DaeMon: Architectural Support for Efficient Data Movement in Fully Disaggregated Systems
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Published:2023-02-27
Issue:1
Volume:7
Page:1-36
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ISSN:2476-1249
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Container-title:Proceedings of the ACM on Measurement and Analysis of Computing Systems
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language:en
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Short-container-title:Proc. ACM Meas. Anal. Comput. Syst.
Author:
Giannoula Christina1ORCID, Huang Kailong2ORCID, Tang Jonathan2ORCID, Koziris Nectarios3ORCID, Goumas Georgios3ORCID, Chishti Zeshan4ORCID, Vijaykumar Nandita2ORCID
Affiliation:
1. University of Toronto & National Technical University of Athens, Toronto, ON, Canada 2. University of Toronto, Toronto, ON, Canada 3. National Technical University of Athens, Athens, Greece 4. Intel Corporation, Portland, OR, USA
Abstract
Resource disaggregation offers a cost effective solution to resource scaling, utilization, and failure-handling in data centers by physically separating hardware devices in a server. Servers are architected as pools of processor, memory, and storage devices, organized as independent failure-isolated components interconnected by a high-bandwidth network. A critical challenge, however, is the high performance penalty of accessing data from a remote memory module over the network. Addressing this challenge is difficult as disaggregated systems have high runtime variability in network latencies/bandwidth, and page migration can significantly delay critical path cache line accesses in other pages.This paper conducts a characterization analysis on different data movement strategies in fully disaggregated systems, evaluates their performance overheads in a variety of workloads, and introduces DaeMon, the first software-transparent mechanism to significantly alleviate data movement overheads in fully disaggregated systems. First, to enable scalability to multiple hardware components in the system, we enhance each compute and memory unit with specialized engines that transparently handle data migrations. Second, to achieve high performance and provide robustness across various network, architecture and application characteristics, we implement a synergistic approach of bandwidth partitioning, link compression, decoupled data movement of multiple granularities, and adaptive granularity selection in data movements. We evaluate DaeMon in a wide variety of workloads at different network and architecture configurations using a state-of-the-art simulator. DaeMon improves system performance and data access costs by 2.39× and 3.06×, respectively, over the widely-adopted approach of moving data at page granularity.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture,Safety, Risk, Reliability and Quality,Computer Science (miscellaneous)
Reference114 articles.
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1. Morpheus: An Adaptive DRAM Cache with Online Granularity Adjustment for Disaggregated Memory;2023 IEEE 41st International Conference on Computer Design (ICCD);2023-11-06 2. Architectural Support for Efficient Data Movement in Fully Disaggregated Systems;ACM SIGMETRICS Performance Evaluation Review;2023-06-26 3. Architectural Support for Efficient Data Movement in Fully Disaggregated Systems;Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems;2023-06-19
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