| 1. |
- Frasca, Fausto, et al.
(author)
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Accelerating Stream Processing Queries with Congestion-aware Scheduling and Real-time Linux Threads
- 2023
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In: Proceedings of the 20th ACM International Conference on Computing Frontiers 2023, CF 2023. ; , s. 144-153
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Conference paper (peer-reviewed)abstract
- 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).
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| 2. |
- Palyvos-Giannas, Dimitrios, 1991, et al.
(author)
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Lachesis: A Middleware for Customizing OS Scheduling of Stream Processing Queries
- 2021
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In: Middleware 2021 - Proceedings of the 22nd International Middleware Conference. - New York, NY, USA : ACM. - 9781450385343 ; , s. 365-378
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Conference paper (peer-reviewed)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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