R2B: high-efficiency and fair I/O scheduling for multi-tenant with differentiated demands

Big data applications have differentiated requirements for I/O resources in cloud environments. For instance, data analytic and AI/ML applications usually have periodical burst I/O traffic, and data stream processing and database applications often introduce fluctuating I/O loads based on a guaranteed I/O bandwidth. However, the existing resource isolation model (i.e., RLW) and methods (e.g., Token-bucket, mClock, and cgroup) cannot support the fluctuating I/O load and differentiated I/O demands well, and thus cannot achieve fairness, high resource utilization, and high performance for applications at the same time. In this paper, we propose a novel efficient and fair I/O resource isolation model and method called R2B, which can adapt to the differentiated I/O characteristics and requirements of different applications in a shared resource environment. R2B can simultaneously satisfy the fairness and achieve both high application efficiency and high bandwidth utilization.