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Pattern learning for scheduling microservice workflow to cloud containers

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Abstract

Patterns are crucial for efficiently scheduling microservice workflow applications to containers in cloud computing scenarios. However, it is challenging to learn patterns of microservice workflows because of their complex precedence constrained structures provided by users with more lightweighted, diversified, and personalized services. In this paper, we propose a graph neural network is designed to identify patterns within a set of microservice workflows by mining the common substructures of workflows. Based on the learned patterns, a pattern-based scheduling algorithm framework is developed for microservice workflows with soft deadline constraints to minimize the average tardiness. A sorting strategy is introduced based on urgency and pattern coverage rate. For simplification of the task sorting process, the pattern-based task sorting algorithm (PB-TS) is devised. Furthermore, a resource selection phase is incorporated to the pattern-based resource selection algorithm (PB-RS) to minimize the candidate resource space. Experimental results demonstrate the proposed method is much efficient as compared to three classical algorithms.

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Data availability

Some data used in this study were obtained from the publicly accessible data (cluster-trace-v2018) described in Sect. 5. Other data are available from the author, please contact vinson@seu.edu.cn.

References

  1. Fowler M, Lewis J (2014) Microservices a definition of this new architectural term. http://martinfowler.com/articles/microservices.html [Online]

  2. Roig EB (2017) Building microservices

  3. Wu Q, Ishikawa F, Zhu Q, Xia Y, Wen J (2017) Deadline-constrained cost optimization approaches for workflow scheduling in clouds. IEEE Trans Parallel Distrib Syst PP(12), 1–1

  4. Kurhinen H et al (2014) Developing microservice-based distributed workflow engine

  5. Balalaie A, Heydarnoori A, Jamshidi P (2016) Microservices architecture enables devlops: migration to a cloud-native architecture. IEEE Softw 33(3):42–52

    Article  Google Scholar 

  6. Wang S, Ding Z, Jiang C (2021) Elastic scheduling for microservice applications in clouds. IEEE Transactions on Parallel and Distributed Systems 32(1):98–115

    Article  Google Scholar 

  7. Adam O, Lee YC, Zomaya AY (2017) Stochastic resource provisioning for containerized multi-tier web services in clouds. IEEE Transactions on Parallel and Distributed Systems 28(7):2060–2073

    Article  Google Scholar 

  8. Venumadhav A (2013) A survey of various workflow scheduling algorithms in cloud environment. Ijsrp Org 22(8):1483–1496

    Google Scholar 

  9. Topcuoglu H, Hariri S, Wu M-y (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE transactions on parallel and distributed systems 13(3), 260–274

  10. Arabnejad H, Barbosa JG (2014) List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Transactions on Parallel and Distributed Systems 25(3):682–694

    Article  Google Scholar 

  11. Kanemitsu H, Hanada M, Nakazato H (2016) Clustering-based task scheduling in a large number of heterogeneous processors. IEEE Press

  12. Nirmala, SJ, Setlur AR, Singh HS, Khoriya S (2018) An Efficient Fault Tolerant Workflow Scheduling Approach using Replication Heuristics and Checkpointing in the Cloud

  13. Żotkiewicz M, Guzek M, Kliazovich D, Bouvry P (2016) Minimum dependencies energy-efficient scheduling in data centers. IEEE Transactions on Parallel and Distributed Systems 27(12):3561–3574

    Article  Google Scholar 

  14. Yu J, Buyya R, Tham CK (2005) Cost-based scheduling of scientific workflow applications on utility grids. In: e-Science and Grid Computing, 2005. First International Conference On, p. 8. Ieee

  15. Yuan Y, Li X, Wang Q, Zhu X (2009) Deadline division-based heuristic for cost optimization in workflow scheduling. Information Sciences 179(15):2562–2575

    Article  Google Scholar 

  16. Cai Z, Li X, Gupta JN (2013) Critical path-based iterative heuristic for workflow scheduling in utility and cloud computing. In: International Conference on Service-Oriented Computing, pp. 207–221. Springer

  17. Abrishami S, Naghibzadeh M, Epema DH (2012) Cost-driven scheduling of grid workflows using partial critical paths. IEEE Transactions on Parallel and Distributed Systems 23(8):1400–1414

    Article  Google Scholar 

  18. Abrishami S, Naghibzadeh M, Epema D (2013) Deadline-constrained workflow scheduling algorithms for infrastructure as a service clouds. Future Generation Computer Systems 29(1):158–169

    Article  Google Scholar 

  19. Rodriguez MA, Buyya R (2014) Deadline based resource provisioningand scheduling algorithm for scientific workflows on clouds. IEEE transactions on cloud computing 2(2):222–235

    Article  Google Scholar 

  20. Chen Z-G, Zhan Z-H, Li H-H, Du K-J, Zhong J-H, Foo YW, Li Y, Zhang J (2015) Deadline constrained cloud computing resources scheduling through an ant colony system approach. In: Cloud Computing Research and Innovation (ICCCRI), 2015 International Conference On, pp. 112–119. IEEE

  21. Chen Z-G, Du K-J, Zhan Z-H, Zhang J (2015) Deadline constrained cloud computing resources scheduling for cost optimization based on dynamic objective genetic algorithm. In: Evolutionary Computation (CEC), 2015 IEEE Congress On, pp. 708–714. IEEE

  22. Li H, Wang D, Zhou M, Fan Y, Xia Y (2022) Multi-swarm co-evolution based hybrid intelligent optimization for bi-objective multi-workflow scheduling in the cloud. IEEE Transactions on Parallel and Distributed Systems 33(9):2183–2197. https://doi.org/10.1109/TPDS.2021.3122428

    Article  Google Scholar 

  23. Melnik M, Nasonov D (2019) Workflow scheduling using neural networks and reinforcement learning. Procedia Computer Science 156:29–36

    Article  Google Scholar 

  24. Kintsakis AM, Psomopoulos FE, Mitkas PA (2019) Reinforcement learning based scheduling in a workflow management system. Engineering Applications of Artificial Intelligence 81(MAY):94–106

    Article  Google Scholar 

  25. Cui D, Ke W, Peng Z, Zuo J (2016) Multiple dags workflow scheduling algorithm based on reinforcement learning in cloud computing. In: International Symposium on Intelligence Computation and Applications

  26. Jiahao W, Zhiping P, Delong C, Qirui L, Jieguang H (2018) A multi-object optimization cloud workflow scheduling algorithm based on reinforcement learning

  27. Ma G, Ahmed NK, Willke TL, Yu PS (2021) Deep graph similarity learning: a survey. Springer US (3)

  28. Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs

  29. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks

  30. Shanthamallu US, Thiagarajan JJ, Song H, Spania, A (2019) Gramme: Semisupervised learning using multilayered graph attention models. IEEE Transactions on Neural Networks and Learning Systems PP(99), 1–12

  31. Velikovi P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2017) Graph attention networks

  32. Xu K, Hu W, Leskovec J, Jegelka S (2018) How powerful are graph neural networks?

  33. Sun Z, Wang H, Wang H, Shao B, Li J (2012) Efficient subgraph matching on billion node graphs. VLDB Endowment

  34. Wang D, Peng C, Zhu W (2016) Structural deep network embedding. In: Acm Sigkdd International Conference on Knowledge Discovery & Data Mining

  35. Li Y, Gu C, Dullien T, Vinyals O, Kohli P (2019) Graph Matching Networks for Learning the Similarity of Graph Structured Objects

  36. Thönes J (2015) Microservices. IEEE Software 32(1):116–116

    Article  Google Scholar 

  37. Newman S (2015) Building Microservices: Designing Fine-grained Systems. “ O’Reilly Media, Inc.”, ???

  38. Sill A (2016) The design and architecture of microservices. IEEE Cloud Computing 3(5):76–80

    Article  Google Scholar 

  39. Fazio M, Celesti A, Ranjan R, Liu C, Chen L, Villari M (2016) Open issues in scheduling microservices in the cloud. IEEE Cloud Computing 3(5):81–88

    Article  Google Scholar 

  40. Zheng C, Thain D (2015) Integrating containers into workflows: A case study using makeflow, work queue, and docker. In: Proceedings of the 8th International Workshop on Virtualization Technologies in Distributed Computing, pp. 31–38. ACM

  41. Gerlach W, Tang W, Keegan K, Harrison T, Wilke A, Bischof J, DSouza M, Devoid S, Murphy-Olson D, Desai N (2014) Skyport-container-based execution environment management for multi-cloud scientific workflows. In: Data-Intensive Computing in the Clouds (DataCloud), 2014 5th International Workshop On, pp. 25–32. IEEE

  42. Gerlach W, Tang W, Wilke A, Olson D, Meyer F (2015) Container orchestration for scientific workflows. In: Cloud Engineering (IC2E), 2015 IEEE International Conference On, pp. 377–378. IEEE

  43. Bhamare D, Samaka M, Erbad A, Jain R, Gupta L, Chan HA (2017) Multi-objective scheduling of micro-services for optimal service function chains. In: Communications (ICC), 2017 IEEE International Conference On, pp. 1–6. IEEE

  44. Amaral M, Polo J, Carrera D, Mohomed I, Unuvar M, Steinder M (2015)Performance evaluation of microservices architectures using containers. In: Network Computing and Applications (NCA), 2015 IEEE 14th International Symposium On, pp. 27–34 . IEEE

  45. Tihfon GM, Park S, Kim J, Kim Y-M (2016) An efficient multi-task paas cloud infrastructure based on docker and aws ecs for application deployment. Cluster Computing 19(3):1585–1597

    Article  Google Scholar 

  46. Peinl R, Holzschuher F, Pfitzer F (2016) Docker cluster management for the cloud-survey results and own solution. Journal of Grid Computing 14(2):265–282

    Article  Google Scholar 

  47. Liu A, Gao M, Tang J (2023) Multi-mode instance-intensive workflow task batch scheduling in containerized hybrid cloud. IEEE Transactions on Cloud Computing 1–15. https://doi.org/10.1109/TCC.2023.3344194

  48. Yu X, Wu W, Wang Y (2023) Integrating cognition cost with reliability qos for dynamic workflow scheduling using reinforcement learning. IEEE Transactions on Services Computing 16(4):2713–2726. https://doi.org/10.1109/TSC.2023.3253182

    Article  Google Scholar 

  49. Li Z, Yu H, Fan G, Zhang J (2023) Cost-efficient fault-tolerant workflow scheduling for deadline-constrained microservice-based applications in clouds. IEEE Transactions on Network and Service Management 20(3):3220–3232. https://doi.org/10.1109/TNSM.2023.3241450

    Article  Google Scholar 

  50. AL-Naday M, Karagiannis V, De Block T, Volckaert B (2023) Federated scheduling of fog-native applications over multi-domain edge-to-cloud ecosystem. In: 2023 19th International Conference on Network and Service Management (CNSM), pp. 1–7 https://doi.org/10.23919/CNSM59352.2023.10327839

  51. Li W, Li X, Ruiz R (2021) Scheduling microservice-based workflows to containers in on-demand cloud resources. In: 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

  52. Hoenisch P, Weber I, Schult, S, Zhu L, Fekete A (2015) Four-fold auto-scaling on a contemporary deployment platform using docker containers. In: International Conference on Service-Oriented Computing, pp. 316–323. Springer

  53. Wu H, Hua X, Li Z, Ren S (2016) Resource and instance hour minimization for deadline constrained dag applications using computer clouds. IEEE Transactions on Parallel and Distributed Systems 27(3):885–899

    Article  Google Scholar 

  54. Ming M, Humphrey M (2011) Auto-scaling to minimize cost and meet application deadlines in cloud workflows. In: Conference on High Performance Computing Networking, Storage and Analysis, SC 2011, Seattle, WA, USA, November 12-18, 2011

  55. Rimal BP, Maier M (2017) Workflow scheduling in multi-tenant cloud computing environments. IEEE Transactions on Parallel and Distributed Systems 28(1):290–304

    Article  Google Scholar 

  56. Bao L, Wu C, Bu X, Ren N, Shen M (2019) Performance modeling and workflow scheduling of microservice-based applications in clouds. IEEE Transactions on Parallel and Distributed Systems, 2114–2129

  57. Gan Y, Jackson B, Hu K, Pancholi M, Ritchken B (2019) An open-source benchmark suite for microservices and their hardware-software implications for cloud & edge systems. In: the Twenty-Fourth International Conference

  58. Sriraman A, Wenisch TF (2018) \(\mu\) suite: A benchmark suite for microservices. In: 2018 IEEE International Symposium on Workload Characterization (IISWC)

  59. Zhou X, Peng X, Xie T, Sun J, Xu C, Ji C, Zhao W Poster: Benchmarking microservice systems for software engineering research. In: IEEE/ACM International Conference on Software Engineering: Companion

  60. Hakim AR, Fithriani I, Novita M (2021) Properties of burr distribution and its application to heavy-tailed survival time data. Journal of Physics Conference Series 1725:012016

    Article  Google Scholar 

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Acknowledgements

This work is supported by the National Key Research and Development Program of China (No. 2022YFB3305500), the National Natural Science Foundation of China (Nos. 62273089, 62102080), Natural Science Foundation of Jiangsu Province (No. BK20210204), and Collaborative Innovation Center of Wireless Communications Technology.

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  1. School of Computer Science and Engineering, Southeast University, Nanjing, 211189, Jiangsu, China

    Wenzheng Li, Xiaoping Li & Long Chen

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Li, W., Li, X. & Chen, L. Pattern learning for scheduling microservice workflow to cloud containers. Int. J. Mach. Learn. & Cyber. 15, 3701–3714 (2024). https://doi.org/10.1007/s13042-024-02115-5

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