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Multidimensional Benchmarking Framework for AQMs of Network Congestion Control Based on AHP and Group-TOPSIS

Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Perak, Malaysia

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A. A. Zaidan

Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Perak, Malaysia

E-mail Address: [email protected]

Corresponding author.

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B. B. Zaidan

Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Perak, Malaysia

Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, ROC

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O. S. Albahri

Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Perak, Malaysia

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M. A. Alsalem

Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Perak, Malaysia

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, and

A. S. Albahri

Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Perak, Malaysia

Informatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq

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https://doi.org/10.1142/S0219622021500127Cited by:23 (Source: Crossref)

Abstract

This paper aims to propose a grouping framework for benchmarking the active queue management (AQM) methods of network congestion control based on multicriteria decision-making (MCDM) techniques to assist developers of AQM methods in selecting the best AQM method. Given the current rapid development of the AQM techniques, determining which of these algorithms is better than the other is difficult because each algorithm performs better in a specific metric(s). Current benchmarking studies benchmark the AQM methods from a single incomplete prospective. In each proposed AQM method, the benchmarking was achieved with reference to some evaluation measures that are relatively close to the desired goal being followed during the development of the AQM methods. Furthermore, the benchmarking frameworks of AQM methods are complicated and challenging because of the following reasons: (1) the technical details of the AQM methods are adapted and the input parameters are selected according to the sensitivity of the AQM methods; and (2) a framework is developed and designed for simulating AQM methods, the simulated network and the collected results. For this purpose, a set of criteria for AQM comparison are determined. These criteria are performance, processing overhead and configuration. The benchmarking framework is developed based on the crossover of three groups of multi-evaluation criteria and several AQM methods as a proof of concept. The AQM families that are implemented and utilized in experiments to generate the data that are used as a proof of concept of our proposed framework are the parameter-based (pars) and fuzzy-based AQM methods. Accordingly, constructing the decision matrix (DM) that will be used to generate the final results is necessary. Subsequently, the underlying AQM methods are benchmarked and ranked using MCDM techniques, namely, integrated analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS). The validation was performed objectively. The $mean \pm standard$ deviation was computed to ensure that the AQM methods ranking undergo systematic ranking. Results illustrate that (1) the integration of AHP and TOPSIS solves the AQM method benchmarking problems; (2) results of the individual TOPSIS context clearly show variances among the ranking results of the six experts; (3) the ranks of the AQM methods obtained from internal and external TOPSIS group decision-making are nearly similar, with random early detection method being ranked as the best one; and (4) in the objective validation, significant differences were found between the groups’ scores, thereby indicating that the ranking results of internal and external TOPSIS group decision-making were valid.

Keywords:

References

1. A. A. Zaidan et al., A survey on communication components for IoT-based technologies in smart homes, Telecommunication Systems 69(1) (2018) 1–25. Crossref, ISI, Google Scholar
2. H.-Q. Wang, H.-S. Wong, H. Zhu and T. T. C. Yip, A neural network-based biomarker association information extraction approach for cancer classification, Journal of Biomedical Informatics 42(4) (2009) 654–666. Crossref, ISI, Google Scholar
3. L. Zhang and H. Xiaojuan, Multiple SVM-RFE for multi-class gene selection on DNA Microarray data, in 2015 Int. Joint Conf. Neural Networks (IJCNN), 2015, pp. 1–6. Crossref, Google Scholar
4. D. Yongqiang et al., Feature selection of high-dimensional biomedical data using improved SFLA for disease diagnosis, in 2015 IEEE Int. Conf. Bioinformatics and Biomedicine (BIBM), 2015, pp. 458–463. Crossref, Google Scholar
5. J. Rawat, A. Singh, H. S. Bhadauria and J. Virmani, Computer aided diagnostic system for detection of leukemia using microscopic images, Procedia Computer Science 70 (2015) 748–756. Crossref, Google Scholar
6. H. Salem, G. Attiya and N. El-Fishawy, Gene expression profiles based Human cancer diseases classification, in 2015 11th Int. Computer Engineering Conf. (ICENCO), 2015, pp. 181–187. Crossref, Google Scholar
7. L. M. de Campos, A. Cano, J. G. Castellano and S. Moral, Bayesian networks classifiers for gene-expression data, in 2011 11th Int. Conf. Intelligent Systems Design and Applications, 2011, pp. 1200–1206. Crossref, Google Scholar
8. K. Chitra and D. G. Padamavathi, Adaptive CHOKe: An algorithm to increase the fairness in Internet routers, International Journal of Advanced Networking and Applications 1(6) (2010) 382–386. Google Scholar
9. M. M. Hamdi, S. A. Rashid, M. Ismail, M. A. Altahrawi, M. F. Mansor and M. K. AbuFoul, Performance evaluation of active queue management algorithms in large network, in 2018 IEEE 4th Int. Symp. Telecommunication Technologies (ISTT), 2018, IEEE, pp. 1–6. Crossref, Google Scholar
10. M. Baklizi, H. Abdel-Jaber, A. A. Abu-Shareha, M. M. Abualhaj and S. Ramadass, Fuzzy logic controller of gentle random early detection based on average queue length and delay rate, International Journal of Fuzzy Systems 16(1) (2014) 9–19. ISI, Google Scholar
11. S. Petrovic-Lazarevic and A. Abraham, Hybrid fuzzy-linear programming approach for multi criteria decision making problems, arXiv preprint cs/0405019. Google Scholar
12. J. Malczewski, GIS and Multicriteria Decision Analysis (John Wiley & Sons, 1999). Google Scholar
13. B. N. Abdullateef, N. F. Elias, H. Mohamed, A. Zaidan and B. Zaidan, An evaluation and selection problems of OSS-LMS packages, SpringerPlus 5(1) (2016) 248. Crossref, Google Scholar
14. A. Zaidan, B. Zaidan, M. Hussain, A. Haiqi, M. M. Kiah and M. J. D. S. S. Abdulnabi, Multi-criteria analysis for OS-EMR software selection problem: A comparative study, Decision Support Systems 78 (2015) 15–27. Crossref, ISI, Google Scholar
15. Q. M. Yas, A. Zadain, B. Zaidan, M. B. Lakulu and B. Rahmatullah, Towards on develop a framework for the evaluation and benchmarking of skin detectors based on artificial intelligent models using multi-criteria decision-making techniques, International Journal of Pattern Recognition and Artificial Intelligence 31(3) (2017) 1759002. Link, ISI, Google Scholar
16. J. Koo, S. Ahn and J. Chung, A comparative study of queue, delay, and loss characteristics of AQM schemes in QoS-enabled networks, Computing and Informatics 23(4) (2004) 317–335. ISI, Google Scholar
17. M. Baklizi, H. Abdel-Jaber, S. Ramadass, N. Abdullah and M. Anbar, Performance assessment of AGRED, RED and GRED congestion control algorithms, Information Technology Journal 11(2) (2012) 255. Crossref, Google Scholar
18. G. F. Ahammed and R. Banu, Analyzing the performance of active queue management algorithms, International Journal of Computer Networks and Communications 2(2) (2010). Google Scholar
19. B. Abbasov and S. Korukoglu, Effective RED: An algorithm to improve RED’s performance by reducing packet loss rate, Journal of Network and Computer Applications 32(3) (2009) 703–709. Crossref, ISI, Google Scholar
20. J. Chen, C. Hu and Z. Ji, Self-tuning random early detection algorithm to improve performance of network transmission, Mathematical Problems in Engineering 2011 (2010) Article ID 872347, 17 pages, 2011. https://doi.org/10.1155/2011/872347. ISI, Google Scholar
21. J. Hong, C. Joo and S. Bahk, Active queue management algorithm considering queue and load states, Computer Communications 30(4) (2007) 886–892. Crossref, ISI, Google Scholar
22. R. Stanojevic, R. N. Shorten and C. M. Kellett, Adaptive tuning of drop-tail buffers for reducing queueing delays, IEEE Communications Letters 10(7) (2006) 570–572. Crossref, ISI, Google Scholar
23. W. Chen, Y. Li and S.-H. Yang, An average queue weight parameterization in a network supporting TCP flows with RED, in 2007 IEEE Int. Conf. Networking, Sensing and Control, 2007, IEEE, pp. 590–595. Crossref, Google Scholar
24. S. Liu, T. Başar and R. Srikant, TCP-Illinois: A loss-and delay-based congestion control algorithm for high-speed networks, Performance Evaluation 65(6) (2008) 417–440. Crossref, ISI, Google Scholar
25. S. Chebli, A. El Akkary, N. Sefiani and N. J. I. Elalami, PI stabilization for congestion control of AQM routers with tuning parameter optimization, IJIMAI 4(1) (2016) 52–55. Crossref, Google Scholar
26. M. Baklizi, H. Abdel-Jaber, M. M. Abu-Alhaj, N. Abdullah, S. Ramadass and A. Almomani, Dynamic stochastic early discovery: A new congestion control technique to improve networks performance, International Journal of Innovative Computing, Information and Control 9(3) (2013) 1118–1126. Google Scholar
27. A. Chydzinski and L. Chrost, Analysis of AQM queues with queue size based packet dropping, International Journal of Applied Mathematics and Computer Science 21(3) (2011) 567–577. Crossref, ISI, Google Scholar
28. M. H. Etbega, M. Woodward, H. Abdel-Jaber and A. G. Ali, A new version of adaptive red with reduced dependency on parameterisation, ed: Citeseer (2004). Google Scholar
29. S. Mohammadi, H. M. Pour, M. Jafari and A. Javadi, Fuzzy-based PID active queue manager for TCP/IP networks, in 2010 10th Int. Conf. Information Sciences Signal Processing and their Applications (ISSPA), 2010, IEEE, pp. 434–439. Crossref, Google Scholar
30. S. Kunniyur and R. Srikant, End-to-end congestion control schemes: Utility functions, random losses and ECN marks, IEEE/ACM Transactions on Networking 11(5) (2003) 689–702. Crossref, ISI, Google Scholar
31. C. Chrysostomou, A. Pitsillides, G. Hadjipollas, A. Sekercioglu and M. Polycarpou, Fuzzy explicit marking for congestion control in differentiated services networks, in Proc. Eighth IEEE Int. Symp. Computers and Communication, 2003. (ISCC 2003), 2003, IEEE, pp. 312–319. Crossref, Google Scholar
32. R. Loukas, S. Kohler, P. Andreas and P. Tran-Gia, Fuzzy RED: Congestion control for TCP/IP Diff-Serv, in MELECON 2000: 10th Mediterranean Electrotechnical Conf., 2000, Vol. 1, 2000, pp. 19–22. Google Scholar
33. M. Khatari, A. Zaidan, B. Zaidan, O. S. Albahri and M. A. Alsalem, Multi-criteria evaluation and benchmarking for active queue management methods: Open issues challenges and recommended pathway solutions, International Journal of Information Technology and Decision Making 18(4) (2019) 1187–1242. Link, ISI, Google Scholar
34. B. Bahaa, A. A. Zaidan, H. A. Karim and N. N. Ahmad, A new digital watermarking evaluation and benchmarking methodology using an external group of evaluators and multi-criteria analysis based on ‘large-scale data’, Software: Practice and Experience 47(10) (2017) 1365–1392. Crossref, ISI, Google Scholar
35. B. Bahaa and A. A. Zaidan, Software and hardware FPGA-based digital watermarking and steganography approaches: Toward new methodology for evaluation and benchmarking using multi-criteria decision-making techniques, Journal of Circuits, Systems and Computers 26(7) (2017) 1750116. Link, ISI, Google Scholar
36. B. Zaidan, A. Zaidan, H. Abdul Karim and N. N. Ahmad, A new approach based on multi-dimensional evaluation and benchmarking for data hiding techniques, International Journal of Information Technology and Decision Making (2017) 1–42. Google Scholar
37. F. Jumaah, A. Zaidan, B. Zaidan, R. Bahbibi, M. Qahtan and A. Sali, Technique for order performance by similarity to ideal solution for solving complex situations in multi-criteria optimization of the tracking channels of GPS baseband telecommunication receivers, Telecommunication Systems 68(3) (2018) 425–443. Crossref, ISI, Google Scholar
38. B. Rahmatullah, A. Zaidan, F. Mohamed and A. Sali, Multi-complex attributes analysis for optimum GPS baseband receiver tracking channels selection, in 2017 4th Int. Conf. Control, Decision and Information Technologies (CoDIT), 2017, IEEE, pp. 1084–1088. Crossref, Google Scholar
39. O. H. Salman, A. Zaidan, B. Zaidan, Naserkalid, M. Hashim, Novel methodology for triage and prioritizing using “big data” patients with chronic heart diseases through telemedicine environmental, International Journal of Information Technology and Decision Making 16(5) (2017) 1211–1245. Link, ISI, Google Scholar
40. Q. M. Yas, A. Zaidan, B. Zaidan, B. Rahmatullah and H. A. J. M. Karim, Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions, Measurement 114 (2018) 243–260. Crossref, ISI, Google Scholar
41. I. Tariq et al., MOGSABAT: A metaheuristic hybrid algorithm for solving multi-objective optimisation problems, Neural Computing and Applications (2018). Google Scholar
42. O. S. Albahri et al., Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ML and novel MCDM methods, Computer Methods and Programs in Biomedicine (2020) 105617. Crossref, ISI, Google Scholar
43. O. S. Albahri et al., Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. Journal of Infection and Public Health (2020). Crossref, ISI, Google Scholar
44. A. A. Zaidan, B. B. Zaidan, M. A. Alsalem, F. Momani and O. Zughoul, Novel multi-perspective hiring framework for the selection of software programmer applicants based on AHP and group TOPSIS techniques, International Journal of Information Technology and Decision Making (2020). Link, ISI, Google Scholar
45. B. Zaidan and A. J. M. Zaidan, Comparative study on the evaluation and benchmarking information hiding approaches based multi-measurement analysis using TOPSIS method with different normalisation, separation and context techniques, Measurement 117 (2018) 277–294. Crossref, ISI, Google Scholar
46. A. Zaidan et al., A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: Coherent taxonomy, open issues and recommendation pathway solution, Health and Technology 8(4) (2018) 223–238. Crossref, ISI, Google Scholar
47. N. Kalid, A. Zaidan, B. Zaidan, O. H. Salman, M. Hashim and H. Muzammil, Based real time remote health monitoring systems: A review on patients prioritization and related “big data” using body sensors information and communication technology, Journal of Medical Systems 42(2) (2018) 30. Crossref, ISI, Google Scholar
48. F. Jumaah, A. Zadain, B. Zaidan, A. Hamzah and R. J. M. Bahbibi, Decision-making solution based multi-measurement design parameter for optimization of GPS receiver tracking channels in static and dynamic real-time positioning multipath environment, Measurement 118 (2018) 83–95. Crossref, ISI, Google Scholar
49. R. T. Mohammed, Review of the research landscape of multi-criteria evaluation and benchmarking processes for many-objective optimisation methods: Coherent taxonomy, challenges and recommended solution, International Journal of Information Technology and Decision Making (IJITDM) 19(6) (2020) 1619–1693. Link, ISI, Google Scholar
50. C. Lin, G. Kou, Y. Peng and F. E. Alsaadi, Aggregation of the nearest consistency matrices with the acceptable consensus in AHP-GDM, Annals of Operations Research (2020) 1–17. ISI, Google Scholar
51. G. Kou, P. Yang, Y. Peng, F. Xiao, Y. Chen and F. E. Alsaadi, Evaluation of feature selection methods for text classification with small datasets using multiple criteria decision-making methods, Applied Soft Computing 86 (2020) 105836. Crossref, ISI, Google Scholar
52. E. Almahdi, A. Zaidan, B. Zaidan, M. Alsalem, O. Albahri and A. S. Albahri, Mobile patient monitoring systems from a benchmarking aspect: Challenges, open issues and recommended solutions, Journal of Medical Systems 43(7) (2019) 207. Crossref, ISI, Google Scholar
53. M. Alsalem et al., Multiclass benchmarking framework for automated acute Leukaemia detection and classification based on BWM and group-VIKOR, Journal of Medical Systems 43(7) (2019) 212. Crossref, ISI, Google Scholar
54. E. Almahdi, A. Zaidan, B. Zaidan, M. Alsalem, O. Albahri and A. S. Albahri, Mobile-based patient monitoring systems: A prioritisation framework using multi-criteria decision-making techniques, Journal of Medical Systems 43(7) (2019) 219. Crossref, ISI, Google Scholar
55. K. Mohammed et al., Real-time remote-health monitoring systems: A review on patients prioritisation for multiple-chronic diseases, taxonomy analysis, concerns and solution procedure, Journal of Medical Systems 43(7) (2019) 223. Crossref, ISI, Google Scholar
56. M. Talal et al., Comprehensive review and analysis of anti-malware apps for smartphones, Telecommunication Systems 72(2) (2019) 285–337. Crossref, ISI, Google Scholar
57. O. Enaizan et al., Electronic medical record systems: Decision support examination framework for individual, security and privacy concerns using multi-perspective analysis, Health and Technology 10(3) (2020) 795–822. Crossref, ISI, Google Scholar
58. H. Zhang, G. Kou and Y. Peng, Soft consensus cost models for group decision making and economic interpretations, European Journal of Operational Research 277(3) (2019) 964–980. Crossref, ISI, Google Scholar
59. G. Kou, D. Ergu, C. Lin and Y. Chen, Pairwise comparison matrix in multiple criteria decision making, Technological and Economic Development of Economy 22(5) (2016) 738–765. Crossref, ISI, Google Scholar
60. G. Kou, Y. Lu, Y. Peng and Y. Shi, Evaluation of classification algorithms using MCDM and rank correlation, International Journal of Information Technology and Decision Making 11(1) (2012) 197–225. Link, ISI, Google Scholar
61. G. Kou, Y. Peng and G. Wang, Evaluation of clustering algorithms for financial risk analysis using MCDM methods, Information Sciences (Ny) 275 (2014) 1–12. Crossref, ISI, Google Scholar
62. G. Kou and C. Lin, A cosine maximization method for the priority vector derivation in AHP, European Journal of Operational Research 235(1) (2014) 225–232. Crossref, ISI, Google Scholar
63. G. Kou, D. Ergu and J. Shang, Enhancing data consistency in decision matrix: Adapting Hadamard model to mitigate judgment contradiction, European Journal of Operational Research 236(1) (2014) 261–271. Crossref, ISI, Google Scholar
64. H. Wang, G. Kou and Y. Peng, Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending, Journal of the Operational Research Society (2020) 1–12. ISI, Google Scholar
65. M. M. Salih, B. Zaidan, A. Zaidan and M. A. Ahmed, Survey on fuzzy TOPSIS state-of-the-art between 2007 and 2017, Computers and Operations Research 104 (2019) 207–227. Crossref, ISI, Google Scholar
66. A. Zaidan et al., Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: A new evaluation and benchmarking methodology, Neural Computing and Applications 32(12) (2019) 8315–8366. Crossref, ISI, Google Scholar
67. N. Napi et al., Medical emergency triage and patient prioritisation in a telemedicine environment: A systematic review Health and Technology 9(5) (2019) 679–700. Crossref, ISI, Google Scholar
68. M. Alaa et al., Assessment and ranking framework for the English skills of pre-service teachers based on fuzzy Delphi and TOPSIS methods, IEEE Access 7 (2019) 126201–126223. Crossref, ISI, Google Scholar
69. N. Ibrahim et al., Multi-criteria evaluation and benchmarking for young learners’ English language mobile applications in terms of LSRW skills, IEEE Access 7 (2019) 146620–146651. Crossref, ISI, Google Scholar
70. A. Albahri et al., Based multiple heterogeneous wearable sensors: A smart real-time health monitoring structured for hospitals distributor, IEEE Access 7 (2019) 37269–37323. Crossref, ISI, Google Scholar
71. O. Albahri et al., Fault-tolerant mHealth framework in the context of IoT-based real-time wearable health data sensors, IEEE Access 7 (2019) 50052–50080. Crossref, ISI, Google Scholar
72. A. S. Albahri, Multi-biological laboratory examination framework for the prioritisation of patients with Covid-19 based on integrated AHP and group VIKOR methods, International Journal of Information Technology and Decision Making (2020). Link, ISI, Google Scholar
73. H. Abdel-Jaber, F. Thabtah, M. Woodward, A. Jaffar and H. Al Bazar, Random early dynamic detection approach for congestion control, Baltic Journal of Modern Computing 2(1) (2014) 16. Google Scholar
74. S. Floyd and V. Jacobson, Random early detection gateways for congestion avoidance, IEEE/ACM Transactions on Networking 1(4) (1993) 397–413. Crossref, ISI, Google Scholar
75. M. Qader, B. Zaidan, A. Zaidan, S. Ali, M. Kamaluddin and W. J. M. Radzi, A methodology for football players selection problem based on multi-measurements criteria analysis, Measurement 111 (2017) 38–50. Crossref, ISI, Google Scholar
76. O. Albahri, A. Zaidan, B. Zaidan, M. Hashim, A. Albahri and M. A. Alsalem, Real-time remote health-monitoring systems in a medical centre: A review of the provision of healthcare services-based body sensor information, open challenges and methodological aspects, Journal of Medical Systems 42(8) (2018) 164. Crossref, ISI, Google Scholar
77. N. Kalid et al., Based on real time remote health monitoring systems: A new approach for prioritization “large scales data” patients with chronic heart diseases using body sensors and communication technology, Journal of Medical Systems 42(4) (2018) 69. Crossref, ISI, Google Scholar
78. M. Alsalem et al., Systematic review of an automated multiclass detection and classification system for acute Leukaemia in terms of evaluation and benchmarking, open challenges, issues and methodological aspects, Journal of Medical Systems 42(11) (2018) 204. Crossref, ISI, Google Scholar
79. A. Albahri, A. Zaidan, O. Albahri, B. Zaidan and M. A. Alsalem, Real-time fault-tolerant mHealth system: Comprehensive review of healthcare services, opens issues, challenges and methodological aspects, Journal of Medical Systems 42(8) (2018) 137. Crossref, ISI, Google Scholar
80. K. H. Abdulkareem, N. Arbaiy, A. A. Zaidan et al., A new standardisation and selection framework for real-time image dehazing algorithms from multi-foggy scenes based on fuzzy Delphi and hybrid multi-criteria decision analysis methods, Neural Computing and Applications (2020), https://doi.org/10.1007/s00521-020-05020-4. Crossref, ISI, Google Scholar
81. K. Mohammed et al., Novel technique for reorganisation of opinion order to interval levels for solving several instances representing prioritisation in patients with multiple chronic diseases, Computer Methods and Programs in Biomedicine 185 (2020) 105151. Crossref, ISI, Google Scholar
82. K. Mohammed et al., A uniform intelligent prioritisation for solving diverse and big data generated from multiple chronic diseases patients based on hybrid decision-making and voting method, IEEE Access 8 (2020) 91521–91530. Crossref, ISI, Google Scholar
83. K. H. Abdulkareem, A novel multi-perspective benchmarking framework for selecting image dehazing intelligent algorithms based on BWM and group VIKOR techniques, International Journal of Information Technology and Decision Making (2020). Link, ISI, Google Scholar

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Multidimensional Benchmarking Framework for AQMs of Network Congestion Control Based on AHP and Group-TOPSIS

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Multidimensional Benchmarking Framework for AQMs of Network Congestion Control Based on AHP and Group-TOPSIS

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References

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