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CSADE: a delay-sensitive scheduling method based on task admission and delay evaluation on edge–cloud collaboration

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Abstract

Compared with cloud computing, edge–cloud collaboration can avoid long transmitting delay to cloud since tasks are close to edge, which makes edge–cloud collaboration suitable for delay-sensitive applications. However, the complex environment of edge–cloud poses new challenge to task scheduling. A Collaborative Scheduling strategy based on task Admission and Delay Evaluation (CSADE) is proposed to deal with the challenge and ensure the quality of service (QoS). In order to schedule maximum tasks to edge and guarantee QoS, a new dynamic delay model in resource manager is proposed to accurately estimate the average delay. Based on the average delay and execution time, task evaluator prevents the impossible tasks to avoid waste of resources on both edge and cloud. The scheduling policy in task scheduler fully leverages the conditions of edge, cloud and tasks to guarantee the QoS. The fault-tolerant mechanism would launch and adjust the scheduling strategy when task Scheduling in emergencies and resource node failures. Thus CSADE ensures the QoS for delay-sensitive applications from three levels, i.e., the accurate quantitative system delay in resource manager, the strict task admission evaluation in task evaluator, the edge-first elastic scheduling strategy and the fault-tolerant mechanism in task scheduler. Comparative experimental results on simulated datasets and real datasets verify that CSADE can reduce average delay time and QoS violation rate obviously.

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References

  1. Guim, F., Metsch, T., Moustafa, H., et al.: Autonomous lifecycle management for resource-efficient workload orchestration for green edge computing. IEEE Trans. Green Commun. Netw. 6(1), 571–582 (2022)

    Article  Google Scholar 

  2. Ghosh, A.M., Grolinger, K.: Edge–cloud computing for internet of things data analytics: embedding intelligence in the edge with deep learning. IEEE Trans. Industr. Inf. 17(3), 2191–2200 (2021)

    Google Scholar 

  3. Han, F., Zheng, M., Ling, Q.: An improved multiobjective particle swarm optimization algorithm based on tripartite competition mechanism. Appl. Intell. 52, 5784–5816 (2022)

    Article  Google Scholar 

  4. Li, H., Ota, K., Dong, M.: Learning IoT in edge: deep learning for the internet of things with edge computing. IEEE Netw. 32(1), 96–101 (2018)

    Article  Google Scholar 

  5. Arabnejad, V., Bubendorfer, K., Ng, B.: Budget and deadline aware e-science workflow scheduling in clouds. IEEE Trans. Parallel Distrib. Syst. 30(1), 29–44 (2019)

    Article  Google Scholar 

  6. Li, X., Qian, L., Ruiz, R.: Cloud workflow scheduling with deadlines and time slot availability. IEEE Trans. Serv. Comput. 11(2), 329–340 (2018)

    Article  Google Scholar 

  7. Chen, H., Wen, J., Pedrycz, W., Wu, G.: Big data processing workflows oriented real-time scheduling algorithm using task-duplication in geo-distributed clouds. IEEE Trans. Big Data 6(1), 131–144 (2020)

    Article  Google Scholar 

  8. Chen, C.H., Lin, J.W., Kuo, S.Y.: Mapreduce scheduling for deadline-constrained jobs in heterogeneous cloud computing systems. IEEE Trans. Cloud Comput. 6(1), 127–140 (2018)

    Article  Google Scholar 

  9. Khabbaz, M., Assi, C.M.: Modelling and analysis of a novel deadline-aware scheduling scheme for cloud computing data centers. IEEE Trans. Cloud Comput. 6(1), 141–155 (2018)

    Article  Google Scholar 

  10. Rahman, M., Li, X., Palit, H.: Modeling and analyzing dynamic fault-tolerant strategy for deadline constrained task scheduling in cloud computing. IEEE Trans. Syst. Man Cybern. Syst. 50(4), 1260–1274 (2020)

    Article  Google Scholar 

  11. Reshmi, R., Saravanan, D.: Load prediction using (dog-alms) for resource allocation based on IFP soft computing approach in cloud computing. Soft. Comput. 24, 15307–15315 (2020)

    Article  Google Scholar 

  12. Chen, Y., Zhang, Y., Xia, H., et al.: A hybrid tensor factorization approach for QoS prediction in time-aware mobile edge computing. Appl. Intell. 52, 8056–8072 (2022)

    Article  Google Scholar 

  13. Hu, B., Cao, Z., Zhou, M.C.: Scheduling real-time parallel applications in cloud to minimize energy consumption. IEEE Trans. Cloud Comput. 11, 1–1 (2019)

    Google Scholar 

  14. Arisdakessian, S., Wahab, O.A., Mourad, A., Otrok, H., Kara, N.: Fogmatch: an intelligent multi-criteria IoT-FOG scheduling approach using game theory. IEEE/ACM Trans. Netw. 28(4), 1779–1789 (2020)

    Article  Google Scholar 

  15. Abdel-Basset, M., et al.: Energy-aware marine predators algorithm for task scheduling in IoT-based fog computing applications. IEEE Trans. Industr. Inf. 6, 1–17 (2020)

    Google Scholar 

  16. Li, X., et al.: A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing. IEEE Trans. Industr. Inf. 15(7), 4225–4234 (2019)

    Article  Google Scholar 

  17. Meng, J., Tan, H., Li, X.Y., Han, Z., Li, B.: Online deadline-aware task dispatching and scheduling in edge computing. IEEE Trans. Parallel Distrib. Syst. 31(6), 1270–1286 (2020)

    Article  Google Scholar 

  18. Yi, C., Cai, J., Su, Z.: A multi-user mobile computation offloading and transmission scheduling mechanism for delay-sensitive applications. IEEE Trans. Mob. Comput. 19(1), 29–43 (2020)

    Article  Google Scholar 

  19. Li, Y., et al.: Learning-aided computation offloading for trusted collaborative mobile edge computing. IEEE Trans. Mob. Comput. 8, 1–18 (2019)

    Google Scholar 

  20. Wang, S., et al.: Delay-aware microservice coordination in mobile edge computing: a reinforcement learning approach. IEEE Trans. Mob. Comput. 12, 1–16 (2019)

    Google Scholar 

  21. Tuli, S., Ilager, S., Ramamohanarao, K., et al.: Dynamic scheduling for stochastic edge–cloud computing environments using A3C learning and residual recurrent neural networks. IEEE Trans. Mob. Comput. 21(3), 940–954 (2022)

    Article  Google Scholar 

  22. Islam, M.T., Karunasekera, S., Buyya, R.: Performance and cost-efficient spark job scheduling based on deep reinforcement learning in cloud computing environments. IEEE Trans. Parallel Distrib. Syst. 33(7), 1695–1710 (2022)

    Article  Google Scholar 

  23. Attiya, I., Elaziz, M.A., Abualigah, L., et al.: An improved hybrid swarm intelligence for scheduling iot application tasks in the cloud. Ptoc. IEEE Trans. Industr. Inf. 18(9), 6264–6272 (2022)

    Article  Google Scholar 

  24. Yuan, H., Zhou, M.C.: Profit-maximized collaborative computation offloading and resource allocation in distributed cloud and edge computing systems. IEEE Trans. Autom. Sci. Eng. 18(3), 1277–1287 (2021)

    Article  Google Scholar 

  25. Yang, R., Yu, F.R., Si, P., et al.: Integrated blockchain and edge computing systems: a survey, some research issues and challenges. IEEE Commun. Surveys Tutor. 21(2), 1508–1532 (2019)

    Article  Google Scholar 

  26. Rodrigues, T. K., Suto, K., Nishiyama, H., et al.: Machine learning meets computation and communication control in evolving edge and cloud: Challenges and future perspective. In: IEEE Communications Surveys and Tutorials (2019)

  27. Lin, C.C., Deng, D.J., Chinh, Y.L., Chiu, H.T.: Smart manufacturing scheduling with edge computing using multiclass deep Q network. IEEE Trans. Industr. Inf. 15(7), 4276–4284 (2019)

    Article  Google Scholar 

  28. Tang, Z., Jia, W., Zhou, X., Yang, W., You., Y.: Representation and reinforcement learning for task scheduling in edge computing. IEEE Trans. Big Data 4, 1–15 (2020)

  29. Kannan, R.S., et al.: Grandslam: guaranteeing slas for jobs in microservices execution frameworks. In: Proceedings of the Fourteenth EuroSys Conference 2019, pp. 1–16 (2019)

  30. Rawajbeh, M.A., Sayenko, V.I., Alhadid, I.H., et al.: Evaluation of functional maturity for a network information service-design and case analysis. Int. J. Ad Hoc Ubiquitous Comput. 38(1–3), 3–16 (2021)

    Article  Google Scholar 

  31. Rawajbeh, M.A.: Performance evaluation of a computer network in a cloud computing environment. ICIC Express Lett. 13, 719–727 (2019)

    Google Scholar 

  32. Alhadid, I., et al.: An intelligent web service composition and resource-optimization method using k-means clustering and knapsack algorithms. Mathematics 9(17), 2023 (2021)

    Article  Google Scholar 

  33. Wang, J., Zhao, L., Liu, J., Kato., N.: Smart resource allocation for mobile edge computing: a deep reinforcement learning approach. IEEE Trans. Emerg. Top. Comput. 3, 1 (2019)

  34. Wang, J., et al.: Edge cloud offloading algorithms: issues, methods, and perspectives. ACM Comput. Surveys (CSUR) 52(1), 1–23 (2019)

    Article  Google Scholar 

  35. Qiu, X., Liu, L., Chen, W., Hong, Z., Zheng., Z.: Online deep reinforcement learning for computation offloading in blockchain-empowered mobile edge computing. IEEE Trans. Veh. Technol. 68(8), 8050–8062 (2019)

  36. Zhang, Y., Tang, B., Luo, J., et al.: Deadline-aware dynamic task scheduling in edge–cloud collaborative computing. Electronics 11(15), 2464 (2022)

    Article  Google Scholar 

  37. Ruan, L., Yan, Y., Guo, S., et al.: Priority-based residential energy management with collaborative edge and cloud computing. Proc. IEEE Trans. Industr. Inf. 16(3), 1848–1857 (2020)

    Article  Google Scholar 

  38. Duan, R., Prodan, R., Li, X.: Multi-user multi-task computation offloading in green mobile edge cloud computing. IEEE Trans. Serv. Comput. 12(5), 726–738 (2019)

    Article  Google Scholar 

  39. Liu, Y., Yu, H., Xie, S., Zhang, Y.: Deep reinforcement learning for offloading and resource allocation in vehicle edge computing and networks. IEEE Trans. Veh. Technol. 68(11), 11158–11168 (2019)

    Article  Google Scholar 

  40. Dinh, T.Q., La, Q.D., Quek, T.Q.S., Shin, H.: Learning for computation offloading in mobile edge computing. IEEE Trans. Commun. 66(12) (2018)

  41. Al-Qerem, A., Alauthman, M., Almomani, A., et al.: IoT transaction processing through cooperative concurrency control on fog-cloud computing environment. Soft. Comput. 24, 5695–5711 (2020)

    Article  Google Scholar 

  42. Wang, J.: Artificial intelligence-based affinity task offloading under resource adjustment in a 5g network. Appl. Intell. 52, 8167–8188 (2022)

    Article  Google Scholar 

  43. Yin, L., Li, P., Luo, J.: Smart contract service migration mechanism based on container in edge computing. J. Parallel Distrib. Comput. 152(9), 157–166 (2021)

    Article  Google Scholar 

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

  45. Gupta, H., Dastjerdi, A.V., Ghosh, S.K., Buyya, R.: iFogsim: a toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software 47(9), 1275–1296 (2017)

  46. Zuo, X., Zhang, G., Tan, W.: Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid IAAS cloud. IEEE Trans. Autom. Sci. Eng. 11(2), 564–573 (2014)

    Article  Google Scholar 

  47. Han, D., Chen, W.: QoS differential scheduling of URLLC under FIFO service discipline: a cross-layer approach. IEEE Wirel. Commun. Lett. 9(9), 1370–1373 (2020)

    Article  Google Scholar 

  48. Jawade, P., Borkar, G. M., Ramachandram, S.: Confinement forest-based enhanced min-min and max-min technique for secure multicloud task scheduling. Trans. Emerg. Telecommun. Technol. e4515 (2022)

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Acknowledgements

This work was supported by Guangdong Basic and Applied Basic Research Foundation, China under Project (Project No. 2023A1515012874, 2020A1515010727), Guangdong Province Special Project (Project No. 2021S0053), Maoming City Science and Technology Plan Project (Project No.2020500), National Natural Science Foundation of China (Project No.61973094).

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Authors and Affiliations

  1. Guangdong University of Petrochemical Technology, Maoming, China

    Liyun Zuo, Jieguang He & Lei Zhang

  2. Shandong University, Jinan, China

    Yonghui Xu

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  1. Liyun Zuo
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Liyun Zuo wrote the main manuscript text, Lei Zhang modified the manuscript text, and she is the corresponding author. All authors reviewed the manuscript.

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Zuo, L., He, J., Xu, Y. et al. CSADE: a delay-sensitive scheduling method based on task admission and delay evaluation on edge–cloud collaboration. Cluster Comput 27, 1541–1558 (2024). https://doi.org/10.1007/s10586-023-04017-7

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  • DOI: https://doi.org/10.1007/s10586-023-04017-7

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