SSDKeeper: Self-Adapting Channel Allocation to Improve the Performance of SSD Devices
Renping Liu, Xianzhang Chen, Yujuan Tan, Runyu Zhang, Liang Liang, Duo Liu
Abstract
Solid state drives (SSDs) have been widely deployed in high performance data center environments, where multiple tenants usually share the same hardware. However, traditional SSDs distribute the users' incoming data uniformly across all SSD channels, which leads to numerous access conflicts. Meanwhile, SSDs that statically allocate one or several channels to one tenant sacrifice device parallelism and capacity. When SSDs are shared by tenants with different access patterns, inappropriate channel allocation results in SSDs performance degradation. In this paper, we propose a self-adapting channel allocation mechanism, named SSDKeeper, for multiple tenants to share one SSD. SSDKeeper employs a machine learning assisted algorithm to take full advantage of SSD parallelism while providing performance isolation. By collecting multi-tenant access patterns and training a model, SSDKeeper selects an optimal channel allocation strategy for multiple tenants with the lowest overall response latency. Experimental results show that SSDKeeper improves the overall performance by 24% with negligible overhead.