Dispersed Computing Resource Discovery Model and Algorithm for Polymorphic Migration Network Architecture

ZHOU Chengcheng; ZHANG Lukai; ZENG Guangping; LIN Fuhong

doi:10.23919/cje.2022.00.305

Volume 32 Issue 4

Jul. 2023

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2023 > 32(4): 821-839

ZHOU Chengcheng, ZHANG Lukai, ZENG Guangping, et al., “Dispersed Computing Resource Discovery Model and Algorithm for Polymorphic Migration Network Architecture,” Chinese Journal of Electronics, vol. 32, no. 4, pp. 821-839, 2023, doi: 10.23919/cje.2022.00.305

Citation:

ZHOU Chengcheng, ZHANG Lukai, ZENG Guangping, et al., “Dispersed Computing Resource Discovery Model and Algorithm for Polymorphic Migration Network Architecture,” Chinese Journal of Electronics, vol. 32, no. 4, pp. 821-839, 2023, doi: 10.23919/cje.2022.00.305

Citation:

PDF( 4385 KB)

Dispersed Computing Resource Discovery Model and Algorithm for Polymorphic Migration Network Architecture

doi: 10.23919/cje.2022.00.305

1.
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Transport Planning and Research Institute, Ministry of Transport, Beijing 100029, China

Funds: This work was supported by the National Natural Science Foundation of China (62072031, 61971032)

More Information

Author Bio:
Chengcheng ZHOU received the M.S. degree in School of Information Technology from University of Sydney in 2018. She is currently a Ph.D. candidate in School of Computer and Communication Engineering, University of Science and Technology Beijing. Her research interests include artificial intelligence, mobile edge computing and dispersed computing. She won one “Provincial and Ministry Science and Technology Progress Award” and one “Provincial and Ministry Natural Science Award” in 2019 and 2021, respectively. (Email: czho9311@163.com)

Lukai ZHANG received the Ph.D. degree in transportation planning and management from Beijing Jiaotong University in 2021. He is now an Engineer at the Transport Planning and Research Institute, China. His research interests include system integration and optimal control techniques. In the past three years, he has published 7 papers (SCI, SSCI, EI) as the first author in the field of optimization algorithms. (Email: zhanglk@tpri.org.cn)

Guangping ZENG received the Ph.D. degree from the University of Science and Technology Beijing, China. He worked as a Postdoctoral Researcher with the Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, USA, for two years. He is currently a Full Professor and the Ph.D. Supervisor of computer science and technology with the University of Science and Technology Beijing, China, and the Deputy Director of the Beijing Key Laboratory of Materials Science Knowledge Engineering. His interested fields include distributed and migrating computing, Linux operating systems and embedded systems, intelligent robotics and softman technology, smart systems and soft computing as well as natural language processing, and data mining. (Email: zenggpustb@163.com)

Fuhong LIN (corresponding author) received the M.S. degree and Ph.D. degree from Beijing Jiaotong University, Beijing, China, in 2006 and 2010, respectively, both in electronics engineering. Now he is a Professor in Department of Computer and Communication Engineering, University of Science and Technology Beijing, China. His research interests include edge/fog computing, network security, and AI. His two papers won “Top 100 Most Cited Chinese Papers Published in International Journals” in 2015 and 2016. He won “Provincial and Ministry Science and Technology Progress Award 2” in 2017 and 2019. (Email: fhlin@ustb.edu)
Received Date: 2022-09-07
Accepted Date: 2023-01-12

Available Online: 2023-02-06

Publish Date: 2023-07-05

Abstract

Abstract

Dynamic resource discovery in a network of dispersed computing resources is an open problem. The establishment and maintenance of resource pool information are critical, which involves both the polymorphic migration of the network and the time and energy costs resulting from node selection and frequent interactions of information between nodes. The resource discovery problem for dispersed computing can be considered a dynamic multi-level decision problem. A bi-level programming model of dispersed computing resource discovery is developed, which is driven by time cost, energy consumption and accuracy of information acquisition. The upper-level model is to design a reasonable network structure of resource discovery, and the lower-level model is to explore an effective discovery mode. Complex network topology features are used for the first time to analyze the polymorphic migration characteristics of resource discovery networks. We propose an integrated calibration method for energy consumption parameters based on two discovery modes (i.e., agent mode and self-directed mode). A symmetric trust region based heuristic algorithm is proposed for solving the system model. The numerical simulation is performed in a dispersed computing network with multiple modes and topological states, which proves the feasibility of the model and the effectiveness of the algorithm.
- Dispersed computing,
- Resource discovery,
- Polymorphic migration network,
- Bi-level programming model,
- Heuristic algorithm

FullText(HTML)

References(19)

References

[1]	M. R. Schurgot, M. Wang, A. E. Conway, et al., “A dispersed computing architecture for resource-centric computation and communication,” IEEE Communications Magazine, vol.57, no.7, pp.13–19, 2019. doi: 10.1109/MCOM.2019.1800776
[2]	C. C. Zhou, C. Gong, H. W. Hui, et al., “A task-resource joint management model with intelligent control for mission-aware dispersed computing,” China Communications, vol.18, no.10, pp.214–232, 2021. doi: 10.23919/JCC.2021.10.016
[3]	H. G. Yang, G. Li, G. Y. Sun, et al., “Dispersed computing for tactical edge in future wars: Vision, architecture, and challenges,” Wireless Communications and Mobile Computing, vol.2021, article no.8899186, 2021. doi: 10.1155/2021/8899186
[4]	J. J. Wu, C. K. Tse, F. C. M. Lau, et al., “Analysis of communication network performance from a complex network perspective,” IEEE Transactions on Circuits and Systems I:Regular Papers, vol.60, no.12, pp.3303–3316, 2013. doi: 10.1109/TCSI.2013.2264697
[5]	P. Loreti and L. Bracciale, “Optimized neighbor discovery for opportunistic networks of energy constrained IoT devices,” IEEE Transactions on Mobile Computing, vol.19, no.6, pp.1387–1400, 2020. doi: 10.1109/tmc.2019.2908402
[6]	W. Liu, T. Nishio, R. Shinkuma, et al., “Adaptive resource discovery in mobile cloud computing,” Computer Communications, vol.50, pp.119–129, 2014. doi: 10.1016/j.comcom.2014.02.006
[7]	Z. Y. Xiao, Z. Han, A. Nallanathan, et al., “Antenna array enabled space/air/ground communications and networking for 6G,” IEEE Journal on Selected Areas in Communications, vol.40, no.10, pp.2773–2804, 2022. doi: 10.1109/JSAC.2022.3196320
[8]	Z. Y. Xiao, L. P. Zhu, Y. M. Liu, et al., “A survey on millimeter-wave beamforming enabled UAV communications and networking,” IEEE Communications Surveys & Tutorials, vol.24, no.1, pp.557–610, 2022. doi: 10.1109/COMST.2021.3124512
[9]	I. Filali, F. Huet, and C. Vergoni, “A simple cache based mechanism for peer to peer resource discovery in grid environments,” in Proceedings of the 8th IEEE International Symposium on Cluster Computing and the Grid, Lyon, France, pp.602–608, 2008.
[10]	H. Huang and L. Q. Wang, “P&P: a combined push-pull model for resource monitoring in cloud computing environment,” in Proceedings of the IEEE 3rd International Conference on Cloud Computing, Miami, FL, USA, pp.260–267, 2010.
[11]	S. Zhou and X. F. Meng, “A location and time-aware resource searching scheme in mobile P2P ad hoc networks,” The Journal of Supercomputing, vol.76, no.9, pp.6809–6833, 2020. doi: 10.1007/s11227-019-03139-3
[12]	O. Wolfson, B. Xu, H. B. Yin, et al., “Resource discovery using spatio-temporal information in mobile ad-hoc networks,” in Proceedings of the 5th International Workshop on Web and Wireless Geographical Information Systems, Lausanne, Switzerland, pp.129–142, 2005.
[13]	W. C. Chung and R. S. Chang, “A new mechanism for resource monitoring in grid computing,” Future Generation Computer Systems, vol.25, no.1, pp.1–7, 2009. doi: 10.1016/j.future.2008.04.008
[14]	I. Murturi, C. Avasalcai, C. Tsigkanos, et al., “Edge-to-edge resource discovery using metadata replication,” in Proceedings of IEEE 3rd International Conference on Fog and Edge Computing, Larnaca, Cyprus, pp.1–6, 2019.
[15]	S. K. Battula, S. Garg, J. Montgomery, et al., “An efficient resource monitoring service for fog computing environments,” IEEE Transactions on Services Computing, vol.13, no.4, pp.709–722, 2020. doi: 10.1109/TSC.2019.2962682
[16]	W. Sun and Y. X. Yuan, “Optimization theory and methods: nonlinear programming,” Springer Science & Business Media, New York, USA, 2006.
[17]	R. Lotfi, N. Mardani, and G. W. Weber, “Robust bi-level programming for renewable energy location,” International Journal of Energy Research, vol.45, no.5, pp.7521–7534, 2021. doi: 10.1002/er.6332
[18]	L. Sahoo, S. Sen, K. Tiwary, et al., “Modified floyd-warshall’s algorithm for maximum connectivity in wireless sensor networks under uncertainty,” Discrete Dynamics in Nature and Society, vol.2022, article no.5973433, 2022. doi: 10.1155/2022/5973433
[19]	N. Hou, F. Z. He, Y. Zhou, et al., “An efficient GPU-based parallel tabu search algorithm for hardware/software co-design,” Frontiers of Computer Science, vol.14, no.5, article no.145316, 2020. doi: 10.1007/s11704-019-8184-3