Resource abstraction and data placement for distributed hybrid memory pool

作     者：Tingting CHEN Haikun LIU Xiaofei LIAO Hai JIN Tingting CHEN;Haikun LIU;Xiaofei LIAO;Hai JIN

作者机构：National Engineering Research Center for Big Data Technology and SystemServices Computing Technology and System Lab/Gluster and Grid Computing LabSchool of Gomputing Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2021年第15卷第3期

页      面：47-57页

核心收录：

学科分类：08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：would like to thank the anonymous reviewers for their insightful comments.This work was supported jointly by National Key Research and Development Program of China(2017YFB1001603) National Natural Science Foundation of China(NSFC)(Grants Nos.61672251,61732010,61825202) 

主　　题：load balance distributed hybrid memory clouds 

摘      要：Emerging byte-addressable non-volatile memory(NVM)technologies offer higher density and lower cost than DRAM,at the expense of lower performance and limited write *** have been many studies on hybrid NVM/DRAM memory management in a single physical ***,it is still an open problem on how to manage hybrid memories efficiently in a distributed *** paper proposes Alloy,a memory resource abstraction and data placement strategy for an RDMA-enabled distributed hybrid memory pool(DHMP).Alloy provides simple APIs for applications to utilize DRAM or NVM resource in the DHMP,without being aware of the hardware details of the *** propose a hotness-aware data placement scheme,which combines hot data migration,data replication and write merging together to improve application performance and reduce the cost of *** evaluate Alloy with several micro-benchmark workloads and public benchmark *** results show that Alloy can significantly reduce the DRAM usage in the DHMP by up to 95%,while reducing the total memory access time by up to 57%compared with the state-of-the-art approaches.