Abstract
Intelligent workload consolidation in storage systems leads to better Return On Investment (ROI), in terms of more efficient use of data center resources, better Quality of Service (QoS), and lower power consumption. This is particularly significant yet challenging in a cloud environment, in which a large set of different workloads multiplex on a shared, heterogeneous infrastructure. However, the increasing availability of fine grained workload logging facilities allows better insights to be gained from workload profiles. As a consequence, consolidation can be done more deeply, according to a detailed understanding of how well given workloads mix. We describe IO Tetris, which takes a first look at fine-grained consolidation in large-scale storage systems by leveraging temporal patterns found in real-world I/O traces gathered from enterprise storage environments. The core functionality of IO Tetris consists of two stages. A grouping stage performs hierarchical grouping of storage workloads to find complementary groupings that consolidate well together over time and conflicting ones that do not. After that, a migration stage examines the discovered groupings to determine how to maximize resource utilization efficiency while minimizing migration costs. Experiments based on customer I/O traces from a high-end enterprise class IBM storage controller show that a non-trivial number of IO Tetris groupings exist in real-world storage workloads, and that these groupings can be leveraged to achieve better storage consolidation in a cloud setting.