Relieving Compression-Induced Local Wear on Non-Volatile Memory Block via Sliding Writes.

Due to its non-volatility and large capacity, NVM devices gradually take place at various levels of memories. However, their limited endurance is still a big concern for large-scale data centres. Compression algorithms have been used to save NVM space and enhance the efficiency of those lifetime extension methods. However, their own influence on the NVM lifetime is not clear. In order to fully investigate the impact of compression on NVM, this paper first studies bit flips involved in several typical compression algorithms. It is found that more bit flips would happen in the shrunken area of a memory block. This induces the phenomenon of intra-block wear unevenness, which sacrifices NVM lifetime. We propose a new metric called local bit flips to describe this phenomenon. In order to relieve the intra-block wear unevenness caused by compression, this paper proposes a sliding write method named SlidW to distribute the compressed data across the whole memory block. We first divide the memory block into several areas, and then consider five cases about the relationship between new data size and left space. Then, we place the new data according to the case. Comprehensive experimental results show that SlidW can efficiently balance wear and enhance NVM lifetime.

Application-Oriented Data Migration to Accelerate In-Memory Database on Hybrid Memory.

With the advantage of faster data access than traditional disks, in-memory database systems, such as Redis and Memcached, have been widely applied in data centers and embedded systems. The performance of in-memory database greatly depends on the access speed of memory. With the requirement of high bandwidth and low energy, die-stacked memory (e.g., High Bandwidth Memory (HBM)) has been developed to extend the channel number and width. However, the capacity of die-stacked memory is limited due to the interposer challenge. Thus, hybrid memory system with traditional Dynamic Random Access Memory (DRAM) and die-stacked memory emerges. Existing works have proposed to place and manage data on hybrid memory architecture in the view of hardware. This paper considers to manage in-memory database data in hybrid memory in the view of application. We first perform a preliminary study on the hotness distribution of client requests on Redis. From the results, we observe that most requests happen on a small portion of data objects in in-memory database. Then, we propose the Application-oriented Data Migration called ADM to accelerate in-memory database on hybrid memory. We design a hotness management method and two migration policies to migrate data into or out of HBM. We take Redis under comprehensive benchmarks as a case study for the proposed method. Through the experimental results, it is verified that our proposed method can effectively gain performance improvement and reduce energy consumption compared with existing Redis database.