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Achieving high performance in virtualized data centers requires bothdeploying high throughput storage clusters, i.e. based on Solid State Disks(SSDs), as well as optimally consolidating the workloads across storage nodes.Nowadays, the only practical solution for cloud storage providers to offerguaranteed performance is to grossly over-provision the storage nodes. Thecurrent workload scheduling mechanisms used in production do not have theintelligence to optimally allocate block storage volumes based on theperformance of SSDs. In this paper, we introduce Serifos, an autonomousperformance modeling and load balancing system designed for SSD-based cloudstorage. Serifos takes into account the characteristics of the SSD storageunits and constructs hardware dependent workload consolidation models. ThusSerifos is able to predict the latency caused by workload interference and theaverage latency of concurrent workloads. Furthermore, Serifos leverages an I/Oload balancing algorithm to dynamically balance the volumes across the cluster.Experimental results indicate that Serifos consolidation model is able tomaintain the mean prediction error of around 10% for heterogeneous hardware. Asa result of Serifos load balancing, we found that the variance and the maximumaverage latency are reduced by 82% and 52%, respectively. The supported ServiceLevel Objectives (SLOs) on the testbed improve 43% on average latency, 32% onthe maximum read and 63% on the maximum write latency.

Serifos: Workload Consolidation and Load Balancing for SSD Based Cloud Storage Systems