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An RDMA-enabled In-memory Computing Platform for R-tree on Clusters

Authors:

Xiaodong ZhangAuthors Info & Claims

ACM Transactions on Spatial Algorithms and Systems (TSAS), Volume 8, Issue 2

Article No.: 15, Pages 1 - 26

https://doi.org/10.1145/3503513

Published: 12 February 2022 Publication History

Abstract

R-tree is a foundational data structure used in spatial databases and scientific databases. With the advancement of networks and computer architectures, in-memory data processing for R-tree in distributed systems has become a common platform. We have observed new performance challenges to process R-tree as the amount of multidimensional datasets become increasingly high. Specifically, an R-tree server can be heavily overloaded while the network and client CPU are lightly loaded, and vice versa.

In this article, we present the design and implementation of Catfish, an RDMA-enabled R-tree for low latency and high throughput by adaptively utilizing the available network bandwidth and computing resources to balance the workloads between clients and servers. We design and implement two basic mechanisms of using RDMA for a client-server R-tree data processing system. First, in the fast messaging design, we use RDMA writes to send R-tree requests to the server and let server threads process R-tree requests to achieve low query latency. Second, in the RDMA offloading design, we use RDMA reads to offload tree traversal from the server to the client, which rescues the server as it is overloaded. We further develop an adaptive scheme to effectively switch an R-tree search between fast messaging and RDMA offloading, maximizing the overall performance. Our experiments show that the adaptive solution of Catfish on InfiniBand significantly outperforms R-tree that uses only fast messaging or only RDMA offloading in both latency and throughput. Catfish can also deliver up to one order of magnitude performance over the traditional schemes using TCP/IP on 1 and 40 Gbps Ethernet. We make a strong case to use RDMA to effectively balance workloads in distributed systems for low latency and high throughput.

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Cited By

Wang FLee RTeng DZhang XSaltz J(2024)High-Performance Spatial Data Analytics: Systematic R&D for Scale-Out and Scale-Up Solutions from the Past to NowProceedings of the VLDB Endowment10.14778/3685800.368591217:12(4507-4520)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.14778/3685800.3685912
Geng LWang HMeng JFan DBen-Romdhane SPichumani HPhegade VZhang X(2024)RR-Compound: RDMA-Fused gRPC for Low Latency, High Throughput, and Easy InterfaceIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.340439435:8(1488-1505)Online publication date: Aug-2024
https://doi.org/10.1109/TPDS.2024.3404394
Weng SFan ZGou J(2024)A fast DBSCAN algorithm using a bi-directional HNSW index structure for big dataInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02104-815:8(3471-3494)Online publication date: 4-Mar-2024
https://doi.org/10.1007/s13042-024-02104-8

Index Terms

An RDMA-enabled In-memory Computing Platform for R-tree on Clusters
1. Information systems
  1. Data management systems
    1. Data structures
      1. Data access methods
        Multidimensional range search
  2. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems
2. Networks
  1. Network services
    1. Cloud computing

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Published In

cover image ACM Transactions on Spatial Algorithms and Systems

ACM Transactions on Spatial Algorithms and Systems Volume 8, Issue 2

June 2022

253 pages

ISSN:2374-0353

EISSN:2374-0361

DOI:10.1145/3506671

Editor:
Walid G. Aref
Purdue University, USA

Issue’s Table of Contents

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Publication History

Published: 12 February 2022

Accepted: 01 December 2021

Revised: 01 September 2021

Received: 01 March 2021

Published in TSAS Volume 8, Issue 2

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Cited By

Wang FLee RTeng DZhang XSaltz J(2024)High-Performance Spatial Data Analytics: Systematic R&D for Scale-Out and Scale-Up Solutions from the Past to NowProceedings of the VLDB Endowment10.14778/3685800.368591217:12(4507-4520)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.14778/3685800.3685912
Geng LWang HMeng JFan DBen-Romdhane SPichumani HPhegade VZhang X(2024)RR-Compound: RDMA-Fused gRPC for Low Latency, High Throughput, and Easy InterfaceIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.340439435:8(1488-1505)Online publication date: Aug-2024
https://doi.org/10.1109/TPDS.2024.3404394
Weng SFan ZGou J(2024)A fast DBSCAN algorithm using a bi-directional HNSW index structure for big dataInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02104-815:8(3471-3494)Online publication date: 4-Mar-2024
https://doi.org/10.1007/s13042-024-02104-8

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