NotNets: Accelerating Microservices by Bypassing the Network

Remote procedure calls are the workhorse of distributed systems. However, as software engineering trends, such as micro-services and serverless computing, push applications towards ever finer-grained decompositions, the overhead of RPC-based communication is becoming too great to bear. In this paper, we argue that point solutions that attempt to optimize one aspect of RPC logic are unlikely to mitigate these ballooning communication costs. Rather, we need a dramatic reappraisal of how we provide communication. Towards this end, we propose to emulate message-passing RPCs by sharing message payloads and metadata on CXL 3.0-backed far memory. We provide initial evidence of feasibility and analyze the expected benefits.

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Publication Details

Published
2024-08-29
DOI
https://doi.org/10.1145/3678015.3680494
Citations
1
Primary Topic
Software System Performance and Reliability
Type
article
Field-Weighted Citation Impact
0.35
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article

NotNets: Accelerating Microservices by Bypassing the Network

Matthew Joseph Adiletta, Robert Soulé, Frank Hady, Peter Alvaro et al.
1 citations
Software System Performance and Reliability
0.35
article

NotNets: Accelerating Microservices by Bypassing the Network

Matthew Joseph Adiletta, Robert Soulé, Frank Hady, Peter Alvaro, Ramesh Illikkal, Adrian Cockroft, Esteban Ramos, David Cheng
article en
1 citations

Abstract

Remote procedure calls are the workhorse of distributed systems. However, as software engineering trends, such as micro-services and serverless computing, push applications towards ever finer-grained decompositions, the overhead of RPC-based communication is becoming too great to bear. In this paper, we argue that point solutions that attempt to optimize one aspect of RPC logic are unlikely to mitigate these ballooning communication costs. Rather, we need a dramatic reappraisal of how we provide communication. Towards this end, we propose to emulate message-passing RPCs by sharing message payloads and metadata on CXL 3.0-backed far memory. We provide initial evidence of feasibility and analyze the expected benefits.

Intel (United States) (US), University of California, Santa Cruz (US), Yale University (US), Orion Genomics (United States) (US), Santa Cruz County Office of Education (US)
Openalex Percentile: Top 40%
Software System Performance and Reliability
0.35
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