Abstract
Edge computing is a new promising paradigm helps users to execute their tasks on edge network which is closer to them rather than cloud. It can reduce application response time especially for those are critical to time such as healthcare applications, real-time apps, game playing, or traffic systems. Edge User Allocation (EUA) problem is responsible for allocate user application into edge servers on the edge network as app vendors’ needing. In this paper, we propose a heuristic called Nearest Edge Server with Highest Capacity (NESHC) to solve the EUA problem. We use a real-word dataset in our extensive experiments. The results show that NESHC can reduce elapsed CPU time and outperform baseline approach (Optimal) and two state-of-the-art approaches (ICSOC19 and TPDS20). The reduction of delay causes increasing of user allocated into edge servers and leveraging the overall utilization of edge network system.
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References
Ahmad, R.W., Gani, A., Hamid, S.H.A., Shiraz, M., Yousafzai, A., Xia, F.: A survey on virtual machine migration and server consolidation frameworks for cloud data centers. J. Netw. Comput. Appl. 52, 11–25 (2015)
Almutairi, J., Aldossary, M.: A novel approach for IoT tasks offloading in edge-cloud environments. J. Cloud Comput. 10(1), 1–19 (2021)
Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp. 13–16 (2012)
Cong, P., Zhou, J., Li, L., Cao, K., Wei, T., Li, K.: A survey of hierarchical energy optimization for mobile edge computing: a perspective from end devices to the cloud. ACM Comput. Surv. (CSUR) 53(2), 1–44 (2020)
Elgendy, I.A., Zhang, W.Z., Liu, C.Y., Hsu, C.H.: An efficient and secured framework for mobile cloud computing. IEEE Trans. Cloud Comput. 9(1), 79–87 (2018)
Elgendy, I.A., Zhang, W., Tian, Y.C., Li, K.: Resource allocation and computation offloading with data security for mobile edge computing. Future Gener. Comput. Syst. 100, 531–541 (2019)
Ferreto, T.C., Netto, M.A., Calheiros, R.N., De Rose, C.A.: Server consolidation with migration control for virtualized data centers. Future Gener. Comput. Syst. 27(8), 1027–1034 (2011)
He, Q., et al.: A game-theoretical approach for user allocation in edge computing environment. IEEE Trans. Parallel Distrib. Syst. 31(3), 515–529 (2019)
Jennings, B., Stadler, R.: Resource management in clouds: survey and research challenges. J. Netw. Syst. Manage. 23, 567–619 (2015)
Lai, P., et al.: Optimal edge user allocation in edge computing with variable sized vector bin packing. In: Pahl, C., Vukovic, M., Yin, J., Yu, Q. (eds.) ICSOC 2018. LNCS, vol. 11236, pp. 230–245. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03596-9_15
Lai, P., et al.: Edge user allocation with dynamic quality of service. In: Yangui, S., Bouassida Rodriguez, I., Drira, K., Tari, Z. (eds.) ICSOC 2019. LNCS, vol. 11895, pp. 86–101. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33702-5_8
Lai, P., et al.: Cost-effective app user allocation in an edge computing environment. IEEE Trans. Cloud Comput. 10(3), 1701–1713 (2020)
Peng, Q., et al.: Mobility-aware and migration-enabled online edge user allocation in mobile edge computing. In: 2019 IEEE International Conference on Web Services (ICWS), pp. 91–98. IEEE (2019)
Rababah, B., Alam, T., Eskicioglu, R.: The next generation internet of things architecture towards distributed intelligence: Reviews, applications, and research challenges. J. Telecommun. Electr. Comput. Eng. (JTEC) 12(2) (2020)
Sahni, Y., Cao, J., Zhang, S., Yang, L.: Edge mesh: a new paradigm to enable distributed intelligence in internet of things. IEEE Access 5, 16441–16458 (2017)
Satyanarayanan, M.: The emergence of edge computing. Computer 50(1), 30–39 (2017)
Tyagi, H., Kumar, R.: Cloud computing for IoT. Internet Things (IoT) Concepts Appl., 25–41 (2020)
Yousefpour, A., et al.: All one needs to know about fog computing and related edge computing paradigms: a complete survey. J. Syst. Archit. 98, 289–330 (2019)
Acknowledgment
Authors received research funds from 59 the Basque Government as the head of the Grupo de Inteligencia Computacional, Universidad del Pais Vasco, UPV/EHU, from 2007 until 2025. The current code for the grant is IT1689-22. Additionally, authors participate in Elkartek projects KK-2022/00051 and KK-2021/00070. The Spanish MCIN 5has also granted the authors a research project under code PID2020-116346GB-I00.
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Alawneh, H., David Nuñez-Gonzalez, J., Graña, M. (2023). Exploring Delay Reduction on Edge Computing Architectures from a Heuristic Approach. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in Computer Science(), vol 14001. Springer, Cham. https://doi.org/10.1007/978-3-031-40725-3_11
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DOI: https://doi.org/10.1007/978-3-031-40725-3_11
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