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A Cost-Efficient Workflow as a Service Broker Using On-demand and Spot Instances

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Abstract

In recent times, cloud computing has become a popular platform for running various applications, with workflows being one of the most common types. However, efficient execution of workflows on cloud resources requires expertise in resource management techniques. Moreover, combining and running workflows from different users can be more cost-effective due to enhanced resource utilization. Therefore, a workflow broker system is necessary to act as an intermediary between users and cloud providers. Although workflow brokers have been extensively studied in recent years, maximizing profits for both brokers and users by utilizing different pricing models has not been adequately addressed. This paper proposes a workflow broker that uses a combination of on-demand and spot instances to minimize workflow execution costs while adhering to deadline constraints. The proposed system classifies resources into multiple classes based on their reliability, with on-demand resources being the most reliable and spot instances classified by their maximum price. The system assigns workflows’ tasks to resource classes based on their criticality, determined by their slack time. Furthermore, to reduce virtual machine provisioning delay, we have utilized container technology to execute workflow tasks. This was achieved by provisioning large virtual machines and executing multiple containerized tasks on each virtual machine. Three different pricing policies are also proposed in this work with the aim of ensuring the broker’s profit, while also offering a reasonable discount to the users. Simulation results indicate that the proposed system not only reduces final costs but also provides higher broker profits and executes more workflows within the deadline compared to previous works.

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Data Availibility

The workflow datasets analysed during the current study are generated using Pegasus workflow generator available in https://confluence.pegasus.isi.edu/display/pegasus/WorkflowGenerator

References

  1. Yi, S., Kondo, D., Andrzejak, A.: Reducing costs of spot instances via checkpointing in the amazon elastic compute cloud. In: 2010 IEEE 3rd International Conference on Cloud Computing, pp. 236–243 (2010)

  2. Bharathi, S., Chervenak, A., Deelman, E., Mehta, G., Su, M.-H., Vahi, K.: Characterization of scientific workflows. In: 2008 Third Workshop on Workflows in Support of Large-scale Science, IEEE pp. 1–10 (2008)

  3. A.Rodriguez, M., Buyya, R.: Scheduling dynamic workloads in multitenant scientific workflow as a service platforms. Future Gener Comput Syst 79, 739–750 (2018)

  4. Hogan, M., Liu, F., Sokol, A., Tong, J.: Nist cloud computing standards roadmap. NIST Spec Publ 35, 6–11 (2013)

    Google Scholar 

  5. Wang, J., Korambath, P., Altintas, I., Davis, J., Crawl, D.: Workflow as a service in the cloud: Architecture and scheduling algorithms. Procedia Comput Sci 29, 546–556 (2014)

    Article  Google Scholar 

  6. Étienne Michon, Gossa, J., Genaud, S., Unbekandt, L., Kherbache, V.: Schlouder: A broker for iaas clouds. Future Gener Comput Syst 69, 11–23 (2017)

  7. Amazon EC2. https://aws.amazon.com/ec2

  8. Amazon Spot Instances. https://aws.amazon.com/blogs/compute/new-amazon-ec2-spot-pricing/

  9. Lin, L., Pan, L., Liu, S.: Methods for improving the availability of spot instances: A survey. Computers in Ind 141, 103718 (2022)

    Article  Google Scholar 

  10. Poola, D., Ramamohanarao, K., Buyya, R.: Fault-tolerant workflow scheduling using spot instances on clouds. Procedia Comput Sci 29, 523–533 (2014)

    Article  Google Scholar 

  11. Vinay, K., Kumar, S.M.D., Raghavendra, S., KR, V.: Cost and faulttolerant aware resource management for scientific workflows using hybrid instances on clouds. Multimed Tools Appl 77(8), 10171–10193 (2018)

  12. Zhou, A.C., He, B., Liu, C.: Monetary cost optimizations for hosting workflow-as-a-service in iaas clouds. IEEE Trans Cloud Comput 4(1), 34–48 (2016)

    Article  Google Scholar 

  13. Sampaio, A.M., Barbosa, J.G.: Constructing reliable computing environments on top of amazon ec2 spot instances. Algorithms 13(8) (2020). https://doi.org/10.3390/a13080187

  14. Piraghaj, S.F., Dastjerdi, A.V., Calheiros, R.N., Buyya, R.: Container cloudsim: An environment for modeling and simulation of containers in cloud data centers. Softw Pract Exp 47(4), 505–521 (2017)

    Article  Google Scholar 

  15. Ye, L., Xia, Y., Yang, L., Yan, C.: Shws: Stochastic hybrid workflows dynamic scheduling in cloud container services. IEEE Trans Autom Sci Eng 1–17 (2021)

  16. Rajasekar, P., Palanichamy, Y.: Scheduling multiple scientific workflows using containers on iaas cloud. J Ambient Intell Humaniz Comput 12, 7621–7636 (2021)

    Article  Google Scholar 

  17. Saeedizade, E., Ashtiani, M.: Ddbws: a dynamic deadline and budget-aware workfow scheduling algorithm in workfow-as-a-service environments. J Supercomput 77(12), 14525–14564 (2021)

    Article  Google Scholar 

  18. Tarafdar, A., Karmakar, K., Das, R.K., Khatua, S.: Multi-criteria scheduling of scientific workflows in the workflow as a service platform. Comput Electr Eng 105, 108458 (2023). https://doi.org/10.1016/j.compeleceng.2022.108458

    Article  Google Scholar 

  19. Zolfaghari, B., Abrishami, S.: A multi-class workflow ensemble management system using on-demand and spot instances in cloud. Future Gener Comput Syst 137, 97–110 (2022)

    Article  Google Scholar 

  20. Liu, J., Ren, J., Dai, W., Zhang, D., Zhou, P., Zhang, Y., Min, G., Najjari, N.: Online multi-workflow scheduling under uncertain task execution time in iaas clouds. J Syst Archit 9(3), 1180–1194 (2021)

    Google Scholar 

  21. Ma, X., Xu, H., Gao, H., Bian, M.: Real-time multiple-workflow scheduling in cloud environments. IEEE Trans Netw Service Manag 18(4) (2021)

  22. Adhikari, M., Koley, S.: Cloud computing: A multi-workflow scheduling algorithm with dynamic reusability. Arab J Sci Eng 43, 645–660 (2018)

    Article  Google Scholar 

  23. Chakravarthi, K.K., Shyamala, L.: Topsis inspired budget and deadline aware multi-workflow scheduling for cloud computing. J Syst Archit 114, 101916 (2021)

    Article  Google Scholar 

  24. Arabnejad, V., Bubendorfer, K., Ng, B.: Dynamic multi-workflow scheduling: A deadline and cost-aware approach for commercial clouds. Future Gener Comput Syst 100, 98–108 (2019)

  25. Ma, X., Xu, H., Gao, H., Bian, M.: Real-time multiple-workflow scheduling in cloud environments. IEEE Trans Netw Service Manag 18(4), 4002–4018 (2021). https://doi.org/10.1109/TNSM.2021.3125395

    Article  Google Scholar 

  26. Chen, H., Zhu, X., Liu, G., Pedrycz, W.: Uncertainty-aware online scheduling for real-time workflows in cloud service environment. IEEE Trans Services Comput 14(4), 1167–1178 (2021). https://doi.org/10.1109/TSC.2018.2866421

  27. Gerlach, W., Tang, W., Keegan, K., Harrison, T., Wilke, A., Bischof, J., D’Souza, M., et al.: Skyport: container-based execution environment management for multi-cloud scientific workflows. In: 2014 5th International Workshop on Data-Intensive Computing in the Clouds, 79, pp. 25–32 (2014)

  28. Filgueira, R., Silva, R.F.D., Krause, A., Deelman, E., Atkinson, M.: Asterism: Pegasus and dispel4py hybrid workflows for data-intensive science. In: 2016 Seventh International Workshop on Data-Intensive Computing in the Clouds (DataCloud), pp. 1–8 (2016)

  29. Poola, D., Ramamohanarao, K., Buyya, R.: Enhancing reliability of workflow execution using task replication and spot instances. ACM Trans Auton Adapt Syst (TAAS) 10(4), 1–21 (2016)

  30. Chen, L., Li, X., Ruizb, R.: Idle block based methods for cloud workflow scheduling with preemptive and non-preemptive tasks. Future Gener Comput Syst 89, 659–669 (2018)

  31. A.Monge, D., Garino, C.G.: Adaptive spot-instances aware autoscaling for scientific workflows on the cloud. In: In Latin American High Performance Computing Conference, 485. Berlin, Heidelberg, pp. 13–27 (2014)

  32. A.Monge, D., Pacini, E., Mateos, C., Alba, E., Garino, C.G.: Cmi: An online multi-objective genetic autoscaler for scientific and engineering workflows in cloud infrastructures with unreliable virtual machines. J Netw Comput Appl 149(8), 102464 (2020)

  33. Pham, T.-P., Fahringer, T.: Evolutionary multi-objective workflow scheduling for volatile resources in the cloud. IEEE Trans Cloud Comput 1–1 (2020)

  34. Sampaio, A.M., Barbosa, J.G.: Workflow scheduling with amazon ec2 spot instances: Building reliable compute environments. International J Mach Learn Comput 10(1), 140–147 (2020)

  35. Pham, T.-P., Durillo, J.J., Fahringer, T.: Predicting workflow task execution time in the cloud using a two-stage machine learning approach. IEEE Trans Cloud Comput 8(1), 256–268 (2020). https://doi.org/10.1109/TCC.2017.2732344

  36. da Silva, R.F., Juve, G., Rynge, M., Deelman, E., Livny, M.: Online task resource consumption prediction for scientific workflows. Parallel Process Lett 25(03), 1541003 (2015). https://doi.org/10.1142/S0129626415410030

    Article  MathSciNet  Google Scholar 

  37. Rodriguez, M.A., Buyya, R.: A taxonomy and survey on scheduling algorithms for scientific workflows in iaas cloud computing environments. Concurr Comput Pract Experience 29(8), 4041. https://doi.org/10.1002/cpe.4041

  38. Abrishami, S., Naghibzadeh, M., Epema, D.H.J.: Cost-driven scheduling of grid workflows using partial critical paths. IEEE Trans Parallel Distrib Syst 23(8), 1400–1414 (2011)

    Article  Google Scholar 

  39. Cai, Z., Li, X., Ruiz, R., Li, Q.: Price forecasting for spot instances in cloud computing. Future Gener Comput Syst 79, 38–53 (2018)

    Article  Google Scholar 

  40. Calheiros, R.N., Ranjan, R., Beloglazov, A., Rose, C.A.F.D., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Experience 41(1), 23–50 (2011)

  41. Mao, M., Humphrey, M.: A performance study on the vm startup time in the cloud. In: 2012 IEEE Fifth International Conference on Cloud Computing, pp. 423–430 (2012)

  42. Pegasus workflow generator. https://confluence.pegasus.isi.edu/display/pegasus/WorkflowGenerator

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Bahareh Taghavi, Behrooz Zolfaghari & Saeid Abrishami

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  1. Bahareh Taghavi
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  2. Behrooz Zolfaghari
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  3. Saeid Abrishami
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Contributions

All authors contributed in conceptualization and proposing the algorithm. Bahareh Taghavi contributed in implementation and preparing results. The first draft of the manuscript was written by Bahareh Taghavi and Behrooz Zolfaghari and Saeid Abrishami commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Saeid Abrishami.

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Taghavi, B., Zolfaghari, B. & Abrishami, S. A Cost-Efficient Workflow as a Service Broker Using On-demand and Spot Instances. J Grid Computing 21, 40 (2023). https://doi.org/10.1007/s10723-023-09676-9

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