Flux Research Group / School of Computing

Towards Fair Sharing of Block Storage in a Multi-tenant Cloud

Xing Lin, Yun Mao, Feifei Li, and Robert Ricci

Proceedings of the 4th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud) 2012.

Operating Systems, Storage


A common problem with disk-based cloud storage services is that performance can vary greatly and become highly unpredictable in a multi-tenant environment. A fundamental reason is the interference between workloads co-located on the same physical disk. We observe that different IO patterns interfere with each other significantly, which makes the performance of different types of workloads unpredictable when they are executed concurrently. Unpredictability implies that users may not get a fair share of the system resources from the cloud services they are using. At the same time, replication is commonly used in cloud storage for high reliability. Connecting these two facts, we propose a cloud storage system designed to minimize workload interference without increasing storage costs or sacrificing the overall system throughput. Our design leverages log-structured disk layout, chain replication and a workload-based replica selection strategy to minimize interference, striking a balance between performance and fairness. Our initial results suggest that this approach is a promising way to improve the performance and predictability of cloud storage.