Queueing-based storage performance modeling and placement in OpenStack environments

Abstract

In enterprise data centers, reliable performance models on storage devices are desirable for efficient storage management and optimization. However, many cloud environments consist of heterogeneous storage devices, e.g., a mixture of commodity disks, where accurate performance models are of particular challenge to attain. In this paper, we propose a lightweight queueing-based storage performance modeling framework, which is able to infer the maximum IO load that a storage device can sustain, as well as its IO load v.s. response time performance curve. Our inference framework views the underlying storage resources as blackboxes and only utilizes historical measurements of the IO and response time on the devices. In an OpenStack environment, we also develop a new storage volume placement algorithm using our performance inference and modeling framework. Experimental results show that our solution can provide up to 80% increase of the IO throughput, in tandem with a 40% reduction of the average response time, compared to the performance provided by the default OpenStack policy.