I/O Workload Management for All-Flash Datacenter Storage Systems Based on Total Cost of Ownership

TL;DR

This paper develops a TCO model for all-flash datacenter storage, incorporating a new empirical write amplification model, and proposes resource management schemes to optimize workload allocation and reduce costs.

Contribution

It introduces a novel TCO model combined with an empirical WAF model and proposes new workload management schemes for cost-effective all-flash storage.

Findings

MINTCO reduces TCO significantly.

The models maintain high throughput and space utilization.

Proposed schemes outperform existing management strategies.

Abstract

Recently, the capital expenditure of flash-based Solid State Driver (SSDs) keeps declining and the storage capacity of SSDs keeps increasing. As a result, all-flash storage systems have started to become more economically viable for large shared storage installations in datacenters, where metrics like Total Cost of Ownership (TCO) are of paramount importance. On the other hand, flash devices suffer from write amplification, which, if unaccounted, can substantially increase the TCO of a storage system. In this paper, we first develop a TCO model for datacenter all-flash storage systems, and then plug a Write Amplification model (WAF) of NVMe SSDs we build based on empirical data into this TCO model. Our new WAF model accounts for workload characteristics like write rate and percentage of sequential writes. Furthermore, using both the TCO and WAF models as the optimization criterion, we design new flash resource management schemes (MINTCO) to guide datacenter managers to make workload allocation decisions under the consideration of TCO for SSDs. Based on that, we also develop MINTCO-RAID to support RAID SSDs and MINTCO-OFFLINE to optimize the offline workload-disk deployment problem during the initialization phase. Experimental results show that MINTCO can reduce the TCO and keep relatively high throughput and space utilization of the entire datacenter storage resources.