CosTLO: Cost-Effective Redundancy for Lower Latency Variance on Cloud Storage Services
Zhe Wu, Curtis Yu, Harsha V. Madhyastha · 2015
Abstract—We present CosTLO, a system that reduces the high latency variance associated with cloud storage services by augmenting GET/PUT requests issued by end-hosts with redundant requests, so that the earliest response can be con-sidered. To reduce the cost overhead imposed by redun-dancy, unlike prior efforts that have used this approach, CosTLO combines the use of multiple forms of redundancy. Since this results in a large number of configurations in which CosTLO can issue redundant requests, we conduct a comprehensive measurement study on S3 and Azure to identify the configurations that are viable in practice. In-formed by this study, we design CosTLO to satisfy any ap-plication’s goals for latency variance by 1) estimating the latency variance offered by any particular configuration, 2) efficiently searching through the configuration space to se-lect a cost-effective configuration among the ones that can offer the desired latency variance, and 3) preserving data consistency despite CosTLO’s use of redundant requests. We show that, for the median PlanetLab node, CosTLO can halve the latency variance associated with fetching content from Amazon S3, with only a 25 % increase in cost. 1