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Accelerating Stream...
Accelerating Stream Processing Queries with Congestion-aware Scheduling and Real-time Linux Threads
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- Frasca, Fausto (författare)
- Universita di Pisa,University of Pisa
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- Gulisano, Vincenzo Massimiliano, 1984 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Mencagli, Gabriele (författare)
- Universita di Pisa,University of Pisa
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- Palyvos-Giannas, Dimitrios, 1991 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Torquati, Massimo (författare)
- Universita di Pisa,University of Pisa
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(creator_code:org_t)
- 2023
- 2023
- Engelska.
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Ingår i: Proceedings of the 20th ACM International Conference on Computing Frontiers 2023, CF 2023. ; , s. 144-153
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https://research.cha... (primary) (free)
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https://doi.org/10.1...
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https://research.cha...
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Abstract
Ämnesord
Stäng
- Stream Processing Engines (SPEs) have been used by companies and industries to develop queries able to extract insights from data streams. The Edge/IoT context poses additional challenges, since streaming queries need to run closer to data producers to save latency, i.e., on resource-constrained devices. Lachesis is a middleware helping Linux to schedule more efficiently threads of the SPE, which revealed useful especially for devices with limited CPU resources. Lachesis does not require any architectural change to the SPE implementation. It collects metrics from the SPE, and computes high-level priorities that are converted into hints to the Operating System to affect its actual scheduling of threads. This paper extends the initial contribution of Lachesis in two main directions: i) we optimize the policy assigning to threads a priority proportional to their actual load by accurately studying the implementation of Storm and Flink, two popular SPEs; ii) instead of restricting the OS scheduling to traditional SCHED_OTHER threads as done previously by Lachesis, we leverage the real-time capability of the modern Linux kernel. Our experimental evaluation shows that both enhancements provide important benefits compared with the previous version of Lachesis: we get +9.75% (average) throughput (+19% peak) with-27% latency on average (-40% peak).
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Nyckelord
- Apache Flink
- Real-time Threads
- Data Stream Processing
- Linux Scheduler
- Apache Storm
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)