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

被引:0
|
作者
Zhao, Wenze [1 ]
Du, Yajuan [1 ,2 ]
Zhang, Mingzhe [3 ]
Liu, Mingyang [1 ]
Jin, Kailun [1 ]
Ausavarungnirun, Rachata [1 ,4 ]
机构
[1] Wuhan Univ Technol, Sch Comp & Artificial Intelligence, Wuhan 430070, Peoples R China
[2] Wuhan Univ Technol, Shenzhen Res Inst, Shenzhen 518000, Peoples R China
[3] Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
[4] King Mongkuts Univ Technol North Bangkok KMUTNB, Sirindhorn Int Thai German Grad Sch Engn, Bangkok 10800, Thailand
基金
中国国家自然科学基金;
关键词
in-memory database; hybrid memory; data migration; DRAM;
D O I
10.3390/mi13010052
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
收藏
页数:13
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