Abstract
The edge computing paradigm has experienced quick development in recent years. This paradigm is featured by pushing the storage and computational resources closer to the end-user on edge network. For this purpose, service providers are allowed to add resources on enriched servers at access points (APs) in networks for hosting a number of end-users tasks. However, the deployment of edge servers is still a technological challenge with respect to the end-user pricing model, resource capacity of the server, worthy server, and the management of the latency between users and the servers, etc. A careful investigation into existing methods shows that most of the existing approaches are limited in many ways: (1) they tended to consider the single service provider configuration with a single pricing model only, and (2) they tended to ignore real-time performance variations of edge resources. In this work, we present a meta-heuristic-based method for resource allocation. It overcomes the above limitations and aims to reduce the overall cost of the edge user by appropriately managing budget and deadline constraints. We performed extensive case studies based on real-world commercial edge computing infrastructures and multiple data Workflow templates. Experimental results clearly show that our approach outperforms other state-of-the-art ones.
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References
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012). 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). ACM.
Josilo, S. (2020). Task Placement and Resource Allocation in Edge Computing Systems (Ph.D. Thesis). KTH Royal Institute of Technology.
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.
Ryu, J.-W., Pham, Q.-V., Luan, H. N., Hwang, W.-J., Kim, J.-D., & Lee, J.-T. (2019). Multi-access edge computing empowered heterogeneous networks: A novel architecture and potential works. Symmetry, 11(7), 842.
Khodashenas, P. S., Ruiz, C., Riera, J. F., Fajardo, J. O., Taboada, I., Blanco, B., … Sallent, O. (2016). Service provisioning and pricing methods in a multi-tenant cloud enabled RAN. In 2016 IEEE Conference on Standards for Communications and Networking (CSCN) (pp. 1–6). IEEE.
Fernando, N., Loke, S. W., & Rahayu, W. (2013). Mobile cloud computing: A survey. Future Generation Computer Systems, 29(1), 84–106.
Jia, M., Cao, J., & Liang, W. (2017). Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Transactions on Cloud Computing, 5(4), 725–737.
Brummett, T., Sheinidashtegol, P., Sarkar, D., & Galloway, M. (2015). Performance metrics of local cloud computing architectures. In 2015 IEEE 2nd international conference on cyber security and cloud computing (pp. 25–30). IEEE.
Chiang, M., & Zhang, T. (2016). Fog and IoT: An overview of research opportunities. IEEE Internet of Things Journal, 3(6), 854–864.
Nguyen, T. D., Nguyen, T.-D., Nguyen, V. D., Pham, X.-Q., & Huh, E.-N. (2018). Cost-effective resource sharing in an internet of vehicles-employed mobile edge computing environment. Symmetry, 10(11), 594.
Pham, X.-Q., Nguyen, T.-D., Nguyen, V., & Huh, E.-N. (2019). Joint node selection and resource allocation for task offloading in scalable vehicle-assisted multi-access edge computing. Symmetry, 11(1), 58.
Xu, J., Palanisamy, B., Ludwig, H., & Wang, Q. (2017). Zenith: Utility-aware resource allocation for edge computing. In 2017 IEEE international conference on edge computing (EDGE) (pp. 47–54). IEEE.
Satyanarayanan, M., Bahl, P., Caceres, R., & Davies, N. (2009). The case for vm-based cloudlets in mobile computing. IEEE Pervasive Computing, 4, 14–23.
Clinch, S., Harkes, J., Friday, A., Davies, N., & Satyanarayanan, M. (2012). How close is close enough? Understanding the role of cloudlets in supporting display appropriation by mobile users. In 2012 IEEE international conference on pervasive computing and communications (pp. 122–127). IEEE.
Abbas, Z., Anjum, M. R., Younus, M. U., & Chowdhry, B. S. (2021). Monitoring of gas distribution pipelines network using wireless sensor networks. Wireless Personal Communications, 117(3), 2575–2594.
Younus, M. U., & Kim, S. W. (2019). Proposition and real-time implementation of an energy-aware routing protocol for a software defined wireless sensor network. Sensors, 19(12), 2739.
Xu, Z., Liang, W., Xu, W., Jia, M., & Guo, S. (2015). Capacitated cloudlet placements in wireless metropolitan area networks. In 2015 IEEE 40th Conference on Local Computer Networks (LCN) (pp. 570–578). IEEE.
Gu, L., Zeng, D., Guo, S., Barnawi, A., & Xiang, Y. (2017). Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Transactions on Emerging Topics in Computing, 5(1), 108–119.
Younus, M. U. (2018). Analysis of the impact of different parameter settings on wireless sensor network lifetime. International Journal of Advanced Computer Science and Applications, 9(3), 16–21.
El Haber, E., Nguyen, T. M., & Assi, C. (2019). Joint optimization of computational cost and devices energy for task offloading in multi-tier edge-clouds. IEEE Transactions on Communications.
Liu, M., & Liu, Y. (2018). Price-based distributed offloading for mobile-edge computing with computation capacity constraints. IEEE Wireless Communications Letters, 7(3), 420–423.
Qin, Z., Qiu, X., Ye, J., & Wang, L. (2020). User-edge collaborative resource allocation and offloading strategy in edge computing. Wireless Communications and Mobile Computing, 2020(11), 1–12.
Shah-Mansouri, H., & Wong, V. W. (2018). Hierarchical fog-cloud computing for IoT systems: A computation offloading game. IEEE Internet of Things Journal, 5(4), 3246–3257.
Yao, H., Bai, C., Xiong, M., Zeng, D., & Fu, Z. (2017). Heterogeneous cloudlet deployment and user-cloudlet association toward cost effective fog computing. Concurrency and Computation: Practice and Experience, 29(16), e3975.
Schad, J., Dittrich, J., & Quiané-Ruiz, J.-A. (2010). Runtime measurements in the cloud: Observing, analyzing, and reducing variance. Proceedings of the VLDB Endowment, 3(1–2), 460–471.
Garey, M. R., & Johnson, D. S. (1981). Approximation algorithms for bin packing problems: A survey. In G. Ausiello & M. Lucertini (Eds.), Analysis and design of algorithms in combinatorial optimization. (pp. 147–172). Springer.
Ostermann, S., Iosup, A., Yigitbasi, N., Prodan, R., Fahringer, T., & Epema, D. (2009). A performance analysis of EC2 cloud computing services for scientific computing. In International conference on cloud computing (pp. 115–131). Springer.
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Bukhari, S.S.H., Kabir, A., Younus, M.U. et al. Novel Cost Efficient Resource Allocation Technique Based on Deadline and Budget Constraints for Edge Users. Wireless Pers Commun 120, 269–286 (2021). https://doi.org/10.1007/s11277-021-08453-9
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DOI: https://doi.org/10.1007/s11277-021-08453-9