Kyriakos Psarakis, George Christodoulou, George Siachamis, Marios Fragkoulis, Asterios Katsifodimos
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引用次数: 0
Abstract
Developing stateful cloud applications, such as low-latency workflows and microservices with strict consistency requirements, remains arduous for programmers. The Stateful Functions-as-a-Service (SFaaS) paradigm aims to serve these use cases. However, existing approaches provide weak transactional guarantees or perform expensive external state accesses requiring inefficient transactional protocols that increase execution latency. In this paper, we present Styx, a novel dataflow-based SFaaS runtime that executes serializable transactions consisting of stateful functions that form arbitrary call-graphs with exactly-once guarantees. Styx extends a deterministic transactional protocol by contributing: i) a function acknowledgment scheme to determine transaction boundaries required in SFaaS workloads, ii) a function-execution caching mechanism, and iii) an early-commit reply mechanism that substantially reduces transaction execution latency. In addition, Styx's elasticity supports state migration for load balancing using scale-up and scale-down operations when workloads introduce uneven overhead among workers. Experiments with the YCSB, TPC-C, and Deathstar benchmarks show that Styx outperforms state-of-the-art approaches by achieving at least one order of magnitude higher throughput while exhibiting near-linear scalability and low latency. Moreover, state migration experiments with YCSB and TPC-C show that Styx's approach to state migration outperforms the baseline, a stop and restart migration approach tailored to Styx, by adapting swiftly to workload changes while maintaining low latency.
期刊介绍:
The journal is dedicated to the publication of scholarly contributions in areas of data management such as database system technology and information systems, including their architectures and applications. Further, the journal’s scope is restricted to areas of data management that are covered by the combined expertise of the journal’s editorial board.
Submissions with a substantial theory component are welcome, but the VLDB Journal expects such submissions also to embody a systems component.
In relation to data mining, the journal will handle submissions where systems issues play a significant role. Factors that we use to determine whether a data mining paper is within scope include:
The submission targets systems issues in relation to data mining, e.g., by covering integration with a database engine or with other data management functionality.
The submission’s contributions build on (rather than simply cite) work already published in database outlets, e.g., VLDBJ, ACM TODS, PVLDB, ACM SIGMOD, IEEE ICDE, EDBT.
The journal''s editorial board has the necessary expertise on the submission''s topic.
Traditional, stand-alone data mining papers that lack the above or similar characteristics are out of scope for this journal. Criteria similar to the above are applied to submission from other areas, e.g., information retrieval and geographical information systems.