first hundreds of observations of an access pattern. After having observed an access pattern for a few minutes (not necessarily contiguously), we demonstrate that the system will be able to optimize its performance for the rest of the life of the system (years).