AutoTiering: Automatic data placement manager in multi-tier all-flash datacenter

Abstract

In the year of 2017, the capital expenditure of Flashbased Solid State Drivers (SSDs) keeps declining and the storage capacity of SSDs keeps increasing. As a result, the "selling point" of traditional spinning Hard Disk Drives (HDDs) as a backend storage -low cost and large capacity -is no longer unique, and eventually they will be replaced by low-end SSDs which have large capacity but perform orders of magnitude better than HDDs. Thus, it is widely believed that all-flash multi-tier storage systems will be adopted in the enterprise datacenters in the near future. However, existing caching or tiering solutions for SSD-HDD hybrid storage systems are not suitable for all-flash storage systems. This is because that all-flash storage systems do not have a large speed difference (e.g., 10x) among each tier. Instead, different specialties (such as high performance, high capacity, etc.) of each tier should be taken into consideration. Motivated by this, we develop an automatic data placement manager called "AutoTiering" to handle virtual machine disk files (VMDK) allocation and migration in an all-flash multitier datacenter to best utilize the storage resource, optimize the performance, and reduce the migration overhead. AutoTiering is based on an optimization framework, whose core technique is to predict VM's performance change on different tiers with different specialties without conducting real migration. As far as we know, AutoTiering is the first optimization solution designed for all-flash multi-tier datacenters. We implement AutoTiering on VMware ESXi [1], and experimental results show that it can significantly improve the I/O performance compared to existing solutions.

Analysis of Valuable Clustering Techniques for Deep Web Access and Navigation

“…It helps in optimizing the performance and decreasing migration operating cost. It makes the issue of polynomial time simpler and resolvable [20]. The auto-tier and auto-replica [19], [20] approach are machine learning approaches which can be further enhanced in future to operate on deep web databases.…”

Euro-Par 2018: Parallel Processing

“…To maximize data-locality, different data partitioning techniques have been proposed to avoid remote join operations for queries [7]. More generally, various advanced data placement and replication strategies have been proposed for data center storage systems to reduce the network overhead for particular workloads [8,9]. Different from them, we focus on exploring data locality using online scheduling rather than pre-processing.…”

Efficient decoding algorithm for 3D video over wireless channels

“…Several optimization frameworks have been proposed in the literature for distributed processing or handling of data to maximize the performance under given resource limitations [2,3,13,[17][18][19]. AutoReplica [17] provides a solution to effectively balance the trade-off between I/O performance and fault tolerance using SSD-HDD tier storages.…”