Empirical Analysis of Disaggregated Cloud Memory on Memory Intensive Applications

• Published in: Journal of semiconductor technology and science Vol. 23; no. 5; pp. 273 - 282
• Main Authors: Jeong, Yeonwoo, Jung, Gyeonghwan, Park, Kyuli, Kim, Youngjae, Park, Sungyong
• Format: Journal Article
• Language: English
• Published: 대한전자공학회 01.10.2023
• Subjects: 전기공학; memory capacity extension; disaggregation; Disaggregated cloud memory; memory
• DOI: 10.5573/JSTS.2023.23.5.273
• ISSN: 1598-1657; 2233-4866

Disaggregated Cloud Memory (DCM) is a hypervisor-based solution that allows client node to extend local memory by leveraging underutilized memory from remote node. These two nodes are generally connected through Remote Direct Memory Access (RDMA)-based high-bandwidth InfiniBand networks. DCM has been a viable alternative to mitigate the performance degradation of memory-intensive applications in memory-constrained environments. There has also been a growing interest in developing memory-intensive applications with managed languages (we call managed applications) such as Java and Python. These managed languages are easy to use but introduce unpredictability in memory usage at runtime. Despite the advantage of memory extension in DCM, the empirical studies that analyze the performance impact and overhead of running managed applications in DCM are lacking. This paper presents the results of a comprehensive study of DCM on both managed and unmanaged applications. The experimental results revealed that the performance degradation of unmanaged applications in DCM is only less than 6% due to fast remote paging and optimized page eviction policy. However, Garbage Collection (GC) severely degrades the performance of managed applications when page fault occurs, while DCM mitigates the performance degradation efficiently. KCI Citation Count: 0