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Weight Queue Dynamic Active Queue Management Algorithm S S symmetry Article Weight Queue Dynamic Active Queue Management Algorithm Mahmoud Baklizi Department of Computer Networks Systems, The World Islamic Science & Education University W.I.S.E, Amman 00962, Jordan; [email protected] Received: 16 November 2020; Accepted: 11 December 2020; Published: 14 December 2020 Abstract: The current problem of packets generation and transformation around the world is router congestion, which then leads to a decline in the network performance in term of queuing delay (D) and packet loss (PL). The existing active queue management (AQM) algorithms do not optimize the network performance because these algorithms use static techniques for detecting and reacting to congestion at the router buffer. In this paper, a weight queue active queue management (WQDAQM) based on dynamic monitoring and reacting is proposed. Queue weight and the thresholds are dynamically adjusted based on the traffic load. WQDAQM controls the queue within the router buffer by stabilizing the queue weight between two thresholds dynamically. The WQDAQM algorithm is simulated and compared with the existing active queue management algorithms. The results reveal that the proposed method demonstrates better performance in terms mean queue length, D, PL, and dropping probability, compared to gentle random early detection (GRED), dynamic GRED, and stabilized dynamic GRED in both heavy or no-congestion cases. In detail, in a heavy congestion status, the proposed algorithm overperformed dynamic GRED (DGRED) by 13.3%, GRED by 19.2%, stabilized dynamic GRED (SDGRED) by 6.7% in term of mean queue length (mql). In terms of D in a heavy congestion status, the proposed algorithm overperformed DGRED by 13.3%, GRED by 19.3%, SDGRED by 6.3%. As for PL, the proposed algorithm overperformed DGRED by 15.5%, SDGRED by 19.8%, GRED by 86.3% in term of PL. Keywords: congestion algorithms; GRED; SDGRED; implementation; queue weight 1. Introduction Computer network devices are utilized in smart homes, smart buildings, organizations, universities, companies, and countries. The wide distribution of computer networks is a consequence of many factors, and among these is the emerging of the Internet-of-Things (IoT). The ever-increasing use of computer networks has resulted in increasing computer crimes and cyber-attacks. Moreover, the connected terminals throughout these networks add a heavy load on the network resources and devices [1]. As a result, the amount of data transferred across the network devices, such as computers and routers, greatly increases. To share the network resources [2–6], the transferred data are divided into packets. Every device that generates packets must be temporarily stored at the router buffer before forwarding to the next router or its destination. When the router buffer becomes full, congestion occurs, and it will lead to the case of packet loss (Figure1)[7–10]. Symmetry 2020, 12, 2077; doi:10.3390/sym12122077 www.mdpi.com/journal/symmetry Symmetry 2020, 12, 2077 2 of 16 Symmetry 2020, 12, x FOR PEER REVIEW 2 of 15 FigureFigure 1. 1. CongestedCongested router. router. TCPTCP protocol hashas its its congestion congestion control, control, however, however, congestion congestion control control at the at router the router buffer buffer that results that resultsin using in TCP using over TCP the underlyingover the underlying networks isnetworks not considered is not [ 4considered,11]. Congestion [4,11]. plays Congestion a prominent plays role a prominentin the deterioration role in the of deterioration network performance. of network This performance. phenomenon This increasesphenomenon the queuing increases delay the queuing (D) and delaypacket (D) loss and (PL packet) and decreases loss (PL) and the numberdecreases of the packets number transferred of packet tos theirtransferred destination, to their which destination, is called whichthroughput is called (T) [throughput10,12–14]. To (T) face [10,12–14]. such a challenge, To face such many a activechallenge, queue many management active queue (AQM) management algorithms (AQM)were developed algorithms and were improved developed to detect and congestion improved atto andetect early congestion stage and improveat an early network stage and performance. improve networkSeveral examples performance. include Several the enhanced examples adaptive include gentle the random enhanced early adaptive detection (GRED)gentle random [15], stabilized early detectiondynamic GRED(GRED) (SDGRED) [15], stabilized [16], Markov-modulateddynamic GRED (SDGRED) queuing systems[16], Markov-modulated [17], FL Intelligent queuing Traffic systemsManagement [17], [FL18 ],Intelligent fuzzy logic Traffic approach Management for congestion [18], fuzzy control logic [ 19approach], and dynamic for congestion GRED (DGRED) control [19], [20], andMarkov-modulated dynamic GRED [ 17(DGRED)], FLACC [20], [19 ],Markov-modulated and dynamic stochastic [17], earlyFLACC discovery [19], and [20 dynamic]. These algorithms, stochastic earlyalthough discovery they have [20]. great These influence algorithms, in easing although the congestion, they have sugreatffer frominfluence some in limitations. easing theFor congestion, example, sufferthe DGRED from some algorithm limita adoptstions. For the conceptexample, of the target DGRED average algorithm queue lengthadopts (Ttheaql ),concept and thus of target drops averagea large numberqueue length of packets (Taql),to and statically thus drops stabilize a large the number queue of length packets at to a specificstatically length. stabilize In the addition, queue lengthGRED at algorithms a specific length. use static In addition, thresholds GRED and algorithms qw, which leaduse static to bursty thresholds traffic and problems qw, which and lead rapid to burstycongestion traffic in problems the router and bu rapidffer, congestion respectively. in Therefore,the router buffer, the router respectively. buffer rapidly Therefore, increases the router and bufferbecomes rapidly full. increases and becomes full. TheThe performance measures, measures, delay delay (D), (D), packet packet loss loss (P LL)) and and throughput throughput (T) (T) have have a a primary primary concernconcern because theythey areare used used to to evaluate evaluate whether whethe ther the network network performance performance is satisfactory is satisfactory or not or [not21]. [21].The existingThe existing algorithms algorithms do not do provide not provide the best the performancebest performance according according to the to mean the mean queue queue length length (mql), (Dmql and), D P Land. This PL. is becauseThis is because the existing the existing algorithms algori usedthms a static used technique a static technique for detecting for anddetecting reacting and to reactingcongestion to congestion at the router at butheff er.router Therefore, buffer. aTher dynamicefore, algorithma dynamic must algorith be developedm must be todeveloped manage theto managepacket dropping the packet based dropping on the based dynamic on the weight dynamic queue. weight queue. ThisThis study proposes a new algorithm called we weightight queue dynamic active queue management algorithmalgorithm (WQDAQM)(WQDAQM) to to address address the limitationsthe limitation of thes existingof the onesexisting and improveones and network improve performance. network performance.The proposed The algorithm proposed is integrated algorithm withis integrated the TCP with protocol the TCP to maintain protocol the to maintain established the conversation established conversationfor data exchange for data in high exchange performance in high [22 performance]. The specific [22]. objective The specific of this studyobjective is to of decrease this study the meanis to decreasequeue length the mean (mql), queue PL, and length D compared (mql), PL with, and other D compared algorithms. with The other proposed algorithms. algorithm The proposed controls algorithmand manages controls the packet and manages dropping the by maintainingpacket dropping the queue by maintaining weight and the thresholdsqueue weight dynamically. and the thresholdsThe rest of dynamically. the paper is organizedThe rest of asthe follows, paper is Section organized2 presents as follows, the literature Section 2 review,presents and the Sectionliterature3 review,discusses and the Section proposed 3 discusses WQDAQM the proposed algorithm, WQDAQM Section4 explains algorithm, the simulationSection 4 explains mechanism, the simulation Section5 mechanism,discusses the Section results 5 of discusses all compared the results algorithms, of all andcompared Section 6algorithms, provides the and conclusion. Section 6 provides the conclusion. 2. Related Works Several algorithms were created to manage the queue and control congestion at the router buffer [3,8,11,23]. The default algorithm for congestion control is the drop tail (DT) algorithm [24]. This algorithm uses the first-in first-out (FIFO) principle, in which the arriving packets are dropped once the router buffer overflows, as illustrated in Figure 2 [11]. The DT does not have thresholds, and when the maximum capacity of the router buffer is reached, the new arriving packets are dropped directly. Symmetry 2020, 12, 2077 3 of 16 2. Related Works Several algorithms were created to manage the queue and control congestion at the router buffer [3,8,11,23]. The default algorithm for congestion control is the drop tail (DT) algorithm [24]. This algorithm uses the first-in first-out (FIFO) principle, in which the arriving packets are dropped once the
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