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DARQ Matter Binds Everything: Performant and Composable Cloud Programming via Resilient Steps

Author(s)
Li, Tianyu; Chandramouli, Badrish; Burckhardt, Sebastian; Madden, Samuel
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Abstract
Providing strong fault-tolerant guarantees for the modern cloud is difficult, as application developers must coordinate between independent stateful services and ephemeral compute, and handle various failure-induced anomalies. We propose Composable Resilient Steps (CReSt), a new abstraction for resilient cloud applications. CReSt uses fault-tolerant steps as its core building block, which allows participants receive, process, and send messages as a single uninterruptible atomic unit. Composability and reliability are orthogonally achieved by reusable CReSt implementations, for example, leveraging reliable message queues. Thus, CReSt application builders focus solely on translating application logic into steps, and infrastructure builders focus on efficient CReSt implementations. We propose one such implementation, called DARQ (for Deduplicated Asynchronously Recoverable Queues). At its core, DARQ is a storage service that encapsulates CReSt participant state and enforces CReSt semantics; developers attach ephemeral compute nodes to DARQ instances to implement stateful distributed components. Services built with DARQ are resilient by construction, and CReSt-compatible services naturally compose without loss of resilience. For performance, we propose a novel speculative execution scheme to execute CReSt steps without waiting for message persistence in DARQ, effectively eliding cloud persistence overheads; our scheme maintains CReSt’s fault-tolerance guarantees and automatically restores consistent system state upon failure. We showcase the generality of CReSt and DARQ using two applications: cloud streaming and workflow processing. Experiments show that DARQ is able to achieve extremely low latency and high throughput across these use cases, often beating state-of-the-art customized solutions.
Date issued
2023-06-20
URI
https://hdl.handle.net/1721.1/151085
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Journal
Proceedings of the ACM on Management of Data
Publisher
ACM
Citation
Li, Tianyu, Chandramouli, Badrish, Burckhardt, Sebastian and Madden, Samuel. 2023. "DARQ Matter Binds Everything: Performant and Composable Cloud Programming via Resilient Steps." Proceedings of the ACM on Management of Data, 1 (2).
Version: Final published version
ISSN
2836-6573

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