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EQGSA-DPW: A Quantum-GSA Algorithm-Based Data Placement for Scientific Workflow in Cloud Computing Environment

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Abstract

The processing of scientific workflow (SW) in geo-distributed cloud computing holds significant importance in the placement of massive data between various tasks. However, data movement across storage services is a main concern in the geo-distributed data centers, which entails issues related to the cost and energy consumption of both storage services and network infrastructure. Aiming to optimize data placement for SW, this paper proposes EQGSA-DPW a novel algorithm leveraging quantum computing and swarm intelligence optimization to intelligently reduce costs and energy consumption when a SW is processed in multi-cloud. EQGSA-DPW considers multiple objectives (e.g., transmission bandwidth, cost and energy consumption of both service and communication) and improves the GSA algorithm by using the log-sigmoid transfer function as a gravitational constant G and updating agent position by quantum rotation angle amplitude for more diversification. Moreover, to assist EQGSA-DPW in finding the optima, an initial guess is proposed. The performance of our EQGSA-DPW algorithm is evaluated via extensive experiments, which show that our data placement method achieves significantly better performance in terms of cost, energy, and data transfer than competing algorithms. For instance, in terms of energy consumption, EQGSA-DPW can on average achieve up to \(25\%\), \(14\%\), and \(40\%\) reduction over that of GSA, PSO, and ACO-DPDGW algorithms, respectively. As for the storage services cost, EQGSA-DPW values are the lowest.

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References

  1. Liu, J., Pacitti, E., Valduriez, P., Mattoso, M.: A survey of data-intensive scientific workflow management. J. of Grid. Comp. 13, 457–493 (2015)

    Article  Google Scholar 

  2. Bousselmi, K., Brahmi, Z., Gammoudi, M.M.: Energy efficient partitioning and scheduling approach for scientific workflows in the cloud. In: 2016 IEEE International Conference on Services Computing (SCC), pp. 146–154 (2016). IEEE

  3. Pierson, J.-M.: Large-Scale Distributed Systems and Energy Efficiency: A Holistic View, pp. 1–312 (2015). https://doi.org/10.1002/9781118981122

  4. Yang, T., Pen, H., Li, W., Zomaya, A.Y.: An energy-efficient virtual machine placement and route scheduling scheme in data center networks. Future Gener. Comp. Syst. 77, 1–11 (2017)

    Article  Google Scholar 

  5. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: Gsa: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)

    Article  Google Scholar 

  6. Choudhary, A., Gupta, I., Singh, V., Jana, P.K.: A gsa based hybrid algorithm for bi-objective workflow scheduling in cloud computing. Future Gener. Comp. Syst. 83, 14–26 (2018)

    Article  Google Scholar 

  7. Ji, J., Gao, S., Wang, S., Tang, Y., Yu, H., Todo, Y.: Self-adaptive gravitational search algorithm with a modified chaotic local search. Ieee Access. 5, 17881–17895 (2017)

    Article  Google Scholar 

  8. Soleimanpour-Moghadam, M., Nezamabadi-Pour, H., Farsangi, M.M.: A quantum inspired gravitational search algorithm for numerical function optimization. Inf. Sci. 267, 83–100 (2014)

    Article  MathSciNet  Google Scholar 

  9. Nezamabadi-Pour, H.: A quantum-inspired gravitational search algorithm for binary encoded optimization problems. Eng. Appl. Artif. Intell. 40, 62–75 (2015)

    Article  Google Scholar 

  10. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: Bgsa: binary gravitational search algorithm. Nat. Comp. 9, 727–745 (2010)

    Article  MathSciNet  Google Scholar 

  11. Gao, Y., Li, K., **, Y.: Compact, popularity-aware and adaptive hybrid data placement schemes for heterogeneous cloud storage. IEEE Access. 5, 1306–1318 (2017)

    Article  Google Scholar 

  12. Wu, X., Liu, Y., Chen, C.: Aco-dpdgw: an ant colony optimization algorithm for data placement of data-intensive geospatial workflow. Earth. Sci. Inf. 12, 641–658 (2019)

    Article  Google Scholar 

  13. Du, X., Tang, S., Lu, Z., Gai, K., Wu, J., Hung, P.C.: Optimal data placement for data-sharing scientific workflows in heterogeneous edge-cloud computing environments. ar**v:2104.06274. (2021)

  14. Kchaou, H., Kechaou, Z., Alimi, A.M.: A pso task scheduling and it2fcm fuzzy data placement strategy for scientific cloud workflows. J. Comput. Sci. 64 (2022)

  15. Ferdaus, M.H., Murshed, M., Calheiros, R.N., Buyya, R.: An algorithm for network and data-aware placement of multi-tier applications in cloud data centers. J. Net. Comp. Appl. 98, 65–83 (2017)

    Article  Google Scholar 

  16. Xu, X., Fu, S., Qi, L., Zhang, X., Liu, Q., He, Q., Li, S.: An iot-oriented data placement method with privacy preservation in cloud environment. J. Net. Comp. Appl. 124, 148–157 (2018)

    Article  Google Scholar 

  17. Khalajzadeh, H., Yuan, D., Grundy, J., Yang, Y.: Cost-effective social network data placement and replication using graph-partitioning. In: 2017 IEEE International Conference on Cognitive Computing (ICCC), pp. 64–71 (2017). IEEE

  18. Chen, Z., Hu, J., Min, G., Chen, X.: Effective data placement for scientific workflows in mobile edge computing using genetic particle swarm optimization. Concurr. Comput: Prac. Exp. 33 (2019) https://doi.org/10.1002/cpe.5413

  19. Chen, Z., Lin, K., Lin, B., Chen, X., Zheng, X., Rong, C.: Adaptive resource allocation and consolidation for scientific workflow scheduling in multi-cloud environments. IEEE Access. 8, 190173–190183 (2020) https://doi.org/10.1109/ACCESS.2020.3032545

  20. Mseddi, A., Salahuddin, M.A., Zhani, M.F., Elbiaze, H., Glitho, R.H.: Efficient replica migration scheme for distributed cloud storage systems. IEEE Trans. Cloud Comp. 9(1), 155–167 (2018)

    Article  Google Scholar 

  21. Chen, Z., Zhao, X., Lin, B.: Fuzzy Theory-Based Data Placement for Scientific Workflows in Hybrid Cloud Environments. Discret. Dyn. Nat. Soc. 2020, 1–13 (2020). https://doi.org/10.1155/2020/8105145

    Article  Google Scholar 

  22. Khalajzadeh, H., Yuan, D., Zhou, B.B., Grundy, J., Yang, Y.: Cost effective dynamic data placement for efficient access of social networks. J. Parallel. Distri. Comp. 141, 82–98 (2020)

    Article  Google Scholar 

  23. Zhao, L., Yang, Y., Munir, A., Liu, A.X., Li, Y., Qu, W.: Optimizing geo-distributed data analytics with coordinated task scheduling and routing. IEEE Trans. Parallel Distri. Syst. 31(2), 279–293 (2019)

    Article  Google Scholar 

  24. Li, C., Liu, J., Wang, M., Luo, Y.: Fault-tolerant scheduling and data placement for scientific workflow processing in geo-distributed clouds. J. Syst. Soft. 187 (2022). https://doi.org/10.1016/j.jss.2022.111227

  25. Bouhouch, L., Zbakh, M., Tadonki, C.: Dynamic data replication and placement strategy in geographically distributed data centers. Concurr. Comput: Prac. Exp. 35(14), 6858 (2023). https://doi.org/10.1002/cpe.6858

    Article  Google Scholar 

  26. Yassir, S., Zbakh, M., Claude, T.: Graph-based model and algorithm for minimising big data movement in a cloud environment. Int. J. High Perform. Comp. Net. 14(3), 365–375 (2019)

    Google Scholar 

  27. Derouiche, R., Brahmi, Z., Gammoundi, M.M., Galan, S.G.: E-dpsiw-fca: Energy aware fca-based data placement strategy for intensive workflow. Scala. Comp: Prac. Exp. 20(3), 541–562 (2019)

    Google Scholar 

  28. Seyyedabbasi, A., Aliyev, R., Kiani, F., Gulle, M.U., Basyildiz, H., Shah, M.A.: Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems. Knowl.-Based Syst. 223 107044 (2021)

  29. Du, X., Tang, S., Lu, Z., Gai, K., Wu, J., Hung, P.C.: Scientific workflows in iot environments: A data placement strategy based on heterogeneous edge-cloud computing. ACM Trans. Manag. Inf. Syst. (TMIS) 13(4), 1–26 (2022)

    Article  Google Scholar 

  30. Zhou, Z., Abawajy, J.H., Chowdhury, M.U., Hu, Z.-G., Li, K., Cheng, H., Elaiwi, A.A.A., Li, F.: Minimizing sla violation and power consumption in cloud data centers using adaptive energy-aware algorithms. Future Gener. Comput. Syst. 86, 836–850 (2017)

    Article  Google Scholar 

  31. Zhou, Z., Shojafar, M., Alazab, M., Abawajy, J., Li, F.: Afed-ef: An energy-efficient vm allocation algorithm for iot applications in a cloud data center. IEEE Trans. Green Commu. Net. 5(2), 658–669 (2021). https://doi.org/10.1109/TGCN.2021.3067309. Publisher Copyright: IEEE Copyright: Copyright 2021 Elsevier B.V., All rights reserved

  32. Zhou, Z., Shojafar, M., Li, R., Tafazolli, R.: Evct: An efficient vm deployment algorithm for a software-defined data center in a connected and autonomous vehicle environment. IEEE Trans. Green Commu. Net. 6(3), 1532–1542 (2022). https://doi.org/10.1109/TGCN.2022.3161423

    Article  Google Scholar 

  33. Kim, Y., Kim, J.-H., Han, K.-H.: Quantum-inspired multiobjective evolutionary algorithm for multiobjective 0/1 knapsack problems. In: 2006 IEEE International Conference on Evolutionary Computation, pp. 2601–2606 (2006). IEEE

  34. Zhang, G.: Quantum-inspired evolutionary algorithms: a survey and empirical study. J. Heuristics. 17(3), 303–351 (2011)

    Article  Google Scholar 

  35. Halliday, D., Resnick, R., Walker, J.: Fundamentals of Physics. John Wiley & Sons, ??? (2013)

  36. Ji, B., Yuan, X., Li, X., Huang, Y., Li, W.: Application of quantum-inspired binary gravitational search algorithm for thermal unit commitment with wind power integration. Energy. Convers. Manag. 87, 589–598 (2014)

    Article  Google Scholar 

  37. Zhao, F., Xue, F., Zhang, Y., Ma, W., Zhang, C., Song, H.: A hybrid algorithm based on self-adaptive gravitational search algorithm and differential evolution. Expert. Syst. Appl. 113, 515–530 (2018)

    Article  Google Scholar 

  38. Lin, B., Zhu, F., Zhang, J., Chen, J., Chen, X., **ong, N.N., Lloret Mauri, J.: A time-driven data placement strategy for a scientific workflow combining edge computing and cloud computing. IEEE Trans. Ind. Inf. 15(7), 4254–4265 (2019). https://doi.org/10.1109/TII.2019.2905659

    Article  Google Scholar 

  39. Tso, F.P., Oikonomou, K., Kavvadia, E., Hamilton, G., Pezaros, D.P.: S-core: Scalable communication cost reduction in data center environments. School of Computing Science, University of Glasgow, Tech. Rep. TR-2013-338. (2013)

  40. Kim, H., Kim, Y.: An adaptive data placement strategy in scientific workflows over cloud computing environments. In: NOMS 2018-2018 IEEE/IFIP Network Operations and Management Symposium, pp. 1–5 (2018). IEEE

  41. Zhou, Z., Shojafar, M., Abawajy, J., Yin, H., Lu, H.: Ecms: An edge intelligent energy efficient model in mobile edge computing. IEEE Trans. Green Commu. Net. 6(1), 238–247 (2022). https://doi.org/10.1109/TGCN.2021.3121961

    Article  Google Scholar 

  42. Cotes-Ruiz, I.T., Prado, R.P., García-Galán, S., Muñoz-Expósito, J.E., Ruiz-Reyes, N.: Dynamic voltage frequency scaling simulator for real workflows energy-aware management in green cloud computing. PLOS ONE. 12(1), 1–30 (2017) https://doi.org/10.1371/journal.pone.0169803

  43. Jiang, S., Wang, Y., Ji, Z.: Convergence analysis and performance of an improved gravitational search algorithm. Appl. Soft Comp. 24, 363–384 (2014)

    Article  Google Scholar 

  44. Zhang, A., Sun, G., Ren, J., Li, X., Wang, Z., Jia, X.: A dynamic neighborhood learning-based gravitational search algorithm. IEEE Trans. Cyber. 48(1), 436–447 (2016)

    Article  Google Scholar 

  45. Zhang, N., Li, C., Li, R., Lai, X., Zhang, Y.: A mixed-strategy based gravitational search algorithm for parameter identification of hydraulic turbine governing system. Knowl-Based Syst. 109, 218–237 (2016)

    Article  Google Scholar 

  46. Wang, Y., Yu, Y., Gao, S., Pan, H., Yang, G.: A hierarchical gravitational search algorithm with an effective gravitational constant. Swarm. Evol. Comp. 46, 118–139 (2019)

    Article  Google Scholar 

  47. Bansal, J.C., Joshi, S.K., Nagar, A.K.: Fitness varying gravitational constant in gsa. Appl. Intell. 48, 3446–3461 (2018)

    Article  Google Scholar 

  48. Hsieh, M.-S., Wu, S.-C.: Modified quantum evolutionary algorithm and self-regulated learning for reactor loading pattern design. Ann. Nucl. Energy. 127, 268–277 (2019)

    Article  Google Scholar 

  49. **ong, H., Wu, Z., Fan, H., Li, G., Jiang, G.: Quantum rotation gate in quantum-inspired evolutionary algorithm: A review, analysis and comparison study. Swarm. Evol. Comp. 42, 43–57 (2018)

    Article  Google Scholar 

  50. Kazimipour, B., Li, X., Qin, A.K.: Initialization methods for large scale global optimization. In: 2013 IEEE Congress on Evolutionary Computation, pp. 2750–2757 (2013). IEEE

  51. Montiel, O., Rubio, Y., Olvera, C., Rivera, A.: Quantum-inspired acromyrmex evolutionary algorithm. Scientific reports. 9(1), 12181 (2019)

    Google Scholar 

  52. Talbi, H., Draa, A.: A new real-coded quantum-inspired evolutionary algorithm for continuous optimization. Appl. Soft Comp. 61, 765–791 (2017)

    Article  Google Scholar 

  53. Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future Gener. Comp. Syst. 29(3), 682–692 (2013)

    Article  Google Scholar 

  54. Liu, Z., **ang, T., Lin, B., Ye, X., Wang, H., Zhang, Y., Chen, X.: A data placement strategy for scientific workflow in hybrid cloud. In: 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), pp. 556–563 (2018). IEEE

  55. Derouiche, R., Brahmi, Z., Gammoudi, M.M.: Fca-based energy aware-data placement strategy for intensive workflow in cloud computing. Procedia. Comp. Sci. 159, 387–397 (2019)

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Computer Science and Engineering, Taibah University, 4333, Medina, Saudi Arabia

    Zaki Brahmi

  2. RIADI Lab, Manouba University, 41477, Manouba, Tunisia

    Zaki Brahmi & Rihab Derouiche

  3. Sousse University, Sousse, Tunisia

    Zaki Brahmi

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  1. Zaki Brahmi
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Appendix A: Workflows features

Appendix A: Workflows features

Table 4 Workflows features
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Brahmi, Z., Derouiche, R. EQGSA-DPW: A Quantum-GSA Algorithm-Based Data Placement for Scientific Workflow in Cloud Computing Environment. J Grid Computing 22, 57 (2024). https://doi.org/10.1007/s10723-024-09771-5

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