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Cloud-native workflow scheduling using a hybrid priority rule and dynamic task parallelism

Authors:

Rakesh NagiAuthors Info & Claims

SoCC '22: Proceedings of the 13th Symposium on Cloud Computing

Pages 72 - 77

https://doi.org/10.1145/3542929.3563495

Published: 07 November 2022 Publication History

Abstract

Demand for efficient cloud-native workflow scheduling is growing as many data science workloads are composed of several tasks with dependencies. As container technology becomes more prevalent in cloud communities, containerized workflow orchestration tools are introduced and become standard for scheduling workflows. However, current schedulers use simple heuristics and rely on the user's choice on priority and parallelism level of tasks without accounting for workflow-specific information.

We introduce a workflow-aware scheduling algorithm that uses workflow information for scheduling tasks, without user input, with an objective of improving resource utilization and minimizing weighted workflow completion time, duration multiplied by user specific workflow priority. Our scheduler comprises of two strategies, a hybrid priority rule inspired by production planning ideas, and a task splitting rule based on a convex task processing time curve for the parallelism level. Using simulation, we demonstrate that our algorithm (1) produces an efficient balance of weighted workflow completion time and resource utilization and (2) outperforms deterministic parallelism.

References

[1]

Kunal Agrawal, Yuxiong He, Wen Jing Hsu, and Charles E Leiserson. 2006. Adaptive scheduling with parallelism feedback. In Proceedings of the eleventh ACM SIGPLAN symposium on Principles and practice of parallel programming. 100--109.

Digital Library

[2]

Kunal Agrawal, Jing Li, Kefu Lu, and Benjamin Moseley. 2016. Scheduling parallel DAG jobs online to minimize average flow time. In Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms. SIAM, 176--189.

[3]

Savaş Balin. 2011. Parallel machine scheduling with fuzzy processing times using a robust genetic algorithm and simulation. Information Sciences 181, 17 (2011), 3551--3569.

Digital Library

[4]

Angel Beltre, Pankaj Saha, and Madhusudhan Govindaraju. 2019. Kubesphere: An approach to multi-tenant fair scheduling for kubernetes clusters. In 2019 IEEE Cloud Summit. IEEE, 14--20.

[5]

David Bernstein. 2014. Containers and Cloud: From LXC to Docker to Kubernetes. IEEE Cloud Computing 1, 3 (2014), 81--84.

[6]

Nigel Brown. 2017. Scheduling Services on a Docker Swarm Mode Cluster. https://semaphoreci.com/community/tutorials/scheduling-services-on-a-docker-swarm-mode-cluster. [Online; accessed 02-June-2022].

[7]

Mukoe Cheong, Hyunsung Lee, Ikjun Yeom, and Honguk Woo. 2019. SCARL: Attentive reinforcement learning-based scheduling in a multi-resource heterogeneous cluster. IEEE Access 7 (2019), 153432--153444.

[8]

Tsui-Ping Chung and Feng Chen. 2019. A complete immunoglobulin-based artificial immune system algorithm for two-stage assembly flow-shop scheduling problem with part splitting and distinct due windows. International Journal of Production Research 57, 10 (2019), 3219--3237.

[9]

Satyaki Ghosh Dastidar and Rakesh Nagi. 2007. Batch splitting in an assembly scheduling environment. International Journal of Production Economics 105, 2 (2007), 372--384.

[10]

Docker. 2021. What is a Container? https://www.docker.com/resources/what-container/. [Online; accessed 05-June-2022].

[11]

Ali Ekici, Özlem Ergun, Pinar Keskinocak, and Michail G Lagoudakis. 2010. Optimal job splitting on a multi-slot machine with applications in the printing industry. Naval Research Logistics (NRL) 57, 3 (2010), 237--251.

[12]

Isaac Klop. 2018. Containerized Workflow Scheduling. (2018).

[13]

Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, and Mohammad Alizadeh. 2019. Learning scheduling algorithms for data processing clusters. In Proceedings of the ACM Special Interest Group on Data Communication. 270--288.

[14]

Taeho Park, Taehyung Lee, and Chang Ouk Kim. 2012. Due-date scheduling on parallel machines with job splitting and sequence-dependent major/minor setup times. The International Journal of Advanced Manufacturing Technology 59, 1 (2012), 325--333.

[15]

Sheldon M Ross. 2014. Introduction to probability models. Academic press.

Digital Library

[16]

César A Sáenz-Alanís, M Angélica Salazar-Aguilar, and Vincent Boyer. 2016. A parallel machine batch scheduling problem in a brewing company. The International Journal of Advanced Manufacturing Technology 87, 1 (2016), 65--75.

[17]

Chenjie Wang, Changchun Liu, Zhi-hai Zhang, and Li Zheng. 2016. Minimizing the total completion time for parallel machine scheduling with job splitting and learning. Computers & Industrial Engineering 97 (2016), 170--182.

Digital Library

[18]

Jingyang Xu and Rakesh Nagi. 2013. Identical parallel machine scheduling to minimise makespan and total weighted completion time: a column generation approach. International Journal of Production Research 51, 23--24 (2013), 7091--7104.

Cited By

Shan CGao RHan QLiu TYang ZZhang JXia Y(2025)KCES: A Workflow Containerization Scheduling Scheme Under Cloud-Edge Collaboration FrameworkIEEE Internet of Things Journal10.1109/JIOT.2024.346623112:2(2026-2042)Online publication date: 15-Jan-2025
https://doi.org/10.1109/JIOT.2024.3466231
Agnihotri PKoldehofe BStiegele PHeinrich RBinnig CLuthra M(2024)ZeroTune: Learned Zero-Shot Cost Models for Parallelism Tuning in Stream Processing.2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00163(2040-2053)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00163

Index Terms

Cloud-native workflow scheduling using a hybrid priority rule and dynamic task parallelism

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Information & Contributors

Information

Published In

cover image ACM Conferences

SoCC '22: Proceedings of the 13th Symposium on Cloud Computing

November 2022

574 pages

ISBN:9781450394147

DOI:10.1145/3542929

General Chair:
Ada Gavrilovska
Georgia Institute of Technology
,
Program Chairs:
Deniz Altınbüken
Google Research
,
Carsten Binnig
TU Darmstadt

Copyright © 2022 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 07 November 2022

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SoCC '22

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SoCC '22: ACM Symposium on Cloud Computing

November 7 - 11, 2022

California, San Francisco

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Overall Acceptance Rate 169 of 722 submissions, 23%

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Cited By

Shan CGao RHan QLiu TYang ZZhang JXia Y(2025)KCES: A Workflow Containerization Scheduling Scheme Under Cloud-Edge Collaboration FrameworkIEEE Internet of Things Journal10.1109/JIOT.2024.346623112:2(2026-2042)Online publication date: 15-Jan-2025
https://doi.org/10.1109/JIOT.2024.3466231
Agnihotri PKoldehofe BStiegele PHeinrich RBinnig CLuthra M(2024)ZeroTune: Learned Zero-Shot Cost Models for Parallelism Tuning in Stream Processing.2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00163(2040-2053)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00163

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