Multi-Criteria Evaluation and Benchmarking for Active Queue Management Methods: Open Issues, Challenges and Recommended Pathway Solutions

The evaluation and benchmarking processes of active queue management (AQM) methods are complicated and challenging. Several evaluation criteria/metrics must be considered before an AQM method can yield satisfactory performance using specific metric(s). Further investigations are required to highlight the limitations of how criteria/metrics are determined and how their procedures accord with the evaluation and benchmarking processes of AQM. In this paper, we presented comprehensive insights into the multi-criteria evaluation and benchmarking of AQM methods based on two critical directions. First, current AQM evaluation criteria are collected, analyzed and categorized. Second, these AQM evaluation criteria highlight conflicting issues and benchmarking techniques to identify weak points, and possible solutions are discussed. The findings of this study are as follows: (1) The limitations and problems of existing AQM evaluation and benchmarking methods, such as multi-evaluation criteria, criteria trade-off, benchmarking and criteria significance, are presented and emphasized. (2) Multi-criteria decision-making using multiple criteria, such as performance, processing overhead and configuration, can be used to benchmark numerous AQM methods to determine solutions for future directions.

[1]  R. Srikant,et al.  End-to-end congestion control schemes: utility functions, random losses and ECN marks , 2003, TNET.

[2]  James Aweya,et al.  A control theoretic approach to active queue management , 2001, Comput. Networks.

[3]  Gu Da-gang A New Adaptive BLUE Algorithm , 2010, 2010 International Conference on Electrical and Control Engineering.

[4]  Chunming Qiao,et al.  Advances in internet congestion control , 2003, IEEE Communications Surveys & Tutorials.

[5]  A. A. Zaidan,et al.  A new digital watermarking evaluation and benchmarking methodology using an external group of evaluators and multi‐criteria analysis based on ‘large‐scale data’ , 2017, Softw. Pract. Exp..

[6]  Donald F. Towsley,et al.  On designing improved controllers for AQM routers supporting TCP flows , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[7]  Mehrbakhsh Nilashi,et al.  Organizational decision to adopt hospital information system: An empirical investigation in the case of Malaysian public hospitals , 2015, Int. J. Medical Informatics.

[8]  Jin-Wook Chung,et al.  A Comparative Study of Queue, Delay, and Loss Characteristics of AQM Schemes in QoS-enabled Networks , 2012, Comput. Artif. Intell..

[9]  Van Jacobson,et al.  Random early detection gateways for congestion avoidance , 1993, TNET.

[10]  Marios M. Polycarpou,et al.  Fuzzy explicit marking for congestion control in differentiated services networks , 2003, Proceedings of the Eighth IEEE Symposium on Computers and Communications. ISCC 2003.

[11]  Shankar P. Bhattacharyya,et al.  PI stabilization of first-order systems with time delay , 2001, Autom..

[12]  S. S. Masoumzadeh,et al.  Deep Blue: A Fuzzy Q-Learning Enhanced Active Queue Management Scheme , 2009, 2009 International Conference on Adaptive and Intelligent Systems.

[13]  A. A. Zaidan,et al.  Mobile Patient Monitoring Systems from a Benchmarking Aspect: Challenges, Open Issues and Recommended Solutions , 2019, Journal of Medical Systems.

[14]  Marimuthu Palaniswami,et al.  Stabilizing RED using a Fuzzy Controller , 2007, 2007 IEEE International Conference on Communications.

[15]  Andris Freivalds,et al.  A dynamic multi-attribute utility theory–based decision support system for patient prioritization in the emergency department , 2014 .

[16]  A. A. Zaidan,et al.  Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions , 2018 .

[17]  B. B. Zaidan,et al.  Electronic medical record systems: decision support examination framework for individual, security and privacy concerns using multi-perspective analysis , 2018, Health and Technology.

[18]  Steven Low,et al.  Random early marking: an optimisation approach to Internet congestion control , 1999, IEEE International Conference on Networks. ICON '99 Proceedings (Cat. No.PR00243).

[19]  Mostefa Mesbah,et al.  EEG rhythm/channel selection for fuzzy rule-based alertness state characterization , 2016, Neural Computing and Applications.

[20]  Ilker Murat Ar,et al.  Evaluating the Relative Efficiency of Commercial Banks in Turkey: An Integrated AHP/DEA Approach , 2013 .

[21]  Moshe Zukerman,et al.  RaQ: A robust active queue management scheme based on rate and queue length , 2007, Comput. Commun..

[22]  Mohammed El Amine Bechar,et al.  Statistical Comparisons of the Top 10 Algorithms in Data Mining for Classification Task , 2016 .

[23]  Yassine Hadjadj-Aoul Towards AQM Cooperation for Congestion Avoidance in DiffServ/MPLS Networks , 2009 .

[24]  B. B. Zaidan,et al.  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 , 2018, Journal of Medical Systems.

[25]  B. B. Zaidan,et al.  Real-Time Fault-Tolerant mHealth System: Comprehensive Review of Healthcare Services, Opens Issues, Challenges and Methodological Aspects , 2018, Journal of Medical Systems.

[26]  Yi Peng,et al.  A Group Decision Making Model for Integrating Heterogeneous Information , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[27]  Jingjun Zhang,et al.  An Improved Adaptive Active Queue Management Algorithm Based on Nonlinear Smoothing , 2011 .

[28]  A. A. Zaidan,et al.  A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution , 2018, Health and Technology.

[29]  B. B. Zaidan,et al.  Towards on Develop a Framework for the Evaluation and Benchmarking of Skin Detectors Based on Artificial Intelligent Models Using Multi-Criteria Decision-Making Techniques , 2017, Int. J. Pattern Recognit. Artif. Intell..

[30]  Cory C. Beard,et al.  Design and analysis of multi-level active queue management mechanisms for emergency traffic , 2005, Comput. Commun..

[31]  Serdar Korukoglu,et al.  Effective RED: An algorithm to improve RED's performance by reducing packet loss rate , 2009, J. Netw. Comput. Appl..

[32]  B. B. Zaidan,et al.  Based Real Time Remote Health Monitoring Systems: A Review on Patients Prioritization and Related "Big Data" Using Body Sensors information and Communication Technology , 2018, Journal of Medical Systems.

[33]  Ali Nazari,et al.  Prediction compressive strength of Portland cement-based geopolymers by artificial neural networks , 2012, Neural Computing and Applications.

[34]  Shahram Mohammadi,et al.  Fuzzy-based PID active queue manager for TCP/IP networks , 2010, 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010).

[35]  Mauro Biagi,et al.  Soft Multi-Criteria Decision Algorithm for Vertical Handover in Heterogeneous Networks , 2011, IEEE Communications Letters.

[36]  Shuang-Hua Yang,et al.  The mechanism of adapting RED parameters to TCP traffic , 2009, Comput. Commun..

[37]  Michael E. Woodward,et al.  Two Different Approaches of Active Queue Management , 2007, 2007 IEEE International Conference on Networking, Sensing and Control.

[38]  B. B. Zaidan,et al.  Multi-criteria analysis for OS-EMR software selection problem: A comparative study , 2015, Decis. Support Syst..

[39]  Dalila B.M.M. Fontes,et al.  Multicriteria Decision Making: A Case Study in the Automobile Industry , 2013 .

[40]  Jung-Shian Li,et al.  Random early detection with flow number estimation and queue length feedback control , 2006, J. Syst. Archit..

[41]  Naixue Xiong,et al.  A novel self-tuning feedback controller for active queue management supporting TCP flows , 2010, Inf. Sci..

[42]  Robert Shorten,et al.  Adaptive tuning of drop-tail buffers for reducing queueing delays , 2006, IEEE Communications Letters.

[43]  A. A. Zaidan,et al.  An evaluation and selection problems of OSS-LMS packages , 2016, SpringerPlus.

[44]  Yi Peng,et al.  Soft consensus cost models for group decision making and economic interpretations , 2019, Eur. J. Oper. Res..

[45]  Attahiru Sule Alfa,et al.  Queueing Theory for Telecommunications - Discrete Time Modelling of a Single Node System , 2010 .

[46]  Fadi A. Thabtah,et al.  Performance Analysis of the Proposed Adaptive Gentle Random Early Detection Method under NonCongestion and Congestion Situations , 2011, DEIS.

[47]  Hong Chen,et al.  Active queue management of delay network based on constrained model predictive control , 2011, 2011 Chinese Control and Decision Conference (CCDC).

[48]  Fadi A. Thabtah,et al.  Analytical Models based discrete-Time Queueing for the Congested Network , 2012, Int. J. Model. Simul. Sci. Comput..

[49]  Wu Chen,et al.  An Average Queue Weight Parameterization in a Network Supporting TCP Flows with RED , 2007, 2007 IEEE International Conference on Networking, Sensing and Control.

[50]  Xiande Zhao,et al.  An application of quality function deployment to improve the quality of teaching , 1998 .

[51]  A. A. Zaidan,et al.  A New Approach based on Multi-Dimensional Evaluation and Benchmarking for Data Hiding Techniques , 2017 .

[52]  Marcelo S. Alencar,et al.  On the performance of M-QAM for Nakagami channels subject to gated noise , 2018, Telecommun. Syst..

[53]  F. M. Jumaah,et al.  Decision-making solution based multi-measurement design parameter for optimization of GPS receiver tracking channels in static and dynamic real-time positioning multipath environment , 2018 .

[54]  Richelle V. Adams,et al.  Active Queue Management: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[55]  Shan Suthaharan,et al.  Reduction of queue oscillation in the next generation Internet routers , 2007, Comput. Commun..

[56]  Jie Zhu,et al.  Stability analysis in an AVQ model of Internet congestion control algorithm , 2012 .

[57]  Bin Zhao,et al.  The Yellow active queue management algorithm , 2005, Comput. Networks.

[58]  B. B. Zaidan,et al.  Novel Methodology for Triage and Prioritizing Using "Big Data" Patients with Chronic Heart Diseases Through Telemedicine Environmental , 2017, Int. J. Inf. Technol. Decis. Mak..

[59]  E. Stanley Lee,et al.  An extension of TOPSIS for group decision making , 2007, Math. Comput. Model..

[60]  Kannan Govindan,et al.  ELECTRE: A comprehensive literature review on methodologies and applications , 2016, Eur. J. Oper. Res..

[61]  Yutaka Takahashi,et al.  A queue management algorithm for fair bandwidth allocation , 2007, Comput. Commun..

[62]  A. A. Zaidan,et al.  Comparative study on the evaluation and benchmarking information hiding approaches based multi-measurement analysis using TOPSIS method with different normalisation, separation and context techniques , 2018 .

[63]  Zheng Xue-feng,et al.  Research on the Improved Way of RED Algorithm S-RED , 2016 .

[64]  B. B. Zaidan,et al.  Multiclass Benchmarking Framework for Automated Acute Leukaemia Detection and Classification Based on BWM and Group-VIKOR , 2019, Journal of Medical Systems.

[65]  T. L. Saaty A Scaling Method for Priorities in Hierarchical Structures , 1977 .

[66]  B. B. Zaidan,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 , 2018, Journal of Medical Systems.

[67]  Gang Kou,et al.  Consensus reaching in social network group decision making: Research paradigms and challenges , 2018, Knowl. Based Syst..

[68]  Yang Chen,et al.  Pairwise comparison matrix in multiple criteria decision making , 2016 .

[69]  Gang Kou,et al.  Analysing discussions in social networks using group decision making methods and sentiment analysis , 2018, Inf. Sci..

[70]  Sureswaran Ramadass,et al.  Performance Assessment of AGRED, RED and GRED Congestion Control Algorithms , 2012 .

[71]  Stanley Zionts,et al.  MCDM---If Not a Roman Numeral, Then What? , 1979 .

[72]  R. Baltussen,et al.  Priority setting of health interventions: the need for multi-criteria decision analysis , 2006, Cost effectiveness and resource allocation : C/E.

[73]  C. E. M. Pearce,et al.  Closed queueing networks with batch services , 1990, Queueing Syst. Theory Appl..

[74]  B. B. Zaidan,et al.  Software and Hardware FPGA-Based Digital Watermarking and Steganography Approaches: Toward New Methodology for Evaluation and Benchmarking Using Multi-Criteria Decision-Making Techniques , 2017, J. Circuits Syst. Comput..

[75]  Konstantin E. Samouylov,et al.  Carrying out consensual Group Decision Making processes under social networks using sentiment analysis over comparative expressions , 2019, Knowl. Based Syst..

[76]  Ayman El-Sayed,et al.  Enhanced Random Early Detection (ENRED) , 2014 .

[77]  Saewoong Bahk,et al.  Active queue management algorithm considering queue and load states , 2007, Comput. Commun..

[78]  Ahmad Fakharian,et al.  Design of H Congestion Controller for TCP Networks Based on LMI Formulation , 2015 .

[79]  B. B. Zaidan,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 , 2018, Journal of Medical Systems.

[80]  B. B. Zaidan,et al.  Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS , 2015, J. Biomed. Informatics.

[81]  B. B. Zaidan,et al.  Survey on fuzzy TOPSIS state-of-the-art between 2007 and 2017 , 2019, Comput. Oper. Res..

[82]  Kang Min Lee,et al.  Congestion Control of Active Queue Management Routers Based on LQ-Servo Control , 2008, Eng. Lett..

[83]  Steven H. Low,et al.  REM: active queue management , 2001, IEEE Netw..

[84]  R. Srikant,et al.  TCP-Illinois: A loss- and delay-based congestion control algorithm for high-speed networks , 2008, Perform. Evaluation.

[85]  K. Secnik,et al.  The association between diabetes related medical costs and glycemic control: A retrospective analysis , 2006, Cost effectiveness and resource allocation : C/E.

[86]  A. A. Zaidan,et al.  A methodology for football players selection problem based on multi-measurements criteria analysis , 2017 .

[87]  Bin Wang,et al.  Subsidized RED: an active queue management mechanism for short-lived flows , 2005, Comput. Commun..

[88]  Kang G. Shin,et al.  The BLUE active queue management algorithms , 2002, TNET.

[89]  Andrzej Chydzinski,et al.  Analysis of AQM queues with queue size based packet dropping , 2011, Int. J. Appl. Math. Comput. Sci..

[90]  Sureswaran Ramadass,et al.  Fuzzy Logic Controller of Gentle Random Early Detection Based on Average Queue Length and Delay Rate , 2014 .

[91]  R. Srikant,et al.  An adaptive virtual queue (AVQ) algorithm for active queue management , 2004, IEEE/ACM Transactions on Networking.

[92]  Aduwati Sali,et al.  Multi-complex attributes analysis for optimum GPS baseband receiver tracking channels selection , 2017, 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT).

[93]  Irfan-Ullah Awan,et al.  Controlling mean queuing delay under multi-class bursty and correlated traffic , 2011, J. Comput. Syst. Sci..

[94]  Thomas L. Saaty,et al.  Marketing Applications of the Analytic Hierarchy Process , 1980 .

[95]  Teh-Lu Liao,et al.  Active queue management controller design for TCP communication networks: Variable structure control approach , 2009 .

[96]  K. I. Mohammed,et al.  Based Multiple Heterogeneous Wearable Sensors: A Smart Real-Time Health Monitoring Structured for Hospitals Distributor , 2019, IEEE Access.