Towards Efficient NVDIMM-based Heterogeneous Storage Hierarchy Management for Big Data Workloads
Renhai Chen, Zili Shao, Duo Liu, Zhiyong Feng, Tao Li · 2019
In this paper, we propose a holistic solution to address several important and challenging issues in storage data management in light of emerging NVDIMM-based architecture: namely, new performance modeling, NVDIMM-based migration, and architectural support for NVDIMMs on migration optimization. In particular, a novel NVDIMM-based heterogeneous storage performance model is proposed to effectively address bus contention issues caused by placing NVDIMMs on the memory bus. We also develop an NVDIMM-based lazy migration scheme to effectively minimize adverse effects caused by memory traffic interferences during storage data management processes. Finally, the NVDIMM-based architectural support for migration optimization is proposed to increase channel parallelism in the destination NVDIMMs and bypass buffer caches in the source NVDIMMs, so that the impact of memory traffic can be alleviated. We present detailed evaluation and analysis to quantify how well our techniques can enhance the I/O performances of big workloads via efficient heterogeneous storage hierarchy management. Our experimental results show that overall the proposed techniques yield up to 98% performance improvement over the state-of-the-art techniques.