scheduling wireless virtual networks functions

240 IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, VOL. 13, NO. 2, JUNE 2016

Scheduling Wireless Virtual Networks FunctionsRoberto Riggio, Abbas Bradai, Davit Harutyunyan, Tinku Rasheed, Member, IEEE, and Toufik Ahmed

Abstract—Network function virtualization (NFV) sits firmly onthe networking evolutionary path. By migrating network functionsfrom dedicated devices to general purpose computing platforms,NFV can help reduce the cost to deploy and operate large ITinfrastructures. In particular, NFV is expected to play a pivotalrole in mobile networks where significant cost reductions can beobtained by dynamically deploying and scaling virtual networkfunctions (VNFs) in the core network. However, in order to achieveits full potential, NFV needs to extend its reach also to the radioaccess segment. Here, mobile virtual network operators shall beallowed to request radio access VNFs with custom resource allo-cation solutions. Such a requirement raises several challenges interms of performance isolation and resource provisioning. In thiswork, we formalize the wireless VNF placement problem in theradio access network as an integer linear programming prob-lem and we propose a VNF placement heuristic, named wirelessnetwork embedding (WiNE), to solve the problem. Moreover, wepresent a proof-of-concept implementation of an NFV manage-ment and orchestration framework for enterprise WLANs. Theproposed architecture builds on a programmable network fabricwhere pure forwarding nodes are mixed with radio and packetprocessing capable nodes.

Index Terms—Network Management, Resource allocation,Virtual Network Embedding, Virtual Network FunctionsPlacement, Network Function Virtualization, Wireless Networks.

I. INTRODUCTION

N ETWORK Function Virtualization (NFV) promises toreduce the cost to deploy and operate large networks by

migrating network functions from dedicated hardware appli-ances to software instances running on general purpose vir-tualized networking and computing infrastructures. This, intime, shall improve the flexibility and the scalability of mobilenetworks in that the deployment of new applications and ser-vices will be quicker (software Vs. hardware developmentlife–cycles) and different network functions can share the sameresources paving the way to further economies of scale. Thisprogressive process of network softwarization is set to play apivotal role in fifth generation mobile networks. In this context,the Network–as–a–Service business model shall allow opera-tors to tap into new revenue streams by further abstracting the

Manuscript received December 9, 2015; revised March 6, 2016; acceptedMarch 13, 2016. Date of publication April 1, 2016; date of current versionJune 8, 2016. This work was supported by the European Union’s H2020Research and Innovation Action under Grant H2020-ICT-644843 (VITAL) andGrant H2020-ICT-671639 (COHERENT). The associate editor coordinatingthe review of this paper and approving it for publication was A. Pras.

R. Riggio, D. Harutyunyan, and T. Rasheed are with the CREATE-NET,Trento 38123, Italy (e-mail: [email protected]; [email protected]; [email protected]).

A. Bradai is with the XLIM Institute, University of Poitiers, Poitiers 86130,France (e-mail: [email protected]).

T. Ahmed is with the CNRS-LaBRI, University of Bordeaux/Bordeaux-INP,33400, France (e-mail: [email protected]).

Digital Object Identifier 10.1109/TNSM.2016.2549563

physical network into service specific slices possibly operatedby different mobile virtual network operators (MVNOs). Theenvisioned vertical applications range from high–definitionvideo delivery to machine–to–machine applications.

In order to cope with the diverse range of requirements thatsprout for such use cases, future wireless and mobile networkswill further rely on virtualized resources and on dynamic ser-vice orchestration. Although a rich body of literature existson VNF placement [1], virtual network embedding [2], andcomponent placement [3], most of these works focus on theproblem of mapping an input virtual network request (often inthe form of a VNF Forwarding Graph) onto a physical virtual-ized network substrate (often offering computational as well asnetworking resources). However, these works implicitly assumethat once a VNF is mapped on a node, the virtualization layer(i.e. the hypervisor) will take care of scheduling the variousVNFs ensuring both logical isolation and an efficient use of thesubstrate resources [4]. Such an assumption does not hold any-more if radio nodes are added to the set of virtualized resourcesavailable in the substrate network (alongside computationaland networking resources). In this case, in fact, the amount ofresources available at each substrate radio node is a stochasticquantity depending on both channel fluctuations and end–usersdistribution.

In this work, we investigate the VNF placement and schedul-ing problems in the Radio Access Network (RAN) domain. Inthis scenario we expect MVNOs to specify their requests interms of a VNF Forwarding Graph. Such VNFs can includefunctions such as load–balancing and firewall, as well as virtualradio nodes. Moreover, in order to satisfy the diverse require-ments imposed by future applications and services, MVNOsshall be allowed to deploy custom resource allocation schemeswithin their network slice. At the same time, the underlyingsystem shall both enforce strict performance isolation betweenMVNOs and ensure efficient resource utilization across the net-work in spite of the non–deterministic nature of the wirelessmedium.

The contribution of this paper is twofold: (i) we formal-ize the VNF placement problem for radio access networks,and (ii) we propose a slice scheduling mechanism that ensuresresource and performance isolation between different slices.The proposed solutions work jointly, i.e. performance isolationis ensured if slices are accepted under the constraints imposedby our VNF placement problem formulation. This paper buildsupon our previous work [5] by refining the VNF placementheuristic WiNE (Wireless Network Embedding), extending thesimulation study to additional types of VNF requests, and ana-lyzing in deeper detail how the type of VNFs impacts onthe substrate network utilization. Numerical simulations showthat the proposed heuristic can approximate the performance

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RIGGIO et al.: SCHEDULING WIRELESS VIRTUAL NETWORKS FUNCTIONS 241

of the optimal ILP–based placement algorithm. Moreover, theefficiency of the heuristic increases with the size of the substratenetwork hinting at the fact that WiNE may be capable of closingthe gap with the ILP–based placement algorithm for realisticsubstrate networks. Finally, we also report on a updated proof–of–concept implementation of the proposed solution and on itsfield evaluation. The programmable data–path, the controllerand the SDK have been released under a permissive license foracademic use1.

The rest of this paper is structured as follows. In Sec. II wediscuss the related work. The physical network model, the VNFrequest model, and ILP problem formulation are presented inSec. III. The VNF placement heuristics and its evaluation arepresented in Sec IV and in Sec. V. The proof–of–concept ispresented in Sec VI while some illustrative VNFs and theirevaluation are presented in Sec. VII. Finally, Sec. VIII drawsthe conclusions pointing out the future work.

II. RELATED WORK

The recent advances in general purpose computing plat-forms paved the way to a new generation of software routers.However, many of these solutions focus on improving the pureraw packet processing speed [6]–[8] but do not tackle the prob-lem of deploying and orchestrating VNFs. In parallel, thereare significant efforts toward VNF management and orchestra-tion. In particular the European Telecommunications StandardsInstitute (ETSI) has recently tackled the NFV concept [9] whileOPNFV [10], OpenMANO [11], and OpenBATON [12] areworking toward an open–source carrier grade platform for NFV.

A. Virtual Network Embedding

The amount of literature on virtual network embedding(VNE) topic is considerable. Seminal works in this domaininclude VINEYard [13] for single domain VNE and PolyVINE[14] for multi–domain VNE. For a comprehensive surveyon VNE algorithms we point the reader to [2]. In [15] theauthors put forward a novel model that reflects the time–varyingresource requirements of a virtual network request. The authorsalso consider VNE in the context of opportunistic resource shar-ing at the level of the entire substrate network. In [16], theauthors present the SiMPLE VNE algorithm. SiMPLE exploitspath diversity in order to protect virtual networks from singlelink failures. However, to the best of the authors’ knowledge,none of these works formulate the VNE problem for hybridwired/wireless networks with the goal of ensuring performanceisolation between tenants.

B. VNF Placement

The VNF placement problem is conceptually similar to thecomponent placement problem in data–centers and cloud com-puting environments. The amount of literature in this domainis thus humbling [4], [17]–[19]. A survey on resource manage-ment in cloud computing environments can be found in [3]. In[17], the authors study the problem of placing virtual machine

1On–line resources available at: http://empower.create-net.org/

instances on physical containers in such a way to reducecommunication overhead and latency. In [18], the authors pro-pose a novel design for a scalable hierarchical applicationcomponents placement in cloud computing environments. Theproposed solution operates in a distributed fashion ensuringscalability while at the same time providing performances veryclose to that of a centralized algorithm. This work is extendedin [19] where several algorithms for efficient management ofcomponent–based applications in cloud environments are pro-posed. In [4] the elasticity overhead and the trade–off betweenbandwidth and host resource consumption are jointly consid-ered by the authors when formulating the VNF placementproblem. In [20], a joint node and link mapping algorithmis proposed. Finally, the authors of [1], [21], [22] tackle theproblem of dynamic VNF placement. Targeting resource allo-cation in data–centers, these works do not tackle the problem ofvirtualized radio function placement.

C. Wireless & Mobile Networks

The topic of radio resources virtualization has receivedsignificant attention in the literature. In [23], a WLAN virtu-alization approach named Virtual WiFi is proposed extendingthe VNE problem from the wired to the wireless domain.Kernel–based virtual machines are used as a virtual wirelessLAN devices while time domain multiplexing is used in orderto provide isolation between the virtual wireless devices. In[24], [25], wireless network virtualization is applied to wirelessmesh networks. A virtual network traffic shaper is introducedin [26], [27] for air time fairness in 802.16e networks. In [28],[29] the problem of virtualizing OFDMA–based wireless net-works (i.e. WiMAX and LTE) is studied. The authors tackle theproblem both at the radio and the core network level openingthe way to interesting infrastructure sharing scenarios. Similarconsideration can be also made for [30] where a frameworkfor sharing a single WiMAX base station is proposed. WirelessVirtualization of 802.11 devices is the focus of [31]. In all thecases above, however, the channel–aware placement of VNFsover radio and wired resources is not formulated nor is theperformance isolation challenge between multiple MVNOstackled.

D. Middlebox Management

Systems like OpenNF [32] and its derivatives [33], [34]focus on providing a platform for consistent VNF management.However their focus is on maintaining backward compatibilitywith currently available VNFs such as Bro [35] for IDS andSquid [36] for caching web proxies. Conversely, in this work weset to explore the possibilities opened by a fully programmablenetworking substrate where also radio access is treated as astandard VNF. Similar considerations can be made also forSplit/Merge [37], CoMB [38], and XoMB [39].

III. NETWORK MODEL

In the VNF placement problem, the input consists of ServiceFunction Chains (SFC) composed by a variable number of




TABLE ISUBSTRATE NETWORK PARAMETERS

VNFs, whereas the substrate network, called Network FunctionVirtualization Infrastructure (NFVI), provides the physical con-straints in terms of bandwidth and capacity [9]. In this context,the term capacity is not related only to pure computationalresources, such as number of CPU cores, memory, and/orstorage. Instead it refers also to packet forwarding and radioprocessing capabilities. Before introducing the proposed, solu-tion we need to detail specific notations for the NFVI and theSFC requests.

A. Network Function Virtualization Infrastructure Model

Let Gn f vi = (Nn f vi , En f vi ) be an undirected graph model-ing the physical network, where Nn f vi is the set of n = |Nn f vi |physical nodes that compose the substrate network and En f vi

is the set of edges or links. An edge enm ∈ En f vi if and onlyif a point–to–point connection exists between n ∈ Nn f vi andm ∈ Nn f vi . With respect to the physical network, links areactual wiring media, e.g., an Ethernet cable interconnectingthe two nodes. Four weights, ωs

c(n), ωsm(n), ωs

s (n), ωsr (n), are

assigned to each node n ∈ Nn f vi : ωsc,m,s(n) ∈ N

+ and ωsr (n) ∈

R+, 0 ≤ ωs

r (n) ≤ 1 representing the packet and radio process-ing resources available on that node. Nodes with all weightsequal to 0 (zero) are assumed to be pure packet forwardingnodes. Nodes with ωs

c > 0, ωsm > 0, ωs

s > 0, and ωsr = 0 are

assumed to be pure packet processing nodes. Finally, nodeswith ωs

c = ωsm = ωs

s = 0 and ωsr > 0 are assumed to be pure

radio access nodes.Another weight ωs

e(enm) is assigned to each link enm ∈

En f vi : ωse(e

nm) ∈ N+ representing the capacity of the link con-

necting two nodes. In order to avoid exceeding the nominalcapacity of the substrate links, traffic shaping is implementedat the nodes with packet and/or radio processing capabili-ties. Finally, let Pn f vi be the set of all substrate paths andPn f vi (s, t) the shortest path between nodes s, t ∈ Nn f vi . Table Isummarizes the NFVI parameters.

The weights ωsc,m,s(n) associated with the packet process-

ing nodes represent, respectively, the amount of CPU, memory,and storage resources available on that node, while the weightsωs

r (n) are specific to the radio access nodes and represent thenormalized amount of wireless resources available at that node.Notice how with the term radio access nodes we refer to thegeneric nodes providing end–users terminals with wireless con-nectivity and how this model makes no assumptions on the typeof resources that can be available at radio nodes. For example,

Fig. 1. NFVI network model. The figure shows the three basic virtualresources: forwarding, packet processing, and radio access.

in a 802.11–based network ωsr (n) could model the amount of

airtime available at a certain Access Point (AP). Similarly, inan OFDMA–based network (e.g., LTE), ωs

r (n) could be used tomodel the available radio resources in time and frequency at acertain eNodeB (eNB). For the sake of simplicity and withoutany loss of generality in this work we assume that all radio–enabled nodes initially have the same amount of resourcesωs

r (n) = 1,∀n ∈ Nn f vi .A sample substrate network is sketched in Fig. 1. The net-

work is composed by 8 nodes interconnected together. In orderto improve readability link weights have been omitted. Thesubstrate network in this example consists of 4 radio accessnodes (at the bottom of the picture), and 4 switches, 2 of whichsupporting just basic forwarding capabilities.

B. Service Function Chain Requests

Users are allowed to request SFCs as a directed graphsGs f c = (Ns f c, Es f c). Where Ns f c denotes the set of nodes(i.e. VNFs) and Es f c ⊆ Ns f c × Ns f c denotes the set of virtuallinks. An edge enm ∈ Es f c if and only if the packets from VNFn ∈ Ns f c must be forwarded to the VNF m ∈ Ns f v. Notice thatas opposed to the previous NFVI model, nodes in SFC requestsrepresent virtual network functions through which packets mustundergo before leaving the network. Packet processing nodesand links in the SFC request shares the same weights as forthe NFVI substrate network (ωv

c, ωvm, ωv

s , ωve). On the other

hand, provisioning of radio resources can be made either interms of fraction of available radio resources (ωv

r ) or in termsof bandwidth (ωv

b). In the former case, the users will specifythe percentage of radio resources they want to be assigned toa certain node, while in the latter case the users will spec-ify the amount of bandwidth assigned to the node. A singleSFC request can mix bandwidth–based and resource–basedprovisioning models.

Due to their stochastic nature, available bandwidth is a time–varying quantity in wireless networks. Channel fading, but alsoto the distribution of end–users, can greatly influence the net-work performance. For example, users at the center of the cellcan, in general, use more efficient modulations and codingschemes thus achieving higher throughput for a fixed amount ofradio resources than users at the edges of the cell. As a result,when the bandwidth–based provisioning model is employed,




TABLE IISERVICE FUNCTION CHAIN REQUEST PARAMETERS

also the actual channel conditions experienced by the end–usersmust be taken into account.

Let us call b(n) the actual aggregated throughput of the vir-tual radio node n ∈ Ns f c in the fraction of resources currentlyassigned the node. We can then introduce an additional parame-ter named reference throughput �v

b(n) ≥ ωvb(n) upon which the

bandwidth reservation ωvb(n) is enforced. We can then define

the effective target bandwidth ω̃vb(n) for the virtual radio node

n as follows:

ω̃vb(n) =

{ωv

b(n) if b(n) ≥ �vb(n)

ωvb(n)

b(n)�v

b(n)if b(n) < �v

b(n)(1)

The parameter �vb(n) represents a threshold. When the actual

throughput b(n) of the virtual radio node n is above the thresh-old, then the bandwidth reservation is respected. Conversely,when b(n) < �v

b(n) the requested bandwidth ωvb(n) is linearly

scaled down. Choosing a small value for �vb(n) means that

the network can utilize more resources in order to satisfy thebandwidth requirements, which in time could result in a higherpricing. Conversely, a high value for �v

b(n) means that the net-work will try to satisfy the bandwidth requirements only forthe tenants that are making a better use of the wireless spec-trum. Notice how, as it will be more clear in Sec. VI, tenantscan make a poor use of the wireless spectrum also due to cus-tom scheduling disciplines that favor fairness at the expense ofthe aggregated throughput. This could be a reasonable trade–off in network slices aimed at emergency response applicationswhere it is critical that all mobile clients get the same shareof resources even if this could lead to a sub–optimal spectrumutilization.

If we define ωvr (n) = ω̃v

b(n)

b(n), then, in order for the SFC request

to be feasible, it must hold:

∑n∈N b

s f c

ω̃vb(n)

b(n)+

∑n∈Nr

s f c

ωvr (n) ≤ ωv

r (m) = 1 (2)

Which means that the sum of the fractions of radio resourcesallocated to virtual nodes must be less than or equal to theresources available at the substrate node m. Table II summarizesthe SFC request parameters.

For example, consider the case depicted in Fig. 2a. Here twotenants (A and B) requested two slices with aggregated capacityof, respectively, 6 Mb/s and 4 Mb/s, while the reference band-width (�v

b) for both tenants has been set to 10 Mb/s. Moreover,

for simplicity and without losing generality, let us assume thatonly one client is active in either slice. If the cell capacity b(n)

experienced by both clients is equal to, or higher than, 10 Mb/sit is easy to see how both slices can be accommodated by thesystem, thus in this case b(n) ≥ �v

b(n) and ω̃vb(n) = ωv

b(n) forboth slices.

However, if the cell capacity experienced by the wirelessclient in the tenant B’s slice is reduced to 5 Mb/s, then thesame request can not be accommodated anymore. This is dueto the fact that serving the same amount of data to the wire-less client in the tenant B’s slice now requires twice the amountof radio resources it did before (see Fig 2b). In this case sinceb(B) < �v

b(B), thus:

ω̃vb(B) = ωv

b(B)b(B)

�vb(B)

= 45

10= 2

This results in the resource allocation sketched in Fig 2c whichpenalizes only the performance of the tenant B without affect-ing tenant A. It is worth noticing that this example can be easilygeneralized to an OFDMA system were resources are assignedacross a time/frequency matrix.

A few sample SFC requests are sketched in Fig. 3. Notice thatwireless terminals are not represented since they are outside thecontrol of the orchestration framework. The linear SFC requestin Fig. 3a consists of three VNFs including a WiFi hotspot, afirewall, and a load balancer. The WiFi hotspot request willalso include parameters such as the name of the network andthe authentication parameters (e.g. type of encryption, RADIUSserver to be used, etc.) however these kind of information arepurely functional and are thus omitted in this section. TheSFC request in Fig. 3b implements a performance enhancingnetwork service, namely duplicates filtering in dense urban sce-narios. Finally, the SFC request in Fig. 3c accounts for an accessenforcement scenario whereby multiple security related VNFsare deployed in parallel. Notice how all radio access VNF usethe bandwidth–based resource provisioning model.

C. Virtual Network Function Placement

In this section we shall provide the optimal ILP formulationfor the SFC embedding problem, while in the next section wewill present a scalable heuristic. The overall objective is to com-pute the optimal VNF placement based on the available radioresources. The chosen objective function is:

min

⎛⎝ ∑

n∈Nn f vi

∑n′∈Ns f c

(wv

c(n′)�c(n)+ wv

m(n′)�m(n)

+wvs (n′)�s(n)+ wv

r (n′)�r (n)

)�n′

n

+∑

e∈En f vi

∑e′∈Es f c

ωve(e′)�e(e)�

e′e

⎞⎠

where, �n′n , �e′

e ∈ 0, 1 are two binary variables indicatingrespectively if the VNF n′ ∈ Ns f v has been mapped to noden ∈ Nn f vi and if the virtual link e′ ∈ Es f c has been mapped tothe substrate link e ∈ En f vi .




Fig. 2. Impact of channel conditions on slice isolation. Bandwidth demands can be met when channel conditions are good for both slices (a). Conversely,bandwidth targets can not be achieved when one of the tenants is experiencing poor channel conditions (b). Isolation can be preserved by linearly scaling downthe bandwidth target for the tenant with adverse channel conditions (c).

Fig. 3. Sample SFC Requests. Notice how resource requests for all radio VNFsare bandwidth–based.

A valid solution is the one where the resources utilized bythe SFC request are at most equal to the available resources onthe substrate network nodes:

∑n′∈Ns f c

ωvc(n′)�n′

n ≤ ωsc(n) ∀n ∈ Nn f vi (3)

∑n′∈Ns f c

ωvm(n′)�n′

n ≤ ωsm(n) ∀n ∈ Nn f vi (4)

∑n′∈Ns f c

ωvs(n′)�n′

n ≤ ωss (n) ∀n ∈ Nn f vi (5)

∑n′∈Ns f c

ωvr (n′)�n′

n ≤ ωsr (n) ∀n ∈ Nn f vi (6)

and links: ∑e′∈Es f c

ωve(e′)�e′

e ≤ ωse(e) ∀e ∈ En f vi (7)

In this work we also assume that every VNF in the SFC requestshall be mapped to a different substrate node:∑

n′∈Ns f c

�n′n ≤ 1 ∀n ∈ Nn f vi (8)

Every VNF in the SFC request shall be mapped only once:∑n∈Nn f vi

�n′n = 1 ∀n′ ∈ Ns f c (9)

In terms of radio resources requirements, the following con-strain, deriving from (1) and (2), enforces that for every radioprocessing node n ∈ Nn f vi a feasible request has been made:

∑n′∈N b

s f c

ωvb(n′)

�vb(n′)

�n′n +

∑n′∈Nr

s f c

ωvr (n′)�n′

n ≤ 1 ∀n ∈ Nn f vi

(10)

Finally, the following constraint enforces that for each linkenm ∈ Es f c there must be a continuous path allocated betweenthe pair of physical nodes on top of which the VNFs n, m ∈Ns f c have been mapped.

j>i∑j∈Nn f vi

�enm

ei j −j<i∑

j∈Nn f vi

�enm

e ji = �ni −�m

i

∀i ∈ Nn f vi ∀enm ∈ Es f c (11)

IV. HEURISTIC

The ILP formulation described in the previous sections cannot be applied to realistic scenarios due to its limited scalability.For example, embedding a 6–nodes linear SFC request over ak = 8 fat–tree substrate topology can take several hours on IntelCore i7 laptop (3.0 GHz CPU, 16 Gb RAM) using the Matlab®

ILP solver (intlinprog). Notice also that even a k = 8 fat–treesubstrate topology is rather small if compared with realistic




Algorithm 1. Compute list of candidate substrate nodes

1: procedure FindCandidates(Nn f vi , Ns f c)

2: for n ∈ Ns f c do3: for p ∈ Nn f vi do4: If ωs

c,m,s,r (p) >= ωvc,m,s,r (n) then

5: n.candidates.add(p)

6: end if7: end for8: end for9: end procedure

Algorithm 2. Sort list of candidate substrate nodes

1: procedure SortCandidates (Ns f c)

2: sort (Ns f c)

3: end procedure

deployments where it is common to find k = 24 fat–tree net-works, making the ILP problem formulation intractable. In thissection we present a heuristic, named WiNE, that can handlesimilar (and more complex) requests in tens of minutes.

The proposed heuristic is composed of three steps. In thefirst step, for each virtual node n ∈ Ns f c, the heuristic com-putes the list of candidate substrate nodes (see Alg. 1). Theseare the substrate nodes that can support the virtual nodes inthe SFC request given the input capacity constraints. In thesecond step, the virtual nodes n ∈ Ns f c are sorted in decreas-ing order according to the number of candidate substrate nodes(see Alg. 2). In the third step, the sorted list of virtual nodes istraversed starting with the virtual node with more embeddingopportunities. For each candidate substrate node p ∈ Nn f vi

the heuristic considers all the neighboring nodes m ∈ Ns f c ofthe virtual node n. The heuristic, then, assigns the node n to thesubstrate node m ∈ Nn f vi with the lowest virtual edge mappingcost (see Alg. 3).

We define the virtual edge mapping cost W : Es f c × Nn f vi ×Nn f vi → R between a virtual edge enm ∈ Es f c and a pair ofsubstrate nodes p, q ∈ Nn f vi as follows:

W (enm, p, q) = �c,m,s,r (p)ωvc,m,s,r (n)

+�c,m,s,r (q)ωvc,m,s,r (m)

+∑

e∈Pn f vi (p,q)

�e(e)ωve(e

nm)

This represents the cost of embedding the virtual edge enm ∈Es f c over the path Pn f vi (p, q) between the substrate nodesp, q ∈ Nn f vi given that virtual nodes n, m are mapped on,respectively, the substrate nodes p, and q. Minimizing thevirtual edge mapping cost for node m essentially means thatsubstrate nodes that are far away from node’s m embeddingopportunities are penalized. This results in virtual nodes in anSFC request to be placed close to each other over the substratenetwork, which in time means that less substrate resources areneeded to support a given number of requests.

Algorithm 3. Nodes and links assignment

1: procedure NodeAndLinkAssignment (Gn f vi , Gs f c)

2: for n ∈ Ns f c do3: for p ∈ n.candidates do4: ff p.used then5: continue6: end if7: for m ∈ n.neighbors do8: if m.mapped then9: cost = W (enm, p, m.mapped)

10: else11: cost = +∞12: for q ∈ m.candidates do13: cost = min(cost, W (enm, p, q))

14: end for15: end if16: mapping_cost (p)+ = cost17: end for18: end for19: p← argmin(mapping_cost (p))

20: n.mapped ← p21: p.used ← T rue22: for m ∈ n.neighbors do23: if m.mapped then24: Allocate path Pn f vi (n.mapped,

m.mapped)

25: end if26: end for27: end for28: end procedure

V. EVALUATION

The goal of this section is to compare the relative perfor-mance of the ILP–based placement algorithm with the per-formance of our placement heuristic using different syntheticsubstrate networks and different SFC requests. In this section,we shall first describe the simulation environment and then theperformance metrics. Simulations are carried out in a discreteevent simulator implemented in Matlab®.

A. Simulation Environment

The ILP–based placement algorithm and the proposed place-ment heuristic are evaluated in three different scenarios. In thefirst scenario, linear VNF requests, similar to the one depictedin Fig. 3a, are considered. In the second scenario, branchedVNF requests, similar to the one depicted in Fig. 3b, are con-sidered. Finally, in the third scenario, VNF requests with loops,similar to the one depicted in Fig. 3c, are used. The numberof VNFs in each SFC request as well as the actual amount ofradio, computational, memory, storage, and link resources arerandomly generated for each request.

The reference substrate network is a k–ary fat–tree withk = 4, 6, 8, where leaf nodes are WiFi APs rather than servers.This results in a total of, respectively, 16, 54, and 128 WiFi APs.The computational, memory, storage, radio, and link resources




Fig. 4. Acceptance ratio using the ILP–based algorithm and the heuristics with different virtual and substrate topologies.

Fig. 5. Average embedding cost using the ILP–based algorithm and the heuristics with different virtual and substrate topologies.

Fig. 6. Average computational nodes utilization using the ILP–based algorithm and the heuristics with different virtual and substrate topologies.

for the substrate network are initially all set to 100. The cost ofusing each unit of node �c,m,s,r (n) and link �e(e) resources isset to 1. The number of VNFs in each SFC request depends onthe SFC type. In the case of linear and branched SFC requeststhe number is randomly picked in the set {3, 6}, while in thecase of cyclic SFC requests the number is randomly picked inthe set {4, 6}. The computational, memory, and storage require-ments for each SFC requests are uniformly distributed between[5, 30], while the radio and link requirements are uniformlydistributed between [5, 60].

The metrics used in this study are the standard ones adoptedin several other related works [13], [40], [41]. For each scenariothe number of accepted requests, the average embedding cost,the average node and link utilization, and the execution timeusing either the ILP–based placement or the proposed heuristicare considered. In this study we assume that a fixed numberof SFC requests are embedded sequentially onto the substratenetwork. In particular in each run, the simulator tries to embed30 randomly generated SFC requests. Reported results are theaverage of 10 simulations.

B. Simulation Results

Figures 4 and 5 shows the percentage of accepted SFCrequests for different substrate networks and the averageembedding cost. As expected the ILP–based placement algo-rithm is more efficient than WiNE in mapping the incomingrequests. This can be seen in terms of both a higher number ofaccepted requests as well as a lower average embedding cost.Notice however that, at least for linear SFCs, WiNE actuallyhas a lower embedding cost. This does not mean that WiNE ismore efficient than the ILP–based algorithm but rather that, byaccepting a lower number of SFC requests, WiNE also utilizes,on average, less substrate resources. It is also worth noticingthat the efficiency of the proposed heuristic increases with thesize of the substrate network. This hints toward the fact thatWiNE may be capable of closing the gap with the ILP–basedplacement algorithm for realistic substrate networks.

Figure 6, 7 and 8 summarize the substrate resource utiliza-tion. As it can be noticed the ILP–based placement algorithmis characterized by a higher utilization ratio for both radioand computational nodes. This results in fewer substrate nodes




Fig. 7. Average radio nodes utilization using the ILP–based algorithm and the heuristics with different virtual and substrate topologies.

Fig. 8. Average link utilization using the ILP–based algorithm and the heuristics with different virtual and substrate topologies.

Fig. 9. Average execution time using the ILP–based algorithm and the heuristic with different virtual and substrate topologies.

being used to support the same number of SFC requests whichin time could result in a more energy efficient operation ifunused nodes can be turned–off. Notice that this is further sup-ported by Fig. 8 where the substrate links utilization is reported.As it can be seen, WiNE is characterized by a higher link utiliza-tion, which means that the proposed heuristic is less efficient infinding shorter paths between VNFs. However, it is also worthnoticing that the gap between the ILP–based placement algo-rithm and WiNE gets smaller as the size of the substrate networkincrease.

Figure 9 shows that the average amount of time requiredto embed a single SFC request using the ILP–based place-ment algorithm is significantly higher than the time requiredto embed the same request using WiNE. The ILP problembecomes essentially intractable for substrate networks withmore than a few tens of nodes (irrespective of the numberof VNFs in the request), while WiNE can effectively embedcomplex SFC requests on substrate networks with hundreds ofnodes in a limited amount of time.

VI. IMPLEMENTATION

A. Overview

We implemented the VNF placement and scheduling solutionpresented in this work in a proof–of–concept NFV managementand orchestration framework, named EmPOWER. Notice that,the prototype currently supports only wireless access networksbased on the 802.11 family of standards and, as a consequence,the applications described in the next section target EnterpriseWLANs and Campus networks scenarios. Nevertheless, as seenin the previous sections, the provisioning model does not makeany assumption about the particular link–layer technology andcan be as well applied to any kind of radio access networkincluding OFDMA networks such as LTE and LTE–Advanced.

Our proof–of–concept is loosely modeled after the ETSI ref-erence NFV Architecture [9]. As it can be seen in Fig. 10, thearchitecture is conceptually divided into three layers. The bot-tom layer consists of the physical as well as the virtualizedresources composing the NFVI. In the second layer we have




Fig. 10. Reference network function virtualization architecture [9].

the actual VNFs which are the software implementation of aparticular network function capable of being executed over theNFVI. We remind the reader that in this work also radio accessis treated as a VNF. Finally, in the third layer we have theOperational Support System (OSS) and the Business SupportSystem (BSS) used by the network administrators to oper-ate and manage their virtual networks. The Management andOrchestration plane covers the orchestration and the manage-ment of physical and/or virtual resources that support the NFVIas well as the life–cycle management of the VNFs, i.e. creation,configuration, monitoring, and destruction.

B. Network Function Virtualization Infrastructure

Our architecture currently accounts for three kinds of NFVIresources, namely: basic forwarding nodes (i.e. OpenFlowswitches), packet processing nodes, and radio access nodes.The latter, in addition to the features supported by the packetprocessing node, also embed specialized hardware in the formof one or more 802.11 Wireless NICs. Figure 11 sketches thesystem architecture.

We name Wireless Termination Points (WTPs) the phys-ical points of attachment in the RAN (e.g. WiFi APs orLTE eNBs) supporting virtualized radio processing capabili-ties. Conversely, the Click Packet Processors (CPPs) are theforwarding nodes with computational capacity. These nodesare essentially programmable switches running an embeddedversion of Linux and capable of performing arbitrary opera-tions on the traffic, e.g. load–balancing, firewalling, deep packetinspection, etc.

As the name suggests the actual packet processing is per-formed by multiple instances of the Click Modular Router [42].Click allows to build complex VNFs using simple and reusablecomponents, called elements. Click includes over 300 elementssupporting functions such as packet classification, access con-trol, deep packet inspection. Elements can be composed in order

to realize complex VNFs. Finally, Click can be easily extendedwith custom processing elements making it possible to supportfeatures that are not provided by the standard elements.

Each WTP/CPP includes an OpenVSwitch instance, one ormore VNFs, and one Agent. The latter is in charge of monitor-ing the status of each VNF as well as handling requests comingfrom the controller. In the current implementation the monitor-ing features include: number of packets/bytes transmitted andreceived as well as the amount of resources (cpu time, memory,storage) utilized by each VNF.

In the current prototype CPPs are built upon the Soekris6501-70 platform consisting in single 1.6 GHz Intel Atom CPU,2 Gbyte if SDRAM, and 12 Gigabit Ethernet Ports. CPPsrun Ubuntu 15.04 Server as operating system. WTPs exploitthe PCEngines ALIX (x86) embedded platform and run theOpenWRT operating system.

C. Virtual Infrastructure Managers

As Virtual Infrastructure Managers (VIMs) we use a combi-nation of frameworks. Ryu [43] is used to configure the switch-ing fabric while for the NFVI we extended the EmPOWERcontroller presented by the authors in [44] in order to supportalso the generalized packet processing nodes. The controllersupports multiple slices on top of the same physical infrastruc-ture. A slice is a virtual network with its own set of WTPs/CPPs.The controller is responsible for the deploying the VNFs on thenetwork devices.

D. Orchestrator

From an architectural standpoint, the VNF placement algo-rithm resides in the Orchestrator which is in charge of decidingwhether a particular request can be accepted or if it must berefused. If a request is accepted, then the Orchestrator is incharge of mapping the request onto the substrate network, i.e.,network resources must be allocated and configured on both thesubstrate nodes and the substrate links and the VNFs must beinstantiated on the selected nodes (see Fig. 11). Notice how, ourproof–of–concept does not impose a specific Orchestrator leav-ing network administrators free to use other currently availablesolutions, such as OpenBaton [12].

VII. APPLICATIONS

In this section we shall describe two SFCs implementedand tested over a small scale testbed deployed at CREATE–NET premises. The testbed consists of 2 OpenFlow–switches,2 CPPs, and 20 WTPs.

A. Radio Access Network Slicing

In this use case we aim at demonstrating the performanceisolation features enabled by our joint VNF placement andscheduling solution. Such use case is supported by introducinga MAC Hypervisor implementing the slice scheduling tech-niques described in the previous sections. Figure 12 sketches




Fig. 11. The EmPOWER network architecture.

Fig. 12. Radio Access Network Slicing Application.

the internal architecture of a radio access node. Notice how, notthe entire WiFi MAC is virtualized. More precisely, WiFi con-trol frames generation (e.g. ACK and RTS/CTS) as well as theactual physical layer are not virtualized due to their tight tim-ing/computational constraints. On the other hand each tenantcan have its own Radio Resource Management (RRM) instanceas well as its own management place. In this work with RRMwe refer to wireless clients scheduling and to rate adaptation,which can be defined on a per–tenant basis2. For example, onetenant can use a wireless station scheduler aimed at ensuringfairness while another tenant could opt for a scheduler aimed atimproving the aggregated slice bandwidth.

2Notice how, the hypervisor implementation details are out of the scope ofthis work and are thus omitted.

The hypervisor monitors the channel utilization as well as theamount of traffic generated by each Tenant. Such information isused to compute the effective aggregated goodput of each slice(b(n)). It is worth stressing that both channel activity and good-put are two quantities that can be monitored by the hypervisorwith low overhead and without affecting the operation of theRRM policy implemented by Tenants. For example a Tenantwith wireless clients experiencing poor channel conditions willsee its aggregated goodput reduced due to re–transmissions.Similarly a Tenant implementing wireless clients schedulingpolicies aimed at maximizing fairness will also see a reductionin goodput if some of its wireless clients require less efficientmodulation and coding schemes.

The network setup used for this set of measurements con-sists of two tenants A and B. The bandwidth requests comingfrom Tenant A and B are, respectively, 4 and 2 Mb/s, while thereference bandwidth (�v

b) is 6 Mb/s for both tenants. A totalof three wireless clients is active in the networks. Clients 1and 2 belongs to Tenant A while Client 3 belongs to TenantB. Traffic is generated from a server sharing the same back-haul with the APs and consists of three UDP streams (onefor each client). Each stream has constant inter–departure timeand packet size resulting in a transmission rate of 10 Mb/sfor each stream. In order to simulate a hotspot with limitedcapacity the rate control algorithm used by the AP has beenmodified in order to always use the lowest transmission rate (6Mb/s) . Measurements have been carried in two different sce-narios differentiated by the channel conditions experienced byclient number 1 which is positioned in such a way to experiencechannel conditions raging from Good to Poor.

As it can be seen from Fig. 13a, when Client 1 is experi-encing good channel conditions the proposed VNF scheduling




Fig. 13. Isolation across clients in different networks. Client 1 and 2 are inTenant 1’s network, while Client 3 is in Tenant 2’s network.

method can satisfy the bandwidth request for both tenants(notice that Tenant A request of 4 Mb/s has been equal-ity partitioned among the two clients). On the other hand,when Client 1 starts experiencing poor channel conditions (seeFig. 13b) the legacy resource provisioning mechanism allocatesthe same bandwidth to all the clients. This behavior, known asIEEE 802.11 performance anomaly [45], allows a node whichexperiences poor channel conditions to monopolize the wire-less medium lowering the performance of the whole system.Conversely, the proposed resource provisioning mechanism canmeet the bandwidth reservations made by Tenant A by linearlyscaling down the amount of resources allocated to Tenant B thatis experiencing poor channel conditions.

B. Data/Management Plane Offloading

Wireless, and in particular mobile networks, have been sofar designed around the requirements of the downlink, i.e. cellor AP selection is performed using downlink signal strengthmeasurements. In the recent years, however, we have wit-nessed a mushrooming of new uplink–centric applications suchas Machine Type Communications (MTC), Internet of Things(IoT), and Vehicle to Infrastructure (V2I) as well as of symmet-ric mobile applications. This calls for a paradigm shift wherethe traffic originated from a wireless client is received by onenode while the traffic destined to the same client is transmit-ted by another node. This kind of network setup is usuallyreferred to as uplink/downlink decoupling and, in its most gen-eral form, can consists of two possibly non overlapping sets oftransmitting and receiving nodes.

The EmPOWER platform [44] allows for network configu-rations where a wireless client can be attached to one AP inthe downlink direction and to one or more APs in the uplinkdirection. This feature allows to exploit the broadcast nature ofthe wireless medium and to opportunistically receive the sametransmission at multiple in–range APs. However, if not properlycontrolled such a feature can lead to an overload in the networkcore. For example, a wireless client scheduled on N APs in theuplink direction could increase the load on the network coreby a factor of N . Moreover, a straightforward implementationof such a mechanism could generate a significant increase inthe number of duplicate packets which in time could triggeranomalous behaviors at the transport layer.

As a result of the fact that the WiFi MAC is virtualizedit is possible to offload parts of it to dedicated processing

Fig. 14. Data/Management Plane Offloading application. Data–plane (dupli-cates filtering) and Management–plane (probe response generation) operationsare offloaded to two different CPPs.

nodes as VNFs that can be then shared among several APs. Inthis use case we implemented a VNF which filters out dupli-cate 802.11 frames based on their sequence number. Figure 14sketches a slightly more generic case were both data–plane(duplicates filtering) and management (probe response genera-tion) functionalities are offloaded to two different CPPs. In thissection however we shall evaluate only the performances of theduplicate filtering VNF.

Traffic originated from clients is received at one or moreWTPs where it is encapsulated (802.11 over Ethernet) and thenforwarded to a CPP where the duplicates filtering VNF isdeployed. This VNF is also responsible for decapsulating the802.11 frame and converting it into an Ethernet frame beforeforwarding it to its intended destination.

In order to evaluate this VNF we exploited a network setupcomposed of a single client and three APs. Traffic is injectedfrom the wireless client as a single UDP stream. Packet trans-mission rate and payload are kept fixed at, respectively, 100packets/s and 1472 bytes. Impairments on the link betweenclient and APs are simulated by randomly dropping receivedframes with probability P L at all receiving APs. For all mea-surements a total of 6000 frames were generated. Confidenceintervals were very small for all the data points and have there-fore be omitted in order to improve readability. Measurementshave been taken using three packet dropping probabilities,namely: 0.05 (good channel conditions), 0.2 (medium channelconditions), and 0.8 (poor channel conditions)

As it can be seen from Fig. 15a, the end–to–end packet deliv-ery ratio increases with the number of available uplinks. Theproposed uplink/downlink decoupling solution can provide asmall performance improvement even when the channel condi-tions are good (P L = 0.05). On the other hand the performanceimprovements are significant when the channel conditions getworse. In particular this solution allows to turn an essentiallybroken channel (4 out of 5 dropped packets) into an usablechannel (1 out of 2 dropped packets). Finally, in Fig. 15b andFig. 15c we can see the impact of the duplicate filtering VNFon the bandwidth utilization. As expected without the VNFthe bandwidth utilization increases with the number of uplinks,while using the VNF filtering the bandwidth utilization does notexceed the nominal goodput.




Fig. 15. Packet loss (a) and bandwidth utilization (b and c) for a single client scheduled at multiple APs in the uplink direction. The duplicate filtering VNF canreduce the load on the backhaul by selectively dropping duplicate packets.

VIII. CONCLUSIONS

NFV is rapidly emerging as a flexible solution to deployand operate future mobile networks. However, in order to fullydeliver on its promises, the concept of NFV must be extendedalso to the radio access segment of the mobile network. In thispaper, we tackled this challenge by presenting a novel formu-lation of the VNF placement problem encompassing also radioaccess VNFs. We then introduced a ILP–based algorithm forsmall networks and a scalable heuristic, named WiNE, for largerdeployments. Numerical simulations showed that the proposedheuristic can approximate the performance of the ILP–basedplacement algorithms for realistic substrate networks. Finally,we reported on a preliminary proof–of–concept implementa-tion of a NFV Management and Orchestration framework forEnterprise WLANs.

As future work, we plan to investigate the resiliency proper-ties of WiNE in case of nodes and link failures and to study howVNF placement can be optimized by taking into account wire-less clients distribution and user mobility. We also plan to verifythe applicability of our problem formulation to OFDMA–basedradio access networks like LTE and LTE–Advanced and toextend the prototype (both the hypervisor and the controller)adding support for VNF migration and scaling as well as forMobile Edge Computing capabilities.

REFERENCES

[1] H. Moens and F. De Turck, “VNF-P: A model for efficient placement ofvirtualized network functions,” in Proc. IEEE 10th Int. Conf. Netw. Serv.Manage. (CNSM), Rio de Janeiro, Brazil, 2014, pp. 418–423.

[2] A. Fischer, J. Botero, M. Till Beck, H. de Meer, and X. Hesselbach,“Virtual network embedding: A survey,” IEEE Commun. Surveys Tuts.,vol. 15, no. 4, pp. 1888–1906, Apr. 2013.

[3] B. Jennings and R. Stadler, “Resource management in clouds: Survey andresearch challenges,” J. Netw. Syst. Manage., vol. 23, no. 3, pp. 567–619,Jul. 2015.

[4] M. Ghaznavi, A. Khan, N. Shahriar, K. Alsubhi, R. Ahmed, andR. Boutaba, “Elastic virtual network function placement,” in Proc. IEEECloudNet, Niagara Falls, ON, Canada, 2014, pp. 255–260.

[5] R. Riggio, A. Bradai, T. Rasheed, J. Schulz-Zander, S. Kuklinski, andT. Ahmed, “Virtual network functions orchestration in wireless net-works,” in Proc. IEEE 11th Int. Conf. Netw. Serv. Manage. (CNSM),Barcelona, Spain, 2015, pp. 108–116.

[6] M. Dobrescu et al., “RouteBricks: Exploiting parallelism to scale soft-ware routers,” in Proc. ACM Symp. Oper. Syst. Principles (SOSP), BigSky, MT, USA, 2009, pp. 15–28.

[7] S. Han, K. Jang, K. Park, and S. Moon, “PacketShader: A GPU-accelerated software router,” SIGCOMM Comput. Commun. Rev., vol. 40,no. 4, pp. 195–206, Aug. 2010.

[8] K. K. Ram, A. L. Cox, M. Chadha, and S. Rixner, “Hyper-switch: Ascalable software virtual switching architecture,” in Proc. USENIX Annu.Tech. Conf. (ATC), San Jose, CA, USA, 2013, pp. 13–24.

[9] European Telecommunications Standards Institute (ETSI), “ETSI GSNFV 002 network functions virtualisation (NFV); Architectural frame-work,” Dec. 2014.

[10] Linux Foundation. OPNFV [Online]. Available: https://www.opnfv.org/[11] Telefonica. OpenMANO [Online]. Available: https://github.com/

nfvlabs/[12] Fraunhofer FOKUS. OpenBATON [Online]. Available: http://openbaton.

github.io/[13] M. Chowdhury, M. R. Rahman, and R. Boutaba, “ViNEYard: Virtual net-

work embedding algorithms with coordinated node and link mapping,”IEEE/ACM Trans. Netw., vol. 20, no. 1, pp. 206–219, Feb. 2012.

[14] M. Chowdhury, F. Samuel, and R. Boutaba, “PolyViNE: Policy-basedvirtual network embedding across multiple domains,” in Proc. ACMWorkshop Virtualized Infrastruct. Syst. Archit. (VISA), New Delhi, India,2010, pp. 49–56.

[15] S. Zhang, Z. Qian, J. Wu, S. Lu, and L. Epstein, “Virtual network embed-ding with opportunistic resource sharing,” IEEE Trans. Parallel Distrib.Syst., vol. 25, no. 3, pp. 816–827, Mar. 2014.

[16] M. A. Khan, N. Shahriar, R. Ahmed, and R. Boutaba, “Simple:Survivability in multi-path link embedding,” in Proc. IEEE 11th Int. Conf.Netw. Serv. Manage. (CNSM), Barcelona, Spain, 2015, pp. 210–218.

[17] D. Breitgand, A. Epstein, A. Glikson, A. Israel, and D. Raz, “Networkaware virtual machine and image placement in a cloud,” in Proc. IEEE9th Int. Conf. Netw. Serv. Manage. (CNSM), Zurich, Switzerland, 2013,pp. 9–17.

[18] M. Barshan, H. Moens, and F. De Turck, “Design and evaluation of a scal-able hierarchical application component placement algorithm for cloudresource allocation,” in Proc. IEEE 10th Int. Conf. Netw. Serv. Manage.(CNSM), Rio de Janeiro, Brazil, 2014, pp. 175–180.

[19] M. Barshan, H. Moens, S. Latre, and F. De Turck, “Algorithms forefficient data management of component-based applications in cloudenvironments,” in Proc. IEEE Netw. Oper. Manage. Symp. (NOMS),Krakow, Poland, 2014, pp. 1–8.

[20] R. Guerzoni et al., “A novel approach to virtual networks embeddingfor SDN management and orchestration,” in Proc. IEEE Netw. Oper.Manage. Symp. (NOMS), Krakow, Poland, 2014, pp. 1–7.

[21] S. Clayman, E. Maini, A. Galis, A. Manzalini, and N. Mazzocca, “Thedynamic placement of virtual network functions,” in Proc. IEEE Netw.Oper. Manage. Symp. (NOMS), Krakow, Poland, 2014, pp. 1–9.

[22] F. Bari, S. R. Chowdhury, R. Ahmed, and R. Boutaba, “On orchestrat-ing virtual network functions,” in Proc. IEEE 11th Int. Conf. Netw. Serv.Manage. (CNSM), Barcelona, Spain, 2015, pp. 50–56.

[23] L. Xia et al., “Virtual WiFi: Bring virtualization from wired to wireless,”SIGPLAN Not., vol. 46, no. 7, pp. 181–192, Mar. 2011.

[24] P. Lv, X. Wang, and M. Xu, “Virtual access network embedding in wire-less mesh networks,” Ad Hoc Netw., vol. 10, no. 7, pp. 1362–1378, 2012.

[25] P. Lv, Z. Cai, J. Xu, and M. Xu, “Multicast service-oriented virtualnetwork embedding in wireless mesh networks,” IEEE Commun. Lett.,vol. 16, no. 3, pp. 375–377, Mar. 2012.

[26] G. Bhanage, R. Daya, I. Seskar, and D. Raychaudhuri, “VNTS: A vir-tual network traffic shaper for air time fairness in 802.16e systems,” inProc. IEEE Int. Conf. Commun. (ICC), Cape Town, South Africa, 2010,pp. 1–6.

[27] G. Bhanage, D. Vete, I. Seskar, and D. Raychaudhuri, “SplitAP:Leveraging wireless network virtualization for flexible sharing ofWLANs,” in Proc. IEEE GLOBECOM, Miami, FL, USA, 2010, pp. 1–6.




[28] R. Kokku, R. Mahindra, H. Zhang, and S. Rangarajan, “NVS: A substratefor virtualizing wireless resources in cellular networks,” IEEE/ACMTrans. Netw., vol. 20, no. 5, pp. 1333–1346, Oct. 2012.

[29] R. Kokku, R. Mahindra, H. Zhang, and S. Rangarajan, “CellSlice:Cellular wireless resource slicing for active RAN sharing,” in Proc. IEEE5th Int. Conf. Commun. Syst. Netw. (COMSNETS), Bangalore, India,2013, pp. 1–10.

[30] G. Bhanage, I. Seskar, R. Mahindra, and D. Raychaudhuri, “Virtualbasestation: Architecture for an open shared WiMAX framework,” inProc. ACM Workshop Virtualized Infrastruct. Syst. Archit (VISA), NewDelhi, India, 2010, pp. 1–8.

[31] G. Smith, A. Chaturvedi, A. Mishra, and S. Banerjee, “Wireless vir-tualization on commodity 802.11 hardware,” in Proc. ACM WorkshopWireless Netw. Testbeds Exp. Eval. Charact. (WinTECH), Montreal, ON,Canada, 2007, pp. 75–82.

[32] A. Gember-Jacobson et al., “OpenNF: Enabling innovation in networkfunction control,” in Proc. ACM SIGCOMM, Chicago, IL, USA, 2014,pp. 163–174.

[33] B. Kothandaraman, M. Du, and P. Sköldström, “Centrally controlled dis-tributed VNF state management,” in Proc. ACM HotMiddlebox, London,U.K., 2015, pp. 37–42.

[34] A. Gember-Jacobson and A. Akella, “Improving the safety, scalabil-ity, and efficiency of network function state transfers,” in Proc. ACMHotMiddlebox, London, U.K., 2015, pp. 43–48.

[35] The Bro Network Security Monitor [Online]. Available:https://www.bro.org/

[36] The Squid Caching Proxy [Online]. Available: http://www.squid-cache.org/

[37] S. Rajagopalan, D. Williams, H. Jamjoom, and A. Warfield, “Split/merge:System support for elastic execution in virtual middleboxes,” in Proc.USENIX Netw. Syst. Des. Implement. (NSDI), Lombard, IL, USA, 2013,pp. 227–240.

[38] V. Sekar, N. Egi, S. Ratnasamy, M. K. Reiter, and G. Shi, “Designand implementation of a consolidated middlebox architecture,” in Proc.USENIX Netw. Syst. Des. Implement. (NSDI), San Jose, CA, USA, 2012,pp. 323–336.

[39] J. W. Anderson, R. Braud, R. Kapoor, G. Porter, and A. Vahdat,“xOMB: Extensible open middleboxes with commodity servers,” in Proc.ACM/IEEE ABCS, Austin, TX, USA, 2012, pp. 49–60.

[40] Y. Minlan, Y. Yi, J. Rexford, and M. Chiang, “Rethinking virtual net-work embedding: Substrate support for path splitting and migration,”SIGCOMM Comput. Commun. Rev., vol. 38, no. 2, pp. 17–29, Mar. 2008.

[41] Y. Zhu and M. Ammar, “Algorithms for assigning substrate networkresources to virtual network components,” in Proc. IEEE INFOCOM,Barcelona, Spain, Apr. 2006, pp. 1–12.

[42] E. Kohler, R. Morris, B. Chen, J. Jannotti, and M. F. Kaashoek, “The clickmodular router,” ACM Trans. Comput. Syst., vol. 18, no. 3, pp. 263–297,Aug. 2000.

[43] Ryu SDN Framework [Online]. Available: http://osrg.github.io/ryu/[44] R. Riggio, M. Marina, J. Schulz-Zander, S. Kuklinski, and T. Rasheed,

“Programming abstractions for software-defined wireless networks,”IEEE Trans. Netw. Serv. Manage., vol. 12, no. 2, pp. 146–162, Jun. 2015.

[45] M. Heusse, F. Rousseau, G. Berger-Sabbatel, and A. Duda, “Performanceanomaly of 802.11b,” in Proc. IEEE INFOCOM, San Francisco, CA,USA, 2003, pp. 836–843.

Roberto Riggio received the Ph.D. degree in commu-nications engineering from the University of Trento,Trento, Italy, in 2008. He is currently a SeniorResearcher Staff Member with the Future NetworksTeam, CREATE-NET, Trento, Italy, and a GuestLecturer with the University of Trento. His researchinterests include performance isolation in multitenantmobile networks; software defined mobile network-ing and deep programmable mobile networks. He hasbeen involved in several European projects includ-ing three 5G-PPP projects and has directly generated

more than 1 MC in competitive funding. He has authored more than 60 paperspublished in internationally refereed journals and conferences. He has servedon the TPC of leading conferences in the networking field and is an AssociateEditor for the International Journal of Network Management (Wiley) and forthe Wireless Networks Journal (Springer).

Abbas Bradai received the M.S. degree in com-puter science from the National Institute of ComputerScience (ESI ex-INI), Algiers, Algeria, and theUniversity of Rennes 1, Rennes, France, in 2009,and the Ph.D. degree from the LaBRI/University ofBordeaux, Talence, France, in 2012. He is actuallyan Associate Professor with University of Poitiers,Poitiers, France, and a Research Fellow with XLIMLaboratory, Poitiers, France. His research interestsinclude multimedia communications over wired andwireless networks, cognitive radio, software defined

network and virtualization. He is/was involved in many French and Europeanprojects (FP7, H2020) such as ENVISION and VITAL.

Davit Harutyunyan received the first level degreein telecommunication engineering and the master’sdegree in telecommunication engineering (both withhons.) from the National Polytechnic University ofArmenia, Yerevan, Armenia, in 2011 and 2015,respectively. He is currently pursuing the Ph.D.degree at the University of Trento, Trento, Italy.Since 2015, he has been a member of the FutureNetworks Team, CREATE-NET, Trento, Italy. Hisresearch interests include cellular networks and soft-ware defined mobile networking.

Tinku Rasheed (M’05) received the bachelor’sdegree in telecommunications from University ofKerala, Thiruvananthapuram, India, in 2002, the M.S.degree from Aston University, Birmingham, U.K.,in 2003, and the Ph.D. degree from the Departmentof Computer Science, University of Paris-Sud XI.,Orsay, France, in 2007. He is a Senior Research StaffMember and the Head of the Future Networks Areawithin CREATE-NET, Trento, Italy. Before join-ing CREATE-NET, he was Research Engineer withOrange Labs for four years. He has extensive indus-

trial and academic research experience in the mobile wireless communicationarea, end-to-end network architectures and services. He has several grantedpatents and has authored more than 60 articles published in major journals andconferences. He is a member of the ACM.

Toufik Ahmed is a Full Professor with ENSEIRB-MATMECA School of Engineers, InstitutPolytechnique de Bordeaux (Bordeaux-INP),Talence, France, and is involved with researchactivities in CNRS-LaBRI Lab-UMR 5800 at theUniversity Bordeaux, Bordeaux, France. His researchinterests include end-to-end Quality of Service(QoS) management and provisioning for multimediawired and wireless networks, media streaming, andcross-layer optimization. He has also worked on anumber of national and international projects. He has

served as a TPC member for international conferences and journals.



scheduling wireless virtual networks functions

Education