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An intelligent long-lived TCP based on real-time
traffic regulation
Mohammad Al Shinwan
1
&Laith Abualigah
1
&Nguyen Dinh Le
2
&Chulsoo Kim
2
&
Ahmad M. Khasawneh
1
Received: 4 October 2019 /Revised: 28 February 2020 /Accepted: 13 March 2020
#Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Transmission control protocol (TCP) transaction is one of the chief protocols of Internet
protocols. It is divided into two categories of data flow. (1) A small fraction of the TCP
connections carried a small portion of the traffic called short-lived TCP. (2) A small fraction of
the remaining TCP connections carried a large portion of the traffic called long-lived TCP. The
main problem here is the transmission time; other data usually harm its flows, such as User
Datagram Protocol (UDP) or short-lived TCP, and cause the unfairness in the network. In this
paper, a novel framework is proposed to improve network throughput and to reduce the impact
of long-lived TCP to other data flows. In this framework, each TCP connection passed by an
edge network device and it will be observed for determining the long-lived TCP flows. Then,
the detected long-lived TCP flow will be regulated based on predicting real-time traffic levels.
Moreover, to highlight the benefits of the proposed framework, an analytical model is
proposed to compare the proposed framework with the conventional TCP in terms of network
performance. Experiments are conducted using the ns-2 benchmark in order to verify the
results of the analytical model. The results showed that the analytical outcomes are promising
and matched well with the outcomes of the ns-2 experiments. In the case of a high error rate,
the proposed framework achieves higher reliability and reveals lower resource consumption.
Keywords Long-lived TCP.Isolation .Segment-by-segment .Network performance .Anovel
framwork
1 Introduction
The transmission control protocol (TCP) conveys the vast majority of the traffic with more
than 90% of bytes. The reliability of data transmission keeps TCP remains the dominating
transport protocol in the internet network [14,27]. However, with the increase of applications
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https://doi.org/10.1007/s11042-020-08856-z
*Mohammad Al Shinwan
mohmdsh@aau.edu.jo
Extended author information available on the last page of the article
running on top of TCP and the demand for a high-speed network, TCP reveals some
shortcomings such as low network throughput due to slow ramp-up mechanism or unfairness
in network resource sharing with other data flows [11,25]. For these reasons, researchers are
encouraged to investigate TCP in more detail in order to provide a better solution for solving
these existed problems [3,4,16,20,25].
In order to understand the root causes of unfairness problem, the complexity of TCP traffic
is needed to be classified. In this study, TCP traffic is divided into short-lived and long-lived
TCP based on their size of data and their life transmission time [23,32]. In another study, by
investigating the lifetime and transfer size of each TCP flow, L. Guo and I. Matta discovered
TCP flows follow a heavy-tailed distribution in which only a small percentage (e.g., less than
20%) of flows are long-lived TCP (e.g., more than 20 packets), but they carry more than 80%
of the total traffic in bytes [6,17]. The vulnerability of long-lived TCP can state that the
unfairness problem flows in sharing network resources [5]. Because of its significant amount
of data, long-lived TCP flows need a long transmission time to finish its transaction. Hence, it
is usually affected by other flows in network resource sharing. In detail, UDP flows usually
occupy more than their fair share of the bandwidth. Short-lived TCP affects long-lived TCP by
reducing its network throughput by up to 10% as the study in [9,15,34].
The basis of TCP congestion control lies in the additive increase multiplicative decrease
mechanism (AIMD) [10]. The congestion window is halved for every window containing a
packet loss and is increased roughly by one segment per round-trip time (RTT). By this
mechanism, standard TCP is successful at low speeds; however, it is not suitable for high-
speed communication due to its slow grow-up of congestion window size when congestion
occurs. In other words, standard TCP is not efficient for high speeds of data communication,
which is currently a high demand for all TCP applications [31,35]. For these reasons, it is
crucial to develop a new transport mechanism for improving network performance and reduce
the impaction between long-lived TCP flows and other network flows [30].
In this paper, a new transport mechanism is proposed with a novel mechanism to solve the
stated unfairness problems and to improve the network throughput of long-lived TCP by
modifying the edge network Equationuipment in the following process. First, edge routers will
be responsible for classifying flows and marking packets, whether they belong to long-lived or
other flows. Second, once a flow is classified into a long-lived TCP flow, edge routers will be
able to control the flow by providing a set of traffic regulation mechanisms. A mechanism for
predicting the traffic level is firstly defined. Based on the predicted traffic level, we can decide
to regulate the long-lived TCP by either reducing its data rate or splitting its connection to
increasing the data speed. In order to highlight the advantage of the proposed transport
mechanism, we develop two probability models for estimating the packet loss rate and several
traversed hops, which respectively denote for the reliability and resource consumption and
then compare with the conventional TCP. Furthermore, we perform an extensive set of ns-2
simulation experiments in a variety of scenarios in order to verify the analytical models. The
analytical results and the supporting experimental outcomes show the following:
&When the error rate is high, ALL-TCP achieves higher reliability than the conventional
system.
&In the proposed model, the lost packets are retransmitted from the edge router instead of
retransmitting from the source node; hence the number of traversal hops will be reduced.
In other words, the proposed model will be better than conventional in terms of resource
consumption.
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&The experiments show that our proposed model obtains higher throughput than the
conventional. The reason is that the RTT of each segment is shorter than the end-to-end
RTT, and the transmission process in each segment is independent of the others.
The rest of the paper is organized as follows. Section 2 outlines related works and positions our
analysis concerning prior studies. In Section 3, we present the proposed framework in the
viewpoint of architecture and operation mechanism. In Section 4, we develop the analytical
models to highlight the advantages of our new transportation mechanism in terms of reliability
and resource consumption and further conduct an extensive of ns-2 experiments to validate the
accuracy of our analytical model. Finally, Section 5 concludes the paper with final remarks.
2 Related work
TCP has been recognized as the most important transport layer protocol for the Internet. It is
distinguished by its reliable transmission, flow control, and congestion control [8,26]. The
literature on TCP protocol is considerable; however, it is out of the scope of this paper to present
a comprehensive survey. Hence, the most related papers have been reviewed in this section in
terms of long-lived TCP regarding network performance and unfairness issues. There is a real
unfairness issue amongst long-lived TCP, UDP, and short-lived TCP in sharing network re-
sources. As mentioned earlier in the introduction of this paper, UDP flows usually use more than
their fair share of the bandwidth. However, there is no existing solution for solving the impact of
UDP to other network flows because UDP is connectionless; it does not provide mechanisms to
control the end-to-end data flow [24]. Hence, most of the researches focus on TCP traffics,
connection-oriented protocol. The complexity of TCP unfairness is not only reflected by the
competition in network resources between UDP and TCP traffics but also between the short and
long-lived TCP. Short-lived TCP flows may reduce up to 10% in the throughput of the long-lived
flows [17]. In order to resolve the unfairness problems, several types of research have been
proposed. We can classify previous works into two aspects. First, the solutions that are given for
general network traffic (i.e., TCP and UDP). Second, specific consideration will explore the
unfairness of TCP traffics in detail regarding long-lived and short-lived TCP flows.
The general unfairness problems were early recognized and mentioned in [13,28]. These
papers focused on improving network fairness by either modifying TCP protocol or by
employing non-tail-drop buffer management at routers. In particular, in [2], Morris proposes
solutions to regulate the data traffics by providing per-flow information at all routers. In [28],
Seddigh et al. propose a new packet dropping mechanism. These solutions show significant
results; however, they require modifications on all routers which are unfeasible in deploy-
ments. The studies in [1,7,19,33] aim to provide traffic metering and dropping mechanisms
at the edge routers only with expected bandwidth or fairness guarantees. These researches are
considered as an improvement to different services [22], which only provides several class
priorities. Their dropping mechanism will base on the user profiles, which supplied vary of
dropping level. Their result outcomes show the number of benefits to the network service
provider. However, it is challenging to deploy in the overall network because the idea of the
open internet is nowadays considered a significant stream in the network society. Many
research aims to provide free internet service with high performance. In this paper, we aim
to improve the network performance of long-lived TCP and reduce the unfairness in the
network but not aiming at any specific users or any individual services. Most of the routers
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nowadays are green routers, which can modify their power plan to balancing their service rate
according to network traffic, switching, and transmission capacity.
These are also several researches focused on analyzing the fairness between long-lived and
short-lived TCP. The competition between long-lived and short-lived is early mentioned in [36].
The solution for solving this problem is proposed by I. Matta and L. Guo in [18]. In this paper,
they provide a mechanism for isolating low and long TCP flows and then enhance response time
and fairness of low flows. The significant results are given. However, the bandwidth (load) control
is needed to perform at each core router. X. Wu and I. Nikolaidis proposed algorithms to classify
schemes based on lifetime (short us. long-lived flows) and RTT attributes, as well as their
combinations in order to provide a better TCP classification schemes. The double objective
served by the classification schemes is to satisfy the need for reduced response time, which is the
primary concern of short-lived flows, while at the same time ensuring fairness among long-lived
flows [4]. In [12,17], they try to improve the network fairness and to improve the network
performance of short-lived TCP flows by suggesting to either using a considerable initial window
value or sharing the network measurement information from previous records. These papers
require modifying the TCP protocol at the end-host terminals and may lead to congestion collapse.
From different approaches, we provide a new framework to improve the network performance
of long-lived TCP flow and to reduce its impaction to other network traffic flows. The following
proposed mechanisms highlight our advantages by comparing them with previous studies. First,
in order to avoid the unfairness problem, long-lived TCP is controlled separately by classifying
the TCP traffics based on the amount of data passing by edge routers. Second, the FARIMA
technology, a well-known predicting mechanism in an internet network, is used for determining
the traffic level, which is a critical input factor for regulating network traffic. Third, based on the
predicted traffic level, the edge device will decide whether it will activate the splitting mechanism
for increasing the data transmission process or activate the slow-down mechanism for reducing
the transmission speed of controlled, long-lived TCP flows when our edge network equipment is
nearly overflowed. We believe that those upgraded mechanisms will improve the performance of
long-lived TCP and significantly reduce its impaction to other data flows.
3 Methodology
In this section, the proposed methodology is represented to show the general procedure in
solving the mentioned problem. Figure 1presents an overview of our proposed framework.
Several modifications are prerequisites in the network edge routers. Because in the current
operation of almost the router operating system, all IP packets received from the Ethernet
driver will be intercepted and processed by the routing mechanism for selecting the next
forwarding hop. Hence, in the first step, a hooking function is provided in order to intercept all
IP packets and forward them into our provided ALL-TCP layer. Hooking function refers to a
variety of techniques employed to prevent calls to pre-existing employment and wind around
them to change the function’s behaviour at runtime. Second, in the proposed provided layer, a
traffic level prediction function is proposed for estimating the traffics state at the next time
interval, which is an essential input factor for traffic regulation mechanisms. Next, the packet
classification mechanism is provided for selecting long-lived TCP flows from network traffics.
In this stage, we can separate TCP traffics from other traffics based on the protocol field in the
IP header and then determine the long-lived flow by counting the number of packets of each
flow passing the network edge router (i.e., more significant than 30 packets). Finally, the traffic
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regulation mechanism is designed for regulating the long-lived TCP flows based on the traffic
level predicted in the second step [33]. Algorithm 1shows the algorithm of the proposed
method. The complexity of the proposed algorithm approximates O (n*t), where n denote the
number of packet and t denote the complexity of the hooking function. The follows descrip-
tions regarding the proposed method process:
&Hooking function: The hooking function is provided to intercept all IP packets and forward
them into the suggested ALL-TCP layer.
&Traffic level prediction function: A traffic level prediction function is presented for
estimating the traffics state at the next time interval, which is an essential input factor
for traffic regulation mechanisms.
&Packet classification mechanism: The packet classification mechanism is provided for
selecting long-lived TCP flows from network traffics. In this stage, we can separate TCP
traffics from other traffics based on the protocol field in the IP header and then determine
the long-lived flow by counting the number of packets of each flow passing the network
edge router (i.e., more significant than 30 packets).
&Traffic regulation mechanism: The traffic regulation mechanism is designed for regulating the
long-lived TCP flows based on the traffic level predicted in traffic level prediction function [33].
In order to implement these mechanisms, two solutions are used depending on the operating
system. If theinternetwork operating system (IOS) is developed in the edge router, the device is
upgraded by adding the ALL-TCP layer in its upper protocol stacks. A hooking function is also
needed to integrate into the IOS kernel in order to intercept all IP packets and forward them to
the ALL-TCP layer for further processing. After processing in our provided layer, packets are
redirected to the routing and forwarding module for transmitting to the next hop. In the second
solution, the edge router implements a like-Unix operating system, so a modified IP and TCP
layers are needed to comfort with our framework. The following sub-sections discuss the
following two main mechanisms, traffic level prediction and traffic regulation in more detail.
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3.1 Traffic level prediction
It is important to choose the right prediction model amongst several current prediction
techniques (i.e., artificial neural network, autoregressive integrated moving average (ARIMA),
fractional ARIMA (FARIMA), and wavelet-based predictors). Because each prediction tech-
nology requires matched input parameters based on the characters of analytical data. In the
field of the internet network, Feng and Shu [36] compared amongst these prediction technol-
ogies by using mean square error and normalized mean square error as performance metrics
[29]. The result shows that FARIMA obtains a better accuracy result than others. Also, it may
be noted that internet traffic is mainly based on the normal distribution which can be presented
by: Y(t) = X(t) + μ,whereμis the mean rate, and X(t) is a stochastic process with a continuous
spectrum and zero mean. The FARIMA process is also in this form. Due to the two above
reasons, FARIMA is chosen for predicting the network traffic level in our edge device.
FARIMA is a prediction model, which is fitted by time series data either to better understand the
data or to predict future points in the series. The goal of FARIMA is to estimate network traffic Y(t +
τ) from the measured traffic history {Y(r) ∣rϵ(−∞,t]}.Ifgivenτas the next control time interval.
τis a significant importance factor because it effects the trade-off between the processing time and
Fig. 1 The overview of proposed software architecture
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the accuracy of prediction decision. For reducing the overhead of the network node processor and
the update message in the routing protocol, Y(t + τ) must be predicted with an acceptable value of τ.
Assume that a confident prediction requires that the normalized interval τ-step prediction
error (error τðÞ¼
b
YtþτðÞ−YtþτðÞ
b
YtþτðÞ ) should not exceed a percentage ε(e.g. 20%) with a
probability Ρε. The maximum prediction interval (MPI) is defined as the follows Equation:
MPI ¼maxnτjΡ
error
τ;εðÞ≤Ρεð1Þ
where Ρerror ¼Pr error τðÞ>εðÞ.Ρerror τ;εðÞis Equation to P(Z > 0) where Z is a random
variable of the Gaussian distribution with a probability density function N −εμ;σ2
ε;τ
.
Based on the Gaussian assumptions identified in this paper, the traffic could be presented
by: Y(t) = X(t) + μ,whereμis the mean rate, and X(t) is a stochastic process with a continuous
spectrum and zero mean. By applying the wold’s decomposition theorem, Gaussian process
Y(t) can be represented by one-sided moving average as the follows Equation:
Yt
ðÞ¼∑þ∝
u¼0hunt−u
ðÞ
þμð2Þ
where n(t) and hudenote Gaussian white noise and the possibly infinite vector of moving
average weights (coefficients or parameters), respectively. The optimal τ−step predictor of the
Gaussian process X(t + τ)is b
XtþτðÞ¼EXtþτðÞjXsðÞ;s≤t½. Applying Kolmogorov’sap-
proach, b
XtþτðÞcan expressed as the follows Equationuation:
b
XtþτðÞ¼∑þ∞
u¼0huþτnt−uðÞ:ð3Þ
Comparing to Eqs. (2) and (3), the unpredictable part in X(t + τ)is ∑
τ−1
u¼0
huntþτ−uðÞ.Soτ−
step predictor variance c
σ2
τcan be expressed in Eq. (4).
c
σ2
τ¼∑τ−1
u¼0h2
u¼σ2−∑þ∞
u¼0h2
uð4Þ
where σ2is the variance of X(t). σ2
ε;τcanbecalculatedbyusingEq.(5).
σ2
ε;τ¼∑τ−1
u¼0h2
uþε2∑þ∞
u¼τh2
u¼1−ε2
c
σ2
τþε2σ2:ð5Þ
From Eqs. (3) and (4), Perror τ;εðÞ≤Pεin Eq. (1) is equivalent to σ2
ε;τ≤ε2μ2
Ф21−Pε
ðÞ
where Ф(x) is
the inverse CDF of N(0,1). From Eqs. (1) and (5), MPI can be given as:
MPI ¼max τjb
σ
2
r
σ2≤1
C2Ф21−Pε
ðÞ
−1
ε2
1−ε2
8
<
:9
=
;ð6Þ
where C=σ/μis the variance coefficient of Y(t).
3.2 Traffic regulation
Based on the traffic level, Y(t + τ) which is predicted at the next interval of the control time of
τ, ALL-TCP agent will decide to either accept or refuse the new long-lived TCP flows [21].
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In the first case, when the predicted traffic level is lower than the minimum threshold α.
The new long-lived TCP flows will be accepted to enter the proposed traffic regulation
module, where the long-lived TCP flow will be split into two sub-connections that work
independently. From the functionality point of view, each edge network router operates as a
destination host by sending acknowledgment messages for every received packet from the
preceding node. Also, it contains functions that operate similarly to those in the conventional
source host, in which it follows existing algorithms for sending data packets in response to
incoming ACKs, multiple duplicate acknowledgments, or retransmit timeouts.
In the second case, if the predicted traffic level excesses the maximum threshold β,ALL-TCP
will reduce the data rate of incoming traffic to avoid congestion state instead of accepting new
connections. Random early detection protocol (RED) [11] is used to reduce the data rate of
incoming traffic with a few modifications needed. In which, the algorithms for calculating the
average queue length is replaced by FARIMA. The behavior of RED with UDP and short-lived
TCP flows are remained the same as it does in original version. Howeve, we control the long-lived
TCP flows is changed where the long-lived TCP flow that has the highest amount of buffered data
in amongst existed long-lived TCP flows in edge router is selected for reducing data rate. This
behavior will guarantee the fairness between long-lived TCP flows in resource sharing. For
selecting the flow that has highest amount the stored data in buffer, we need to measure the
incoming and outgoing packets rate of each long-lived TCP at the next time interval, t + τ
separately. If the predicted incoming and outgoing packets of the preceding and the next segment
at the next time interval are Yi(t+τ), Yo(t+τ) and the current stored data in the buffer is δ,then
expected buffering data for the long-lived TCP flow can be measured as the follows Equation:
BtþτðÞ¼YitþτðÞ−δþYotþτðÞ ð7Þ
The predicted incoming and outgoing packets of preceding and next segments are similarly
determined based on the congestion window size at the next time interval of τ. For example, when
the current congestion window size of the preceding segment is cwndiand its incoming packets
arrive the edge router at the time before the predicted time interval, then Yi(t+τ)=cwnd
i+1
,
otherwise Yi(t+τ)=0.IfW
max presents for the maximum window size and an Ack message are
sent to the sender after receiving a number of ηdata frames, then the value of the next congestion
window size in the case of congestion free can be obtained by the follow Equation:
cwndiþ1¼Wmax;if cwndiþ1≥Wmax
cwndiþcwndi=η;Otherwie
ð8Þ
In order to reduce the jitter problem at the end of the host terminal and to keep the allocated
memory for each flow in the edge router at a minimal value, the data transmission rate between
sub-segments of each long-lived TCP flow should be regulated based on the following ratio:
γ¼δþcwndiþt
cwndoþt
ð9Þ
This ratio should be regulated at a value that higher or at least equal to 1 and does not exceed a
defined maximum threshold (i.e., γ≤2). When γis out of the allowed range, the mechanism
need to adjust the bias by delay sending packets or acknowledgment message of the sub-
segment which has higher throughput. This mechanism is similar to TCP delayed acknowl-
edgment that is proposed in [13].
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4 Analysis and simulation
In the proposed traffic regulation module, each long-lived TCP is running based on segment-
by-segment transportation (SBST). Hence, in Section A, we explore the advantages of SBST
by comparing it with conventional end-to-end transportation (ETET) regarding reliability and
resource consumption. In section B, simulated experiments by using the ns-2 tool are
conducted to verify the analytical model and to investigate the network throughput of long-
lived TCP further.
It’s hard to tune the SBST and ETET, because of the SBST is related to the data
transformation to segments at the source node and reconstructed at the receiver. On the other
hand, the ETET is related to different feature such as the flow controls rate and error detection,
which make the tuning is hard to implement.
4.1 Fundamental of SBST and ETET
In this section, the network reliability and resource consumption of each transportation mecha-
nism is measured by modeling the behavior of a packet. Which is when sent from the sender to the
receiver. The reliability is presented by overall packet loss probability, while resource consump-
tion can be measured based on the number of traversed routers that a packet needs to pass by in
order to reach the receiver. The following assumptions are used in the proposed model:
&pi: Packet loss probability at router i.
&psi: Probability of a packet passing by router number i successfully.
&pe: End-to-end packet loss probability.
&ps: Probability of successfully sending a packet from the source to the destination.
&n: The total number of routers in the network.
&n1: The number of routers of the first segment.
&n2: The number of routers of the second segment.
&R: Maximum number of retransmissions.
4.1.1 Packet loss probability in ETET
The probability of sending a packet successfully from the sender to the receiver is defined as:
ps¼∏n
i¼11−pi
ðÞ:ð10Þ
Hence, the probability of sending a packet unsuccessfully after R retransmission times is
donated as follows:
pe¼1−ps
½
R¼1−∏n
i¼11−pi
ðÞ
R:ð11Þ
Assume that a packet is dropped at a random position k when transmitting to the receiver, then
the probability of this event can be described as:
pek¼pk∏k−1
i¼11−pi
ðÞ:ð12Þ
In one transmission time, the number of traversed routers of event that a packet may not reach
the receiver is defined as the follows Equation:
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hf¼∑n−1
k¼1
kpek
1−∏n
i¼11−pi
ðÞ
¼∑n−1
k¼1
kp
k∏k−1
i¼11−pi
ðÞ
1−∏n
i¼11−pi
ðÞ
:ð13Þ
Hence, the expected of total traversed routers of event that a packet cannot reach the receiver
after a maximum number of retransmissions, R, is given as follows Equation:
hR¼Rhf¼R∑n−1
k¼1
kp
k∏k−1
i¼11−pi
ðÞ
1−∏n
i¼11−pi
ðÞ
:ð14Þ
The expected of traversed routers of event that a packet reaches a receiver after a number of r
retransmissions time (r < R) are obtained by the follows Equation:
hr¼r−1ðÞhfþn:ð15Þ
The probability of event that a packet may not reach the receiver after a maximum number of
retransmissions, R, is presented by the follows Equation:
pR¼1−∏n
i¼11−pi
ðÞ
R:ð16Þ
The probability of event that a packet reaches to the receiver after a number of retransmissions
r (r < R) is given as the follows Equation:
pr¼∏r
j¼11−∏n
i¼11−pi
ðÞ
j−1∏n
i¼11−pi
ðÞ:ð17Þ
From Eqs. (14), (15), (16), and (17), the total expected of traversed routers when a packet is
transmitted through an end-to-end connection is defined as the follows Equation:
ht¼∑R−1
r¼1prhr
ðÞþpRhR:ð18Þ
4.1.2 Packet loss probability in ETET
Packet loss probability in SBST is measured by aggregating the packet loss probability in the
first and the second segments. The probability of event that a packet is transmitted successfully
through the first segment is given as the follows Equation:
psi1 ¼∏n1
i¼11−pi
ðÞ:ð19Þ
The probability of event in which a packet cannot reach the receiver after a maximum number
of retransmissions, R, can then be:
pe1¼1−psi1
R¼1−∏n1
i¼11−pi
ðÞ
R:ð20Þ
Hence, the probability of an event in which a packet is transmitted successfully by passing
through the first and second segments is defined sequentially as:
ps1¼1−pe1¼1–1−∏n1
i¼11−pi
ðÞ
Rð21Þ
ps2¼1−pe2¼1–1−∏n2
i¼11−pi
ðÞ
Rð22Þ
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From Eq. (21)and(22), the probability of an event in which a packet is transmitted
successfully from the sender to the receiver is described as follows:
ps¼ps1ps2ð23Þ
Therefore, the probability of an event in which a packet cannot reach the receiver after a
maximum number of retransmissions, R can be obtained as follows:
pe¼1−ps¼1–1–−∏n1
i¼11p
i
ðÞ
R
no
1–1∏n−n1
i¼11−pi
ðÞ
R
no
ð24Þ
4.1.3 Resource consumption in SBST
Similar to Eq. (18), the expected total traversed routers that a packet needs to pass through the
first segment (ht1) can be calculated as:
ht1¼∑R−1
r¼1pr1hr1
þpR1hR1;ð25Þ
where pr1;hr1represent for the packet loss probability and number of traversed routers of event
that a packet is transmitted successfully though the first segment, respectively. While, pR1,
hR1denote for the packet loss probability and number of traversed routers of event that a
packet cannot reach the receiver after a maximum number of retransmission R. The probability
of an event that a packet is transmitted successfully though the second segment after a number
of retransmissions; k can be defined as the follows Equation:
pr2¼1−1−∏n1
i¼11−pi
ðÞ
R
no
1−∏n2
i¼11−pi
ðÞ
k−1ðÞ
∏n2
i¼11−pi
ðÞ
:ð26Þ
The probability of an event that a packet cannot reach the destination after a maximum number
of retransmissions, R, is given as follows Equation:
pR2¼pr11−∏n2
i¼11−pi
ðÞ
R:ð27Þ
Similar to the analyses in (14) and (15), the number of traversed routers of events that a packet
transmits through the second segment in both cases success and failure after a maximum
number of retransmissions, R, can be represented as hr1and hr2, respectively. From Eqs. (26)
and (27), the overall traversed routers of event that a packet is transmitted through the second
segment can be obtained as follows:
ht2¼∑R−1
r2¼1pr2hr2
þpR2hR2:ð28Þ
From Eqs. (25) and (28), the overall number of traversed routers (ht) through which a packet
passes in SBST is identified as:
ht¼ht1þht2¼∑R−1
r¼1pr1hr1
þpR1hR1þ∑R−1
r2¼1pr2hr2
þpR2hR2:ð29Þ
4.1.4 Analytical results
The comparison results can be obtained based on the established Equations, which is given
above by using MATLAB software package. The configure ration of the given parameters are
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set to n= 8, pi = 0.05, and R = 10 according to [3]. The packet loss probability of SBST and
ETET are obtained by using Eqs. (11)and(24), respectively. The results in Fig. 2show that the
SBS is more reliable than the conventional ETET. The number of traversed routers is
determined by using Eqs. (18)and(29). The results shown in Fig. 3present that, in SBST, a
packet needs to pass by a fewer number of routers to reach the receiver than in ETET. In other
words, SBST consumes less network resource than the conventional ETET.
4.2 Simulation experiments
We simulate ALL-TCP by modifying the implementation of the current Tahoe TCP in ns-2,
which generally can be summarized as follows.
First, in order to provide mechanisms for sending packets, we redesign the packet sending
module by driving from the TcpAgent class. Second, for acknowledging the received packet,
an acknowledgment module is provided by driving from the TcpSink class. Each long-lived
1 2 3 4 5 6
5
5.5
6
6.5
7
Maxiumum number of retrans mis ions
spohdesevarT
ETET
SBST
Fig. 2 Network reliability comparison
1 2 3 4 5 6
0
0.05
0.1
0.15
0.2
0.25
0.3
Maxim um number of retransmisi ons
er
uli
affoytilib
a
borP
ETET
SBST
Fig. 3 Resource consumption comparison
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TCP flow is maintained and controlled by each flow separately, which includes both sending
and acknowledgment modules. Third, because ns-2 currently provides a single point of packet
entrance called entry (Which is a connector object). Each packet that enters the Node entry is
directly forwarded to the address classifier module (i.e., Classifier instvar). If the Node is not
the final destination, the address classifier will forward the packet to the link specified in the
routing table. Therefore, we need to provide a hooking function.
In order to intercept packets and forward them to our provided ALL-TCP agent for further
processing, our framework lie on the change in transport mechanism without modifying the
original TCP protocol implemented in the end-host terminals. Hence, in a real implementation,
the edge router is responsible for adapting with a different version of TCP, such as Reno, New-
Reno, or SACK.
As we proposed in the Tcl simulated code, a network with four routers that connects
two host terminals is created. Each link is configured to 4 Mb of bandwidth capacity, and
50 ms link-delay. We use link error instead of creating congestion in routers in order to
create packet losses. The link error probability is similar to packet loss probability, p_i,
which is used in our analytical model. The ALL-TCP agent is attached to router number 2
Fig. 4 Experiment configuration
0500 1000 1500 2000
0
50
100
150
200
250
300
350
400
Number of sent pac kets
stekcappordforebmuN
Taho e TC P
ALL-TCP
Fig. 5 Drop packets with link error 0.5
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when we simulate for the proposed framework. The overview of our experiment config-
uration is shown in Fig. 4.
The first experiment aims to confirm the analytical model in section Packet Loss Probability
in ETET, where the reliability of transmitting a packet via ALL-TCP is proved better than the
conventional TCP. In this experiment, the amounts of dropped packets are measured by
sending 2.000 packets via the network with different link error. The results from Figs. 5,6,
7,and8show the amounts of dropped packets for link error: 0.025, 0.05, 0.075, and 0.1,
respectively. The results show that the packet drop probability of the proposed framework is
always smaller than the conventional Tahoe TCP. This conclusion confirms to our analytical
model in section Packet Loss Probability in ETET.
In the ALL-TCP framework, each long-lived TCP connection is split into sub-connections
that work simultaneously in the case that network traffic is lower than the minimum threshold
value. It is because the round trip time of each sub-connection is shorter than the end-to-end
RTT. Hence, ALL-TCP is expected to gain higher network throughput than the conventional
TCP. In the second experiment, we try to measure the network throughput at the TCP Sink
based on the number of received bytes in a fixed 0.8 s time scale. The results in Fig. 9showed
ALL-TCP achieves higher network throughput than the conventional Tahoe TCP and finishes
0500 1000 1500 2000
0
100
200
300
400
500
600
Number of sent pac kets
stekcappordf
o
re
b
muN
Tahoe TCP
ALL-TCP
Fig. 6 Drop packets with link error 0.75
0500 1000 1500 2000
0
100
200
300
400
500
600
700
800
Number of sent pac kets
stekcappord
f
or
e
b
m
uN
Taho e TC P
ALL-TCP
Fig. 7 Drop packets with link error 0.1
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the transmission of 2000 packets in a shorter time. The results (as shown in Fig. 10) are even
better than if there are two ALL-TCP agents are deployed in the network.
In order to highlight the adaptive ability of the proposed framework, we measure the
recovery time of congestion window size from the time of multiplicative decreasing until it
recovers to the highest value. The results in Fig. 11 shows ALL-TCP quickly retrieve the
highest congestion window size each time it incurs congestion. This advantage helps ALL-
TCP not only get higher network throughput but also it is better in adaption to congestion. As
well as, this Figure shows that ALL-TCP always get higher network throughput than Tahoe
TCP in the case of network configuration with link error set to 0.1.
010 20 30 40 50 60
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Time (in seconds)
)
c
e
s
/bM(
t
uphguohT
Tahoe TCP
ALL-TCP
Fig. 8 Throughput comparison in normal state with one ALL-TCP agent in deployment
010 20 30 40 50 60
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Tim e (i n s e c on ds )
)ces/
b
M(tuphguohT
Tahoe TCP
Wit h 1 ALL-TCP
Wit h 2 ALL-TCP
Fig. 9 Throughput comparision in normal state with two ALL-TCP agents in deployment
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5 Conclusion
In this paper, a novel protocol design is proposed to improve the network performance of long-
lived TCP (ALL-TCP). ALL-TCP firstly separates long-lived TCP from other data flows.
Secondly, the traffic regulation mechanism is proposed in which a number set of functions is
provided to regular, and control long-lived TCP connections in an edge router. The results of
the analytical models and an extensive set of ns-2 simulation showed the follows: 1) ALL-TCP
is more reliable and consumes less energy than the conventional TCP in the case of high error
rate in the network. 2) It always presents higher network throughput in both cases of
congestion state or normal operation due to its shorter RTT. 3) Our modification is on the
edge network equipment only, hence these are no effect to the end-host terminals. It could be
deployed gradually in current TCP/IP networks with high scalability. 4) The network fairness
is obtained because the long-lived TCP is separated from other data flows. For future work, the
050 100 150 200
0
10
20
30
40
50
60
70
80
90
Drop Occurrence
)
c
es(e
m
itda
e
L
Tahoe TCP
ALL-TCP
Fig. 10 Lead time to recover congestion window size after congestion
020 40 60 80 100
0
0.02
0.04
0.06
0.08
0.1
0.12
Time (in sec onds)
)ces/bM(tuphguohT
Tahoe TCP
ALL-TCP
Fig. 11 Throughput comparison with error link 0.1
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proposed mechanism can be modified to improve other TCP versions like BIC TCP or
compound TCP, in hope to improve the enhance performance and fairness of long-lived
TCP flows.
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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Affiliations
Mohammad Al Shinwan
1
&Laith Abualigah
1
&Nguyen Dinh Le
2
&Chulsoo Kim
2
&
Ahmad M. Khasawneh
1
Laith Abualigah
Aligah.2020@gmail.com
Nguyen Dinh Le
nguy.le@inje.ac.kr
Chulsoo Kim
charles@inje.ac.kr
Ahmad M. Khasawneh
a.khasawneh@aau.edu.jo
1
Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
2
Department of Computer Engineering, Inje University Gimhae, Seoul, Republic of Korea
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