Priority IO Scheduling in the Cloud

Current state of the art runtime systems, built for managing cloud environments, almost always assume resource sharing among multiple users and applications. In large part, these runtime systems rely on functionalities of the node-local operating systems to divide the local resources among the applications that share a node. While OSes usually achieve good resource sharing by creating distinct application-level domains across CPUs and DRAM, managing the IO bandwidth is a complex task due to lack of communication between the host and IO device. In our work we focus on controlling the HDD IO bandwidth available to user-level applications in a cloud environment. We introduce priority-based (PBS) IO scheduling, where the ordering of IO commands is decided cooperatively by the host and IO device. We implemented our scheduling policies in the Linux storage stack and Hadoop Distributed File System. Initial results show that in a cloud environment, the real-time commands managed by PBS outperform the real-time IO scheduling of the Linux kernel by up to a factor of ∼ 5 for the worst case latency, and by more than 2x for aver-