research-article

Lachesis: a middleware for customizing OS scheduling of stream processing queries

Authors:

Dimitris Palyvos-Giannas,

Gabriele Mencagli,

Marina Papatriantafilou,

Vincenzo GulisanoAuthors Info & Claims

Middleware '21: Proceedings of the 22nd International Middleware Conference

Pages 365 - 378

https://doi.org/10.1145/3464298.3493407

Published: 02 December 2021 Publication History

Abstract

Data streaming applications in Cyber-Physical Systems enable high-throughput, low-latency transformations of raw data into value. The performance of such applications, run by Stream Processing Engines (SPEs), can be boosted through custom CPU scheduling. Previous schedulers in the literature require alterations to SPEs to control the scheduling through user-level threads. While such alterations allow for fine-grained control, they hinder the adoption of such schedulers due to the high implementation cost and potential limitations in application semantics (e.g., blocking I/O).

Motivated by the above, we explore the feasibility and benefits of custom scheduling without alterations to SPEs but, instead, by orchestrating the OS scheduler (e.g., using nice and cgroup) to enforce the scheduling goals. We propose Lachesis, a standalone scheduling middleware, decoupled from any specific SPE, that can schedule multiple streaming applications, run in one or many nodes, and possibly multiple SPEs. Our evaluation with real-world and synthetic workloads, several SPEs and hardware setups, shows its benefits over default OS scheduling and other state-of-the-art schedulers: up to 75% higher throughput, and 1130x lower average latency once such SPEs reach their peak processing capacity.

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Cited By

Yasukata KIshiguro K(2024)Developing Process Scheduling Policies in User Space with Common OS FeaturesProceedings of the 15th ACM SIGOPS Asia-Pacific Workshop on Systems10.1145/3678015.3680481(38-44)Online publication date: 4-Sep-2024
https://dl.acm.org/doi/10.1145/3678015.3680481
Gulisano VMedvet E(2024)Evolutionary Computation Meets Stream ProcessingApplications of Evolutionary Computation10.1007/978-3-031-56852-7_24(377-393)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56852-7_24
Garcia AGriebler DSchepke CFernandes L(2023)Micro-batch and data frequency for stream processing on multi-coresThe Journal of Supercomputing10.1007/s11227-022-05024-y79:8(9206-9244)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1007/s11227-022-05024-y
Show More Cited By

Index Terms

Lachesis: a middleware for customizing OS scheduling of stream processing queries
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Online analytical processing engines
2. Software and its engineering
  1. Software organization and properties
    1. Contextual software domains
      1. Operating systems
        Process management
        Scheduling

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cover image ACM Conferences

Middleware '21: Proceedings of the 22nd International Middleware Conference

December 2021

398 pages

ISBN:9781450385343

DOI:10.1145/3464298

General Chairs:
Kaiwen Zhang
ÉTS, Canada
,
Abdelouahed Gherbi
ÉTS, Canada
,
Program Chairs:
Nalini Venkatasubramanian
UC Irvine
,
Luís Veiga
IST (U.Lisboa) & INESC-ID, Portugal

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Published: 02 December 2021

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Cited By

Yasukata KIshiguro K(2024)Developing Process Scheduling Policies in User Space with Common OS FeaturesProceedings of the 15th ACM SIGOPS Asia-Pacific Workshop on Systems10.1145/3678015.3680481(38-44)Online publication date: 4-Sep-2024
https://dl.acm.org/doi/10.1145/3678015.3680481
Gulisano VMedvet E(2024)Evolutionary Computation Meets Stream ProcessingApplications of Evolutionary Computation10.1007/978-3-031-56852-7_24(377-393)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56852-7_24
Garcia AGriebler DSchepke CFernandes L(2023)Micro-batch and data frequency for stream processing on multi-coresThe Journal of Supercomputing10.1007/s11227-022-05024-y79:8(9206-9244)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1007/s11227-022-05024-y
Fragkoulis MCarbone PKalavri VKatsifodimos A(2023)A survey on the evolution of stream processing systemsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-023-00819-833:2(507-541)Online publication date: 22-Nov-2023
https://dl.acm.org/doi/10.1007/s00778-023-00819-8

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