Abstract
The edge computing model enables real-time and low-power processing of data, while contributing to data security and privacy protection. However, the heterogeneity and diversity of edge computing devices pose a great challenge to task scheduling and migration. Most of the existing studies only consider the allocation of computational resources, but lack comprehensive consideration of data resources, storage space, etc. In this paper, we proposed intelligent scheduling strategies for computing power resources in heterogeneous edge networks. We define the relevant models and construct a comprehensive matching matrix in terms of task matching with computing resources, data resources, storage resources, load balancing of computing devices and storage space matching, and design an intelligent scheduling algorithm based on iteration and load balancing according to the matching degree of tasks and computing devices in the heterogeneous edge network environment. The iterative and load-balanced scheduling algorithm is based on the least-cost flow solution scheduling strategy, which effectively reduces the task computation response time and improves the computation and storage resource utilization of computing devices. Experimental validation of the proposed intelligent scheduling strategy is carried out based on a simulation environment. The experimental results show that the proposed intelligent scheduling strategy has obvious advantages over random scheduling methods in terms of task processing delay, computing power resource utilization and number of satisfactory tasks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Naveen, S., Kounte, M.R.: Key technologies and challenges in IoT edge computing. In: 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 61–65 (2019)
Yu, R, Zhang, X., Zhang, M.: Smart home security analysis system based on the Internet of Things. In: 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), pp. 596–599 (2021)
Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent IoT applications in edge and fog computing environments. IEEE Trans. Mobile Comput. 20(4), 1298–1311 (2021)
Tan, Z., Yu, F.R., Li, X., Ji, H., Leung, V.C.: Virtual resource allocation for heterogeneous services in full duplex-enabled scans with mobile edge computing and caching. IEEE Trans. Veh. Technol. 67(2), 1794–1808 (2017)
Wang, P., Yao, C., Zheng, Z., Sun, G., Song, L.: Joint task assignment, transmission, and computing resource allocation in multilayer mobile edge computing systems. IEEE Internet Things J. 6(2), 2872–2884 (2018)
Tran, T.X., Pompili, D.: Joint task offloading and resource allocation for multiserver mobile-edge computing networks. IEEE Trans. Veh. Technol. 68(1), 856–868 (2019)
Auluck, N., Azim, A., Fizza, K.: Improving the schedulability of real-time tasks using fog computing. IEEE Trans. Serv. Comput. (2019)
Mehrabi, M., You, D., Latzko, V., Salah, H., Reisslein, M., Fitzek, F.H.P.: De-vice-enhanced MEC: multiaccess edge computing (MEC) aided by end device computation and caching: a survey. IEEE Access 7, 166079–166108 (2019)
Chen, W., Wang, D., Li, K.: Multiuser multitask computation offloading in green mobile edge cloud computing. IEEE Trans. Serv. Comput. 12(5), 726–738 (2019)
Balcazar, E.H., Cerda, J., Avalos, A.: A validation method to integrate non linear non convex constraints into linear programs. In: 2018 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), pp. 1–7 (2018)
Kuendee, P., Janjarassuk, U.: A comparative study of mixed-integer linear pro-gramming and genetic algorithms for solving binary problems. In: 2018 5th International Conference on Industrial Engineering and Applications (ICIEA), pp. 284–288 (2018)
Mohammad, C.W., Shahid, M., Husain, S.Z.: A graph theory based algorithm for the computation of cyclomatic complexity of software requirements. In: 2017 International Conference on Computing, Communication and Automation (ICCCA), pp. 881–886 (2017)
Susymary, J., Lawrance, R.: Graph theory analysis of protein-protein interaction network and graph based clustering of proteins linked with Zika virus using MCL algorithm. In: 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1–7 (2017)
Lu, M., Li, F.: Survey on lie group machine learning. Big Data Mining Analyt. 3(4), 235–258 (2020)
Kalinina, E.A., Khitrov, G.M.: A linear algebra approach to some problems of graph theory. Comput. Sci. Inf. Technol. 2017, 5–8 (2017)
Acknowledgments
This work was supported by the Science and Technology Project of State Grid Corporation “Research on Key Technologies of Power Artificial Intelligence Open Platform” (5700-202155260A-0-0-00).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ji, Z., Zhang, J., Wang, X. (2022). Intelligent Scheduling Strategies for Computing Power Resources in Heterogeneous Edge Networks. In: Wang, Y., Zhu, G., Han, Q., Zhang, L., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2022. Communications in Computer and Information Science, vol 1629. Springer, Singapore. https://doi.org/10.1007/978-981-19-5209-8_18
Download citation
DOI: https://doi.org/10.1007/978-981-19-5209-8_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-5208-1
Online ISBN: 978-981-19-5209-8
eBook Packages: Computer ScienceComputer Science (R0)