Improvement of SCTP congestion control in the LTE-A network

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Improvement of SCTP congestion control in the LTE-A network Ihab Ahmed Najm a,b, Mahamod Ismail a, Jaime Lloret c, Kayhan Zrar Ghafoor d, B.B. Zaidan e, Abd Al-razak Tareq Rahem a a

Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia Tikrit University, College of Computer and Mathematic Sciences, Iraq c Department of Communications, Polytechnic University of Valencia, Camino de Vera 46022, Valencia, Spain d Faculty of Engineering, Koya University, Daniel Miterrand Boulevard, Koya KOY45, Kurdistan e Department of Computer System & Technology, Faculty of Computer Science & IT, University of Malaya, 50603 Lembah Pantai, Kuala Lumpur, Malaysia b

art ic l e i nf o

a b s t r a c t

Article history: Received 19 March 2015 Received in revised form 3 September 2015 Accepted 5 September 2015

Long Term Evolution-Advanced (LTE-A) is the fourth-generation wireless communication of mobile technology. LTE-A offers a scalable coverage as it possesses efficient throughput and ubiquitous connectivity. The congestion control of the LTE-A transport layer negatively influences the overall performance of the throughput. Moreover, the existing slow-start and congestion avoidance mechanisms helps to reduce the LTE-A performance. Thus, this paper improves the congestion control mechanism by incorporating the Stream Control Transmission Protocol (SCTP) in LTE-A. Specifically, the slow-start and congestion avoidance phases will be improved. The proposed mechanism, called ENH-SCTP, reduces the time duration, towards reaching a threshold, by ranking the congestion window, throughput, queue size and packet loss as performance metrics. The ranking can be achieved by adding a value which selection is based on a multi-criteria problem. Concretely we used the multi-criteria decision-making (MCDM) technique, especially the utilization of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The simulation results show that the proposed SCTP in LTE-A performs better than the conventional SCTP. As a consequence, the congestion window, throughput, queue size and packet loss are significantly improved. & 2015 Published by Elsevier Ltd.

Keywords: SCTP Congestion control LTE-A N-selection TOPSIS

1. Introduction In mobile networks, the demand for high data rates is increasing rapidly. Therefore, the initial concept for a successor to Long Term Evolution (LTE) technology was introduced in 2009 as LTE-Advanced (LTE-A). LTE-A was developed by the 3rd Generation Partnership Project (3GPP) and was evaluated by the International Telecommunications Union (ITU). ITU filed LTE-A under LTE Release 10 and Beyond and rated the concept in terms of the International Mobile Telecommunications-Advanced (IMTAdvanced) requirements. LTE-A improves LTE substantially. In terms of generational division, LTE-A is a high-speed wireless network for mobile phones. It is an extension of LTE (Astorga et al., 2013; Kishiyama et al., 2013; Rumney, 2013; Gani et al., 2014; Najm et al., 2014). LTE-A is a complement and supplement to LTE. Moreover, they use the same frequency bands. Nonetheless, LTE-A is designed to be compatible with the services offered worldwide. E-mail addresses: [email protected] (I.A. Najm), [email protected] (M. Ismail), [email protected] (J. Lloret), [email protected] (K.Z. Ghafoor), [email protected] (A.-r. Rahem).

An appropriate level of Internet capability is also necessary to facilitate its implementation in other mobile systems. LTE-A works well at high data rates from over 100 Mbps to 1 Gbps. To apply LTE-A in different frequency bands, that is, 900, 1800, 2100, and 2600 MHz, frequency blocks of up to 100 MHz are employed at 800 MHz. These blocks are combined and then transferred for radio transmission through the multiple-input multiple-output (MIMO) system. For instance, the 8
8 MIMO system increases the power spectral density. Data rates can reach up to 1 Gbps given channel widths of 100 MHz. The peak link of spectral efficiencies are 15 bits/Hz in the downlink and 6.75 bits/ Hz in the uplink (Ratasuk et al., 2010; Rumney, 2013; Gani et al., 2014; Lloret Mauri et al., 2014; Najm et al., 2014). LTE-A is highly flexible given its self-organizing network (SON) feature. It possesses an intelligent topology that facilitates the reception of signals with minimal interference instead of a topology that generates signals with maximum field strength. It is able to adapt to changes in the payload, in field strength, and in either the number of participants or the relations among radio cells (Ratasuk et al., 2010; Tran et al., 2014). Area repeater stations can be used to improve the coverage of a radio cell. Terminals can also be installed outside the actual cell. LTE-A is backward compatible

http://dx.doi.org/10.1016/j.jnca.2015.09.003 1084-8045/& 2015 Published by Elsevier Ltd.

Please cite this article as: Najm IA, et al. Improvement of SCTP congestion control in the LTE-A network. Journal of Network and Computer Applications (2015), http://dx.doi.org/10.1016/j.jnca.2015.09.003i

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with LTE, and its key technologies include carrier aggregation (CA), advanced relaying, and coordinated multipoint transmission (Abd El Al et al., 2004; Yu and Wei, 2010; Ratasuk et al., 2010; Kishiyama et al., 2013). Table 1 illustrates the basic differences between LTE and LTE-A. The current study utilizes LTE-A as a backbone in network topology. This topology launches a specific protocol within the transport layer. As a platform, the transport layer must have specific features derived from the internal protocols. These protocols should include an error detection mechanism that begins from the source, and transitions to the correct destination, that manages and balances bandwidth among connections or sessions, and that re-transmits lost segments or packets. Some applications require reliable environments; thus, in addition to the aforementioned features, the transport layer must recognize head-of-line blocking and either order received segments or protocol data units (Caro Jr et al., 2003; Aydin et al., 2012). The Stream Control Transmission Protocol (SCTP) operates in the transport layer. It combines the features of the user datagram protocol (UDP) and the transmission control protocol (TCP) at the endpoint. Moreover, SCTP reliably transports data over packetbased networks. It checks the data to be posted over the network for faults based on the proper packet sequence. It also maintains the connection between two endpoints throughout the process. SCTP features are compared with those of TCP and UDP in (Stewart and Xie, 2002). The standard SCTP was developed by the Internet Engineering Task Force (Alamgir et al., 2002; Ansay et al., 2011; Aydin et al., 2012; Wallace et al., 2015). It exhibits unique and positive features, such as multihoming and multistreaming, in addition to a fourway handshake (Stewart and Xie, 2002; Najm et al., 2014). In the current study, we aim to address the degradation in performance when SCTP is adapted to LTE-A. Consequently, congestion control mechanisms are performed stepwise, slow-start and congestion avoidance phases are significant, and congestion control window (cwnd) increases moderately slowly. Moreover, both throughput and packet delivery ratio over networks are poor (Ahmed et al., 2003; Duke et al., 2003; Chen et al., 2008; Najm et al., 2014). As per literature, SCTP congestion control is weak, especially when it is integrated into LTE-A. LTE-A is a high-impact infrastructure (Qin-long et al., 2009; Eklund et al., 2010; Kishiyama et al., 2013; Wallace et al., 2015); therefore, the current paper presents an enhanced SCTP (ENH-STP) congestion control method. This method is a viable replacement for SCTP over LTE-A.

Table 1 Comparison of LTE and LTE-A. LTE (Rel. 8–9) LTE category Downlink (in MBit/s) Uplink (in MBit/ s) MIMO antenna system Frequency boundary CA Modulation downlink Modulation uplink

LTEA (Rel. 10)

3 100

4 150

5 300

6 300

7 300

50

50

75

50

150

2
2 MIMO

4
4 MIMO

8 1200 600 a

Up to 20 MHz

min. 2
2 several 8
8 MIMO 20 MHz to 100 MHz per CA

NA QPSK, 16 QAM, 64 QAM

Yes 64 QAM

QPSK, 16 QAM

16 QAM

QPSK, 16 QAM, 64 QAM

64 QAM 64 QAM

a This term indicates that MIMO antenna systems can be combined in several ways, such as 2
4 or 4
4.

The rest of the paper is organized as follows. Section 2 reviews the literature on SCTP congestion control; specifically, that on the congestion control of SCTP RFC 2960, and mainly on studies regarding to slow start and congestion avoidance phases. Section 3 presents the mathematical model of the proposed SCTP congestion control method. Section 4 discusses the simulation conducted to determine the effects of selected parameters, as well as the use of the Multi-Criteria Decision-Making (MCDM). The final section presents the conclusions drawn from the study and our future work.

2. Related work The congestion control mechanism of SCTP determines its performance in various environments, including Ethernet, Wi-Fi, WiMAX, and LTE-A. SCTP is reliable and exhibits the unique features of multistreaming and multihoming. However, these features demand much from the SCTP congestion control algorithm. Moreover, the conventional SCTP congestion control algorithms over LTE-A presenting poor performance in terms of cwnd, throughput and send/receive/lose packets (Abed et al., 2012; Ahuja and Shore, 2013; Najm et al., 2014). Many studies have improved the features of SCTP. For instance, Ahmed et al. (2003) presented a new SCTP congestion control method by increasing the amount of cwnd to improve SCTP over high-latency broadband networks. Specific values were assigned for slow start, and cwnd decreased in the congestion avoidance phase because the current congestion control algorithms must be refined further, especially in highbroadband networks. Nonetheless, the experiment results are based on only two nodes. Following the trend of improving SCTP in simulation environments, Caro et al. (2003) and Caro et al. (2003) compared TCP (selective acknowledgment (SACK) and forward acknowledgment (FACK)) with the New Reno SCTP by adopting the new highest transmission sequence number (TSN) ACKed. Although this method improved only a recovery phase and did not affect the throughput. Following the trend of improving SCTP in heterogeneous networks, Jyh-Ming et al. (2011) presented an improved SCTP based on jitter-based congestion that applies end-to-end semantics over wired–wireless networks. However, the process of calculating a timestamp-based mechanism is unclear, as is the congestion control component into which this mechanism has been integrated. Furthermore, Yu and Wen (2014) proposed a new SCTP congestion control to address performance problems over high-speed networks and improve SCTP. This protocol remains superior to others, including TCP, despite its diminished performance. However, this protocol does not identify the environment in which it has been applied. Following the trend of enhancing SCTP, Ho et al. (2007) proposed an enhanced SCTP called receiver bandwidth estimation SCTP. This protocol depends on the estimated available bandwidth on the receiver side. However, the chunk loss rate method applied is vague. Chen et al. (2008) also employed a new strategy that combines load-sharing strategy with a new congestion control mechanism. This strategy enhances the performance of SCTP over satellite links; however, the proposed solution is applicable only for multihoming. To improve SCTP in wireless networks, Ye Na et al. (2010) proposed an enhanced multihoming SCTP based on SACK. This protocol handles the disconnection in wireless networks. Nonetheless, the conducting results addressed TSN as performance factor. To improve SCTP, Kozlovszky et al. (2006) evaluated and compared SCTP and TCP in terms of speed, latency, and peer-to-peer communication within data acquisition systems. However, the proposed method is based only on Ethernet. To enhance the SCTP in wireless local area networks, Cao and Liu (2010) presented a new congestion control model based on the relationship between the

Please cite this article as: Najm IA, et al. Improvement of SCTP congestion control in the LTE-A network. Journal of Network and Computer Applications (2015), http://dx.doi.org/10.1016/j.jnca.2015.09.003i

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throughput and the congestion control mechanism. However, the mathematical model for the proposed technique is indistinct. In addition, the results of this method must be refined further. Many studies also assessed SCTP in various networks. Alamgir et al. (2002), Chen (2009), Deepak and Chief (2011), Abed et al. (2012), Ahuja and Shore (2013), and Najm et al. (2014) compared and evaluated SCTP and TCP by applying these protocols to highspeed networks. Some of these studies used LTE-A. Brennan and Curran (2001) assessed the effect of the SCTP congestion control mechanism on burst packets after recovery through simulation. Eklund et al. (2010) improved the configuration of the SCTP mechanism for carrier-grade telephony and suggested that SCTP is pivotal in LTE/LTE-A. Hurtig and Brunstrom (2008) enhanced loss recovery and early retransmission. This study also evaluated SCTP performance. Zou et al. (2006) assessed the performance of SCTP subflows and of subflow grouping streams based on the required Quality of Service (QoS). The proposed solution considered individual flows with their own congestion mechanisms to avoid false sharing. Many studies also investigated the congestion control algorithms of SCTP. Specifically, those focused on Concurrent Multipath Transfer (CMT)-SCTP. For instance, Abd El Al et al. (2004) aggregated the bandwidths of the existing interfaces in their proposal. In this simulation study, the load sharing of SCTP aggregated the bandwidths of all active transmission paths among connected terminals. Dreibholz et al. (2010, 2011a, 2011b, 2012) combined CMT with resource pool notation to improve non-CMT transfer and presented new open-source tools to measure and evaluate both CMT-SCTP and multipath TCP. Similarly, Arshad and Saleem (2010) investigated the end-to-end transport layer protocol delay and compared FAST TCP and SCTP through simulation. Subsequently, Chang et al. (2013) launched a QoS-conscious pathswitching policy to improve SCTP performance. Leu et al. (2011) then presented a scheme that selects the fastest path as a primary path before data transmission. Han et al. (2014) employed the transport layer handover mechanism and considered QoS requirements to be the main criteria in path switching. Inwhee and Sijia (2009) and Inwhee and Erzheng (2012) improved SCTP performance through automatic path switching that depends on path condition and best load sharing. In all these papers, the authors developed a new congestion control mechanism. To improve CMT-SCTP, Ansay et al. (2011) proposed a new retransmission policy in multihoming by generating alternate paths for retransmission. To evaluate CMT-SCTP, Aydin et al. (2012) evaluated SCTP multihoming. They compared it with TCP in terms of friendliness. In fact, friendliness is an aggregate coordination protocol; and they claimed that this protocol is TCP-friendly. Duke et al. (2003) applied SCTP over a land mobile satellite channel and employed TCP in the same environment. Subsequently, Hassayoun et al. (2011) introduced dynamic window coupling, which used performance factors such as delay and packet loss to detect and select a favorable path. Wallace and Shami (2014) proposed two modeling techniques: one is based on renewal theory, and the other applies the Markov chain. To enhance CMT-SCTP, Iyengar et al. (2007) improved CMT-SCTP using various retransmission strategies and several constrained receipt buffers. Our work aims to develop suitable congestion control algorithms for SCTP, to adapt it to LTE-A, and to evaluate the performance factors according to the standard SCTP (STD-SCTP) at Stewart and Xie (2000). The analysis of the literature depicted several parameters to assess/evaluate or utilized as evaluation metric, but in this research we use these parameters during the development phase along with multi-criteria evaluation to select the most optimistic threshold to adapt SCTP to LTE-A.

3

3. Proposed SCTP congestion control method The congestion control algorithm is the core engine of SCTP and operates throughout the transport layer. As long as SCTP works across this layer, many tasks can be accomplished, including the sending and receiving of packets, error detection, and the recovery of lost packets. These tasks are accommodated according to the available bandwidth of the network. Moreover, the number of endpoints used to manage these tasks is analyzed in detail, because they are used as the congestion control mechanism. An effective congestion control process ensures that a specific packet successfully reaches its destination and that the retransmission is correct in case the sending process fails. This process is complicated when transfer traffic is high, the available resources for sending are limited, and the network contains numerous endpoints. SCTP congestion control is divided into many phases. Two of these phases, namely, the slow start and congestion avoidance phases, are enhanced in the current study through the proposed SCTP congestion control algorithms. The proposed congestion control for SCTP over LTE-A is highly promising, especially in comparison with existing poor, unrefined congestion control algorithms (Ahmed et al., 2003; Duke et al., 2003; Chen et al., 2008). The algorithms for slow start and congestion avoidance work together to control traffic and to balance the amount of data streamed within a specific network (Abd El Al et al., 2004; Ahuja and Shore, 2013). The congestion control in SCTP can be implemented in the same links of path followed by the SCTP association, where SCTP association is represented and defined by the corresponding transport address on either side of association. This control may moderate the sender more effectively than the algorithms can. Moreover, the sender must not exceed the allowable algorithm regulations (Chen, 2009; Yu and Wen , 2014; Wallace and Shami, 2014). As in TCP, an SCTP endpoint uses the following four variables to determine the transmission process.

 Receiver advertised window size (rwnd) is measured in bytes

 



and is set by the receiver based on the readily available buffer size for incoming packets. rwnd is present within all associations. Congestion control window (cwnd) is also measured in bytes. The cwnd assigned by the sender depends on the network status maintained in the predestination address. Slow start threshold (ssthresh) is measured in bytes and is used by the sender to differentiate slow start and congestion avoidance phases (Alamgir et al. 2002; Ho et al., 2007; Ansay et al., 2011; Aydin et al., 2012; Dreibholz et al., 2012; Najm et al., 2014). Partial byte acknowledgment (pba) is set in the congestion avoidance phase to facilitate cwnd modulation.

The proposed congestion control of SCTP protocol uses modified techniques that are related to the slow start and congestion avoidance phases. Fig. 1 illustrates the operations of the suggested mechanisms and elucidates each step with respect to the congestion control paradigm of the enhanced SCTP. In the data transmission process, a sender must be connected with a receiver. The sender establishes a connection with a specific destination. It sends the first packet and waits for the ACKed in order to detect cwnd and ssthresh. When the receiver sends ACKed to the sender, then both cwnd and ssthresh are known. Therefore, the congestion control is achieved. If the receiver does not send ACKed, the recovery phase is applied. If cwnd is lower than or equal to ssthresh, then the proposed slow start phase is executed. When cwnd is greater than ssthresh, the proposed congestion avoidance phase is initiated. This operation continues until all packets are

Please cite this article as: Najm IA, et al. Improvement of SCTP congestion control in the LTE-A network. Journal of Network and Computer Applications (2015), http://dx.doi.org/10.1016/j.jnca.2015.09.003i

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Fig. 1. Flowchart of the proposed congestion control mechanism.

sent completely. The process of computing the proposed congestion control is described in Section 3.1, while the variables listed in the flowchart are defined in Section 3.2. In the following sections, the aforementioned congestion control phases are described in detail. 3.1. Slow start phase This phase is initiated at the beginning of the data transmission under anonymous conditions or after a long period during which the SCTP searches the network to determine the available bandwidth capacity. ssthresh acts as an indicator of the available bandwidth to avoid mechanisms that exceed the available resources. The slow start phase can also take place after a packet loss repair, as detected by the retransmit timer. If cwnd rssthresh, cwnd increases after each acknowledgment. As a result, the value of cwnd increases exponentially. In this phase, the sender must not demand more than the algorithm allows. The exponential enhancement of slow start is described below: 1. cwnd ¼cwnd þz. z is the minimum either the total size of the previous outstanding DATA chunk(s) acknowledged (Data acked), or , is the path maximum transmission unit (PMTU) of the destination (Brennan and Curran, 2001; Guanhua et al., 2002; Chen 2009). The standard slow start equation is expressed as follows:

Δcwnd slow start ¼ minðData acked; PMTU Þ

ð1Þ

Where Data acknowledged (acked) is the total size of the previous outstanding acknowledged DATA chunk(s), and PMTU is the

path maximum transmission unit (PMTU) of the destination (Brennan and Curran, 2001; Guanhua et al., 2002; Chen 2009). The proposed slow start equation is written as follows:

Δcwnd slow start ¼ minðData acked; PMTU Þ þ A

ð2Þ

where A¼ cwnd/N. N is an integer number that ranges between 1 and 9 and it is determined according to several performance factors. These factors are the number of packets sent per period, the number of packets received per time unit, the total number of dropped packets, congestion window, and network throughput. To obtain the best N value, MCDM is conducted by using TOPSIS, which is explained below. TOPSIS consists of the following steps: Step 1: Construction of the normalized decision matrix In this step, various dimensional attributes are transformed into non-dimensional attributes. This method facilitates the comparison of attributes. Matrix ðxij Þmn is then normalized to the matrix R ¼ ðr ij Þmn using the normalization method. Let D be the decision matrix used to rank the performance of alternative Ai, in our case N is an integer number between 1 to 9 which represents the alternatives to be ranked by TOPSIS. Cj is the evaluation criteria of alternatives N, in particular, we utilize four performance metrics namely, cwnd, throughput, packet lose and queue size, so j¼4. Normalization is the process of converting unit data to unit less-data with a uniform score between 0 and 1. For instance, throughout measure with hundred thousand while packet lose measure with ten. The compassion between these two different scales is not possible, while normalization is a preprocess for any future aggregation and score measurement. This

Please cite this article as: Najm IA, et al. Improvement of SCTP congestion control in the LTE-A network. Journal of Network and Computer Applications (2015), http://dx.doi.org/10.1016/j.jnca.2015.09.003i

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process generates a new matrix R, which is presented as follows: 2 3 r 11 r 12 … r 1n 6 r … r 2n 7 6 21 r 22 7 7: ð3Þ R¼ 6 6 ⋮ ⋮ ⋮ ⋮ 7 4 5 r m1 r m2 … r mn

The values in this matrix are estimated following next equation. qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Xm r ij ¼ xij = x2 : ð4Þ i ¼ 1 ij Step 2: Construction of the weighted normalized decision matrix In this process, a set of weights w ¼ w1 ; w2 ; w3 ; ⋯; wj ; ⋯; wn provided by the decision maker is accommodated in the normalized decision matrix. The resultant matrix can be calculated by multiplying each column from the normalized decision matrix (R) with its associated weight wj. The value of the set of the weights is equal to 1. It should be noted that the importance of criteria is hard to be measured, because all criteria seems to be important. Therefore, in our case study we treated all criteria equally in terms of importance. m X

wj ¼ 1:

ð5Þ

j¼1

This process results in a new matrix V, lows: 2 3 2 w1 r 11 … v1n v11 v12 6 v 7 6 w r v … v 22 2n 7 6 21 6 1 21 7¼6 V ¼6 6 ⋮ 7 6 ⋮ ⋮ ⋮ ⋮ 4 5 4 vm1 vm2 … vmn w1 r m1

which is written as folw2 r 12

…

w2 r 22

…

⋮

⋮

w2 r m2

…

wn r 1n

3

wn r 2n 7 7 7: 7 ⋮ 5 wn r mn ð6Þ

Step 3: Determination of the ideal and negative ideal solutions In this step, the two artificial alternatives A (the ideal alternative) and A  (the negative ideal alternative) are defined as    max vij j jA JÞ; min vij j j A J  Þj i ¼ 1; 2; …; m A ¼ i i n o ¼ v1 ; v2 ; …; vj ; ⋯vn ; ð7Þ A ¼



 min vij j j A JÞ; i

¼ v1 ; v2 ; …; vj ; ⋯vn ;

max vij j j A J  Þj i ¼ 1; 2; …; m



i

ð8Þ

  where J is a subset of i ¼ 1; 2; …; m that contains beneficial attributes (in this case, size, robustness, and complexity). J  is the set that complements J. This variable can be denoted as Jc and corresponds to the set of cost attributes. In our case, the best performance behavior is when metrics (throughput, cwnd, queue size, etc.) have high value (in the case of packet loss is low value). Step 4: Separation measurement based on Euclidean distance In this step, separation is measured by calculating the distance between each alternative in V and the ideal vector A*, according to Euclidean distance, is given by vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uX

2 u n  Si ¼ t ð9Þ vij  vj ; i ¼ ð1; 2; ⋯mÞ: j¼1

Similarly, the separation measurement for each alternative in V

from the negative ideal A  is expressed as vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uX

2 u n vij  vj ; i ¼ ð1; 2; ⋯mÞ: Si  ¼ t

5

ð10Þ

j¼1

At the end of this step, two values, namely, Si and Si  , have been counted for each alternative. These two values represent the distance between each alternative, as well as both the ideal and negative ideal alternatives. In this step we will calculate the distance between each one of the nine alternatives to the positive ideal, consequently, to the negative ideal generated from the previous step. Step 5: Calculation of closeness to the ideal solution In the process, the closeness of Ai to the ideal solution A* is defined as C i ¼ Si  =ðSi  þ Si Þ; 0 o C i o1; i ¼ ð1; 2; ⋯mÞ:

ð11Þ

C i ¼ 1 if and only if Ai ¼ A . Similarly, C i ¼ 0 if and only if Ai ¼ A  . Step 6: Rank of the alternatives according to closeness to the ideal solution The set of the alternative Ai can then be ranked according to C i in descending order. The higher the value, the better the performance is.The aim of the selection process is to choose the most effective performance factors influencing the congestion control mechanism. The best N is selected by substituting a value of N into the proposed equation. The determination of the best N depends on the outcomes of the performance factors. The ideal outcome is as follows: limited packet loss, maximum throughput, maximum number of packets sent and received, maximized queue size utilization, and maximum cwnd. When N value exceeds 9, packet loss is aggravated. This occurrence in turn affects the queue size. N values range between 1 and 9 based on the aforementioned considerations, the experimental results, and the performance factor outcomes. The selection process accommodates all of the given considerations. The aforementioned procedure is supported by the processes of cwnd value selection and of congestion control formation based on the MCDM. Many previous studies applied TOPSIS in network selection, including studies on handover decision problems, handoff decision strategies for heterogeneous wireless networks, and interface selection in heterogeneous wireless networks (Siddiqui and Zeadally, 2006; Stevens-Navarro and Wong, 2006; Phuoc and Boukhatem, 2008; Savitha and Chandrasekar, 2011). We simulated STD-SCTP over LTE-A and observed that cwnd increases gradually in linear increments. Given that cwndr ssthresh, cwnd increases after each acknowledgment according to a particular time period, as indicated in Fig. 2. In fact, Simulation environment and parameters illustrated in 4 and 4.1 respectively. Fig. 2 plotting the cwnd values, where values outcomes through hands-on analysis of STD-SCTP which implemented over LTE-a environment, the values fetched from the testbed which executed inside the simulator. Assuming that a and b are two specific times, F(a) is the cwnd value at time a and F(b) is the cwnd value at time b. Let G(x) be the modified cwnd at times a and b, then G(a) is the modified cwnd value at time a and G(b) is the modified cwnd value at time b, as represented in Fig. 3. As per Fig. 3, G(x) is preferred in terms of cwnd value; therefore, F(x) must approach G(x) to achieve the same value. Let β be the angle between F(x) and G(x), and α is the angle between F(x) and the x-axis. θ is α þ β (the angle between G(x) and x-axis). F(a) is the cwnd value of STD-SCTP at time a, F(b) is the cwnd value of

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6

The congestion avoidance mechanism works simultaneously with slow start because cwnd is always compared with ssthresh. However, congestion avoidance is the dominant phase if cwnd is greater than ssthresh; otherwise, the slow start is established. These phases are chiefly occupied with each other, especially with the variation of the cwnd routes to be occupied by certain phases. The standard congestion avoidance equation is expressed as:

250

200

Packets

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66

150

100

Δcwnd CA ¼ 1
PMTU per RTT:

50

0

ð15Þ

The proposed congestion avoidance equation is written as: 2

0

4

a

6

8

Time (Seconds)

b

10

12

ð16Þ

where Ba 0 and B ¼cwnd/N; on the basis of the N value and the previously discussed selection mechanism, the proposed congestion avoidance equation subtracts B. Reducing congestion avoidance by this amount accelerates the congestion control process instead of downgrading it slowly as in the standard congestion avoidance equation. Furthermore, the increase in slow start and the decreased congestion avoidance optimizes and refines the congestion control of SCTP (Ahmed et al., 2003). Therefore, the proposed congestion avoidance equation should be subject to total average change and converted into a nonhomogeneous equation, as described previously.

STD-SCTP

Fig. 2. Congestion window in standard SCTP over LTE-A.

G(x) G(b) F(x)

F(b)

Δcwnd CA ¼ ð1
PMTU per RTTÞ–B;

G(a) 4. Performance evaluation

F(a)

θ

β

α a

b

Fig. 3. Visualization of the congestion window for standard SCTP over LTE-A and the expected congestion window.

STD-SCTP at time b, G(a) is the cwnd value of ENH-SCTP at time a, and G(b) is the cwnd value of ENH-SCTP at time b. Observing Fig. 3, we obtain Eqs. (12) and (13).  GðbÞ  GðaÞ sin θ ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; 2 2 2 ðb  aÞ þ ðGðbÞ  GðaÞÞ

4.1. Simulation setup ð12Þ

and, F ðbÞ  F ðaÞ sin ðαÞ ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; 2 2 2 ðb  aÞ þ ðF ðbÞ F ðaÞÞ

ð13Þ

Taking into account that ΔQ¼G(b)  G(a), ΔZ¼F(b)  F(a) and ΔT ¼b  a, total average change in time (Hughes-Hallett et al. 2014). Dividing Eq. (12) by Eq. (13), it is obtained  ΔQ sin θ ΔZ  ¼ p ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi : 2 sin θ  β ΔT 2 þ ΔQ ffi2 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi p 2 2 2

In this section, we discuss the experiments we conducted using the network simulator ns-2 (ns-2, 2015) to evaluate the performance of the proposed SCTP protocol versus STD-SCTP (UC Berkeley et al., 2011; Chowdhury and Jony, 2014). Specifically, we focus on the simulation setup and results, as well as on comparing the findings with those of the state-of-the art TCP, SCTP, and ENHSCTP over LTE-A. We conducted a paired sample t-test to illustrate the significant variance between STD-SCTP results and those of ENH-SCTP.

ð14Þ

ΔT þ ΔZ

Thus, ΔZ approaches ΔQ when ß approaches 0. Furthermore, the total average change in F(x) must increase in order to increase the value of cwnd. 3.2. Congestion avoidance phase This phase is established when either the amount of data transmitted or the value of cwnd exceeds the threshold. Thus, congestion avoidance enhances cwnd by 1
MTU per round-trip time (RTT) when cwnd is greater than ssthresh. The sender either has a cwnd value or transmits data that exceeds the outstanding amount for the specific transport address.

In order to perform our simulation, we apply SCTP traffic in a LTE-A topology (see Fig. 4). Each user endpoint (UE) is a HTTP client. The TCP and SCTP of each UE are linked to Node-B (eNB) unidirectionally. The uplink bandwidth is 500 Mbps and the delay is 2 ms for the UE, while the downlink bandwidth is 1 Gbps, and its delay is similar to the uplink bandwidth. We used the typical air queue for both uplink and downlink. The chunk size is 1468 bytes and the message transmission unit (MTU) size is 1500 bytes. Two base stations, namely eNB1 and eNB2, are linked to the group of UEs. Both eNBs are connected to the main gateway (aGW) by two unidirectional links. The upload link bandwidth is 5 Gbps, and its delay is 10 ms. The bandwidth and delay of the download link are similar to those of the upload link. aGW is used as an http/cache server. It is linked to the server through a bi-directional link. This link has a bandwidth of 10 Gbps. Its delay is 100 ms. The queue uses the drop-tail management algorithm. The metric is assumed to almost experience bottleneck. Furthermore, our experiment focuses on measuring ENH-SCTP when there is high traffic. The server also acts as a TCP sink, HTTP server, and the endpoint of SCTP association. 4.2. Simulation results Various experiments were conducted to determine the effect of SCTP and LTE-A parameters on the proposed protocol and on existing SCTP congestion control solutions. The congestion control

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7

UE

eNB1 Drop Tail

aGW

Server

UE

eNB2 aGW: Main gateway. eNB: Base station. UE: User endpoint. Fig. 4. Network topology.

Table 2 Performance factors of N-alternatives. N

cwnd

Throughput

Queue size

Pkt loss

A*

A

Overall score

1 2 3 4 5 6 7 8 9

120 130 145 200 205 212 202 225 235

9,881,687 9,912,326 9,905,462 10,120,778 9,902,374 10,023,750 10,264,182 10,106,678 10,368,886

48,424.92 56,199.3 53,788.4 57,674 51,274.2 51,180.7 57,581.3 52,895.4 59,680.9

209.75 81.75 24 27 43.5 64.5 70.5 94.5 104

0.680099 0.273515 0.162449 0.06392 0.10123 0.155842 0.172185 0.249343 0.2781

0 0.447845 0.64803 0.653065 0.597051 0.530305 0.50819 0.441747 0.425122

0 0.620835 0.799564 0.910848 0.855029 0.772874 0.746926 0.639203 0.604535

mechanism is evaluated using the following performance factors. It is worth mentioning that all performance metrics, like cwnd, throughout, queue size and packet delivery, fetched from the trace file which was generated by the simulation environment. 4.2.1. Effect of N selection The obtained performance metrics are listed in Table 2 with regard to the averages of cwnd, throughout, queue size, and packet loss. They are used for selecting N. In addition, A* is a positive ideal closeness, while A  is a negative ideal closeness. These parameters have been previously described in Section 3. As mentioned in Section 3, the value of N is incorporated into the equations for the proposed SCTP congestion control. When different N values are applied to the ENH-SCTP in nine scenarios, cwnd increases with each increment of N except when N is 7 (in this case, cwnd decreases). Moreover, this value increased sharply when N increased from 3 to 4. By contrast, the combined increments in throughput increments were not sequential. When N increased from 1 to 4, the throughput increases. However, the throughput decreased when N increased to 5. When N was 7, the throughput increased, but when N increased up to 8, the throughput decreased. When N increased to 9, it obtained the maximum throughput. Hence, increasing N generally increases the throughput. Furthermore, joint queue size increased in an irregular manner. Thus, an increase in N both increased and decreased queue size. Finally, N began to increase with high packet loss rate.

Packet loss decreased until N was equal to 3; therefore, this loss increases progressively. As a result, It is difficult to determine the ideal N value. MCDM was thus conducted for this purpose; specifically, TOPSIS was applied. TOPSIS counts the scores of the alternatives by calculating to the closeness of each alternative to the positive ideal. The results of the positive and negative ideals, as well as the closeness of the N-alternatives, are reported in Table 2. The total performance score of each N with respect to the results from nine experiments is reported in Fig. 5. It confirms that N ¼4 is the best solution when all of the criteria are equally important. If we vary the importance of performance factors the overall score of the alternatives will change. In our experiment, these factors are treated equally, whereas other scenarios are neglected. 4.2.2. Effect of congestion window The experimental results show the effect of the congestion window on the congestion control of SCTP and on the existing protocols. Thus, we can observe how cwnd solutions support the increase of cwnd value. The scenario includes STD-SCTP, ENH-SCTP and TCP. This experiment aimed to clarify the behavior of each protocol during high-traffic situations. The behaviors of these protocols were determined by measuring performance factors, such as the number of packets sent, the number of packets received, cwnd plot, throughput, packet loss, and queue size. Each performance parameter was measured at

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8

Score

1200

STDSCTP1

1000

STDSCTP2

800

STDSCTP3

600

STDSCTP4

1 0.9

packets

0.8 0.7 0.6 0.5

ENHSCTP1

400

ENHSCTP2

0.4 0.3

200

0.2

0

ENHSCTP3

0

0.1

10

20

30

40

seconds.

0 1

2

3

4

5

6

7

8

50

ENHSCTP4 TCP

9

Fig. 7. cwnd for standard and enhanced SCTPs.

N Fig. 5. Overall score of N-alternatives. 450 400 350 300

Packets

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66

250 200 150 100 50 0 0

2

4

6

8

10

Time (Seconds) STD-SCTP

ENH-SCTP

Fig. 6. cwnd values for standard SCTP and enhanced SCTP.

various simulation times, that is, 10, 20, 30, and 50 s. The results display a constant pattern in terms of time at 2 and 5 min; therefore, the samples were confined to these time periods. In this study, we selected the performance parameters from 10 s to 50 s according to the diversity of the outcomes, as depicted in Figs. 6 and 7. The cwnd values were plotted based on the number of packets delivered at a specific time. In Fig. 6, the effect of cwnd on STDSCTP is indicated in square marker line, whereas ENH-SCTP is shown in circle marker line. The simulation time was set to 10 s. Both scenarios represent the effect of cwnd on SCTP congestion control. The proposed congestion control of ENH-SCTP displays better results than STD-SCTP during the simulation time period. Specifically, the cwnd of ENH-SCTP reached 400 packets, whereas STD-SCTP reached 200 packets. The performance of STD-SCTP exhibited sustained degradation, as it is shown in Fig. 6. This degradation is detected from 0.3 s to 1.5 s. The degradation in STD-SCTP performance may be attributed to the congestion control of SCTP RFC 2960. ENH-SCTP is clearly superior to STD-SCTP in terms of overall cwnd performance. STD-SCTP performs poorly, especially when it is used in LTE-A. The difference of both SCTPs performance levels is ascribed to their congestion control mechanisms. Furthermore, an increased cwnd denotes improved behavior when these SCTPs are tested in the same environment. A scenario that is more complicated than the aforementioned one is presented in Fig. 7. The cwnd performance results considered eight SCTP connections between UE and eNB, namely, four STD-SCTP connections and four ENH-SCTP connections. A TCP connection was also included. The overall connection was supported by the high-impact LTE-A, and the simulation time is set to 50 s. This simulated high-impact environment precisely highlights

the behavior of each SCTP and TCP in a situation wherein the bandwidth is almost fully utilized or in a situation with a real high-traffic network. The plotting of cwnd depends on the number of packets reached at a specific time. Fig. 7 marks the cwnd of the STD-SCTP group in long dash line and the one of the ENH-SCTP group in plus (þ) marker line. The simulation time was set to 50 s. The cwnd for the ENH-SCTP group generated better results than for STD-SCTP in the same time period. In particular, ENH-SCTP reached 1000 packets, whereas STD-SCTP only reached 730 packets. cwnd for TCP is also depicted in solid line. This variation in cwnd performance results is related to the congestion control mechanisms of STD-SCTP and ENH-SCTP. To determine the significant difference between the mean of cwnd for STD-SCTP and for ENH-SCTP, a paired sample t-test was conducted. In a time period of 10 ms, the cwnd mean for STD-SCTP (m ¼114.12 packets) is less than for ENH-SCT (m ¼200.15 packets). Similarly, the cwnd mean for STD-SCTP at 50 s (m ¼372.36 packets) is less than for ENH-SCT (m ¼488 packets). To determine the significance of the difference between the results of the two SCTPs in terms of cwnd, we applied the equation t(df)¼t state, pr 0.5. df denotes degree of freedom, t state indicates a sample mean of scores that deviate from the expected value of zero, and p denotes the probability of the significance of two tails. Therefore, t(34) ¼ 7.537, p r0.5 in a time period of 10 s; and t(166) ¼ 20.880, p r0.5 in a time period of 50 s. p value is 0.000, which is less than or equal to 0.5 for both scenarios (i.e., 10 and 50 s). Thus, cwnd STDSCTP and cwnd ENH-SCTP differ significantly and indicate that the proposed cwnd improved the standard SCTP. 4.2.3. Effect of packet delivery in the network Packets are another interesting performance measure that can indicate the behavior of ENH-SCTP in comparison with STD-SCTP. This factor includes the number of packets sent, the number of packets received, and the packets dropped or lost. Packets can also measure the behavior of the congestion control. The packets were classified according to packet transmission type (send, receive, drop) for the simulation. This transmission type corresponds to the packet acknowledgment type in the trace file. The total number of packets sent, received, and dropped are calculated at a specific time and are categorized according to the packet type. Fig. 8 shows the highly divergent results between STD-SCTP and ENH-SCTP in terms of the number of packets sent, received, and lost. The sample is based on one connection, and the time period is 10 s. STD-SCTP sent a total of 17,169 packets, whereas ENH-SCTP sent a total of 21,337 packets. The total number of packets received by STD-SCTP was 17,145 packets, whereas ENHSCTP received 21,225 packets. Furthermore, STD-SCTP lost 32 packets, whereas ENH-SCTP lost 27 packets.

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25000 100

2 21225

21337 20000 17145

17169

80

Packets

15000 10000

60

40

5000 32

27 20

0 NUMBER OF PACKETS SENT

NUMBER OF PACKETS RECEIVED

NUMBER OF PACKETS LOST

0 0

Original SCTP

Enhanced SCTP

5

10

15

20

25

30

35

40

45

50

Time (Seconds) STD-SCTP

Fig. 8. Numbers of packets sent, received, and lost.

ENH-SCTP

Fig. 10. Comparison of the throughputs for standard and enhanced SCTP. 120 100 80

Mbps

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66

9

60 40 20 0 0

2

4

6

8

10

Time (Seconds) STD-SCTP

Enh-SCTP

Fig. 9. Throughput for standard and enhanced SCTP.

In sum, the percentages of enhancement with the use of ENHSCTP are as follows:

 The rate of packets sent increased by 20%. This value is calculated using next equation. rate of packets ¼



ðtotal pkt sent of STD SCTPÞ  ðtotal pkt sent of ENH  SCTPÞ : total pkt sent of STD SCTP

ð17Þ

 The rate of packets received increased by 20%. This value is also computed with the formula given above.

 The ratio of packet loss for STD-SCTP was 0.18, whereas that for ENH-SCTP was 0.12. These ratios were calculated using the following equation: ratio of packet loss for STD  SCTP ¼

ððtotal pkt loss of ENH SCTPÞ  100Þ ðtotal pkt recieved of ENH SCTPÞ

ð18Þ

Therefore, ENH-SCTP outperforms STD-SCTP in terms of packet loss ratio and the number of packets lost. Importantly it should be noted that a variance of packets sent, packets received and packets lost between STD-SCTP and ENH-SCTP are not same because the impact of the applied enhanced congestion control mechanisms. 4.2.4. Effect of throughput To clarify the effect of the throughput on SCTP congestion control, we conducted experiments given the same arguments and network topology as in the previous simulations. Throughput represents the average data rate based on the amount of data that successfully reached the destination over a specific data link. It also indicates the amount of bandwidth utilized. Fig. 9 displays the throughput during 10 s.

The favorable performance of the proposed congestion control mechanism is clearly reflected in Fig. 9, which shows the comparison of the throughputs of STD-SCTP and ENH-SCTP, STD-SCTP is assigned in round dot line A short RTT reflects the source capability of SCTP to send packets to the destination at a high rate. The total throughput rates of the SCTPs differ significantly in a 10 s period. At 1 s, the throughput for STD-SCTP declined, whereas that for ENH-SCTP stabilized at a high rate. Furthermore, the throughput for STD-SCTP was minimized at 6.5 s. At 9 s, the throughput of STD-SCTP dropped again and remained blundering until the end of the experiment. Thus, ENH-SCTP outperforms STD-SCTP in terms of throughput. This sample was obtained from the high traffic situation in two bottleneck links. Fig. 10 displays the throughput during 50 s. The favorable performance of the proposed congestion control mechanism is clearly reflected in Fig. 10, which shows the comparison of the throughputs of STD-SCTP and ENH-SCTP. STD-SCTP is indicated in round dot line. A short RTT reflects the source capability of SCTP to send packets to the destination at a high rate. The total throughput rates of the SCTPs differ significantly in a 50 s period. At 1 s, the throughput for STD-SCTP declined and floppy, whereas that for ENH-SCTP stabilized at a high rate. Furthermore, the throughput for STD-SCTP was minimized at 4 s, 6 s, 7 s, 11 s and highest drawback at 14 s, the throughput of STD-SCTP dropped again and remained blundering until the end of the experiment. Thus, ENHSCTP outperforms STD-SCTP in terms of throughput. This sample was obtained from the high traffic situation in two bottleneck links. Figs. 9 and 10 were measured in a time granularity of 0.05 s. This period is derived from the overall simulation time. Furthermore, the sample was taken from one connection between a specific UE and an eNB across a high-traffic bottleneck. The result of the paired sample t-test was obtained using SPSS to demonstrate the significant difference between the throughputs of STD-SCTP and of ENH-SCTP. At 10 s, the mean of the throughput for STD-SCTP (m ¼9,331,841 packets) is lower than for cwnd ENHSCTP (m ¼10,120,778 packets). Furthermore, the mean of the throughput for STD-SCTP (m ¼9,287,305 packets) is lower than for cwnd ENH-SCTP (m ¼ 1,032,0778 packets) at 50 s. We again used the equation t(df)¼ t state, p r0.5 to determine the significance of the difference between the two SCTPs. t(199) ¼ 7.989, p r0.5, and t (1000) ¼ 15.38, p r0.5. The p value was 0.000 at both 10 and 50 s, and 0.000 r0.5. Hence, the difference between the throughputs of STD-SCTP and of ENH-SCTP differs significantly. Overall, the proposed enhancement improved the throughput of ENH-SCTP.

Please cite this article as: Najm IA, et al. Improvement of SCTP congestion control in the LTE-A network. Journal of Network and Computer Applications (2015), http://dx.doi.org/10.1016/j.jnca.2015.09.003i

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10

4.2.5. Effect of queue size This section demonstrates the effect of queue size on the proposed congestion control of SCTP. A queue is composed of a permeability that is authorized through any link connection. This queue manages packet flow within a specific transmission connection; at this point, packets may be either held or dropped. As an indicator or a metric of queue management performance, queue size is employed in the simulation as a direct index of linkage resource utilization. The average queue size of each flow represents the router resource management. In this study, we used LTE/Queue. Fig. 11 illustrates the difference between STD-SCTP and ENH-SCTP in this regard. The queue size for STD-SCTP is marked in round dot line, whereas for ENH-SCTP is in solid line. The time period for the assessment of this performance parameter is 10 s. This sample was obtained from one link connection. The packet rate is based on a scale of thousands to one packet rate level (e.g., a rate of 10 denotes 10 thousand packets). Both SCTPs were initialized at 0.3 s. Then, the queue size of STD-SCTP was minimized starting from 0.4 s until 1.8 s. Although the performance of ENH-SCTP was erratic, it did not drop to a lower level than STD-SCTP. The performance of ENH-SCTP vacillated throughout the simulation time period; nonetheless, it is still more stable than STD-SCTP. While ENH-SCTP performance declined, it did not reach the poor levels of STD-SCTP. The degradations of the latter were significant at 2.1, 3.2, 4.1, 5.05, 6.1, 7.05, 8.1, and 9.1 s. Moreover, the queue utilization of ENH-SCTP also increased. It was almost maximized. STD-SCTP performance frequently decreased; nonetheless, it increased slightly starting from 5.0 s until the end of the simulation period. However, it rarely reached the maximum level. Similar behaviors were observed at 50 s. Therefore, the 10 s sample alone is presented in this paper to avoid repeating our analysis. A paired sample t-test was performed using SPSS to compute the significant difference between the queue sizes of STD-SCTP

80 70 60 Packets

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 Q5 57 58 59 60 61 62 63 64 65 66

50 40

ENH-SCTP

30

STD-SCTP

and ENH-SCTP. The mean of the queue size for STD-SCTP (m ¼29,785 packets) is lower than for cwnd ENH-SCTP (m ¼57,674 packets). Using the equation t(df) ¼t state, p r0.5, we obtain t(197) ¼17.26, p r0.5 given that the p value is 0.000. As 0.000r 0.5, the queue sizes of STD-SCTP and ENH-SCTP differ significantly. Therefore, the proposed enhancement improved the queue size of ENH-SCTP.

4.2.6. STD-SCTP vs. ENH-SCTP The proposed congestion control of SCTP gives superior results than conventional SCTP as obtained across two simulation scenarios. The new mathematical model that incorporated the MCDM was developed for the modified congestion control algorithms to obtain the best values from the proposed equations. Table 3 provides the results for STD-SCTP and ENH-SCTP to allow an easy comparison.

5. Conclusion In order to meet the requirements of effective SCTP congestion control, we enhanced the congestion control of SCTP over LTE-A. The proposed congestion control applied slow start and congestion avoidance algorithms to improve SCTP performance. Moreover, the improvements were based on a new congestion window technique. The new approach employs a MCDM based on performance metrics such as cwnd, throughput, number of packets sent, number of packets received, number of packets lost, and queue size. The selection process utilized the TOPSIS algorithm, which determined the best value according to the aforementioned parameters. The performance of the proposed congestion control protocol was analyzed through simulation. As per the results, the ENH-SCTP outperformed STD-SCTP and TCP in terms of cwnd, packets, throughput, and queue size. Furthermore, our proposal is a viable solution and can be used in a LTE-A network. In future work, we will apply the MCSM concepts in SCTP path selection. SCTP is a promising research topic related to the development of green transport layers. Other aspects may also be considered to enhance SCTP over other queue management algorithms and to enhance video streaming over LTE (Radhakrishnan and Nayak, 2012).

20 10 0 0

10

Uncited reference

Fig. 11. Queue sizes of standard and enhanced SCTPs.

(Stewart, 2015).

2

4

6

8

Seconds

Table 3 Comparison of STD-SCTP with ENH-SCTP. cwnda

Throughputa

Queue sizea

Packets sent

Packets received

Packets lost

Congestion control mechanism. S.S¼ min(Data Acked,PMTU) C.A ¼ 1*PMTU per RTT S.S¼ min(DataAcked,PMTU) þA C.A ¼ 1*PMTU per RTT-B

STD-SCTP

114

9,331,841

29,785

17,169

17,145

32

ENH-SCTP

200

10,120,778

57,674

21,337

21,225

27

In addition, S.S represents a slow start and C.A corresponds to congestion avoidance. S.S and C.A are described in Section 3. The table illustrates the divergence of STD-SCTP from ENH-SCTP based on the values of performance factors such as cwnd, throughput, queue size and packet delivery over a network. The averaged values confirm the superiority of ENH-SCTP over STD-SCTP, especially given the difference in their congestion control mechanisms. a

The mean values used in table are based on 10 s time duration scenario.

Please cite this article as: Najm IA, et al. Improvement of SCTP congestion control in the LTE-A network. Journal of Network and Computer Applications (2015), http://dx.doi.org/10.1016/j.jnca.2015.09.003i

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Please cite this article as: Najm IA, et al. Improvement of SCTP congestion control in the LTE-A network. Journal of Network and Computer Applications (2015), http://dx.doi.org/10.1016/j.jnca.2015.09.003i

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