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Reliability Optimization Scheduling and Energy Balancing for Real-Time Application in Fog Computing Environment

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Advanced Parallel Processing Technologies (APPT 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14103))

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Abstract

Fog computing has the characteristics of stronger localized computing power and less data transmission load, thus better meeting the high energy efficiency, reliability, and real-time response requirements required by intelligent connected vehicle technology applications. Currently, research on fog computing task scheduling has become a hot topic, with existing research mainly focusing on low energy consumption or high real-time parallel task scheduling, which cannot meet the high reliability requirements in intelligent connected vehicle scenarios. Therefore, this paper establishes a fog computing task model based on Directed acyclic graph (DAG) to achieve accurate definition of energy, time and reliability. To achieve quantitative optimization of time and reliability indicators under energy constraints, a fog computing task scheduling algorithm was proposed and compared with existing scheduling algorithms. Then, the proposed algorithm is used to solve the DAG task list optimization problem based on fast Fourier transform (FFT) and Gaussian elimination (GE) structure. The experimental results show that compared with the existing ECLL method, ECLLRS has a more significant effect in satisfying the real-time and reliability of the system under the premise of limited energy budget.

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References

  1. Hong, H.-J.: From cloud computing to fog computing: unleash the power of edge and end devices. In: 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 331–334. Hong Kong, China (2017). https://doi.org/10.1109/CloudCom.2017.53

  2. Jindal, R., Kumar, N., Nirwan, H.: MTFCT: a task offloading approach for fog computing and cloud computing. In: 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, pp. 145–149 (2020). https://doi.org/10.1109/Confluence47617.2020.9058209

  3. Garcia, J., Simó, E., Masip-Bruin, X., Marín-Tordera, E., Sánchez-López, S.: Do we really need cloud? estimating the fog computing capacities in the city of Barcelona. In: 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion), Zurich, Switzerland, pp. 290–295 (2018). https://doi.org/10.1109/UCC-Companion.2018.00070

  4. Minh, Q.T., Kamioka, E., Yamada, S.: CFC-ITS: context-aware fog computing for intelligent transportation systems. IT Prof. 20(6), 35–45 (2018). https://doi.org/10.1109/MITP.2018.2876978

    Article  Google Scholar 

  5. Xue, D.: Task offload optimization management of networked vehicles in edge computing environment. In: 2nd International Signal Processing, Communications and Engineering Management Conference (ISPCEM). Montreal, ON, Canada, vol. 2022, pp. 38–42 (2022). https://doi.org/10.1109/ISPCEM57418.2022.00014

  6. Ra, M.-R., Sheth, A., Mummert, L., Pillai, P., Wetherall, D., Govindan, R.: Odessa: enabling interactive perception applications on mobile devices. In: Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, pp. 43–56, Bethesda, Maryland (2011)

    Google Scholar 

  7. Xiao, X., Xie, G., Li, R., Li, K.: Minimizing schedule length ofenergy consumption constrained parallel applications on heterogeneous distributed systems. In: Trustcom/BigDataSE/ISPA, 2016 IEEE, pp. 1471–1476. IEEE (2016)

    Google Scholar 

  8. Niu, J., Liu, C., Gao, Y., Qiu, M.: Energy efficient task assignment with guaranteed probability satisfying timing constraints for embedded systems. IEEE Trans. Parallel Distrib. Syst. 25(8), 2043–2052 (2014)

    Article  Google Scholar 

  9. Naghibzadeh, M.: Modeling and scheduling hybrid workflows of tasks and task interaction graphs on the cloud. Future Generation Comput. Syst. 65, 33–45 (2016)

    Article  Google Scholar 

  10. Tang, Z., Qi, L., Cheng, Z., Li, K., Khan, S.U., Li, K.: An energy-efficient task scheduling algorithm in DVFS-enabled cloud environment. J. Grid Comput. 14(1), 55–74 (2016)

    Article  Google Scholar 

  11. Xie, G., Jiang, J., Liu, Y., Li, R., Li, K.: Minimizing energy consumption of real-time parallel applications using downward and upward approaches on heterogeneous systems. IEEE Trans. Ind. Inform. 13, 108–1078 (2017)

    Google Scholar 

  12. Xie, G., Zeng, G., Li, R., Li, K.: Energy-aware processor merging algorithms for deadline constrained parallel applications in heterogeneous cloud computing. IEEE Trans. Sustain. Comput. 2(2), 62–75 (2017)

    Article  Google Scholar 

  13. Kwak, J., Kim, Y., Lee, J., Chong, S.: DREAM: dynamic resource and task allocation for energy minimization in mobile cloud systems. IEEE J. Sel. Areas Commun. 33(12), 2510–2523 (2015)

    Article  Google Scholar 

  14. Cuervo, E., et al.: Maui: making smartphones last longer with code offload. In: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, San Francisco, CA, USA, pp. 49-62 (2010)

    Google Scholar 

  15. Contini, D., De Castro, L.F.S., Madeira, E., Rigo, S., Bittencourt, L.F.: Simulating smart campus applications in edge and fog computing. In: 2020 IEEE International Conference on Smart Computing (SMARTCOMP), Bologna, Italy, pp. 326–331 (2020). https://doi.org/10.1109/SMARTCOMP50058.2020.00072

  16. Li, K.: Heuristic computation offloading algorithms for mobile users in fog computing. ACM Trans. Embed. Comput. Syst. (TECS) 20(2), 1–28, 11 (2021). Article no. 11

    Google Scholar 

  17. Shatz, S.M., Wang, J.P.: Models and algorithms for reliability-oriented task-allocation in redundant distributed-computer systems. IEEE Trans. Reliab. 38(1), 16–27 (1989)

    Article  Google Scholar 

  18. Liu, J., Li, K., Zhu, D., Han, J., Li, K.: Minimizing cost of scheduling tasks on heterogeneous multicore embedded systems. ACM Trans. Embed. Comput. Syst. (TECS) 16(2), 36 (2016)

    Google Scholar 

  19. Liu, J., Zhuge, Q., Gu, S., Hu, J., Zhu, G., Sha, E.H.M.: Minimizing system cost with efficient task assignment on heterogeneous multicore processors considering time constraint. IEEE Trans. Parallel Distrib. Syst. 25(8), 2101–2113 (2014)

    Article  Google Scholar 

  20. Xie, G., Chen, Y., Liu, Y., Wei, Y., Li, R., Li, K.: Resource consumption cost minimization of reliable parallel applications on heterogeneous embedded systems. IEEE Trans. Ind. Inform. 13(4), 1629–1640 (2016)

    Article  Google Scholar 

  21. Yuan, N., Xie, G., Li, R., Chen, X.: An effective reliability goal assurance method using geometric mean for distributed automotive functions on heterogeneous architectures. In: 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), pp. 667–674 (2017). https://doi.org/10.1109/ISPA/IUCC.2017.00105

  22. Khan, S.M.T., Barik, L., Adholiya, A., Patra, S.S., Brahma, A.N., Barik, R.K.: Task offloading scheme for latency sensitive tasks In: 5G IOHT on Fog Assisted Cloud Computing Environment, 3rd International Conference for Emerging Technology (INCET). Belgaum, India, vol. 2022, pp. 1–5 (2022). https://doi.org/10.1109/INCET54531.2022.9824699

  23. Li, K.: Scheduling precedence constrained tasks for mobile applications in fog computing. IEEE Trans. Serv. Comput. 16, 2153–2164 (2022)

    Google Scholar 

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Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under Grant No. 62002147, and the China Postdoctoral Science Foundation under Grant No. 2020TQ0134. The authors would like to express their gratitude to the anonymous reviewers for their constructive comments, which have helped to improve the quality of the paper.

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Nanchang University, Nanchang, Jiangxi, China

    Ruihua Liu

  2. School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China

    Huijuan Huang, Yulei He, Xiaochuan Guo, Can Yan, Junhao Dai & Wufei Wu

Authors
  1. Ruihua Liu
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  2. Huijuan Huang
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  3. Yulei He
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  4. Xiaochuan Guo
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  5. Can Yan
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  6. Junhao Dai
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  7. Wufei Wu
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Corresponding author

Correspondence to Wufei Wu .

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Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Chao Li

  2. Tsinghua University, Beijing, Beijing, China

    Zhenhua Li

  3. National University of Defense Technology, Nanjing, China

    Li Shen

  4. Shanghai Jiao Tong University, Shanghai, China

    Fan Wu

  5. Nankai University, Tianjin, China

    Xiaoli Gong

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Liu, R. et al. (2024). Reliability Optimization Scheduling and Energy Balancing for Real-Time Application in Fog Computing Environment. In: Li, C., Li, Z., Shen, L., Wu, F., Gong, X. (eds) Advanced Parallel Processing Technologies. APPT 2023. Lecture Notes in Computer Science, vol 14103. Springer, Singapore. https://doi.org/10.1007/978-981-99-7872-4_11

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  • DOI: https://doi.org/10.1007/978-981-99-7872-4_11

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  • Print ISBN: 978-981-99-7871-7

  • Online ISBN: 978-981-99-7872-4

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