Localized Validation Accelerates Distributed Transactions on Disaggregated Persistent Memory

Author:

Zhang Ming1ORCID,Hua Yu1ORCID,Zuo Pengfei1ORCID,Liu Lurong1ORCID

Affiliation:

1. Huazhong University of Science and Technology

Abstract

Persistent memory (PM) disaggregation significantly improves the resource utilization and failure isolation to build a scalable and cost-effective remote memory pool in modern data centers. However, due to offering limited computing power and overlooking the bandwidth and persistence properties of real PMs, existing distributed transaction schemes, which are designed for legacy DRAM-based monolithic servers, fail to efficiently work on the disaggregated PM. In this article, we propose FORD, a F ast O ne-sided R DMA-based D istributed transaction system for the new disaggregated PM architecture. FORD thoroughly leverages one-sided remote direct memory access to handle transactions for bypassing the remote CPU in the PM pool. To reduce the round trips, FORD batches the read and lock operations into one request to eliminate extra locking and validations for the read-write data. To accelerate the transaction commit, FORD updates all remote replicas in a single round trip with parallel undo logging and data visibility control. Moreover, considering the limited PM bandwidth, FORD enables the backup replicas to be read to alleviate the load on the primary replicas, thus improving the throughput. To efficiently guarantee the remote data persistency in the PM pool, FORD selectively flushes data to the backup replicas to mitigate the network overheads. Nevertheless, the original FORD wastes some validation round trips if the read-only data are not modified by other transactions. Hence, we further propose a localized validation scheme to transfer the validation operations for the read-only data from remote to local as much as possible to reduce the round trips. Experimental results demonstrate that FORD significantly improves the transaction throughput by up to 3× and decreases the latency by up to 87.4% compared with state-of-the-art systems.

Funder

National Natural Science Foundation of China

Key Laboratory of Information Storage System, Ministry of Education of China

Publisher

Association for Computing Machinery (ACM)

Subject

Hardware and Architecture

Reference96 articles.

1. Marcos K. Aguilera, Nadav Amit, Irina Calciu, Xavier Deguillard, Jayneel Gandhi, Stanko Novakovic, Arun Ramanathan, et al. 2018. Remote regions: A simple abstraction for remote memory. In Proceedings of the 2018 USENIX Annual Technical Conference (USENIX ATC’18). 775–787.

2. Resistive random access memory (ReRAM) based on metal oxides;Akinaga Hiroyuki;Proceedings of the IEEE,2010

3. Emmanuel Amaro, Christopher Branner-Augmon, Zhihong Luo, Amy Ousterhout, Marcos K. Aguilera, Aurojit Panda, Sylvia Ratnasamy, and Scott Shenker. 2020. Can far memory improve job throughput? In Proceedings of the 15th EuroSys Conference (EuroSys’20). ACM, New York, NY, Article 14, 16 pages.

4. Thomas E. Anderson, Marco Canini, Jongyul Kim, Dejan Kostic, Youngjin Kwon, Simon Peter, Waleed Reda, Henry N. Schuh, and Emmett Witchel. 2020. Assise: Performance and availability via client-local NVM in a Distributed File System. In Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI’20). 1011–1027.

5. Spin-transfer torque magnetic random access memory (STT-MRAM);Apalkov Dmytro;ACM Journal on Emerging Technologies in Computing Systems,2013

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