1

Effects of C/U Plane Separation
and Bearer Aggregation in Mobile Core Network
Shuya Abe, Go Hasegawa, Member, IEEE, and Masayuki Murata, Member, IEEE

Abstract—In response to the growing demand for cellular
networks, it is essential to improve the capacity of mobile core
networks. Especially, in terms of accommodating machine-tomachine/Internet-of-Things (M2M/IoT) terminals into cellular
networks, the load on the control and the user planes of themobile core network increases massively. To deal with thisproblem, it is possible to apply virtualization technologies suchas software-defined network and network function virtualization.
However, few existing studies evaluate such solutions for mobile
core networks numerically and in detail.
In this paper, we first evaluate mobile core network architectures with virtualization technologies and control/user (C/U)
plane separation using the mathematical analysis. We also propose a novel bearer aggregation method to reduce the control
plane load to accommodate massive M2M/IoT terminals.
The result of numerical evaluation shows that the capacityof the mobile core network can be increased by up to 32.8%
with node virtualization and C/U plane separation, and further
by 201.4% by using bearer aggregation. Moreover, to maintainthe performance of the mobile core network, we should carefully
determine where the bearer aggregation is applied and when
the shared bearer for each terminal is determined based on
application characteristics and the number of accommodated
M2M/IoT terminals.
Index Terms—mobile core network, M2M/IoT communication,
Software Defined Networks, C/U plane separation, bearer aggregation.

I. I NTRODUCTION
Background. With increasing demand for cellular networks
owing to the proliferation of rich user terminals such as smartphones and massive machine-to-machine/Internet-of-Things(M2M/IoT) terminals [1], increasing the capacity of mobile
core networks is important [2]. Some M2M/IoT communications have characteristics different from those of richuser terminals—communication may occur periodically orintermittently with small amounts of data, while the numberof terminals may be enormous. In addition, many M2M/IoT
terminals have almost no mobility, and most of them onlytransmit data (i.e., no data are received).
One possible way to accommodate such terminals is to
exploit a non-cellular wireless network called “Low Power,
Wide Area Network (LPWAN) [3]”. However, the realizationof LPWANs which are not based on cellular network is costly
because it requires the construction of a new network infrastructure. On the other hand, when accommodating massive
S. Abe and M. Murata are with the Graduate School of Information
Science and Technology, Osaka University, Suita 565–0871, Japan (e-mail:s-abe@ist.osaka-u.ac.jp).
G. Hasegawa is with the Cybermedia Center, Osaka University, Toyonaka
560–0043, Japan.
Manuscript received May 26, 2017.

M2M/IoT terminals to existing cellular networks, such as enhanced Machine Type Communication (eMTC), Narrowband
Internet of Things (NB-IoT), and Extended Coverage GSM
IoT (EC-GSM-IoT) [4]–[6], existing network infrastructure
can be utilized. However, as more and more M2M/IoT terminals are accommodated to the cellular networks, the load on
the core networks increases, especially on the control planenodes. In this paper, we focus on the signaling procedures
based on the existing cellular network and propose a method
to improve the network capacity.
In existing studies, the effects of virtualization technologies
such as software-defined network (SDN) and network function
virtualization (NFV) on long-term evolution/evolved packet
core (LTE/EPC) networks have been discussed to address suchproblems [7]–[18]. In [19], our research group considered themobile core network architecture for accommodating massive
M2M/IoT terminals and showed the conceptual idea of thebearer aggregation method. However, these evaluations werebased on a simple analysis model with severe assumptions.
Contribution. In this paper, we evaluate the performance ofmobile core networks with node virtualization and control/user(C/U) plane separation. In addition, we propose a detailed
mechanism of bearer aggregation. The main contributions of
this paper are as follows.
1) Introduction of an analysis method for evaluating theperformance of mobile core networks in terms of accommodating massive M2M/IoT terminals based onqueueing theory and the actual source codes of mobile
core network implementation.
2) Numerical evaluation of the performance of mobile
core networks with node virtualization and C/U plane
separation with SDN.
3) Proposal and evaluation of bearer aggregation method
to concretize the primal idea proposed in [19].
4) Presentation of parameter design according to the characteristics of M2M/IoT terminals.
Outline. In Section II, extant research related to our study
is summarized. Section III presents the architectures of themobile core network used herein. Section IV introduces thebearer aggregation method. Section V describes the mathematical analysis of the performance of the mobile core network.
In Section VI, we provide extensive numerical evaluation
results and discussions. In Section VII, based on the evaluation
results, we show the design of parameter settings for M2M/IoT
terminals with various characteristics. Finally, in Section VIII,
we conclude this paper with a brief summary and an outlineof future work.

2

TABLE I: Comparison of methods: The characteristics of various existing evaluations and the proposed method.
Applying softwarelization technologies to EPC nodes
Qualitative discussions on applying SDN
Overhead evaluation of virtualization and SDN
Evaluation considering signal processing loadAggregating multiple bearers

II. R ELATED W ORK
Various methods have been proposed for improving thecapacity of M2M/IoT communications in mobile cellular
networks [20]. These existing works, as well as the method
proposed in this paper, are listed in Table I.
In [7], the authors showed that efficient resource utilization
can be achieved by implementing EPC nodes as software.
In addition, [8]–[10] showed that low-cost mobility support
can be realized by virtualizing EPC nodes as functions of
NFV and distributing them over the network. The authorsof [11] and [12] showed that applying NFV to EPC nodes
decreases the amount of signaling traffic and the cost ofdevices, infrastructure, and energy consumption. In [12], [13],[16], [17], application of SDN to mobile core networks andvirtualization of nodes in a cloud environment were studied.
The authors of [14] presented the design, implementation,and evaluation of two LTE/EPC architectures, one of which
is based on SDN, and the other is based on NFV. From theevaluation results, an SDN-based EPC is better when handlinglarge amounts of data traffic because it decreases the overheadof forwarding data packets. On the other hand, an NFV-based
EPC is better at handling large signaling load, because every
signaling message is handled with the SDN controller in an
SDN architecture.
In [15], the authors proposed a hybrid SDN/NFV architecture which applies both the SDN decomposition and NFVconcept for LTE/EPC networks. In the proposed architecture,
the data plane functions of SGW and PGW can be locateddynamically in either of a data center (cloud environment)in case of NFV deployment or a transport network in case of
SDN deployment depending on QoS requirement. The authors
evaluated the performance of these solutions and showed that
the SDN decomposition decreases the network delay while
increases the total network load, and that the NFV deployment
increases the traffic delay while it does not increase the
network load.
In these studies, the authors argued that the utilization ofserver resources can be improved and cost can be decreased
by virtualizing nodes and applying SDN and NFV. However,the number of signaling procedures for virtualized functional
modules and additional SDN control messages in mobile core
networks may increase. Accommodating massive M2M/IoT
terminals, the overhead on the control plane nodes cannot beignored, especially when such terminals may be synchronizedwhen in sending data. However, there has been almost no
detailed evaluation of the relationship between SDN andincreased signaling overhead, with the exception of [16] where
the authors evaluated additional network traffic due to the[7]–[11][12]–[16]

[17]

[18]

Proposed

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application of SDN to the mobile core network. In [12], [13],[16], the signal processing load was evaluated on the basis of
the number and size of messages sent and received by EPC
nodes. However, the processing load of signaling messages
is determined by many other factors such as the number of
instructions executed in the node to process messages and node
resources.
In [18], the authors proposed a modified packet core architecture and tunnel management methods, including bearer
aggregation, specific to M2M traffic, and evaluated the performance of a mobile core network by using OpenAirInterface(OAI) [21], a software application for LTE/EPC networks
written in C. However, this method requires the introductionof a new node in the mobile core network and extensive
modification to the signaling message flows. On the other
hand, our proposed method focuses on bearer aggregation withminimal modification of the current mobile core network.
III. N ETWORK A RCHITECTURE
To evaluate the effects of node virtualization and C/U plane
separation in mobile core networks, we consider the following
three network architectures.
A. Conventional Architecture (CA)The conventional mobile core network architecture is shownin Fig. 1(a). It consists of user equipments (UEs), eNodeBs, aserving gateway/packet data network gateway (SGW/PGW), a
mobile management entity (MME), a home subscriber server(HSS), and a policy and charging rules function (PCRF). Notethat SGW and PGW are integrated into a single node, as in
the state-of-the-art implementation design of EPC. We assume
that MME, HSS, and PCRF are virtualized and located in thecloud environment owned by the mobile network operator, andeNodeB and SGW/PGW are located in the transport network
without virtualization. Fig. 1(b) shows the signaling flow
when a UE changes its state from idle to active and requests
start of communication. In this figure, req. and res. mean
the signaling message is a request and a response message,
respectively, and Msg. stands for “message”. Ctxt, Ded.,
Acc mean “Context”, “Dedicated”, and “Accept”, respectively.
The figure includes the number of statements in programs
executed by each node for processing each signaling message.
The number of statements was obtained by analyzing thesource code of OAI. Note that each processing of signaling
messages has a different number of statements, meaning thateach message imposes a different load on the corresponding
mobile core node.

3

Cloud networkof mobile operator+6600(
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shown in Fig. 1(b) is terminated at NAS Security Mode cmp
and a pair of a MME and an SGW can perform as a Cellular
IoT Serving Gateway Node [5].

H1RGH%

B. C/U Plane-separated Architecture (PSA)eǆternal IP network

6*:3*:(875$1

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Control plane signaling(a) Mobile core network model.

Fig. 2(a) shows the architecture of the mobile core network with node virtualization and C/U plane separation in
SGW/PGW. SGW/PGW is separated into SGW/PGWc forcontrol plane functions and SGW/PGWd for data plane functions. SGW/PGWc is virtualized and located in the cloudenvironment, while SGW/PGWd is in the transport network
without virtualization, as in CA. By applying C/U plane
separation, the propagation delay between SGW/PGWc andMME becomes smaller than that in CA.
In this architecture, a GTP module [17], that is, a function
matching the general packet radio service tunneling protocol(GTP) bearers established at SGW, is installed in the cloud
network. This is because all control plane functions are locatedin the cloud environment in PSA. Therefore, it is necessary
for all data packets to pass through the cloud environment
when UEs perform data communication after establishing thebearer, resulting in large propagation delays in the mobile
core network. For the reason, this architecture is unsuitablefor UEs with large amount of communication data such as
smart phones, while it is acceptable for M2M/IoT terminals
with small amount of transmitting and receiving data.
Fig. 2(b) shows the signaling flow when a UE changes its
state from idle to active and requests the start of communication. Compared with Fig. 1(b), a control message related toroute setting by SDN is required to be sent from SGW/PGWc
to SGW/PGWd after signaling bearer establishment is complete. The number of statements necessary for processing thismessage is determined based on the source code of similarfunctions in OAI. The total propagation delay in signaling
messages related to bearer establishment in this architectureis smaller than that in CA since messages between MME and
SGW/PGWC are exchanged within the cloud environment.
C. C/U Plane-separated Architecture with GTP Module Located in Data Plane (PSAg )(b) Signaling flow for bearer establishment.

Fig. 3(a) shows the mobile core network architecture where
the GTP module is implemented in the transport network.
The GTP module can be implemented in the form of special
hardware or software in SGW/PGWd . This architecture canprevent the increase in propagation delay in the data planefound in PSA, while an additional signaling message is required to configure the GTP module for bearer establishment,
as shown in Fig. 3(b).

Fig. 1: Conventional Architecture (CA)IV. B EARER AGGREGATION M ETHOD

The networks where data is carried by RRC or NAS
messages such as small data transmission in NB-IoT [5] canalso be evaluated by assuming that the signaling procedure

One of the problems associated with accommodating massive M2M/IoT terminals in cellular networks is the increase in
the number of bearers to be handled concurrently in the mobile
core network. In this section, a bearer aggregation method is
proposed to decrease the number of concurrent bearers.

4

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3&5)
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GTP
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3&5)

Control plane signaling
SDN signaling(a) Mobile core network model.

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Control plane signaling

SDN signaling(a) Mobile core network model.

(b) Signaling flow for bearer establishment.

(b) Signaling flow for bearer establishment.

Fig. 3: C/U Plane-separated Architecture with GTP module
located at Data Plane (PSAg )Fig. 2: C/U Plane-separated Architecture (PSA)

A. Overview
The bearer aggregation method reduces the load on EPC
nodes by ensuring that one bearer is shared by multiple UEs;
by contrast, in current mobile core networks, a single bearer
corresponds to a single UE.
An illustration of the bearer aggregation method is given

in Fig. 4(a). At the node where the aggregation method isapplied, called an aggregation point, bearers from a group of
UEs are aggregated into a single shared bearer. For example,
in Fig. 4(a), when the aggregation point is an SGW and it
aggregates multiple S1–u bearers between an eNodeB andthe SGW into a single S5/S8 bearer between the SGW anda PGW, packets from UE1 , UE2 , and UE3 passing throughtheir S1–u bearers are injected into a shared S5/S8 bearer to5

B. Virtual IMSI

Aggregating Point
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1

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1
2

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2

3

4

5

6

3
4

4

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5

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(a) Bearer aggregation method.

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ܷ‫ܧ‬ଶ

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ܷ‫ܧ‬ସ
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IMSI

vIMSI

000 00 0000000001

000 00 1000000001

000 00 0000000002

000 00 1000000001

000 00 0000000003

000 00 1000000001

000 00 0000000004

000 00 1000000002

000 00 0000000005

000 00 1000000002

000 00 0000000006

000 00 1000000002(b) IMSI table.

Fig. 4: (a) Bearer Aggregation: Bearers from a group of UEs
are aggregated into a single shared bearer. (b) IMSI table:
MME handles the matching between IMSIs and a vIMSI.

be transmitted to the PGW. By this mechanism, the processing
overhead of the SGW for handling signaling messages isreduced by decreasing the number of concurrent bearers at thenode, while additional processing is required for maintaining
the shared bearers. Note that the number of bearers aggregatedby a single shared bearer, that is defined as aggregation levelin this paper, can vary. Furthermore, the aggregation level does
not affect the U-plane performance since it only omit a partof signaling procedure in C-plane when establishing bearers.
One possible shortcoming of bearer aggregation is that the
Quality of Service (QoS) of the data transmission can beconsidered within a unit of an aggregated bearer, while perUE guarantee can be achieved in the conventional mobile core
network. However, especially when considering M2M/IoT
communication, it is likely that massive but homogeneous
terminals from a single user are accommodated. In such a case,the degraded resolution of QoS guarantee would be acceptable.
For example, 3GPP categorizes M2M/IoT UEs accomodated
to the cellular network into some types [22] and UEs in thesame categories can be aggregated into the same group.
We expect that this method can be applied to the LPWANs
which have the similar signaling procedures such as eMTC.
Moreover, the aggregation method can be applied to large datatransmission in NB-IoT that utilize bearers.

To realize the bearer aggregation method with minimal
modification to the behavior of the conventional mobile core
network, we introduce the concept of virtual International
Mobile Subscriber Identity (vIMSI), which is associated with ashared bearer, in contrast to a normal IMSI, which is assigned
uniquely to each UE and a corresponding bearer. MME
handles matching between IMSIs and vIMSIs by maintaining
an IMSI table (Fig. 4(b)) which represents the current statusof bearer aggregation.
In the signaling flows shown in Fig. 1(b), Fig. 2(b) and
Fig. 3(b), from the time when the flows begin to the time
when a “NAS Security Mode” message is sent from a UE toan MME, the signaling messages include only a normal IMSI
that corresponds to the UE. When an “NAS Security Mode”
response message arrives at the MME, the MME searches the
IMSI table in Fig. 4(b) to locate a vIMSI for the UE. Then,in the following signaling flow, signaling messages include
both the normal IMSI for the UE and the vIMSI for theshared bearer. In addition, the MME notifies the PCRF of
the correspondence between the IMSIs and vIMSIs when it
updates the IMSI table.
C. Design Options
The bearer aggregation method has two design parameters.
One is the node on which aggregation is applied, and the otheris the timing when a group of UEs for a shared bearer isdetermined.
1) Aggregation Point: The bearers are aggregated at SGW
or eNodeB. Table II shows the positive and negative effects
with SGW or eNodeB aggregation.
a) Aggregation at SGW: Multiple S1–u bearersbetween
an eNodeB and an SGW are aggregated into a single S5/S8
bearer between the SGW and a PGW. This leads to a decreasein the number of Modify Bearer req/res. messages andCreate Bearer req/res. messages for creating S5/S8 bearers.
Since an S5/S8 bearer is maintained while a UE is attachedto the network, bearer aggregation at the SGW does not
significantly influence the protocol for the establishment andrelease of S5/S8 bearers.
b) Aggregation at eNodeB: Multiple radio bearers between UEs and an eNodeB are aggregated into a single S1–
u bearer between the eNodeB and an SGW. The number of
S5/S8 bearers is also reduced because an S1–u bearer andan S5/S8 bearer have a one-to-one relationship. Consequently,
the decrease in the signaling overhead owing to aggregation
at the SGW can be realized. Additionally, the number of
Initial Context Setup req/res. messages for establishing S1–ubearers decreases.
However, we believe that this aggregation significantly
affects the protocol. In the current mobile core networks, anS1–u bearer and a corresponding radio bearer are released
simultaneously when a UE becomes idle. In contrast, whenaggregation is applied at eNodeB, a shared S1–u bearer should
be maintained until all UEs in the group for the shared bearer
become idle.

6

TABLE II: Positive and Negative Effect with SGW or eNodeB Aggregation: Depending on aggregation point, required
modification, network capacity and bearer establishment time are different.
Aggregation Point
SGW
eNodeB

Positive Effectrequired small modificationhigh network capacity, small bearer establishment time

Negative Effectlow network capacity, large bearer establishment timerequired large modification

TABLE III: Positive and Negative Effect with Pre-determined or On-demand Aggregation: Depending on aggregation
timing, wait time and network capacity are different.
Aggregation Timing
Pre-determined
On-demand

Positive Effect
not required wait timehigh network capacityc) Aggregation at both SGW and eNodeB: The combination of aggregations at SGW and eNodeB can be considered
to further decrease the number of bearers, in which some of
the aggregated S1–u bearers are again aggregated into a single
aggregated S5/S8 bearer.
In this aggregation, the IMSI table in Fig. 4(b) can also be
used. In the second stage of the aggregation at the SGW, thevIMSI of the aggregated S1–u bearer is added to the IMSI
column and the vIMSI of the S5/S8 bearer is added to thevIMSI column.
This aggregation inherits the advantages and disadvantagesof both aggregations at SGW and at eNodeB. Therefore, it
significantly affects the protocol in the sense that a sharedS1–u bearer should be maintained until all UEs in the groupfor the shared bearer become idle, as in the aggregation at
eNodeB.
2) Aggregation Timing: The group of a UE for bearer
aggregation is determined when the UE attaches to the networkor when the UE becomes active and the communication
request is issued. Table III describes the positive and negative effects with pre-determined aggregation or on-demandaggregation.
a) Pre-determined Aggregation: The group of a UE for
bearer aggregation is determined when the UE attaches tothe network. The assignment of a vIMSI by MME and thenotification to the PCRF are conducted after that.
Fig. 5(a) shows a timeline of the signal processing at
the MME with a pre-determined aggregation method. The
vertical dashed lines represent arrivals of the communication
requests from a group of UEs. When the first UE (UE1 in the
figure) arrives, the corresponding shared bearer is established(Bearer Establishment depicted as a blue box in the figure).
Therefore, the following UEs (UE2 ...UEK ) do not require theestablishment procedure of the shared bearer. However, thedata path setting to data plane nodes (green box) is necessaryfor each UE.
b) On-demand Aggregation: The group of a UE for
bearer aggregation is determined when the UE becomes active
and the communication request is issued, not when the UE
attaches to the network. Therefore, a notification is sent to the
PCRF every time the UE initiates communication. Fig. 5(b)shows an example of the on-demand aggregation method.
Each UE waits until the number of UEs reaches the required
aggregation level, which is the number of UEs in each group.

Negative Effectlow network capacity,required wait time

BearerEstablishment

Node
Processing

Node
Processing

Data Path
Setting for ܷ‫ܧ‬ଵData Path
Setting for ܷ‫ܧ‬ଶ

NodeProcessing
Msg. arrival(from ܷ‫ܧ‬ଵ )Msg. arrival(from ܷ‫ܧ‬ଶ )Msg. arrival(from ܷ‫ܧ‬௄ )Data Path
Setting for ܷ‫ܧ‬௞t(a) Pre-determined aggregation method.
(Wait Time)(Wait Time)

Node
Processing
Node
Processing

AssignBearerNodevIMSI Establishment Processing

Data Path Setting forܷ‫ܧ‬ଵ ǡ ܷ‫ܧ‬ଶ ǡ ǥ ǡ ܷ‫ܧ‬௄tMsg. arrival Msg. arrival(from ܷ‫ܧ‬ଵ ) (from ܷ‫ܧ‬ଶ )Msg. arrival(from ܷ‫ܧ‬௄ )(b) On-demand aggregation method.

Fig. 5: (a) Pre-determined Aggregation: The first UE (UE1 )establishes a shared bearer. (b) On-demand Aggregation: A
shared bearer is established when the communication requestfrom all UEs in a group arrives.

Then, assignment of the vIMSI, sending of a notification to
the PCRF, and establishment of the shared bearer occur. Notethat this method requires only one data path setting procedure
for all UEs in the group. However, a UE experiences a waitingtime between the time it sends a communication request andthe time at which the shared bearer is established.

V. P ERFORMANCE A NALYSIS
We calculate the network capacity and the bearer establishment time to evaluate the performance of mobile core
networks. We define the network capacity as the maximum
number of UEs that can be accommodated with saturated
utilization of nodes in a mobile core network. The bearer
establishment time is defined as the time from when thesignaling flow starts to the time when it ends, assuming the
network models and signaling flows explained in Section III.

7

We first calculate the processing time for a signaling message at a node. We then derive the network capacity and thebearer establishment time based on the processing time.

messages and the server resources of node N. Therefore, λ N ,2 ] are derived as
E[SN ], and E[SNλN

PN · nU,
D · nNPN∑
LN=

E[SN ] =A. Notations
m is one of CA, PSA and PSAg defined in Section III. For
the individual node, we abbreviate UE, eNodeB, MME, and
SGW/PGW as U, B, M, and G, respectively. G c , G d , and G grespectively represent a control plane node, a data plane node,
and a GTP module for SGW/PGW. The propagation delay ofsignaling messages between nodes N1 and N2 is denoted byτN1, N2 . The average processing time for a signaling message
at node N is denoted by t N . CN1, N2 means the number ofsignaling messages transmitted from N1 to N2 . The number ofmessages processed at node N in the signaling flow is denotedby PN . n N represents the number of nodes N in the network.
A N is the server resources of node N in terms of the numberof statements that can be processed per second. L Ni is thenumber of statements required for processing the ith signaling
message at node N. Note that N, N1 , and N2 mean one of U,
B, M, G c , G d , and G g .
We assume that each UE starts the communication at regularintervals of D, which is called a communication period of
a UE. In addition, there is a randomness in the start timing
within the period. Specifically, all UEs initiate communication
requests randomly within a certain time interval D ′(< D). K
represents the aggregation level in bearer aggregation (K = 3
in Fig. 4). We set K to a constant value in this evaluation forsimplicity.

B. Processing TimeTo derive the average processing time at a node N, weemploy the M/G/1/PS queuing model. We assume that thearrival of the signaling messages at a node follows the Poisson
distribution. In the M/G/1/PS model, the mean sojourn time
E[R] can be derived as
ρr
E[S 2 ] 1 − ρr
E[R] =·+· E[S],
1 − ρ 2E[S]1−ρρ = λ · E[S].

(2)

In the analysis, the number of signaling messages to beprocessed per unit time at node N, also called the signaling
processing frequency, is regarded as the job arrival rate. The
time distribution of the processing of signaling messages at
node N is used for the workload distribution, SN . Then,the mean workload E[SN ] can be calculated on the basisof the average number of statements for processing signaling

PN
∑

L 2Ni

i=1

A2N · PN

,

(4)

.

(5)

C. Network Capacity
Solving (2) for nU with (3) and (4), we obtainnU = D · ρ

n N · AN
.
PN
∑
Li

(6)

i=1

By substituting ρ = 1 for (6), we can obtain the number of
UEs that can be accommodated at the node N. We denote it
by nU N max . The network capacity, denoted as nUma x is theminimum value of nU N max for all nodes in the network.
©
ª­®
­ n N · AN ®
­®.
(7)
nUma x = min ­ D®
PNN ∈V m ­
∑
®
­
Li ®
« i=1
¬
We assume that some nodes in the network are located in thecloud environment and their server resources can be configured
while ensuring that the total amount of server resources
remains fixed. When we optimize the server resources of thenodes in the network to obtain the maximum value of nU N max
for all nodes becomes identical, that also equals to the network
capacity in (7). We can calculate the server resources of each
N in such a situation, called as optimized server resources ofnode N and denoted as A′N , as follows. Note that W represents
a set of the nodes whose server resources are optimized.

A′N =

∑
I ∈W

where λ is the job arrival rate, S(x) is the workload distribution, E[S] is the mean workload, r is the maximum
number of parallel processing runs, and the system utilization
is determined as follows:i

A N · PN
i=1

2
E[SN
] =

(
(1)

(3)

)
AI

PN
∑

Li
i=1
·
PI
∑∑

·

1
.
nN

(8)

Li

I ∈W i=1

When applying a bearer aggregation method, both of thesignaling processing frequency λ and the average signaling
processing time E[S] change. λ decreases because PN in (3)gets smaller. On the other hand, E[S] decreases because Li in(4) decreases. Specifically, when applying a bearer aggregation
method with the aggregation level K, the number of signaling
messages related to the bearer establishment decreases to 1/K.
In detail, when i is the signaling processing to be aggregated,
the number of statements Li becomes Li /K with the bearer
aggregation.
From (6), we can see that the network capacity is proportional to the number of bottleneck nodes. Also, the network
capacity is proportional to the amount of server resources.

8

Therefore, when the server resources are optimized, because
a more proportion of server resources is allocated to thebottleneck nodes, the network capacity increases. In addition,
applying bearer aggregation to the mobile core network reduces the number of statements and signaling processings,
resulting in increased network capacity.

Substituting (13) for (11), we obtain∑dT=
dnU N ∈V

PN · D · n N ·(m

2 D · n N · AN −

PN
∑

Li2

i=1
PN
∑

)2 −

Li · nU

D·K
2 . (14)2nU

i=1

D. Bearer Establishment TimeThe bearer establishment time T is the sum of propagation
delay of all signaling messages, Tτ ; processing times of all
messages, Tt ; and the waiting time required when using ondemand aggregation, Tw . We derive the bearer establishment
time by (9).
T = Tτ + Tt + Tw
∑ (
∑
)
=
CN1, N2 · τN1, N2 +(PN · t N ) + Tw,
N1, N2 ∈V m

(9)

N ∈V mwhere Tw is calculated by (10) on the basis of the communication period of a UE, the number of UEs attached to the
network, and the aggregation level.
{
K ·D(Aggregation at SGW)
2nU
Tw = K(10)·D ·n B
(Aggregation at eNodeB)
2nU
In what follows, we assess the effect of nU on T. SinceTτ does not depend on nU , we can obtain the following
equation for PSAg with on-demand aggregation at SGW bydifferentiating T with respect to nU . Note that in case ofaggregation at eNodeB, the second term of the following
equation is multiplied by nB .
dTt
dTw
dT
=
+
dnU
dnU dnU
)
) (
∑ (
dt N
D·K
=
PN
+ − 2
dnU
2nU
N ∈V

(11)

m

dt N
dnU

is expressed as (12). This represents the increase in the
average signaling processing time t N at the node N when thenumber of UEs increases.
dt N
dt N dρ=·dnUdρ dnU(= (2E[S]2 − E[S 2 ])(r − 1)ρr)
(12)
− (2E[S]2 − E[S 2 ])r · ρr−1 + 2E[S]2/2E[S](1 − ρ)2dρ
dnUAssuming that the maximum number r of parallel processing
runs corresponding to each node is one, (12) can be convertedas
dt N
PN · D · n N · E[S 2 ]=dnU2(D · n N − PN · E[S] · nU )2
PN∑
D · nN ·
Li2
=

(

i=1

2 D · n N · AN −

PN
∑
i=1

(13)
)2 .

Li · nU

From (13), we can see that the increase in the average
signaling processing runs corresponding to time is inversely
proportional to the square of the number of accommodated
dt N
terminals. dn
diverges to positive infinity when nU apU
D ·n N ·A N
proaches ∑ P N, which is identical to nU N max in (6) withi=1

Li

ρ = 1. Therefore, with (14), by appropriately allocating server
resources to EPC nodes as in (8), we can maximize thenumber of accommodated UEs with finite value of the bearer
establishment time.
VI. N UMERICAL E VALUATIONIn this section, we show the numerical results of theanalysis in Section V for evaluating the effects of the node
virtualization and C/U plane separation with SDN discussed
in Section III and the bearer aggregation method proposed in
Section IV.
A. Evaluation Candidates and Parameter Settings
We evaluate the performance of six different bearer aggregation methods, each of which combines aggregation point andaggregation timing. For comparison, we evaluate the performance of a model without bearer aggregation. The notations
for these methods are as follows.
• NA: no aggregation• PA–SGW: pre-determined aggregation at SGW• OA–SGW: on-demand aggregation at SGW• PA–eNB: pre-determined aggregation at eNodeB• OA–eNB: on-demand aggregation at eNodeB• PA–SGWeNB: pre-determined aggregation at both of
SGW and eNodeB• OA–SGWeNB: on-demand aggregation at both of SGW
and eNodeB
The communication period of a UE is set to 600 seconds.
The network model has 2,000 eNodeBs, one MME and one
SGW/PGW for CA. In PSA and PSAg , SGW/PGW is divided
into one SGW/PGWc and SGW/PGWd . One GTP module
exists in the network for PSA and PSAg . We change thenumber of UEs to be attached to the network, while each
eNodeB accommodates an identical number of UEs. The
propagation delays of signaling messages between nodes are
configured as follows. Note that the propagation delays do notinclude the processing time for signaling messages.
• UE–eNodeB: 20 msec
• eNodeB–SGW/PGW, SGW/PGW d : 7.5 msec
• eNodeB–MME, SGW/PGWc : 10 msec
• SGW/PGW, SGW/PGW d –MME, SGW/PGWc : 10 msec
• MME–SGW/PGWc : 1 msec
• GTP module–SGW/PGW d : 1 msec

9

The default values of the server resources of nodes as follows.

3•

These values were determined on the basis of discussions
with researchers from a mobile network operator in Japan,
assuming a nation-wide mobile core network and we set the
average values of propagation delays assuming EPC deployedin Japan as follows. The cloud environment which has MME
and SGW/PGWc is located in major metropolitan areas inJapan, namely, Tokyo, Osaka and Nagoya. SGW/PGWs or
SGW/PGWd s locate in these major metropolitan areas andregional hub cities, namely, Sapporo, Sendai, Hiroshima andFukuoka. ENodeBs are deployed to the whole part of Japanand we configured the number of eNodeB from the white paper
from Ministry of Internal Affairs and Communications [23].
We assume that in PSA and PSAg , the server resources
located in the cloud environment (MME and SGW/PGWc )can be optimized so that the loads on the servers are identical,
while the sum of the server resources remains unchanged fromthe above mentioned default values.
The number of statements for processing each signaling
message in the signaling flow in Section IV is determinedon the basis of the source code of OAI. Note that we ignore
the number of statements for maintaining and searching the
IMSI table in the bearer aggregation because we assume it issufficiently smaller than that for other signaling messages.
B. Evaluation Results
1) Network Architecture Comparisons: Fig. 6 shows therelationship between the number of accommodated UEs andthe bearer establishment time when we utilize the mobile
core networks based on the three architecture discussed in
Section III. In the figure, (unoptimized) means that the server
resources are set to the default values and are not optimized,
while (optimized) represents the results with the optimizationof server resources. As shown in the figure, when the numberof UEs reaches a certain value, the bearer establishment timeincreases sharply. This is because the load on one of the nodes
in the network increases to 100%. In what follows, we use that
number of UEs as the network capacity.
Without server resource optimization, there is almost no
difference among the three networks in terms of network
capacity. In these networks, the bottleneck node of the network
capacity is MME, and even when the load on SGW/PGWc
decreases owing to C/U plane separation, the system utilizationof MME remains unaffected.
A comparison of the results obtained with and without
server resource optimization in PSA or PSAg , shows thatthe network capacity increases by 32.8% after server resource
optimization. This is because C/U plane separation reduces theload on SGW/PGWc , and server resource optimization makes

CA (unoptimized)

2.5
Bearer Establishment Time (sec)

UE: 3,000 statements/sec• eNodeB: 300,000 statements/sec• MME: 3,000,000 statements/sec• SGW/PGW: 3,000,000 statements/sec• SGW/PGWc : 3,000,000 statements/sec• SGW/PGWd : 3,000,000 statements/sec• GTP module: 600,000 statements/sec

PSA (unoptimized)
PSAg (unoptimized)

2

PSA (optimized)
1.5

PSAg (optimized)

1

0.5

0
0

2000004000006000008000001x106

1.2x106

Number of UEs

Fig. 6: Comparison of network architecture: The network
capacity increases by applying server resource optimization.
a greater proportion of server resources available for allocation
to the bottleneck nodes.
Moreover, regardless of whether server resources are optimized, CA yields a slightly longer bearer establishment timethan PSA or PSAg . This is because, in PSA and PSAg ,propagation delays are reduced by placing the control plane
function of SGW in the cloud environment. PSA and PSAg
yield almost the same bearer establishment times because thenumber of signaling messages and processing of signaling
messages are almost identical. However, based on the difference between Fig. 2(b) and Fig. 3(b),there is one additionalsignaling message, GTP Setting between the SGW/PGWd
and GTP module, in the case of PSAg . Therefore, the total
propagation delay of PSAg is slightly larger than that of PSA,
and this difference cannot be recognized in Fig. 6.
In the following evaluation, PSAg is utilized.
2) Aggregation Level: Figs. 7(a) and 7(b) show the results with server resource optimization when applying predetermined aggregation at SGW and on-demand aggregation at
eNodeB, respectively. In the figure, K = i indicates the results
obtained by setting the aggregation level K to i.
We can see from these figures that the network capacity
increases further by applying a bearer aggregation method(K > 1) regardless of the combination of aggregation point
and timing. The performance gain is up to 181.8% when wecompare NA K=1 and PA-SGW K=1024. This is because
bearer aggregation reduces the number of signaling messages
to be processed by MME and SGW/PGWc , which, in turn,
reduces the server load. However, when the aggregation levelis higher than 64, the network capacity remains almost unchanged. This is because the signaling overhead that can beremoved by bearer aggregation is sufficiently small and canbe ignored. In the following evaluation, the aggregation levelis set to 64.
Fig. 7(b) shows that when applying the on-demand aggregation at eNodeB, the bearer establishment time becomessignificantly large. This is caused by the waiting time shownin Fig. 5(b). Equation (10) shows that the waiting time is
proportional to the aggregation level and inversely proportional

10

3

3

NA(unoptimized)

NA(K=1)
PA‑SGW(K=4)
PA‑SGW(K=16)
PA‑SGW(K=64)
PA‑SGW(K=256)

2

PA‑SGW(K=1024)
1.5

1

2

1.5

1

0.5

0.5

0

0
0

5000001x106

1.5x106

2x106

2.5x106

3x106(a) Pre-determined aggregation at SGW
NA(K=1)OA‑eNB(K=4)
OA‑eNB(K=16)
OA‑eNB(K=64)
OA‑eNB(K=256)
OA‑eNB(K=1024)

104
103
2

10

101
100
10‑1
0

5000001x106

1.5x106

5000001x106

1.5x106

2x106

2.5x106

3x106

Fig. 8: Effect of combination of server resource optimization and bearer aggregation: In terms of network capacity,
the simultaneous application of server resource optimization
and bearer aggregation yields better performance than applying
them individually.

106
105

0

Number of UEs

Number of UEs

Bearer Establishment Time (sec)NA(optimized)
PA‑SGW(K=64,unoptimized)
PA‑SGW(K=64,optimized)

2.5
Bearer Establishment Time (sec)Bearer Establishment Time (sec)

2.5

2x106

2.5x106

3x106

Number of UEs(b) On-demand aggregation at eNodeB (log-scale)Fig. 7: Effect of aggregation level: The network capacity
increases by applying a bearer aggregation method.

to the number of accommodated UEs. Therefore, when theaggregation level decreases or when the number of accommodated UEs increases, the bearer establishment time decreases.
3) Combination of Server Resource Optimization and
Bearer Aggregation: Fig. 8 shows the results without bothbearer aggregation and server resource optimization (NA (unoptimized)), with only server resource optimization (NA (optimized)), with only bearer aggregation (PA–SGW (K=64, unoptimized)) and with both bearer aggregation and server
resource optimization (PA–SGW (K=64, optimized)). Notethat in this evaluation, we utilize only PA–SGW as the bearer
aggregation method. As can be seen from Fig. 8, whencomparing NA (unoptimized) and NA (optimized), performance is improved by 32.8% with server resource optimization. Comparing NA (unoptimized) and PA–SGW (K = 64,
unoptimized) indicates that the bearer aggregation increases
network capacity by 91.8%. On the other hand, by comparing
NA (unoptimized) and PA–SGW (K = 64, unoptimized), we
can observe that combining server resource optimization andbearer aggregation improves the network capacity by 264.5%,
which is much greater than the performance improvement by

one of both methods. This means that a higher performancegain can be achieved by combining these methods. Thisdifference arises from the amount of resources that can beallocated to the bottleneck node. Server resource optimization
without aggregation cannot greatly reduce the load on thebottleneck nodes, and the server resources that can be allocatedto the bottleneck nodes are limited. However, the combinationof server resource optimization and bearer aggregation greatly
reduces the load on the nodes in the network and increases
the amount of resources that can be allocated to the bottleneck
node.
In the following evaluations, server resource optimization isapplied.
4) Aggregation Point: Fig. 9 shows a comparison of theperformance of bearer aggregation at SGW and that at eNodeB. Figs. 9(a) and 9(b) plot the results of pre-determinedaggregation and on-demand aggregation, respectively.
As shown in Fig. 9(a), with pre-determined aggregation,
the aggregation at eNodeB outperforms that at SGW in termsof network capacity and bearer establishment time. This isbecause the aggregation at eNodeB can largely reduce thenumber of bearers and the corresponding signaling messages
compared to the aggregation at SGW. Note that in this result,
there is almost no difference of bearer establishment time between them. However, we confirmed that especially when theserver resources of eNodeB are small, this difference becomesapparent. Fig. 9(b) shows that with on-demand aggregation,
the aggregation at eNodeB also yields higher network capacity,
albeit with a substantially longer bearer establishment time.
This is because of the abovementioned long waiting timeassociated with the on-demand aggregation.
5) Aggregation Timing: Fig. 10 shows similar results forthe sake of comparing the pre-determined aggregation and ondemand aggregation schemes. Figs. 10(a) and 10(b) show plotsof the results of aggregation at SGW and eNodeB, respectively.

11

3

3

NA(K=1)

PA‑SGW(K=64)

PA‑SGW(K=64)

2.5

PA‑eNB(K=64)

Bearer Establishment Time (sec)Bearer Establishment Time (sec)

2.5

NA(K=1)

2

1.5

1

0.5

OA‑SGW(K=64)

2

1.5

1

0.5

0

0
0

5000001x106

1.5x106

2x106

2.5x106

3x106

0500000

1x106

Number of UEs(a) With pre-determined aggregation.

2x106

2.5x106

3x106(a) With the bearer aggregation at SGW

3

3

10

10

102

NA(K=1)

NA(K=1)OA‑SGW(K=64)
OA‑eNB(K=64)

PA‑eNB(K=64)
OA‑eNB(K=64)

Bearer Establishment Time (sec)Bearer Establishment Time (sec)

1.5x106
Number of UEs

101

100

10‑1
0

5000001x106

1.5x106

2x106

2.5x106

102

101

100

10‑1

3x106

0

500000

1x106

Number of UEs

1.5x106

2x106

2.5x106

3x106

Number of UEs(b) With on-demand aggregation.

(b) With the bearer aggregation at eNodeB

Fig. 9: Effect of aggregation point: The aggregation at
eNodeB results in larger network capacity than that at SGW.

Fig. 10: Effect of aggregation timing: The on-demand aggregation scheme outperforms the pre-determined aggregation
scheme in terms of network capacity.
3

KB:KG=1:8
KB:KG=1:64
KB:KG=8:1
KB:KG=8:8
KB:KG=64:1

2.5
Bearer Establishment Time (sec)As shown in Fig. 10(a), the pre-determined aggregation
scheme yields a slightly shorter bearer establishment time at
the cost of lower network capacity.
When the number of accommodated UEs is small, the bearer
establishment time with the on-demand aggregation scheme
is long owing to the waiting time. In contrast, Fig. 10(b)shows that with the aggregation at eNodeB, the negative effectof the waiting time becomes apparent when the on-demandaggregation scheme is applied. This is because of the longer
waiting time, as discussed in Subsection VI-B4.
6) Aggregation at both SGW and eNodeB: Fig. 11 showsthe effects of bearer aggregation at both SGW and eNodeB
when applying the pre-determined aggregation scheme. In the
figure, KB:KG=i:j indicates the results when the aggregationlevel at eNodeB is set to i and that at SGW is set to j.
According to the above results, to increase the network
capacity, we would increase the aggregation level at eNodeB
or SGW. For example, assume that the current aggregationlevel at eNodeB and SGW is 1:8. When we increase theaggregation level at either eNodeB or SGW by eight times,
the aggregation level changes to 8:8 or 1:64, respectively.
Fig. 11 plots these cases. We can observe that the performance

2

1.5

1

0.5

0
0

5000001x106

1.5x106

2x106

2.5x106

3x106

Number of UEs

Fig. 11: Effect of aggregation at both eNodeB and SGW:
The performance gain becomes different depending on theaggregation level at each aggregation point.

achieved with 8:8 is better than that achieved with 1:64. On
the other hand, when the current aggregation level is 8:1 and12NA(K=1)

1.4

OA‑SGW(K=64)
OA‑eNB(K=64)

1.2
Processing Time (sec)

we want to increase the aggregation level at only one node,
the performances achieved with 8:8 and 64:1 are similar. Thisis caused mainly by the difference in the effect of bearer
aggregation at eNodeB and SGW. That is, the aggregation
at eNodeB decreases the number of S1-u and S5/S8 bearers,
while that at SGW decreases only the number of S5/S8
bearers. The second reason is that the performance gained byincreasing the aggregation level from 1 to 8 is significantly
larger than that gained by increasing the aggregation level from
8 to 64.

1
0.8
0.6
0.4
0.2

VII. D ISCUSSION
A. Server Resource Optimization
From the results in Subsection VI-B3, server resource
optimization can improve network capacity, regardless ofwhether bearer aggregation is applied. This means server
resource optimization in a cloud environment is fundamentallyadvantageous.
B. Aggregation Level
In the aggregation at eNodeB, an aggregated S1–u bearer
remains established until all UEs in the aggregated bearer
become idle. Also, when the S1–u bearer is released, the additional signaling procedure is required. From this perspective, todecrease the signaling procedure in the mobile core network,
the aggregation level should be determined so that at least one
UE in the aggregated bearer being active, that depends on thecommunication characteristics of UEs such as communication
cycle and the degree of communication synchronization ofUEs.
C. Aggregation Point
The results described in Subsection VI-B4 demonstrate thatbearer aggregation at eNodeB outperforms that at SGW in
terms of network capacity. This is because bearer aggregation
at SGW reduces only the number of S5/S8 bearers, whilethat at eNodeB reduces the numbers of both S1–u and S5/S8
bearers. For the same reason, as shown in Fig. 9(a), when
applying the pre-determined aggregation scheme, the bearer
establishment time with the bearer aggregation at eNodeB issmaller than that at SGW. For supporting these discussions,Fig. 12 shows plots of changes in the total processing times ofsignaling messages (Tt in (9)) with the on-demand aggregation
scheme as a function of the number of accommodated UEs.
The figure shows that the aggregation at eNodeB has asmaller total processing time than that at SGW. However,
especially when the number of UEs is small in the on-demandaggregation scheme, the effect of waiting time (Tw in (9)) on
the bearer establishment time is stronger, as shown in Fig. 9(b).

005000001x106

1.5x106

2x106

2.5x106

3x106

Number of UEs

Fig. 12: Processing time comparison: The on-demand aggregation at eNodeB outperforms the on-determined aggregation
at SGW in terms of processing time.

scheme. The main reason is that the pre-determined aggregation scheme requires a data path setting for each UE, while theon-demand aggregation scheme requires only one setting for agroup of UEs. On the other hand, the on-demand aggregation
method increases the MME load owing to the process ofdetermining vIMSI and the corresponding shared bearer fora group of UEs at the start of communication. However,
because the amount of the overhead is inversely proportional
to the aggregation level, when the aggregation level exceeds acertain value, the total load on the nodes located in the cloudenvironment (MME and SGW/PGWc ) decreases.
The difference between the pre-determined and on-demandaggregation schemes affects the utilization of the sharedbearers. Given that UEs in a certain pre-determined groupdo not always communicate simultaneously, utilization of theshared bearers with the pre-determined aggregation scheme
varies according to the UEs’ communication frequency. By
contrast, with the on-demand aggregation scheme, utilizationof each shared bearer is always high because shared bearers
are established only for active UEs.
E. UE’s mobility
In this paper, we assume that UEs do not have any mobility
and no handover occurs. When the mobility of UEs is considered, additional signaling messages are required for UEs
leaving the current shared bearer, re-assigning a new sharedbearer, and handling vIMSIs for handover UEs. From thisviewpoint, the aggregation at SGW is preferable because it
does not affect the handover procedure while the aggregation
at eNodeB significantly affects the signaling procedure. Furthermore, utilization of the shared bearer would degrade owing
to UE handover because the number of UEs in the sharedbearer decreases.

D. Aggregation Timing
The results described in Subsection VI-B5 demonstrate thatthe on-demand bearer aggregation scheme yields a larger
network capacity than the pre-determined bearer aggregation

F. Preferred bearer aggregation settings
From the above discussions, we can determine the recommended combinations of aggregation point and timing

13

TABLE IV: Recommended setting and obtained performance: Varies according to the characteristics of UEs.
UEs’ characteristics
high mobility
massive, high mobility
low latency, low/no mobility

Aggregation point
SGW
SGW
eNodeB

Aggregation timing
pre-determined
on-demand
pre-determined

Required modification
small (MME)small (MME)large (UE, eNodeB and MME)

depending on delay constraints of the M2M/IoT applications,the number of UEs and their mobility. Table IV summarizes
the relationships among the characteristics of UEs, preferable
aggregation point and timing, modifications required for EPC
nodes, and resulting bearer establishment times and network
capacities. Note that the aggregation level is not included inthis table because the ideal aggregation level is not affectedby the total number of UEs and their mobility.
G. Aggregation at both SGW and eNodeB
Finally, the bearer aggregation at both SGW and eNodeB
inherits the characteristics of the aggregations at SGW andeNodeB. The performance gain differs depending on thecurrent aggregation level at each aggregation point.
H. M2M/IoT communication
C/U plane separation and bearer aggregation are not specialized for M2M/IoT communication and we can apply themethods to conventional user communication. However, while
UEs communicate large data in the user communication, many
M2M/IoT UEs transmit and receive small data. Because ofthat, for such M2M/IoT communication, processing of the
C-plane are significant in terms of the load on a mobile
core network. We think that C/U plane separation and bearer
aggregation are more suitable for M2M/IoT communication
because these methods used in this paper can reduce the loadon C-plane.
VIII. C ONCLUSION
In this paper, we evaluated the performance of a mobile core
network with node virtualization and C/U plane separation
based on SDN. We proposed a bearer aggregation method that
decreases the signaling overhead, which is very important fromthe viewpoint of using massive M2M/IoT terminals.
The main results of this study are as follows.
1) We developed a detailed algorithm and a signaling procedure for the bearer aggregation method (Section IV).
2) We presented an analysis for evaluating the performanceof mobile core networks (Section V).
3) We presented numerical results showing that the network
capacity can be increased by up to 32.8% with node
virtualization and C/U plane separation (Fig. 7(a)).
4) We showed the simultaneous application of server resource optimization and bearer aggregation with appropriate aggregation point and timing yields 201.4% larger
network capacity than applying only server resource
optimization (Figs. 9, 10 and 8).
5) We discussed appropriate settings for the aggregation method in accordance with the characteristics of
M2M/IoT terminals (Table IV).

Bearer establishment time
large
large
small

Network capacitylowmediumhigh

We consider that implementation experiments of the proposed methods are required to confirm their effectiveness on
the actual environment. We are currently constructing theproof-of-concept implementation of the proposed methods. In
detail, we plan to apply C/U plane separation and bearer aggregation methods to OAI and conduct experimentations where
massive UEs connects to the mobile network simultaneously.
In future work, we plan to evaluate the effect of virtualization
and C/U plane separation at eNodeB, and the relationshipbetween an aggregation level and the performance in U-planesuch as latency. Moreover, we will evaluate LPWANs which
utilize the existing mobile network infrastructure such as
eMTC and NB-IoT. In addition, we will extend our discussion
to compare conventional bearer-based mobile core networks
with packet-routing-based, i.e., GTP-less networks that do notuse bearers.
ACKNOWLEDGMENT
The authors thank Wataru Takita, Kazuaki Obana, Motoshi
Tamura, and Ryosuke Kurebayashi from NTT DOCOMO, andTakashi Shimizu from NTT Network Innovation Laboratories
for their helpful comments.
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Shuya Abe Shuya Abe received his B.E. degreefrom Osaka University, Japan, in 2016. His researchinterests include Cellular IoT and Software Defined
Networks.

Go Hasegawa Go Hasegawa received the M.E.
and D.E. degrees in Information and Computer
Sciences from Osaka University, Osaka, Japan, in
1997 and 2000, respectively. From July 1997 to June
2000, he was a Research Assistant in the Graduate
School of Economics, Osaka University. He is now
an Associate Professor at the Cybermedia Center,
Osaka University. His research is in the area oftransport architecture for future high speed networks
and overlay networks. He is a member of IEEE and
IEICE.

Masayuki Murata Masayuki Murata received M.E.
and Ph.D. degrees in information science and technology from Osaka University in 1984 and 1988.
In April 1984, he joined the Tokyo Research Laboratory IBM Japan as a researcher. From September
1987 to January 1989, he was an assistant professor
with the Computation Center, Osaka University. In
February 1989, he moved to the Department ofInformation and Computer Sciences, Faculty of Engineering Science, Osaka University. From 1992 to1999, he was an associate professor with the Graduate School of Engineering Science, Osaka University, and since April 1999, hehas been a professor. He moved to the Graduate School of Information Scienceand Technology, Osaka University in April 2004. He has published morethan 300 papers in international and domestic journals and conferences. His
research interests include computer communication networks, performancemodeling, and evaluation. He is a fellow of IEICE and a member of IEEE,
the Association for Computing Machinery (ACM), The Internet Society, and
IPSJ.