Full Text:   <346>

Summary:  <19>

Suppl. Mater.: 

CLC number: TP393

On-line Access: 2024-06-04

Received: 2023-03-05

Revision Accepted: 2024-06-04

Crosschecked: 2023-08-09

Cited: 0

Clicked: 454

Citations:  Bibtex RefMan EndNote GB/T7714


Yuexia FU






Qinqin TANG


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.5 P.685-700


Reputation-based joint optimization of user satisfaction and resource utilization in a computing force network

Author(s):  Yuexia FU, Jing WANG, Lu LU, Qinqin TANG, Sheng ZHANG

Affiliation(s):  China Mobile Research Institute, Beijing 100053, China; more

Corresponding email(s):   fuyuexia@chinamobile.com, wangjingjc@chinamobile.com, lulu@chinamobile.com, qqtang@bupt.edu.cn, zhangsheng@chinamobile.com

Key Words:  Computing force network, Resource scheduling, Performance-based reputation, User satisfaction

Yuexia FU, Jing WANG, Lu LU, Qinqin TANG, Sheng ZHANG. Reputation-based joint optimization of user satisfaction and resource utilization in a computing force network[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(5): 685-700.

@article{title="Reputation-based joint optimization of user satisfaction and resource utilization in a computing force network",
author="Yuexia FU, Jing WANG, Lu LU, Qinqin TANG, Sheng ZHANG",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Reputation-based joint optimization of user satisfaction and resource utilization in a computing force network
%A Yuexia FU
%A Jing WANG
%A Lu LU
%A Qinqin TANG
%A Sheng ZHANG
%J Frontiers of Information Technology & Electronic Engineering
%V 25
%N 5
%P 685-700
%@ 2095-9184
%D 2024
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300156

T1 - Reputation-based joint optimization of user satisfaction and resource utilization in a computing force network
A1 - Yuexia FU
A1 - Jing WANG
A1 - Lu LU
A1 - Qinqin TANG
A1 - Sheng ZHANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 25
IS - 5
SP - 685
EP - 700
%@ 2095-9184
Y1 - 2024
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2300156

Under the development of computing and network convergence, considering the computing and network resources of multiple providers as a whole in a computing force network (CFN) has gradually become a new trend. However, since each computing and network resource provider (CNRP) considers only its own interest and competes with other CNRPs, introducing multiple CNRPs will result in a lack of trust and difficulty in unified scheduling. In addition, concurrent users have different requirements, so there is an urgent need to study how to optimally match users and CNRPs on a many-to-many basis, to improve user satisfaction and ensure the utilization of limited resources. In this paper, we adopt a reputation model based on the beta distribution function to measure the credibility of CNRPs and propose a performance-based reputation update model. Then, we formalize the problem into a constrained multi-objective optimization problem and find feasible solutions using a modified fast and elitist non-dominated sorting genetic algorithm (NSGA-II). We conduct extensive simulations to evaluate the proposed algorithm. Simulation results demonstrate that the proposed model and the problem formulation are valid, and the NSGA-II is effective and can find the Pareto set of CFN, which increases user satisfaction and resource utilization. Moreover, a set of solutions provided by the Pareto set give us more choices of the many-to-many matching of users and CNRPs according to the actual situation.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Abbas N, Zhang Y, Taherkordi A, et al., 2017. Mobile edge computing: a survey. IEEE Int Things J, 5(1):‍450-465.

[2]Bao QZ, Ren XX, Liu CF, et al., 2021. Resource trading with hierarchical game for computing-power network market. Proc 5th Int Joint Conf, p.94-109.

[3]Benblidia MA, Brik B, Merghem-Boulahia L, et al., 2019. Ranking fog nodes for tasks scheduling in fog-cloud environments: a fuzzy logic approach. Proc 15th Int Wireless Communications & Mobile Computing Conf, p.1451-1457.

[4]Buchegger S, Le Boudec JY, 2002. Performance analysis of the CONFIDANT protocol. Proc 3rd ACM Int Symp on Mobile Ad Hoc Networking & Computing, p.226-236.

[5]Buchegger S, Le Boudec JY, 2003a. Coping with False Accusations in Misbehavior Reputation Systems for Mobile Ad-Hoc Networks. EPFL Technical Report IC/2003/31, Elsevier, Lausanne, Switzerland.

[6]Buchegger S, Le Boudec JY, 2003b. The effect of rumor spreading in reputation systems for mobile ad-hoc networks. Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, Article 10.

[7]Chaitra T, Agrawal S, Jijo J, et al., 2020. Multi-objective optimization for dynamic resource provisioning in a multi-cloud environment using lion optimization algorithm. Proc 20th Int Symp on Computational Intelligence and Informatics, p.000083-000090.

[8]Chen YF, Li ZY, Yang B, et al., 2020. A Stackelberg game approach to multiple resources allocation and pricing in mobile edge computing. Fut Gener Comput Syst, 108:273-287.

[9]Cui LZ, Xu C, Yang S, et al., 2019. Joint optimization of energy consumption and latency in mobile edge computing for Internet of Things. IEEE Int Things J, 6(3):‍4791-4803.

[10]Deb K, Pratap A, Agarwal S, et al., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput, 6(2):182-197.

[11]Di Z, Luo T, Qiu C, et al., 2023. In-network pooling: contribution-aware allocation optimization for computing power network in B5G/6G era. IEEE Trans Netw Sci Eng, 10(3):1190-1202.

[12]Dong YQ, Guan CC, Chen YL, et al., 2022. Optimization of service scheduling in computing force network. Int Conf on Service Science, p.147-153.

[13]Du ZP, Li ZQ, Duan XD, et al., 2022. Service information informing in computing aware networking. Int Conf on Service Science, p.125-130.

[14]Fang WD, Zhang CL, Shi ZD, et al., 2016. BTRES: beta-based trust and reputation evaluation system for wireless sensor networks. J Netw Comput Appl, 59:88-94.

[15]Fortes JAB, 2010. Sky computing: when multiple clouds become one. Proc 10th IEEE/ACM Int Conf on Cluster, Cloud and Grid Computing, Article 4.

[16]Ganeriwal S, Balzano LK, Srivastava MB, 2008. Reputation-based framework for high integrity sensor networks. ACM Trans Sens Netw, 4(3):1-37.

[17]Gelman A, Carlin JB, Stern HS, et al., 1995. Bayesian Data Analysis. Chapman and Hall/CRC, New York, USA.

[18]Jara EC, 2014. Multi-objective optimization by using evolutionary algorithms: the p-optimality criteria. IEEE Trans Evol Comput, 18(2):167-179.

[19]Josang A, Ismail R, 2002. The beta reputation system. Proc 15th Bled Electronic Commerce Conf, p.502-2511.

[20]Kan TY, Chiang Y, Wei HY, 2018. Task offloading and resource allocation in mobile-edge computing system. Proc 27th Wireless and Optical Communication Conf, p.1-4.

[21]Kang KX, Ding D, Xie HM, et al., 2022. Adaptive DRL-based task scheduling for energy-efficient cloud computing. IEEE Trans Netw Serv Manag, 19(4):4948-4961.

[22]Li F, Seok MG, Cai WT, 2021. A new double rank-based multi-workflow scheduling with multi-objective optimization in cloud environments. IEEE Int Parallel and Distributed Processing Symp Workshops, p.36-45.

[23]Liu B, Mao JW, Xu L, et al., 2021. CFN-dyncast: load balancing the edges via the network. IEEE Wireless Communications and Networking Conf Workshops, p.1-6.

[24]Liu L, Fan Q, Buyya R, 2018. A deadline-constrained multi-objective task scheduling algorithm in mobile cloud environments. IEEE Access, 6:52982-52996.

[25]Liu XL, Jia SW, 2019. An iterative reputation ranking method via the beta probability distribution. IEEE Access, 7:540-547.

[26]Mao YY, You CS, Zhang J, et al., 2017. A survey on mobile edge computing: the communication perspective. IEEE Commun Surv Tut, 19(4):2322-2358.

[27]Miriyala SS, Subramanian VR, Mitra K, 2018. TRANSFORM-ANN for online optimization of complex industrial processes: casting process as case study. Eur J Oper Res, 264(1):294-309.

[28]Monteiro A, Teixeira C, Pinto JS, 2014. Sky computing: exploring the aggregated cloud resources—part II. Proc 9th Iberian Conf on Information Systems and Technologies, p.1-6.

[29]Mostafa HA, El-Shatshat R, Salama MMA, 2013. Multi-objective optimization for the operation of an electric distribution system with a large number of single phase solar generators. IEEE Trans Smart Grid, 4(2):1038-1047.

[30]Niu XX, Wang HC, Hu S, et al., 2018. Multi-objective online optimization of a marine diesel engine using NSGA-II coupled with enhancing trained support vector machine. Appl Therm Eng, 137:218-227.

[31]Peng CD, Liu HL, Gu FQ, 2017. An evolutionary algorithm with directed weights for constrained multi-objective optimization. Appl Soft Comput, 60:613-622.

[32]Rehani N, Garg R, 2018. Meta-heuristic based reliable and green workflow scheduling in cloud computing. Int J Syst Assur Eng Manag, 9(4):811-820.

[33]Resnick P, Zeckhauser R, 2002. Trust among strangers in Internet transactions: empirical analysis of eBay’s reputation system. In: Baye M (Ed.), The Economics of the Internet and E-Commerce. Emerald Group Publishing Limited, Bingley, England, p.127-157.

[34]Resnick P, Kuwabara K, Zeckhauser R, et al., 2000. Reputation systems. Commun ACM, 43(12):45-48.

[35]Song ZD, Sun HG, Yang HH, et al., 2022. Reputation-based federated learning for secure wireless networks. IEEE Int Things J, 9(2):1212-1226.

[36]Stoica I, Shenker S, 2021. From cloud computing to sky computing. Proc Workshop on Hot Topics in Operating Systems, p.26-32.

[37]Tang XY, Cao C, Wang YX, et al., 2021. Computing power network: the architecture of convergence of computing and networking towards 6G requirement. China Commun, 18(2):175-185.

[38]Tian L, Yang MZ, Wang SG, 2021. An overview of compute first networking. Int J Web Grid Serv, 17(2):81-97.

[39]Xiong L, Liu L, 2004. PeerTrust: supporting reputation-based trust for peer-to-peer electronic communities. IEEE Trans Knowl Data Eng, 16(7):843-857.

[40]Yao HJ, Lu L, Duan XD, 2021. Architecture and key technologies for computing-aware networking. ZTE Technol J, 27(3):7-11(in Chinese).

[41]Yuan HT, Bi J, Zhou MC, et al., 2021. Biobjective task scheduling for distributed green data centers. IEEE Trans Autom Sci Eng, 18(2):731-742.

[42]Zhang P, Peng MG, Cui SG, et al., 2022. Theory and techniques for "intellicise" wireless networks. Front Inform Technol Electron Eng, 23(1):1-4.

[43]Zou Y, Shen F, Yan F, et al., 2021. Reputation-based regional federated learning for knowledge trading in blockchain-enhanced IoV. IEEE Wireless Communications and Networking Conf, p.1-6.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE