a hybrid sdn/nfv architecture for future lte networksad57/documents/icc2017.pdf · abstract—in...

A Hybrid SDN/NFV Architecture for Future LTENetworks

Ali Tawbeh, Haidar Safa, and Ahmad R. DhainiDepartment of Computer Science

American University of BeirutBeirut, Lebanon

Email: {at58, hs33, ad57}@aub.edu.lb

Abstract—In the LTE EPC, many network entities and inter-faces have to be maintained and updated regularly. Moreover,to accommodate more users, new hardware must be integrated,although rarely used. To address these challenges, the EPC canbe moved to the cloud using two modern technologies: SDN andNFV. In this paper, we study the impact of integrating these noveltechnologies on LTE networks. We propose a hybrid approachfor selecting whether to apply NFV or SDN on each gatewayat a given time while minimizing the network load taking intoconsideration key network parameters such as the number ofactive datacenters, the deployment city population, the intensityat a given time, the QoS class identifier (QCI), and the delaybudget. We formulate the SDN decomposition/NFV virtualizationselection as an optimization problem where the objective is tominimize the network load subject to a set of constraints. Ourresults show that our proposed solution is more responsive to thedynamic state of the network such that for a given gateway, ata certain time slot, an SDN decomposition might be the optimalchoice; while at another time slot with a different network state,the NFV architecture might be more suitable.

Index Terms—LTE, Evolved Packet Core, SDN Decomposition,NFV Virtualization.

I. INTRODUCTION

Long Term Evolution (LTE) comprises two main enti-ties: the evolved UMTS terrestrial radio access network (E-UTRAN) and the Evolved Packet Core (EPC) [12]. The EPCis mainly composed of the home subscriber server (HSS),the packet data network gateway (PGW), the service gateway(SGW), and the mobility management entity (MME). ThePGW handles the communication between the EPC and packetdata networks. The SGW plays the role of a router of data fromthe eNB to the correspondent PGW.

The LTE EPC has a static “fit-to-purpose” architecture. Eachhardware component in the EPC is employed to perform onespecific task, which means adding more functionalities willrequire integrating more hardware entities in the core network.Also, accommodating a large number of users during peaktimes is done via duplicating entities that may not be necessar-ily used during idle times. In this context, upgrading the EPCwill result in more complex architecture and protocols leadingto an increase in the capital and operational expenditures;meanwhile the average revenue per subscriber is expectedto keep decreasing [15]. Motivated by the core principles oftwo promising technologies, namely Network functions Vir-tualization (NFV) and Software Defined Networking (SDN),

moving the EPC to the cloud helps achieve cost reduction forthe benefit of enlarging revenues margin [9], [13]. However,despite their potential in building robust, reliable and highperformance service delivery networks, little attention has beengiven to the impact of virtualizing LTE EPS using NFV andSDN on the network load and data plane. In addition, eventhough integrating NFV in mobile core networks may beadvantageous, it requires steering all data traffic to a datacenterwhere the functions are virtualized; this adds extra load on thetransport network and results in longer delay on the data planedepending on the location of data centers. The load overheadin the network increases proportionally with the amount ofcontrol that SDN is granted due to the signaling between thecontroller and the data forwarding elements. In this paper,we study the impact of integrating SDN and NFV in LTEnetworks and propose a hybrid architecture for SGW and PGWgateways, which applies both the SDN decomposition andNFV concept on each gateway while minimizing the networkload.

The remainder of the paper is organized as follows. InSection II, we briefly introduce SDN and NFV and discusssome related work. In Section III, we present the proposedapproach and formulate the selection problem between SDNdecomposition and NFV virtualization as an optimizationproblem where the objective is to minimize the network loadsubject to a set of constraints that include the number ofactive datacenters, the population of the city of deployment,the intensity at the given time, the QoS class identifier (QCI)and the volume of generated traffic in addition to the packetdelay budget. In Section IV, we evaluate the performance ofthe proposed approach. Finally, we present our conclusionsand future work in Section V.

II. BACKGROUND AND RELATED WORK

In SDN, the infrastructure layer consists of programmableswitches and routers that perform data forwarding. On topof the infrastructure layer and below the application layer,there is a control layer that administers the infrastructure layervia an open protocol interface such as OpenFlow [11]. Theapplication layer implements customized business applicationsand network services. This separation abstracts the infrastruc-ture for network services and applications so as the networkappears as a single logical switch maintained by centralized

IEEE ICC 2017 Communications Software, Services, and Multimedia Applications Symposium

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software-based SDN controllers [10]. NFV changes the currentpractice of buying and installing new hardware to integratenew functions or services, by implementing network functionsas software that can be run on general purpose servers with theability of instantiating and moving instances of these functionsbetween different datacenters at different locations as required,without the need of installing new hardware [13]. NFV andSDN can be implemented separately or be combined togetherto achieve greater value. By separating control and data planes,SDN can enhance performance, and introduce flexibility andsimplicity in resolving compatibility issues and maintenancethrough programmability and centralized control. In turn,NFV can provide the infrastructure as virtualized functionsimplemented as software instances that can be connected andcontrolled using SDN [13].

Most related work [1]–[8] lack analyzing the impact of vir-tualization on EPC’s performance. A lightweight mobile cloudOffloading Architecture is presented in [8], which utilizes thevirtualized resources hosted in datacenters only to offload thenetwork traffic within the mobile core. A qualitative analysis ofthe benefits of SDN and NFV to the mobile core are discussedin [1]. Additionally, [6] studied the influence of SDN and NFVon the gateways and datacenter placement within the mobilecore, however considering only uniform traffic demands thatshould not vary with respect to time and intensity of the areaof deployment. This is a major limitation in the approach.In fact, demands are strongly correlated with time variationsand intensity of the area being served. A mobile networkarchitecture with virtual components and SDN control, whichoffers a fine-granular control of the available resources wasproposed in [7]. NFV and SDN concepts were applied on thehigh volume data-plane within the mobile core network. Eventhough both [6] and [7] proposed applying NFV on SGW andPGW, however only [6] suggested decomposing the gatewaysbetween control plane and data plane using SDN. Both worksquantify and minimize the network load, but unlike [6] whichassumes uniform demands, [7] considers time-dependent andpopulation dependent demands. Also, [7] proposed modelsthat take into consideration datacenter available resources andsave power, but only considering full virtualization; i.e., allthe gateway’s functions are implemented in a datacenter andthe gateway is replaced by a basic SDN networking elementthat steers traffic to different datacenters. The drawback ofsuch an architecture is the impact on delay-critical functions.Moreover, both [6] and [7] did not consider the characteristicsof the bearers being established despite the fact that thebearer packet delay budget and resource requirements maydiffer depending on the QCI of the bearer and its otherQoS parameters. Finally, both approaches do not take intoaccount datacenter available resources and locations whichwill certainly affect the total load in the network, and increasedata plane delay.

III. PROPOSED SOLUTION

In this section, we propose a hybrid architecture where SDNdecomposition and NFV virtualization are applied on every

gateway. We also use the LTE QCI to determine the delaybudget for each set of bearers. We then formulate the datacenter placement problem as an optimization problem anddescribe the computation of the parameters.

A. Hybrid Network Architecture

Figure 1 illustrates the proposed hybrid architecture. Here,interconnected gateways are replaced by networking elements(NE), each connected to a datacenter. The left NE replacesan SGW, while the right NE replaces a PGW. L1 denotes thelength of the path between the NE replacing the SGW and thedatacenter, L2 is the length of the path between the two NEs,and L3 is the length of the path between the NE replacing thePGW and the datacenter.

Fig. 1: Hybrid architecture

The application of SDN decomposition on a gateway ne-cessitates enhancing the NE to support the gateway dataplane functions, such as GTP. The control plane functionsof each gateway are implemented as software instances inthe datacenter, depicted as SGW-C and PGW-C. The NEsare connected to the control plane instances via the SDNcontroller, namely CTR (which also resides in the datacenter),via the dashed links which are dedicated to transport controlmessages and exchange flow tables rules (no user data on thislink).

In the NFV scenario, the data (SGW-U and PGW-U) andcontrol planes (SGW-C and PGW-C) functions of each gate-way are implemented as software instances in the datacenter,so as the NEs are only used to forward packets from/to thedatacenter and between them. The NEs are connected to theuser plane instances in the datacenter using “solid lines” linksto transport user data plane packets.

The hybrid architecture can be achieved by implementing asingle control plane instance for each gateway since both theSDN and NFV require running the control part as softwareapplication in the datacenter. While there are two implemen-tations for the data plane functions, the one in the datacentergets activated in case of NFV deployment, while the otherone is implemented in the NEs and gets activated in caseof SDN deployment. For example, in metropolitan (crowded)areas, if an SGW is deployed, in “peak” times (e.g., Mondayafternoon), the NFV deployment can be used to minimize thenetwork load; however, this does not imply that the virtualizeddeployment will be used at all times. The hybrid architectureallows for the activation of the SDN decomposed deploymentduring in light-loaded periods (e.g., late at night) to decreasethe average packet delay.


B. QoS Consideration

In LTE systems, data is transported using bearers accordingto certain quality of service (QoS) requirements. The QoSrequirements are enforced via a QoS Class Identifier (QCI),which maps the bearer to four metrics: the resource type (i.e.,guaranteed-bit-rate, GBR or non-GBR), packet error/loss rate,packet delay budget, and QCI priority. The number of QCIvalues is nine [14].

Our proposed hybrid architecture employs SDN decom-position and NFV on each gateway, which gives a moregranular control over the network and makes it responsiveto the dynamic state of the network. Namely, for a givengateway, at a certain time slot, an SDN decomposition may bethe optimal choice for the current network state (to decreasedelay); however, in another time slot, the state of the networkmay change and thus the NFV architecture may be moresuitable (to decrease delay). The inclusion of QCI-enableddemands may have one deployment suitable for a set ofdemands, but it might not be the case for others. This assertsthe need of a hybrid architecture, which, for a given gateway,at a given time, a set of QCIs may be operating on onedeployment, while the other set will be operating on the otherdeployment.

C. Problem Formulation

Our objective is to minimize the total network load byfinding the optimal data centers placement in each time slot forevery demand given certain packet delay budget constraints.Optimal datacenter placement means that in every time slot,for each set of bearers having the same QCI of the samedemand, we must find the optimal location of the datacenterand choose which deployment to activate, given a certaindelay budget. The possible datacenter locations are where theoperator already has a deployment.

We adopt the QCI standard classes in order to classifythe data flows of each demand since it helps to identify thethreshold of packet delay and to estimate the traffic volumewhen combining it with other QoS parameters, namely theMBR, GBR, UE-AMBR and APN-AMBR.

We define the following notations:• Q: the set of standardized QCI values (from 1 to 9).• C: the set of datacenter locations.• D: the set of demands.• T : the set of time slots.• P : the set of paths.• K: the number of data centers.In our model, Q is the set of QoS standardized classes that

can be assigned to bearers in the LTE’s core network.P is the set of all possible paths that a set of bearers might

take between the SGW and the corresponding PGW. In total,there are four possible paths:

1) Between a virtualized SGW and a virtualized PGW.2) Between a virtualized SGW and a decomposed PGW.3) Between a decomposed SGW and a virtualized PGW.4) Between a decomposed SGW and a decomposed PGW.

Our goal is to minimize the total network load by choosingfor each QCI q of bearers established on demand d, a data-center c with a path p at each time slot t. The problem canbe formulated as follows:

minimize∑q∈Q

∑c∈C

∑d∈D

∑t∈T

∑p∈P

δq,c,d,t,pNq,c,d,t,p

where δq,c,d,t,p is a binary variable that is set to one if attime t, the bearers of QCI q of the demand d are assignedfor the datacenter in location c on the path p. Nq,c,d,t,p is apre-calculated load for the combination q, c, d, t and p. Theconstraints of the minimization problem are:∑

c∈Cδc = K (1)∑

p∈Pδq,c,d,t,p ≤ δc ∀q ∈ Q, d ∈ D, c ∈ C, t ∈ T (2)∑

c∈C

∑p∈P

δq,c,d,t,p = 1 ∀q ∈ Q, d ∈ D, t ∈ T (3)∑c∈C

∑p∈P

δq,c,d,t,pLq,c,d,t,p ≤ Lbudget

∀q ∈ Q, d ∈ D, t ∈ T(4)

Constraint (1) ensures that K datacenters are under opera-tion such that δc is a binary variable that determines whethera datacenter c is selected to be under operation. Constraint (2)ensures that in case a datacenter c is chosen, a path p ∈ P canbe selected for bearers of QCI q ∈ Q, which are establishedfor demand d in time slot t. Constraint (3) forces the selectionof a single path p and a single datacenter c for each QCI q fordemand d in time slot t. The traffic delay budget is met byconstraint (4); for QCI q’s bearers of demand d in time slott, the delay produced by choosing a datacenter c and path p,namely Lq,c,d,t,p, must remain under q’s delay budget.

D. Calculating the Problem Parameters

The next step is to quantify the pre-calculated problemparameters, namely the network load N and the latency, foreach combination of QCI q, datacenter location c, demand d,time slot t, and path p.

1) Calculating network load: Similar to [7], the traffic ofa city ct ∈ CT , where CT is the set of considered cities intime slot, t, can be represented as the product of the intensityin time slot t, denoted i(t), and the population of the city,p(ct): f(ct, t) = i(t)× p(ct).The traffic, TR, at an SGW, which is equivalent to the trafficcaused by a demand since a demand is defined between eachSGW and its PGW, is expressed as:

TRd,t = TRSGW (t) =∑ct∈CT

f(ct, timect,SGW (t))×bct,SGW ,

(5)where f(ct, timect,SGW (t)) is the traffic of city ct at time t,and bct,SGW is a boolean value that is set to 1 if and only if cityct is covered by the considered SGW . However, this formulaignores that the demand is composed of bearers belonging todifferent QCIs; thus, not all of them have the same impacton the total network load. Also the formula does not take


into account the extra load added by the control plane whenadopting a path p where one or both gateways are decomposed.Therefore, we reformulate Eq. (5) to reflect the impact of eachQCI q on the network load by integrating the average bit rateof bearers belonging to q, denoted as BRavgq . To account forthe load added by the control plane of the SDN decomposition,we define a coefficient α denoting the SDN control volumepercentage of the traffic generated by an SGW. This percentagedepends on the protocol adopted by the operator. The loadadded by SDN control plane also depends on the chosen pathdue to the fact that when choosing a path where both gateways(the SGW and the PGW) are decomposed, the amount ofcontrol messages is roughly double the amount of when asingle gateway is decomposed. When none of the gateways isdecomposed, the SDN control messages are absent. Thereforedepending on the chosen path p and the SDN control volumeα, the traffic must be multiplied by a coefficient βp,α, whereβ can be expressed as follows:

βp,α = 1 + γ × α (6)

where γ is the number of gateways decomposed in path p.Consequently, the traffic volume generated by the bearers

of QCI q, taking the path p, on the demand d, at a time t, isexpressed as follows:

TRq,d,t,p = (∑ct∈CT

f(ct, timect,sgw(t))×bct,sgw+BRavgq )×βp,α

(7)BRavgq is the average bit rate of all bearers constituting

demand d and having a QCI q; this value can be computedbased on MBR and GBR value. We add the bit rate to theequation in order to account for the considered QCI. Thereforethe traffic of two different QCIs will have two different values.The QCI with greater bit rate will have a greater traffic value.

Consequently, the network load Nq,c,d,t,p can be expressedas follows:

Nq,c,d,t,p = TRq,d,t,p × lengthc,d,p (8)

where lengthc,d,p is the length of the path p for demand dpassing through the datacenter at location c (i.e., the distanceof the path packets take).

Depending on the path p, lengthc,d,p can be calculated asfollows:

• If both SGW and PGW are virtualized, then the com-munication between the NEs will happen through thedatacenter. Thus, lengthc,d,p = L1 + L3.

• If the SGW is virtualized and the PGW is decomposedthen the NE replacing the SGW needs to communicatewith the data center when receiving packets while thecommunication between the NEs happens through thedirect link since PGW is decomposed and thereforethe data plane de[ployed in its NE is activated. Thus,lengthc,d,p = 2× L1 + L2.

• If the SGW is decomposed and the PGW is virtualizedthen the length of the path is the same as in the previouscase, however L3 will be multiplied by two instead ofL1. Thus, lengthc,d,p = 2× L3 + L2.

• If both SGW and PGW are decomposed, then the commu-nication between the NEs will happen directly betweenthem. Thus, lengthc,d,p = L2.

Since real deployment depicting the routing process betweengateways has not been yet reported in the literature, we abstractthe path lengths between the gateways and use euclideandistances; this does not affect the selection of a path versusanother.

2) Calculating network delay: Tproc is the time needed toprocess the packet at each network node, i.e., at the SGWand the PGW, which is subject to: 1) the demand since itspecifies the involved gateways and the cities connected tothem which affects the number of established bearers, 2)the chosen path since it determines whether each gatewayis virtualized or decomposed, and 3) the time slot sincethe number of active bearers varies according to time. Inour performance evaluation, we use Tproc values that wereestimated in [6] for virtualized gateways and decomposedgateways. Tprop is the propagation delay time on each linkbetween the SGW and the PGW, which is subject to: 1)the demand since it determines the involved gateways andhence their location, 2) the datacenter location, and 3) thechosen path because it determines how to calculate the length.The total network delay between the access edge, i.e. SGW,and the IP domains gateway, i.e. PGW, can be expressed asLc,d,t,p = Tprocd,t,p + Tpropc,d,p .

IV. PERFORMANCE EVALUATION

To evaluate the performance of our solution under realnetwork scenarios, we developed a simulation model thatdepicts the US mobile core gateways based on the US LTEcoverage map [16] and the US population map [17]. We alsouse traffic delay and network load as performance metrics. Theclustered topology used for performance evaluation is basedon the one presented in [6], [7]. It is composed of 4 PGWs rep-resented as red rectangles, and 18 SGWs represented as greenrectangles. We assume that there exists a demand betweeneach SGW and PGW pair; thus, we have |D| = 18 demandsin total. Each gateway is identified by a unique ID number.The distances between the gateways are measured using the“measure distance map” on FreeMapTools [18]. Finally, weassume that any two gateways can be interconnected (i.e., thenetwork is meshed). This assumption is important as it enablesthe gateways to connect to the PGW when it is selected as adatacenter.

To decrease the optimization problem solver’s running timeso as to retrieve results instantly, we obtain the values of theparameters Nq,c,d,t,p and Lq,c,d,t,p offline; this is done viacalculating them for all the combinations of QCIs, datacenterslocations, demands, time slots and paths. In LTE, there are 9QCIs, thus |Q| = 9; there are 22 possible datacenter locations(18 SGWs and 4 PGWs), thus |C| = 22. We assume T ’s unit


is in hours; thus to cover a full day, |T | = 24. Finally, thereare four possible paths for each demand as described in theprevious section; thus, |P | = 4.

A. Traffic and Network Load Measurement

To calculate the total traffic on each SGW, we used thepopulation sizes presented in Table I; these are extracted fromthe US coverage map via summing the population sizes ofall cities situated in the vicinity of each SGW. To obtain thenetwork usage for every time slot, we followed the daily trafficintensity presented in [19].

SGWs sgw1 sgw2 sgw3 sgw4 sgw5 sgw6Population (in Millions) 0.68 11.4 2.0 1.76 1.6 5.8



TABLE I: Population of each SGW

For evaluating Eq. (7), we estimate the average bit rateof each QCI q per the example services for each QCI valuereported in [14]. Based on these values, we estimate the bitrate of each QCI value as shown in Table II.

QCI 1 2 3BRavg 64kbps 384kbps 16kbps

QCI 4 5 6BRavg 20Mbps 1kbps 19Mbps

QCI 7 8 9BRavg 384kbps 20Mbps 20Mbps

TABLE II: Estimated bit rate of each QCI value

To calculate βp,α, we set the value of α to 10% [6]; γ iscomputed as detailed in Eq. 6. Finally, to compute the networkload Nq,c,d,t,p for each parameters combination, we computethe path length lengthc,d,p for each combination based on theassumed US core network.

B. Delay Measurement

As aforementioned, the traffic delay between the mobilecore gateways Lc,d,t,p is computed as the sum of Tprop (whichis distance over speed) and Tproc, which is computed based onthe work reported in [7], summarized in Table III. The tableshows a correlation between the number of established bearersand the processing delay for a virtualized gateway. Indeed, ahigher number of bearers results in longer processing delay.However, the processing delay for decomposed gateways re-mains constant.

No. of bearers 10 100 1k 10kbits/sec 1 M 10 M 100 M 1 G

packets/sec 83 830 8.3K 83KVirtualized GW Tproc 62 µs 83 µs 109 µs 132 µs

Decomposed GW Tproc 15 µs 15 µs 15 µs 15 µs

TABLE III: Average processing delay

The number of bearers depends on the population of thecities within the demand’s gateway coverage range and theintensity of the considered time slot. For example, in crowdedcities, at peak times, the number of established bearers is

higher than at off peak times. Also the number of bearersestablished in cities, is higher than its value in suburbs.Therefore, we compute the total number of bearers at timet for a population pop as follows:

Bt,pop =intensity(t)× pop

σ(9)

where σ is a parameter used to normalize the output of theequation; it can be determined empirically. In our simulations,we set σ = 500 because this value gives a number ofbearers proportional to the population used. To generate anumber of bearers for each QCI of each demand we dividethe total number over the number of QCIs. To do so, we splitBt,pop into nine random numbers, and then assign one randomnumber to each QCI value.

LTE specifies delay budgets for each QCI based on thelatency between user’s UE and the server running the service;thus the specified values are relatively high. In our simulations,we used a fixed value of 4.95ms determined empirically onthe presumed core network topology, in a way that any smallervalue will cause constraint (4) not to be met, resulting ininfeasible model. Relaxation on that value was then appliedsuch that the delay budget values for each QCI are proportionalto the ones in [14] but normalized to the order of the fixedvalue. For example, the values 50ms, 100ms, 150ms and300ms, were mapped to the values 4.95ms, 5.0ms, 5.1msand 5.3ms, respectively.

C. Results

We first consider a topology with a single data center. Fig.2 shows the topology of the US core network after runningthe optimization problem at time slot 15 (between 3:00 pmand 4:00 pm) where the intensity is at its highest value (0.86).The chosen datacenter is PGW with ID 2 circled in orange.The figure also shows the path taken by each demand for QCI3. We observe that all the QCIs of each demand took thesame path, so for simplicity we only depict the paths of QCI3. The path of each demand is represented with a differentcolor. For the SGWs that were not originally connected tothe PGW selected as datacenter, the path goes from the SGWto the datacenter then back to the PGW that it is originallyconnected to it. For example, in Fig. 2, the path of the demandgenerated by SGW 18 goes to PGW 2, which is selected asa datacenter, then goes back to PGW 4, which SGW 18 isoriginally connected to. For the SGWs that were originallyconnected to the PGW that is chosen as the datacenter, thereis a single line going from the SGW to the PGW. This is, forexample, the case of SGW 11 in Fig. 2, where the traffic goesdirectly to the PGW that is connected to (i.e., PGW 2), whichis selected as a datacenter. Regarding the types of paths, eachdemand was either virtualized (both gateways are virtualized),represented by solid line, or decomposed (both gateways aredecomposed), represented by dashed lines. We conclude thatthe farthest SGWs from the datacenter (i.e., the SGWs thatbelong to other PGWs than the one chosen as the datacenter)have taken the full decomposed path, while the nearest SGWs


have taken the virtualized path. This result is expected and canbe justified as follows: the demands from the farthest SGWswill face higher propagation delay, therefore to remain withinthe delay budget, the compensation happens by choosing thedecomposed path since it requires lower processing delay thanthe processing delay required by a virtualized path.

Fig. 2: The new US core network topology at time slot 3:00-4:00pm, for QCI 3 and with one datacenter

Fig. 3 illustrates the effect of increasing the number ofdatacenters to two. It shows that the new datacenter is PGW1, it is chosen instead of PGW 3 or 4 since PGW 1’s SGWsare much more populated than the others because they servebig cities like San Francisco or New York, allowing its SGWsto connect to it via taking virtualized paths to minimize thetotal network load as much as possible.

Fig. 3: The new US core network topology at time slot 3:00-4:00pm, for QCI 3 and with two datacenter

V. CONCLUSIONS AND FUTURE WORK

In this paper, we studied the problem of virtualizing the LTEEPC using SDN and NFV. We proposed a hybrid architecturethat applies both technologies on each gateway, and findsthe optimal path for each set of bearers with the same QCIbetween each connected SGW and PGW without impairingthe QoS requirements. Our simulation results showed that theclosest SGWs to the datacenter took virtualized paths whilethe farthest took SDN decomposed paths. This asserts the factthat SDN decomposition decreases the network delay while itincreases the total network load; in contrast, an NFV gatewaydoes not increase the network load due to the absence of an

additional control layer, at the expense of increasing the trafficdelay.

In our future work, we aim to study how the objectivefunction might vary with respect to time for different numberof datacenters. Also, we want to analyze the variation ofruntime with respect to constraints, time slots, number ofdemands, and number of active datacenters. Moreover, we planto add further mobile core network components such as theMME, with control plane delay budgets.

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