A Mechanism for Uplink Packet Scheduler in LTE
Network in the Context of Machine-to-Machine
Communication
Adyson M. Maia∗ , Dario Vieira† , Miguel F. de Castro∗ , Yacine Ghamri-Doudane‡
∗ GREat
Lab - Federal University of Ceará (UFC), Fortaleza, Brazil
School of Information and Digital Technologies (EFREI), Villejuif, France
‡ University of La Rochelle, La Rochelle, France
Email: adysonmaia@great.ufc.br, dario.vieira@efrei.fr, miguel@great.ufc.br, yacine.ghamri@univ-lr.fr
† Engineering
Abstract—This paper proposes a mechanism for uplink packet
scheduler in LTE network in the context of Machine-to-Machine
(M2M) communication. The proposed approach uses the current
and past information of the system to satisfy the Quality of Service
(QoS) requirements, to ensure fairness in resource allocation and
to control the congestion caused by M2M devices. We carried
out some network simulations by using a NS-3 simulator so
as to show the effectiveness of the proposed approach. The
results indicate that our solution can reduce the impact of M2M
communication on Human-to-Human (H2H) communication and
avoid the problem of starvation, when compared to related
approaches.
Keywords—LTE; Internet of Things; IoT; Machine-to-Machine;
M2M; uplink packet scheduler
I.
I NTRODUCTION
In the Internet of Things (IoT), a great variety of smart
objects (home appliances, automobiles, cell phones, etc.) will
be connected to the Internet. In this new scenario, where these
smart objects will be connected and interacting with each
other, Machine-to-Machine (M2M) communication will play
an important role in the deployment of IoT [1]. M2M communication refers to the transmission and automatic collection of
data from a remote source without, or with only limited, human
intervention through a public network infrastructure (Wi-Fi,
WiMax, UMTS, LTE, etc.) [2].
There is a large market potential for M2M communication,
and the application fields of this communication are very
wide [2]. Furthermore, the quantity of objects that will be
connected in the future IoT is estimated at billions or even
trillions [1]. Due to this vast number of smart objects, a large
volume of traffic generated by the M2M communication is
expected. Accordingly, this will exceed the volume generated
by human-oriented communications (VoIP, media streaming,
web browsing, etc.), also called Human-to-Human (H2H)
communication, which are responsible for most of the traffic
in the Internet at present.
It is expected also that cellular network infrastructures
will play a key role in the deployment of IoT, as well as
for M2M communication, highlighting the significance of the
LTE (Long Term Evolution) network for this deployment [3].
However, improvements in the LTE are necessary because of
the intrinsic characteristics of M2M communication (discussed
in more detail in Section II), which are different from H2H
communication, for which the network was originally designed. Among these improvements, the uplink channel packet
scheduler requires special attention.
Existing solutions in the literature [4] that address scheduling for the current communication (H2H) in LTE are not
suitable for M2M communication, because of the assumption
of the low volume of services and the Quality of Service
requirements (QoS) of H2H communication [5]. Subsequently,
this is not the case for the majority of M2M applications,
which have a huge amount of services and therefore different
QoS requirements [5]. Furthermore, these solutions do not
cope with the congestion caused by the deployment of a huge
number of smart objects in the same area and, therefore, using
the same shared resources.
The current solutions that handle M2M communication [5]–[10], which are discussed in Section III, have some
shortcomings. One of them is that some of these solutions do
not ensure the fair allocation of resources and may lead to the
problem of starvation. Another weakness is related to either the
lack of control over the impact caused by the introduction into
the network of M2M communication on the performance of
the H2H communication, or this kind of impact control being
poorly carried out.
Within this context, in this paper, we discuss the packet
scheduler in the LTE radio network, taking into account
both M2M and H2H communication, and we propose a new
mechanism for scheduling packets to uplink channel, details
of which are presented in Section IV. The proposed scheduler
uses the current traffic information of each device as well
as allocation logs to classify the traffic and also to prioritize
the allocation of radio resources. We argue that the proposed
approach can (i) control the impact of M2M communication
on H2H, (ii) ensure fairness and thus avoid the problem of
starvation and (iii) meet the QoS requirements of applications.
We present in Section V the experiments through simulations
and we also discuss the results gained. Finally, in Section VI,
we discuss the conclusions and future works.
II.
OVERVIEW
This section briefly describes some technologies on M2M
communication and LTE, in order to provide to required
background of this article’s proposal.
A. M2M Communication
M2M and H2H applications have some features in common
such as: secure connection, mobility and package switching [6]. However, since M2M applications perform their
functions without (or with only limited) human intervention,
several other characteristics differentiate them from H2H applications. Amongst these characteristics can be mentioned (i) the
infrequent transmission at regular intervals of a small amount
of data, (ii) delay tolerance and (iii) the main traffic is in uplink
(devices to the server) [11].
The majority of M2M applications can be classified into
two categories [12]. In the category of event-based applications, the devices send messages to the server when an event
occurs in the monitored environment. Furthermore, applications in this category require reliable real-time transmission.
Most M2M applications are included in the second category,
called time-based. In this case, the devices send their collected
data to the server at regular intervals of time (which can vary
from a few milliseconds to several minutes or hours).
As the devices in M2M communication generally send
more data than they receive, we focus in this paper on the
uplink packet scheduler. In the next subsection, we present a
brief description of the packet scheduler in the LTE.
B. Packet Scheduler for LTE Network
The scheduling in the two channels (downlink and uplink)
is performed separately (one scheduler for the downlink and
another one for uplink) in the Medium Access Control (MAC)
layer of the base station. The scheduling of both channels is
performed each TTI (Transmission Time Interval) of 1 ms,
and it is decided which Resource Blocks (RBs), the smallest
unit that the scheduler can allocate to a device, are allocated to
which device. This decision aims to meet the needs of device
resources, as well as to maximize global system performance.
An important characteristic that differentiates the downlink
scheduler from the uplink scheduler is that the latter has the
constraint that all the RBs allocated to a single device should
be continuous with respect to frequency.
III.
R ELATED W ORKS
In this section, we discuss some of the solutions that take
into account M2M communication in the network. These solutions are compared according to their goals and the parameters
used to accomplish them.
In [6], the authors propose a scheduler group-based system
in which devices are grouped into classes, dynamically created,
according to their QoS requirements. The QoS requirements of
the devices are placed into a class made up of two parameters:
(i) the arrival rate of packets and (ii) the maximum tolerable
jitter. However, in scenarios where there are many devices,
the scheduler proposed in [6] is not fair to devices that belong
to classes with lower priorities. In addition, the solution does
not exploit information regarding the quality of the channel
between the device and the base station, which influences the
throughput.
Two solutions are presented in [5] aiming to guarantee QoS
requirements. For that, the authors apply two parameters as
follows: (i) the channel quality between the device and the base
station, and (ii) the maximum tolerable delay as QoS metric.
The two solutions differ between them by the weight of the two
parameters used in order to prioritize the QoS metric (either
the channel quality or maximum tolerable delay). However, the
two solutions are not fair to the devices that have the worst
conditions in the relevant parameter used as metric.
In [10], the authors propose a scheduler that prioritizes
the devices that have a critical deadline. For this, the solution
prioritizes the allocation of resources for devices that have
more data to transmit and the waiting time for resources is
closest to exceed the maximum allowed delay. Nonetheless,
this solution also has no mechanism to ensure fairness in the
allocation.
In a real scenario, both H2H communication and M2M
communication will be present on the network. In this context,
the shortage of radio resources, and consequently, the network
congestion is a problem to take into account. The main cause
of this problem is that although each M2M device transmits
small amounts of data resulting in less need for radio resources,
the large amount of devices requesting resources leads to a
shortage of resources. Accordingly, the H2H communication
will be seriously affected if the scheduling does not take into
account this problem. In this context, none of the solutions
aforementioned deal with the impact of M2M communication
on the H2H communication.
The solution presented in [7] controls this impact by
dividing the scheduler into two parts. In the first part, the
scheduling allocates resources for H2H devices. In the second
part, the scheduling is carried out for the others devices by
using the remaining resources after the first part. However, it
may occur that after the first part of the scheduling there are no
more available resources for M2M devices in scenarios where
there is heavy H2H traffic. As a result, this may lead to the
problem of starvation for M2M devices.
In [8], the authors propose some improvements on the work
introduced in [6]. In this proposed approach [8], the impact of
M2M communication on H2H communication is controlled
by reserving a constant amount of resources for M2M communication. Moreover, to classify traffic in predetermined QoS
classes, the Poisson process, applied to model the burst transmission of event-based applications as proposed in [13], is used
instead of transmission at regular intervals in the time-based
applications. However, in [8], there is neither guarantee of
fairness nor use of the channel quality information. Moreover,
the constant separation of resources for both M2M and H2H
communications cause a poor utilization of these resources.
In [9], the proposed scheduler also reserves a constant
percentage of resources for M2M communication. But in this
case, this percentage refers to an upper limit of resources
that can be allocated for M2M communication. Moreover, this
solution prioritizes the devices with less tolerance to delay,
which results in no guarantee of fair allocation to devices more
delay tolerable.
Based on the shortcomings of the solutions discussed
above, we propose a new mechanism for the packets scheduler
that (i) controls the impact of M2M communication on H2H,
(ii) ensures fairness in allocating resources and tries to (iii)
maximize the satisfaction of QoS requirements.
TABLE I.
Index
10
11
12
13
14
15
16
17
IV.
Q O S CLASSES FOR M2M APPLICATIONS
Maximum Delay
50 ms
50 ms
100 ms
150 ms
300 ms
400 ms
500 ms
1s
Packet Loss
10−6
10−2
10−2
10−2
10−2
10−2
10−2
10−2
Applications
Event Based
Time Based
Time Based
Time Based
Time Based
Time Based
Time Based
Time Based
P ROPOSED M ECHANISM FOR S CHEDULING
In this section, we describe in detail the mechanism
proposed in this paper. In Subsection IV-A, we present the
classification of the device according to its traffic flows.
In Subsection IV-B, we describe the mechanism to control
the impact on the performance of H2H communication with
the introduction of M2M communication. Finally in Subsection IV-C, we present the scheduler algorithm.
B. Calculation of Demand for Resources
One goal of our work is to control the impact of M2M communication on H2H. For this purpose, the proposed mechanism
will prioritize the H2H communication in the scheduling.
However, the amount of resources allocated to the H2H devices
so that they do not suffer large reductions in their performances
is not an easy task. The main reason for this is that the amount
of data (in bits) to be transmitted is not directly related to
the amount of RBs needed for this transmission. Indeed, the
throughput is influenced, beyond the amount of RBs, by the
quality of the transmission channel between the device and
each RB allocated.
To calculate the current demand for resources by H2H
devices, we use the ratio of the average amount of resources
allocated and the average size of data in the transmission buffer
as shown below:
avg
d H (u, t) = BS(u, t) × RB (u, t − 1)
RB
avg
BS (u, t − 1)
(1)
A. QoS Classes for M2M
The LTE standard supports end-to-end quality of service
for the main types of H2H applications. Traffic flows can be
classified into one of nine classes of QoS defined according to
their requirements as specified in [14]. The QoS requirements
are defined by the following parameters: (i) the maximum
tolerable delay and (ii) the rate of packet loss. However, these
nine classes do not encompass the wide range of maximum
delay (a few milliseconds to several minutes) of time-based
applications. Furthermore, the event-based category requires
high priority, low tolerance for delay and high reliability (low
rate of packet loss). Thus, the only possible relationship for
this category is a class that is used exclusively for signaling
messages.
To address this issue, we propose two approaches to
extend the QoS classes to support M2M communication. Both
proposals have the advantage of identifying the devices (M2M
or H2H) only by the QoS class to which their traffic is related.
The proposed approaches are defined as follows:
1)
2)
In the first approach, two classes are added to the
LTE standard. One class is used for event-based
applications and the other ones is used for time-based
applications. Nevertheless, the maximum allowable
delay parameter is not used in the latter class. Instead, each device of time-based applications sends
to the network (base station) its own value for this
parameter.
In the second approach, 1 + N classes are added to
the standard. As in the previous approach, one class is
used for event-based applications, but now N classes
for time-based applications are used. These N classes
differ in the value of the maximum tolerable delay.
Therefore, a device that transmits its data in the time
interval equal to x chooses the class with the largest
maximum allowable delay less or equal to x.
Table I illustrates an example of the proposed approaches.
The first approach uses only the first two classes (Index 10
and 11) and the second one all other classes illustrated in the
table.
d H (u, t) is the current demand for RBs from dewhere RB
vice u at TTI t, BS is the current size of the data in
the buffer, RB avg is the average number of RBs allocated
to the device and BS avg is the average size of data in
the transmission buffer. Moreover, each H2H device has a
min
minimum threshold of RBH
(greater than zero) required
resources. The functions RB avg , BS avg are calculated using
an Exponential Moving Average (EMA).
With the demand for resources calculated, we can reserve
an amount of RBs for H2H communication equal to the sum
of the demand of each H2H device. However, the H2H devices
may require all available resources causing starvation for M2M
devices. To avoid this, a constant minimum percentage of
resources (0 ≤ ρ ≤ 1) is ensured for M2M. Nevertheless, this
minimum percentage of resources is reserved only if there are
sufficient devices request them. Furthermore, as M2M devices
transmit small amounts of data (less than 1000 bits) [13],
then a M2M device will receive a fixed and small number of
min
max
resources RBM
. Therefore, at most UM
(t) M2M devices
will receive resources at TTI t.
The Equations (2a) and (2b) formalize this discussion,
where RB is the set of available resources, UM is the set of
M2M devices requesting resources and RBM (t) is the amount
of resources reserved for M2M communication. Consequently,
the amount of resources reserved for H2H is equal to the
remaining resources as shown in Equation (2c).
max
UM
(t)
= max
%!
$
d H (t)
|RB| − RB
ρ|RB|
,
(2a)
min
min
RBM
RBM
min
max
RBM (t) = RBM
× min (|UM |, UM
(t))
RBH (t) = |RB| − RBM (t)
(2b)
(2c)
C. Scheduler Algorithm
Once the demands for resources are calculated, the RBs are
divided into two consecutive groups regarding the frequency,
one for each type of communication (H2H and M2M) and
with size equal to its demand. Thereby the scheduling for
H2H and M2M can be handled separately. Therefore the
mechanism proposed in this paper focuses only on scheduling
for M2M devices, benefiting from the use of any existing
uplink scheduler in the literature for H2H communication.
Based on the model specified in [15], we divide the
proposed algorithm into two phases as detailed below.
1) First Phase: This phase has the role of choosing which
devices will receive resources. The devices that received fewer
resources over time and that are closest to overcoming the
maximum tolerable delay have higher priority to be selected
for the next phase. Thus, the goal of the first phase is both to
ensure fairness in the allocation of resources and to satisfy the
QoS requirements.
M2M devices are prioritized according to the function
TD
MM
shown in Equation (3), where T avg (u, t) is a moving
average of the throughput that indicates whether the device
u has received a fair amount of resources until the TTI
t. ∆D(u) is a function that measures whether this device
is close to not satisfying its QoS requirements. Lastly, the
constant ω (0 ≤ ω ≤ 1) indicates the weight to ensure
fairness (first component of the Equation (3)) and to satisfy
the QoS requirements (second component of the Equation (3))
in prioritizing the devices.
TD
MM
(u, t)
T avg (u, t − 1)
= (1 − ω) 1 −
maxn∈UM T avg (n, t − 1)
∆D(u)
+ω 1−
maxn∈UM ∆D(n)
(3)
∆D(u) is calculated in two ways. If the last request for
resources was either granted or no request was made, then
∆D(u) is equal to the maximum tolerable delay. Otherwise,
∆D(u) is either equal to the difference between the maximum
delay and the expected time since the last request not granted
or zero if this difference is negative.
min
Given this, at most RBM /RBM
M2M devices that have
TD
higher values by MM
are selected for the next phase. The
devices not selected will need to wait x TTIs to make a new
request. The value of x is chosen, for each device, randomly
in the interval [0, σ∆D], where σ is a constant (0 ≤ σ ≤
1). Therefore the next requests are spread over time in order
to reduce possible network congestion generated by the large
amount of devices requesting resources.
2) Second Phase: This phase creates groups with the same
amount of continuous RBs; one for each device selected by
the previous phase. After the creation of the groups, it remains
only to decide which group of resources will be allocated to
which device. For this, we use the following greedy approach:
1)
2)
3)
4)
For each resources group g and device u, calculate
the expected throughput if the device use this group.
Let S be a set with these values of throughput.
If S 6= ∅, then remove the highest value from this
set. Otherwise, the algorithm terminates.
Allocate resources group g for the device u that is
associated with the removed value only if the device
has not yet received resources.
Repeat step 2.
V.
E XPERIMENTS
A. Simulation Environment
In order to evaluate our proposed mechanism, we evaluated
it against two other solutions in the literature. The first solution
is the scheduler Proportional Fair (PF), which is one of the
most widely used and researched solutions in the literature
for fair resource scheduling of H2H communication [16].
The PF was also used for the scheduling of H2H devices in
the proposed mechanism. The second approach used in our
simulation is the second algorithm proposed in [5].
In order to carry out the experiments, we had to define
the values for two input parameters of the second algorithm
proposed in [5]. The first input parameter was the RBs available for M2M communication, the value of which we have
set as equal to the same parameter as that of our proposed
scheduler. The second input parameter that we have set as a
value is related to the amount of resources required by each
M2M device. In this case, we have assumed that the available
resources are divided equally between the M2M devices.
The simulated scenario was of a near future urban environment, perhaps in the IoT, where there is a high traffic H2H and
M2M. The H2H applications that we used were VoIP, Video
and FTP. The modelings of their traffic flows were based on
the works of [17] (VoIP and FTP) and [18] (Video). These
three traffic flows have been chosen to cover the various QoS
classes in the LTE standard. Moreover, we assumed both that
each device generates only one type of traffic and that each
H2H traffic is generated for an equal number of devices. The
burst transmissions of event-based applications were modeled
by a Poisson process with rate γ = 0.02 packets/TTI [8]. The
transmission interval of each time-based device was uniformly
distributed between 50 and 550 ms. The two categories of
M2M applications have a packet size of 125 bytes [13].
Aiming to simulate an environment with high H2H traffic,
the experiment was composed of 30 H2H devices, 10 for each
type of traffic. The devices (H2H and M2M) were uniformly
distributed around a single base station. We have defined
the number of devices per category of M2M applications
such that the time-based category has a larger number of the
devices, since most M2M applications belong to this category.
Moreover, the bandwidth was set to 5 MHz, 25 RBs available
per TTI (1 ms) in order to simulate an environment with
limited resources even using a small number of devices.
We used Table I to classify the M2M traffic according to
the two approaches discussed in Section IV-A. The minimum
min
min
amount of resources required per device (RBH
and RBM
)
was chosen to be able to transmit all buffered data in a single
TTI if the device has good quality in the channel and the
amount of data is small. The waiting time after the request is
denied was established as a maximum of 10% (σ = 0.1) of
the value of ∆D.
We chose the value of the constant ω of Equation (3)
through previous simulations. In these simulations, we varied
the value of ω between 0 and 1, we used 3 H2H devices, 250
M2M devices and the second approach of Subsection IV-A.
Furthermore, we configured the value of ρ equal to 0.48 so
min
that there were at least 4 (25ρ/RBM
) M2M devices getting
resources per TTI. The decision of the value of ρ is discussed
TABLE II.
Simulation time
Runs
Distribution of devices
Number of base stations
Bandwidth
Number of M2M devices
Number of H2H devices
H2H Traffic
Video
VoIP
FTP
M2M Traffic
Event Based
TIme Based
PARAMETERS OF THE SIMULATIONS
General Parameters
3 s (3000 TTIs)
30
Fixed and uniform within 1400
1
5 MHz (25 RBs)
0, 50, 100, 150, 200, 250; 1/3 Event Based, 2/3
Time Based
10 VoIP, 10 Video, 10 FTP
Packet Size
Inter-arrival Time
1200 bytes
40 bytes
256 bytes
75 ms
20 ms
16.625 ms
125 bytes
Poisson Process, γ = 50 ms
125 bytes
Uniformly distributed [50, 550] ms
Parameters of the Proposed Mechanism
min
min
RBH
, RBM
3
2M
ρ
0, 0.24, 0.48, 0.72, 0.96
ω
0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 1
σ
0.1
Constant of EMA
99
B. Results
As discussed in Section IV-A, two approaches are proposed in this paper to classify M2M traffic according to its
QoS requirements. In the remainder of this paper, the first
approach is referred to as “P roposalV 1” and the second one
as “P roposalV 2”. The second scheduler proposed in [5] is
designated as “Lioumpas2”. Finally, the “Proportional Fair”
scheduler is identified as P F .
1) Impact of M2M communication on H2H: Figure 2(a)
presents the mean values of the throughput for H2H devices
in relation to variation of the number of M2M devices. Note
that since the P roposalV 1, V 2 and Lioumpas2 schedulers
use the same subroutine to separate resources between H2H
and M2M communications, then only the P roposalV 2 and
P F schedulers are shown in the Figures 2(a), 2(b) and 2(c).
Furthermore, to analyze how the parameter ρ influences the
performance of H2H communication, we varied its value
according to Table II.
By analyzing Figure 2(a), we observed that the P F presented a drastic drop in throughput as the number of M2M
devices increased. P roposalV 2 also presents decreases in its
rates in almost all cases and this decrease is greater with the
increase of value of ρ. But even with ρ = 0.96 the drop in
throughput is less steep than the P F .
Fig. 1. Level of non-satisfaction of QoS requirements (ρ = 0.48, 30 H2H
devices, 250 M2M devices) - M2M Traffic.
in the next subsection. Figure 1 presents the results of these
simulations regarding the level of non-satisfaction of QoS
requirements for M2M traffic flows. Therefore, as we can
see in Figure 1, ω = 0.95 had the best results. Moreover,
we can observe in this figure that occurs improvements in
QoS satisfaction when the second component of Equation (3)
has greatest weight. This is explained by the fact that eventbased devices, which are less delay tolerant, begin to have
higher priority in scheduling. However, when the fairness
is disregarded (ω = 1), the satisfaction of QoS is also
compromised.
In order to evaluate the performance of our proposed
solution, we used three metrics: (i) the throughput, (ii) the
percentage of packets that do not satisfy the constraint of
the maximum tolerable delay and (iii) the Jain’s fairness
index [19]. We have defined the simulation time (3 s) to contain
a reasonable amount of schedules (3000). To obtain reliable
results as discussed in [19], we carried out 30 simulations,
generating random and independent samples. In addition, the
confidence interval of 95% was used to analyze the results.
Finally, Table II summarizes the main parameters used in the
simulations. Other parameters not shown in the table have
default values of NS-3 [20] simulator used in the experiments.
Figure 2(b) presents the mean values of the percentage
of packets for H2H traffic flows that exceed the maximum
tolerable delay. Just as in the graph of throughput (Figure
2(a)), we observed that P roposalV 2 performed better than
P F in satisfying QoS requirements (low percentage of packets
that exceeded the maximum delay) of H2H applications. In
addition, we observed that with the increase of value of ρ the
level of non-satisfaction of QoS requirements also tends to
increase.
Analyzing Figures 2(a) and 2(b), we can conclude that low
values of ρ result in low impacts on performance of H2H
communication. However, as can be seen in Figure 2(c), these
low values have negative effects on satisfaction of QoS requirements of M2M traffic flows. Hence, we choose ρ = 0.48,
for the remaining simulations of this paper, because it have a
good trade-off between the metrics discussed in this paragraph.
2) Satisfaction Level of QoS requirements: Figure 3(a)
presents the mean percentage of packets of event-based applications that exceeded the maximum tolerable delay. Similarly,
Figure 3(b) shows the results obtained for the time-based
applications.
In Figure 3(a), we can see that the P roposalV 1 and
P roposalV 2 present an increase in its values. This increase
is associated with the guarantee that the time-based devices
received resources. Also for this reason, the P roposalV 1
and P roposalV 2 present better results, in Figure 3(b), than
Lioumpas2 since this later gives low priority to time-based
devices and does not ensure fair allocation of resources.
However, Lioumpas2 presented better results for event-based
applications when the number of devices was high (greater
than 180).
3) Fairness in the allocation of resources: Figures 4(a) and
4(b) show the mean of the fairness index values for event-
(a) Throughput - H2H Traffic
(b) Level of non-satisfaction of QoS requirements - H2H
Traffic
(c) Level of non-satisfaction of QoS requirements - M2M
Traffic
Fig. 2.
Impact of M2M communication on H2H communication.
(a) Event-based applications
Fig. 3.
(b) Time-based applications
Level of non-satisfaction of QoS requirements for M2M communication (ρ = 0.48, ω = 0.95).
and time-based applications respectively. In order to better
understand the results shown in these figures, the fairness index
can be interpreted as follows: the closer the value of the index
to 1, the fairer the result. Consequently, values closer to zero
indicate that the result is more unfair, hence a greater the
probability of starvation occurs.
The Lioumpas2 scheduler presented reductions in fairness
index for both categories of M2M applications. This reduction
is more severe for time-base applications, which have lower
priorities in this scheduler. P roposalV 1 and P roposalV 2
presented a slight increase in the index of fairness for time-
based applications. This result is related to the increase in
traffic that has the highest priority in these schedulers as
defined in Section V-A. For this same reason, the P F scheduler
has shown an increase in their values, but in this case, for the
time-based applications.
VI.
C ONCLUSION AND F UTURE W ORKS
In this paper, we present a mechanism for the packet scheduler in the uplink of LTE network to treat M2M communication using historical information about resource allocations,
channel quality and QoS requirements of devices to (i) control
(a) Event-based applications
Fig. 4.
(b) Time-based applications
Index of fairness for M2M communication (ρ = 0.48, ω = 0.95).
the impact of M2M communication on H2H, (ii) avoid the
problem of starvation with the fair allocation of resources and
(iii) satisfy the QoS requirements.
From the analysis of the results obtained by the simulations, we observe that our approach to separating the resources
between H2H and M2M communications, so that the amount
of resources allocated to the M2M devices is controlled, is a
viable alternative to controlling the impact of M2M communication on H2H communication. Furthermore, the proposed
scheduler avoids the problem of starvation by ensuring fair
allocation of resources. However, in an environment with
a high volume of H2H traffic and in order to meet the
objectives (i) and (ii) aforementioned, the satisfaction of QoS
requirements of event-based applications is compromised.
Moreover, the results demonstrated that the approach of
extending the QoS classes of the LTE standard to add more
n classes for M2M applications in the time-based category
has similar results to the approach of adding a new control
message which the device uses to send its own values of
QoS requirements. Therefore, the first approach avoids the
generation of more traffic in a congested environment.
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
Interesting future works that we can mention are: (i) the
use of the characteristic of group base of M2M devices in
scheduling and (ii) the adaptation of the constants of the
mechanism proposed in this paper according to the state of
the system.
[14]
[15]
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