Object Proxy Patterns for Accelerating Distributed Applications
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Abstract (arXiv)
Workflow and serverless frameworks have empowered new approaches to distributed application design by abstracting compute resources. However, their typically limited or one-size-fits-all support for advanced data flow patterns leaves optimization to the application programmer -- optimization that becomes more difficult as data become larger. The transparent object proxy, which provides wide-area references that can resolve to data regardless of location, has been demonstrated as an effective low-level building block in such situations. Here we propose three high-level proxy-based programming patterns -- distributed futures, streaming, and ownership -- that make the power of the proxy pattern usable for more complex and dynamic distributed program structures. We motivate these patterns via careful review of application requirements and describe implementations of each pattern. We evaluate our implementations through a suite of benchmarks and by applying them in three substantial scientific applications, in which we demonstrate substantial improvements in runtime, throughput, and memory usage.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2024.
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Citing papers per year: 2024: 3; 2025: 1; 2026: 1.
5 papers.
- 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
- 2025Empowering Scientific Workflows with Federated Agentscited by 16
- 2024Employing Artificial Intelligence to Steer Exascale Workflows with Colmenacited by 3
- 2024Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learningcited by 3
- 2026Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputerscited by 1
- 2026Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputers20 Mar 2026
- 2025Empowering Scientific Workflows with Federated Agents8 May 2025
- 2024Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning24 Sep 2024
- 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework17 Sep 2024
- 2024Employing Artificial Intelligence to Steer Exascale Workflows with Colmena26 Aug 2024