R2B: High-Efficiency and Fair I/O Scheduling for Multi-tenant with Differentiated Demands

Abstract

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. This work aims to help the cloud provider achieve higher utilization by shifting the burden to the cloud customers to specify their type of workload.