Joint bearer aggregation and control-data planeseparation in LTE EPC for increasing M2M

communication capacity

Go HasegawaCybermedia CenterOsaka University

1-32, Machikaneyama-cho, Toyonaka, Osaka 560-0043, JapanEmail: hasegawa@cmc.osaka-u.a.jp

Masayuki MurataGraduate School of Information Science and Technology

Osaka University1-5 Yamadaoka, Suita, Osaka 565-0871, Japan

Email: murata@ist.osaka-u.ac.jp

Abstract—In this paper, we propose a method for increasingthe capacity of Machine-To-Machine (M2M) communication inmobile core networks. The proposed method combines twoapproaches: bearer aggregation inside mobile core networks fordecreasing the load of Evolved Packet Core (EPC) nodes, andapplying a Software Defined Networking (SDN) architecture toseparate the control and data planes and aggregate control planenodes in a cloud network environment for resource sharing. Thecombination of these two approaches is meaningful because theyhave a complementary relationship. We give a mathematicalanalysis and numerical results of a performance evaluationof the proposed method. The evaluation results show that wecan increase the capacity of a mobile core network for M2Mcommunication by around 30% when one of the two approachesis applied, while the performance gain increases up to 124% whenboth approaches are combined.

I. INTRODUCTION

Due to the continuous increase of mobile phone users andthe rapid popularization of rich terminals such as smartphonesand tablets, congestion in data plane for transferring userdata, and that in control plane for controlling radio accessbearers and connections between User Equipments (UEs) andexternal networks, have become a serious problem in 3Gand Long Term Evolution (LTE) mobile networks. There aresome solutions for data plane congestion such as offloading toWiFi or other networks [1-5]. However, such solutions cannotalleviate control plane congestion because the number of UEsto be handled in the mobile core network remains unchangedwith offloading.

Furthermore, some smartphone applications generate pe-riodic communications to corresponding servers that heavilyimpact the control plane load [6]. Furthermore, Machine-to-Machine (M2M) communications via 2G/3G/LTE mobilenetworks are gaining increased attention as a new communi-cation paradigm, driving further mobile network demand. Thecommunication characteristics of M2M terminals significantlydiffers from those of traditional Human-to-Human (H2H) com-munications [7], in that the number of M2M terminals is muchlarger than the number of H2H terminals, while the amountof communication data per each M2M terminal is smallerand communications occur periodically or intermittently. Suchcommunications by rich terminals and M2M terminals havea large impact on the control plane load when traditional

method for accommodating H2H terminals. Furthermore, theAverage Revenue Per User (ARPU) of M2M terminals wouldlikely be substantially smaller than that of H2H terminals [8],meaning that we cannot recover the cost for accommodatingM2M terminals within existing mobile cellular networks undercurrent systems and cost structures.

Existing researches has focused on control plane conges-tion by M2M communications in mobile core networks [9, 10],and some solutions, such as light-weight signaling protocol,has been proposed [11, 12]. One way to decrease control planeload in a mobile core network is to decrease the number ofhandling bearers in the network, because the existing 3GPP-based mobile core network persistently maintains a few bearersfor each UE, regardless of their traffic amount. Group-basedcommunications [13, 14] can help decrease the number ofbearers in a mobile core network because the number ofconnected UEs to the mobile core network decreases. How-ever, such methods require UEs to have short-range wirelessnetwork equipment, such as WLAN or Bluetooth, that shouldbe avoided in order to reduce the cost of M2M terminals.

There has also been recent researches on applying SoftwareDefined Network (SDN) architectures to mobile core networks,aimed at decreasing their operational cost [15-18]. In sucharchitectures, control and data planes on LTE Evolved PacketCore (EPC) nodes are separated and one or both planesare virtualized and located in a cloud network environment.This should allow efficient and flexible operation of mobilecore networks, since it provides server and network resourcesharing among multiple nodes and among multiple mobile corenetworks. At present, however, there are almost no detailedsystem designs and performance evaluations proposed in theliterature.

In this paper, we address the control plane congestion prob-lem in mobile core networks by combining bearer aggregationand control-data plane separation. We propose to aggregatebearers for M2M communications at SGW to decrease thenumber of handled bearers. For control-data plane separation,we separate the planes of a Servicing/Packet data networkGateway (SGW/PGW) and server resources are shared bythe control-plane nodes of SGW and PGW and the MobilityManagement Entity (MME) in the cloud network environment.As explained below, these two methods should be combined

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since they have a complementary relationship. We also give aperformance analysis of the proposed method to confirm itseffectiveness at increasing capacity in M2M communications.

The remainder of this paper is organized as follows. InSection II, we explain the motivation for this work. In Sec-tion III, we present the proposed method for combining beareraggregation and control-data plane separation. We then give aperformance analysis of the proposed method and numericalexamples in Section IV. Finally, we conclude this paper withan overview of future work in Section V.

II. MOTIVATION

In Figure 1, we briefly depict the signaling procedurewhen a UE that has already attached to the network beginsits communication, based on [19]. In this figure we onlyshow the signaling steps related to bearer establishment. Foreach UE, the mobile core network handles two bearers: onebetween evolved NodeB (eNodeB) and the Serving Gateway(SGW), and another between SGW and Packet data networkGateway (PGW). These bearers are persistently maintained andactivated (deactivated) when the UE begins (terminates) thecommunication. Consequently, when many M2M terminals,that corresponding to UEs in M2M communication, connect tothe mobile core network, the number of bearers to be handledincreases significantly. Furthermore, signaling overhead onLTE EPC nodes, such as the Mobility Management Entity(MME), SGW, and PGW, also increases, because each periodicor intermittent communication with M2M terminals requires asignaling procedure. Decreasing the number of handled bearersthrough bearer aggregation is one straightforward approachtoward alleviating this problem.

On the other hand, physical and fixed resource allocationof LTE EPC nodes is not desirable, due to the unpredictabilityof sudden changes in the mobile network traffic and the recentincrease of Mobile Virtual Network Operators (MVNOs).Event-driven and intermittent communication demands byM2M terminals make the situation worse. Applying SDNarchitecture to mobile core networks has been considered asa means to accommodate such dynamic situations. Efficientresource utilization is achieved by separating control and dataplanes and aggregating control plane nodes in a cloud networkenvironment, since we can dynamically allocate server andnetwork resources to control plane nodes according to the nodeload.

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From the above discussion, we believe that a combinationof bearer aggregation and an SDN architecture would decreasethe overhead of mobile core networks when accommodat-ing M2M communications. This is because decreasing nodeoverhead by bearer aggregation should allow more efficientresource sharing in SDN-ready mobile core networks, whileresource sharing with control-data plane separation becomesmore effective when the control-plane load of some EPC nodesis decreased by bearer aggregation.

III. PROPOSED METHOD

A. Bearer aggregation at SGW

Figure 2 briefly depicts a mobile core network. It iscomposed of UEs, eNodeBs, a SGW, a PGW, and networks(eUTRAN, EPC, and external IP network) connecting them.We omit MME and other nodes from the figure to more simplyexplain bearer aggregation. The red lines in the figure representbearers between UEs and eNodeB, and those between eNodeBand SGW. These bearers are established for each UE con-necting to the network. In the original EPC network, a beareris established between SGW and PGW for each UE to relaypackets between UE and the external IP network. Therefore,the number of bearers between SGW and PGW equals thenumber of active UEs accommodated by the network.

Under bearer aggregation at SGW, a single bearer isestablished for multiple UEs, that is, multiple bearers betweeneNodeB and SGW. In Figure 2, the blue lines represent theaggregated bearers. Note that the number of blue lines issignificantly smaller than the number of red lines betweeneNodeBs and SGW. We define the number of UEs aggregatedto a single bearer between SGW and PGW as the aggregationratio K. For example, K = 32 means that 32 bearersbetween eNodeB and SGW, corresponding to 32 UEs, areaggregated into a single bearer between SGW and PGW. Wecan decrease the number of bearers in the mobile core networksand corresponding signaling steps to establish bearers betweenSGW and PGW.

Such bearer aggregation inside the mobile core networksdoes not need to modify the behavior of UEs, which is alarge advantage compared with group-based communicationapproaches [13, 14] especially when accommodating a verylarge number of M2M terminals. On the other hand, it requiresmore considerations such as realizing paging, IP addressmanagement, and traffic charging. In this paper, however, wefocus on the performance gain by bearer aggregation in termsof load reduction in EPC nodes.

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B. Applying SDN to LTE EPC networks

When applying SDN architecture to EPC nodes, we have abroad choice regarding which node should separate control anddata planes, and where to place each plane. Some researchershave focused on plane separation and the plane/function place-ment problem in mobile core networks [15, 16, 18, 20]. Basedon such discussions, we propose a network architecture likethat shown in Figure 3. In this architecture, control-data planeseparation is applied to SGW and PGW, and the control planeof SGW (SGWc in the figure) and PGW (PGWc) are located ina cloud network environment, as well as MME and other EPCnodes. Data plane nodes of SGW (SGWd) and PGW (PGWd)remain in the transport network. Furthermore, we locate GTPtunnel matching functions at the transport network to avoidadditional latency in the data packet path [18]. Note that in theexisting architecture without plane separation, control and dataplanes for SGW and PGW are located at SGWd and PGWdin Figure 3, respectively.

We can expect a decreased delay in signaling steps betweenMME and SGW/PGW since they are located on the same cloudnetwork. On the other hand, additional signaling is requiredbetween SGWc/PGWc and SGWd/PGWd, since GTP tunnelmatching information should be updated when a new UE startscommunication. Furthermore, server resources can be sharedamong MME, SGWc, PGWc, and other EPC nodes in thecloud network, increasing overall capacity for accommodatingUEs under limited server resources.

C. Impact on signaling procedure

The proposed approaches in Subsections III-A and III-Brequire some modifications to the signaling procedure. InFigure 4, we briefly depict a possible signaling procedure withtwo approaches when K UEs start communication, where Kis the aggregation ratio. First, MME waits for bearer activationrequests from K UEs. Then, MME configures an aggregatedbearer between SGW and PGW and allocates it to K UEs(green box in the figure). Blue arrows represent the signalingsteps for activating the aggregated bearer. Red arrows representsignaling steps for configuring GTP tunnel matching at GTPmodules in SGW (SGWgtp) and PGW (PGWgtp). When

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Fig. 4. Signaling procedure and data packet path

we utilize OpenFlow, these signaling messages correspond toFlowMod messages.

Comparing Figures 1 and 4, we can observe increased stepsand decreased steps for starting communications by K UEs,that affects on the load of EPC nodes. In the next sectionwe present a detailed mathematical analysis to compare theperformance of the proposed method with that of the originalone.

IV. PERFORMANCE ANALYSIS

A. Analysis overview

In this section we derive data transfer times by mathemati-cal analysis. Data transfer time is defined as the time durationfrom when a UE starts to establish its bearers for packettransmission to when the data is completely transferred. It isdivided into bearer establishment time and data transmissiontime.

For bearer establishment time, we apply a simple queuingmodel to derive the time required for processing each signalingmessage. We then follow the signaling steps in Figures 1 and 4to derive the bearer establishment time.

For data transmission time, we assume that the destinationserver is located at the external IP network in Figure 3,where we ignore the propagation delay between PGW and thedestination server. We consider both TCP and UDP to confirmthe effect of transport-layer protocol overhead.

In the following analysis, we first derive the time in thecase where only control-data plane separation is applied. Wethen extend the analysis to accommodate bearer aggregation.

B. Notations

The propagation delay of signaling messages betweenEPC nodes is denoted as τNODE1,NODE2, where NODE1 andNODE2 represent one of a terminal or a node in Figures 1and 4, that is, UE, eNodeB, MME, SGW, PGW, SGWc,SGWd, PGWc, PGWd, SGWgtp, and PGWgtp. For example,τSGW,PGW represents the propagation delay between SGWand PGW. We also denote the delay for processing a singlesignaling message at each node by tNODE, where NODErepresents the same meaning as NODE1 and NODE2 inτNODE1,NODE2. For example, tMME means the processingdelay for a single signaling message at MME.

C. Bearer establishment time

The bearer establishment time is the sum of propagationdelays between nodes and the node processing time for allsignaling messages. For the original signaling procedure with-out the proposed approaches, we follow the signaling stepsin Figure 1 and sum up the delays to derive the bearerestablishment delay Torg, as follows.

Torg = LRRC+(6tUE + 4teNodeB + 9tMME + 5tSGW + 3tPGW)

+(10τUE,eNodeB + 10τeNodeB,MME + 5τMME,SGW + 5τSGW,PGW)

In the above equation, LRRC represents the time for estab-lishing a Radio Resource Control (RRC) connection betweenUE and eNodeB.

When applying only the SDN architecture without beareraggregation, additional signaling steps are required betweenSGWc and SGWd and between PGWc and PGWd. Fur-thermore, some control messages are exchanged betweenSGWd/PGWd and SGWgtp/PGWgtp for configuring GTPmodules. We denote the number of required message ex-changes between SGWc/PGWc and SGWd/PGWd and thenumber of required message exchanges between SGWd/PGWdand SGWgtp/PGWgtp as NFM and NGTP, respectively. Byfollowing the signaling steps in Figure 4, we can derivethe bearer establishment time when applying only the SDNarchitecture TSDN as follows.

TSDN = LRRC + (6tUE + 4teNodeB + 9tMME + 5tSGWc + 3tPGWc)

+(10τUE,eNodeB + 10τeNodeB,MME + 5τMME,SGWc + 5τSGWc,PGWc)

+ max(2NFMτSGWc,SGWd + (NFM + 1)tSGWc + NFMtSGWd+2NGTPτSGWd,SGWgtp + (NGTP + 1)tSGWd + NGTPtSGWgtp,

2NFMτPGWc,PGWd + (NFM + 1)tPGWc + NFMtPGWd+2NGTPτPGWd,PGWgtp + (NGTP + 1)tPGWd + NGTPtPGWgtp)

To derive node processing time, tNODE, we exploit theM/G/1/PS queuing model. In the M/G/1/PS model, the meansojourn time E[R] can be derived as

E[R] =ρr

1 − ρ

E[S2]2E[S]

+1 − ρr

1 − ρE[S], (1)

where λ is the job arrival rate, S(x) is the workload dis-tribution, E[S] is the mean workload, r is the maximumnumber of parallel processing, and ρ = λ · E[S] is thesystem utilization. By assuming that an overhead for handlinga signaling message is identical for all nodes, we obtaintNODE for a given node using Equation (1). In detail, weuse the arrival rate of signaling messages at the node for λ.The arrival rate of signaling messages can be calculated fromthe number of signaling messages per one communication,the communication frequency, and the data size of UE andtransport-layer protocol overhead. Tables I and II respectivelysummarize the number of signaling messages to be processedat each node per one communication by TCP and UDP, whereNP is the number of data packets to be transmitted. Wepresent the results of the original network without the proposedapproaches and those of the network with control-data planeseparation.

The workload distribution S(x) corresponds to the distri-bution of processing time for signaling messages by the node.

TABLE I. NUMBER OF SIGNALING MESSAGES PER UECOMMUNICATION (TCP)

Original Control-data plane separationUE 6+(3/2)NP + 2 6+(3/2)NP + 2eNodeB 4+(3/2)NP + 2 4+(3/2)NP + 2MME 9 9SGW 5+(3/2)NP + 2SGWc 5+2NFMSGWd NFM + 2NGTP + 2((3/2)NP + 2)SGWgtp NGTP + (3/2)NP + 2PGW 3+(3/2)NP + 2PGWc 3+2NFMPGWd NFM + 2NGTP + 2((3/2)NP + 2)PGWgtp NGTP + (3/2)NP + 2

TABLE II. NUMBER OF SIGNALING MESSAGES PER UECOMMUNICATION (UDP)

Original Control-data plane separationUE 6+NP 6+NPeNodeB 4+NP 4+NPMME 9 9SGW 5+NPSGWc 5+2NFMSGWd NFM + 2NGTP + 2NPSGWgtp NGTP + NPPGW 3+NPPGWc 3+2NFMPGWd NFM + 2NGTP + 2NPPGWgtp NGTP + NP

For simplicity, we assume that the processing time distributionof signaling messages follows an exponential distribution. Themean workload E[S] is determined by the processing power.Finally, we obtain tNODE by calculating E[R] in Equation (1).

D. Data transmission timeAfter establishing bearers the UE starts sending data pack-

ets to the destination server. We calculated data transmissiontime based on the protocol overheads of TCP and UDP. ForTCP transmission, we assume that the transmission data size issufficiently small to be transmitted in a slow start phase. Thedata transmission time with TCP CTCP(S) can be obtained as

CTCP(S) ≈ T + 2

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Fig. 5. Effect of control-data plane separation

E. Effect of bearer aggregation

By applying bearer aggregation, we can decrease the num-ber of signaling messages per UE communication in Tables Iand II according to the aggregation ratio K. Specifically,signaling messages for activating a bearer between SGWd andPGWd (blue arrows in Figure 4) are required only once for KUEs. Considering this, the number of signaling messages to beprocessed is decreased from 9 to 6.5 + 2.5/K at MME, from5 to 1 + 4/K at SGWc, and from 3 to 3/K at PGWc. Thismeans that increasing K would decrease the node overheadsignificantly, but the positive effect would converge with toolarge a K.

F. Numerical results and discussion

We set Bwireless = 10 Mbps, Bcore = 1 Gbps, and P =128 bytes. Pheader is set to 40 bytes for TCP transmission,and 28 bytes for UDP transmission. Each UE initiates a com-munication in 600 sec intervals and the transmission data sizefor each communication is set to 1,000 bytes. The propagationdelays between nodes and terminals are set as τUE,eNodeB

= 20 msec, τeNodeB,MME = τeNodeB,SGW = τSGW,PGW =τSGWd,PGWd = 7.5 msec, τMME,SGW = τSGWc,SGWd =τPGWc,PGWd = 10 msec, τeNodeB,SGWc = τSGWc,PGWc =1 msec, τSGWd,SGWgtp = τPGWd,PGWgtp = 1 msec, andLRRC = 20 msec. For node processing power, we set tUE

= teNodeB = 1,000 messages/sec, tMME = tSGW = tPGW =tSGWc = tPGWc = tSGWd = tPGWd = 10,000 messages/sec, andtSGWgtp = tPGWgtp = 100 Gbps. Note that these parametersare configured as typical values based on discussions on thecurrent status of mobile core networks operated by the mobilecarrier company in Japan.

For the control-data plane separation in the proposedmethod, we set the processing power of MME, SGWc, andPGWc so that we obtain the minimum value of data transmis-sion time, while the total processing power remains unchangedat 30,000 messages/sec.

Figure 5 shows the changes in data transmission time asa function of the number of accommodated UEs when weutilize TCP (Figure 5(a)) and UDP (Figure 5(b)). We plotthe results of the original method and those of the proposedmethod with only control-data plane separation. From thesefigures we can observe that regardless of the transport-layerprotocol, the data transmission time of the proposed methodis smaller than that of the original method. This means thatwhen applying the SDN architecture, the positive effect ofdecreasing propagation delays of signaling messages is largerthan the negative effect of the additional signaling messages

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Fig. 6. Effects of bearer aggregation

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Fig. 7. Combination of bearer aggregation and control-data plane separation

for SDN control. Furthermore, the number of accommodatedUEs without divergence of the data transmission time, which isdefined as the network capacity, can be increased by using theproposed method. In this case the network capacity increasesby around 30%.

Figure 6 present the results of the proposed method withonly bearer aggregation. K = 1 corresponds to the originalmethod. From these figures we can observe that larger valuesof K result in higher network capacity, up to a certain levelat which the capacity converges with further increases ofK. In this case the network capacity increases by 37% forK = 64. We also confirm that the data transmission delayremains almost unchanged with a smaller number of UEsregardless of K. This means that the negative effect of thebearer aggregation can be ignored.

We finally show the results of a combination of the control-data plane separation and bearer aggregation in Figure 7. Weset K = 64 for bearer aggregation. For comparison, we alsoplot the results of the original method, those of the proposedmethod with only control-data plane separation, and those ofthe proposed method with only bearer aggregation. From thisfigure we can see that combining the two approaches signifi-cantly increases the network capacity. Specifically, the networkcapacity increases by 124% by combining two approaches, butonly 30% and 37% with only one approach. This result clearlyshows the effectiveness of combining the two approaches, aspredicted in Section II.

In Figure 8, we plot the processing power of MME, PGW,and SGW nodes in the proposed method, to confirm theeffect of the server resource sharing in an SDN architecture.In the figure we plot the results with and without beareraggregation for comparison. From this figure, we can see thatmore processing power is allocated to MME, while decreasingthe processing power of SGW and PGW. This is because thebottleneck is the signaling message processing at MME. On

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0 400000 800000 1.2e+06 1.6e+06

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(d) With bearer aggregation, UDPtraffic

Fig. 8. Node processing power of the proposed methods

the other hand, the processing power of SGW increases asthe number of UEs increases, because the signaling overheadof SGW increases. Furthermore, when applying bearer aggre-gation (Figures 8(b) and (d)) the more processing power isallocated to MME, while SGW and PGW have less processingpower. This is because the signaling overhead at SGW andPGW decreases with bearer aggregation and more processingpower can be allocated to MME to further decrease the nodeprocessing time. These results again show that a combinationof bearer aggregation and control-data plane separation is verymeaningful.

V. CONCLUSION

We proposed a method for increasing the capacity ofM2M communications in mobile core networks. The proposedmethod combines two approaches, bearer aggregation andcontrol-data plane separation. We performed mathematicalanalysis to evaluate the performance of the proposed method.From extensive evaluation results we confirm that the networkcapacity, in terms of the number of accommodated M2M ter-minals without divergence of the data transfer time, increasesby 124% by combining two approaches, but only 30% and37% with only one approach. These results clearly show thatthe combination of these two approaches is meaningful sincethey have complementary relationship.

In future work we plan to extend the proposed method toaccommodate wide-area mobile core networks, in which thelocation of EPC nodes and distribution of UEs should be takeninto account. Also important is enhancing the analysis modelin Section IV by determing the detailed procedure for beareraggregation and by considering the overhead of each signalingmessage in detail.

ACKNOWLEDGMENT

The authors thank Wataru Takita, Kazuaki Obana, andMotoshi Tamura from NTT DOCOMO, and Takashi Shimizu

from NTT Network Innovation Laboratories for their helpfulcomments. The authors also appreciate the feedback offeredby Kohei Shiomoto, Noriaki Kamiyama, and Yuusuke Nakanofrom NTT Network Technology Laboratories, and Hideo Mi-hayara from Osaka University.

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