Virtualization and Programmability in Mobile Wireless Networks ...

Virtualization and Programmability in Mobile Wireless Networks: Architecture and Resource Management Kleber V. Cardoso

Mohammad J. Abdel-Rahman and Allen MacKenzie

Luiz A. DaSilva

Instituto de Inform´atica Universidade Federal de Goi´as Goiˆania, GO, Brasil

Dept. of Electrical & Computer Engineering Wireless @ Virginia Tech

CONNECT Centre for Future Networks & Communications Trinity College Dublin

Abstract— We present a high-level end-to-end architecture for virtualization and programmability in next-generation mobile wireless networks. Our architecture envisions three major players: Service Providers, who wish to orchestrate wireless networks with particular characteristics to support particular applications; Resource Providers, who contribute resources such as spectrum, access points, backhaul infrastructure, and computing; and Virtual Network Builders, who marshal resources into networks for Service Providers. We take into account resource sharing and investigate how virtualization and programmability affect resource management. We show that: (i) virtualization reduces cost significantly, (ii) this cost reduction does not degrade the user satisfaction, and (iii) non-virtualized networks need to keep a large amount of idle capacity to satisfy coverage.

I. I NTRODUCTION Virtualization and programmability are two fundamental concepts in the cloud computing paradigm and in the idea of providing any resource as a service, a.k.a. XaaS or *aaS. In the networking context, software-defined networking (SDN) [1] has been the main initiative in this direction. While SDN has established a solid basis in wired networks, a similar solution for mobile wireless networks is still lacking. Softwaredefined wireless networking is evolving quickly and some proposals may eventually reach the same levels of adoption as OpenFlow [2] and NETCONF [3]. However, virtualization and programmability applied in an end-to-end context have aspects that demand a broader view, which cannot be encapsulated by a single software-defined wireless network. Modern mobile wireless networks are a complex ecosystem involving the Internet, cellular networks, WLANs, and other wireless networks. Starting from 4G, cellular networks have become data packet networks that transport voice, among other services. Small cells, including those deployed by end users, outnumber traditional base stations, while WLANs are considered an effective part of mobile wireless networks. This heterogeneity of mobile wireless networks is motivated by CAPEX reduction, since cost has been a recurrent problem in the expansion and update of these networks. Infrastructure sharing among mobile network operators (MNOs) has also been an increasingly popular approach to reduce costs in This material is based upon work supported by the National Science Foundation under Grant No. 1443978.

cellular networks. Virtualization allows for offering to the users and service providers the abstraction of a virtual mobile wireless network that seems similar to a traditional mobile wireless network operated by a single entity. Additionally, the appearance of mobile virtual network operators (MVNOs) reflects a lower barrier for entry into the market of mobile wireless networks, since the infrastructure can be hired instead of built. In this paper, we present our view for the future of the mobile wireless networks through a high-level end-to-end architecture. Our view is grounded in the Network without Borders (NwoB) paradigm [4], which introduces the serviceoriented concept as a natural motivation and guiding principle for virtualization and programmability in mobile wireless networks. Virtual networks are built to support service providers, which can be MVNOs or content and application providers. The NwoB paradigm addresses sharing not only in the context of infrastructure, but also spectrum. Thus, we discuss how aspects such as the regulatory framework affect virtualization of the spectrum. We extend the NwoB paradigm through the concept of Virtual Wireless Networking (VWN), which involves the elements of the whole end-to-end architecture for mobile wireless networks, i.e., entities, relationships, and resources. We also evaluate some resource allocation schemes that illustrate the benefits of network sharing, virtualization and programmability. The rest of the paper is organized as follows. We present a brief literature review in Section II. In Section III, we introduce an architecture for future wireless networks based on virtualization and programmability, describing its main entities and interactions. In Sections IV and V, we study the cost reduction and user satisfaction improvement achieved by sharing and virtualization, by formulating and evaluating different resource allocation optimization problems. In particular, our results show that virtualization not only improves user satisfaction but also reduces the cost of the virtual networks. Non-virtualized resource allocation may lead to user dissatisfaction even when high cost is tolerable. Moreover, we study the relatioship between the cost and user satisfaction, in which the cost of the virtual networks may be reduced significantly without considerably degrading the level of user satisfaction. Finally,

we conclude the paper in Section VI and provide directions for future research. II. R ELATED W ORK The success of SDN in the wired domain is motivating the proposal of new architectures for software-defined mobile wireless networks [5], [6]. These extend the SDN concept to the mobile networking context and also encompass relevant aspects of slicing, isolation, and virtualization of infrastructure resources. However, these works focus on the resources owned and managed by a single operator. Solutions for softwaredefined wireless networking can be potential enablers of sharing among operators that control different sets of resources, and they have not yet been investigated in this context. These works about software-defined wireless networking also have not addressed most of the issues related to resource allocation. The need to define an end-to-end architecture, in the context of multiple software-defined wireless networks, was already identified in the survey work of Yang et al. [7]. The end-to-end architecture that we propose in this paper takes into account heterogeneity of participants, i.e., different operators may adopt different internal architectures and they cooperate in order to provide end-to-end communications, and coexistence with legacy wireless systems. Finally, most current architectures for software-defined wireless networking do not take into consideration the spectrum and how its virtualization can bring benefits. Because an end-to-end architecture may be affected by different economic aspects, some works deal with this subject as part of a business model [8]. Recently, mobile wireless networks started being addressed in this context [8], [9]. CostaPerez et al. [9] approach important aspects, including spectrum sharing. However, the authors do not explore the wide potential of the virtualization concept, focusing instead on the network virtualization substrate (NVS) framework. Additionally, the authors do not discuss the interactions between the participants in the model, nor how resource providers other than cellular networks can get involved in the architecture. In [8], the authors introduce a framework for wireless virtualization that shares some ideas with our proposal. The work discusses aspects such as cost reduction, support of new services, and control and management flexibility. However, we have a different view regarding the role of the MVNOs, and we do not restrict our proposal to cellular networks. In addition, we present initial results that illustrate the benefits of virtualization, not only as an approach to reduce costs but also to improve user satisfaction. III. A RCHITECTURE FOR V IRTUAL W IRELESS N ETWORKING In order to create an environment that is able to support future demands for mobile services and to continue evolving smoothly after that, we envision an approach based on the paradigm of Networks without Borders (NwoB) [4]. A basic concept in the NwoB paradigm is the removal of traditional constraints on spectrum and infrastructure, which implies

the ability to share these resources. In addition, the NwoB paradigm is service-centric, i.e., a network exists to provide a service. In Figure 1a, we introduce our architecture and present the roles that an entity can play. As illustrated, there are three main roles: Service Provider (SP), Virtual Network Builder (VNB), and Resource Provider (RP). The VNB can be specialized into two separate roles: Virtual Network Architect (VNA) and Network Aggregator (NA). The number and diversity of RPs and SPs suggest an hourglass shape for the hierarchy of roles in this architecture, i.e., we anticipate fewer VNBs than RPs or SPs. Some entities may play more than one role in the model, e.g., adopt both the RP and VNB roles. The RP is the owner of a set of resources that can be offered as virtual resources, according to contracts established with VNBs or NAs, for example. In general, an RP will define how to slice and share its resources as virtual ones. However, an RP can also offer this capability to a VNB (or NA) and so delegate how a virtual resource is mapped in the substrate. An RP can be a traditional MNO, but it can also be a backhaul provider, a small-cell network operator, a company that owns WLAN networks, an individual who owns a WLAN or a femtocell access point, or a cloud computing provider. Thus, there will be a large number of heterogeneous RPs. The VNB interacts with the SPs to understand their demands and to deliver virtual mobile networks tailored to them. The VNB also interacts with the RPs to secure access to the required resources, which may be already virtualized or not. The VNB as a whole has as its critical task the composition of virtual resources into virtual networks, which involves subtasks in both the virtual context and the physical substrate. Multiple VNBs may exist, but each VNB can aggregate resources from multiple RPs to support multiple SPs. Thus, it is expected that there are fewer VNBs than RPs or SPs. Figure 1b shows additional details about the VNB and illustrates the components of the VNA and NA. A VNA operates only over virtual resources with abstract information about capacity, coverage, cost, and so on. This isolation of the VNA will be provided by the abstraction layer, which will rely on elements such as standardized virtual objects and a resource description language. An important virtual network service provided by a VNB (or VNA) is the translation of the SP demands into virtual resources, for example, from QoE to virtual infrastructure and spectrum. Other services include the creation, update, and teardown of virtual networks. These services operate over the virtual resources pool. Any need for additional resources is communicated to the negotiation agent, which is responsible for identifying the best approach to obtain additional resources. An NA will keep in touch with a set of RPs that can offer virtualized resources ready for use, resources ready to be virtualized before using, or physical resources. Resources ready to be virtualized before use offer a flexibility similar to physical resources but have the advantage of keeping the virtualization on the RP side instead of the NA side. The component responsible for non-virtualized resources needs to be able to deal with technologies such as IP, MPLS,

(a) Roles that an entity can play.

(b) Virtual network builder.

Fig. 1: Architecture for virtual wireless networking. UTRAN, and 802.11 in order to configure the resources and create virtual counterparts for them. On the other hand, the component responsible for the virtualized resources may enjoy the virtualization capabilities provided by technologies such as SDN, NFV, and EC2 and only needs to create the suitable abstractions to update the virtual resources pool. Thus, while the substrate can also involve virtual resources and other aggregation layers can be also built above the NA, the abstraction layer establishes a transition between the physical and virtual contexts. The concept of an SP in the VWN context has a wider meaning than the traditional use. An SP can be a traditional MVNO that offers data, voice, and messaging services, but it can also be a specialized MVNO that offers data service for specific applications, e.g., support for IoT devices. In addition, an SP could also be an over-the-top service provider, such as Netflix, Skype, or Facebook. Furthermore, an SP may bundle multiple services to the user. This whole context suggests a large number of heterogeneous SPs, asking for virtual mobile networks with different coverage and capacity requirements. A. Interactions In Figure 2, we show the most common interactions, represented by arrows between entities. Interaction A occurs when an SP wants to bundle services from other SPs to offer to users. This interaction also arises when an SP (e.g., a content provider) wants to outsource the virtual network operation to another SP with this expertise (e.g., an MVNO). In general, interaction A will be performed by humans in a time scale of weeks or months. Interaction B is probably one of the most frequent and important in the VWN context, because it involves the demand for and offer of virtual networks. Interaction B takes from weeks to months depending on the level of human intervention demanded. Initially, more human intervention is likely to be required, but as the paradigm evolves this interaction may be extensively automated. The use of optimization algorithms and machine learning will be important to obtain satisfactory results in this context. Interaction C might occur when a VNB cannot cover the geographical area requested by an SP. In this case, this VNB interacts with other VNBs in order to aggregate the required resources. This interaction also appears to support

a demand for ultra dense capacity in an area where a single VNB is not able to marshal sufficient resources to meet the demand. Similar to interaction A, interaction C will be mainly performed by humans in time scale of weeks or months. Interaction D is also frequent and critical in the VWN context, because it involves mapping virtual networks into the substrate. From a VNB to an RP, this interaction includes requests for resources, updates of previous requests, and releases of resources that are no longer required. In the reverse direction, interaction D can also include responses to requests, or updates. These updates can be especially important because they can be used by an RP to indicate to a VNB that a pool of shared resources has changed or is about to change. Such a change could affect a large number of virtual networks that the VNB is providing. Thus, such a change requires the VNB’s attention. Similar to interaction B, interaction D takes from hours to days depending on the level of human intervention; more human intervention is likely to be required initially, but as the paradigm evolves this interaction may be extensively automated. Finally, interaction E describes the communication between RPs in order to establish connection points in the substrate. This interaction also involves the QoS parameters that will allow a proper mapping from the virtual networks to the substrate. Each RP can establish connections with any number of other RPs. Interaction E can happen at very different time scales, from seconds to weeks, depending on factors such as human intervention and physical resources involved.

Fig. 2: Interactions between some typical entities in VWN. B. Resources The creation of a virtual wireless network may involve several types of resources such as spectrum licenses, base stations or access points, backhaul links, and storage devices, among others. Our goal is to obtain virtual resources to operate a network; however, we are also interested in the definition and mapping of virtual resources to physical resources. With a proper instantiation of every resource or set

of resources, virtualization creates a flexible abstraction that is transparent to the details of slicing, isolation, sharing, and composition/aggregation. Wireless network virtualization is a concept with multiple dimensions that crosses multiple layers, involving hardware and software resources [10]. Spectrum as a virtual resource provides a useful abstraction for the physical resource, which has many dimensions, including factors such as frequency band, bandwidth, geographic location, interference, access priority, and sharing model. Thus, while the virtual spectrum may be related to aspects of QoS/QoE and SLA, the mapping process needs to deal with the constraints imposed by the substrate. Virtualizing spectrum may make the details transparent to the VNA and the SPs. However, the proper abstraction for virtual spectrum is still an open issue. Similar to spectrum, the general goals of virtualizing an infrastructure resource are to: (i) create a proper abstraction to operate with and (ii) perform flexible mapping in the substrate. Typically, each type of infrastructure resource may have a different set of parameters, and the virtualization process is expected to be able to connect these heterogeneous resources together. When the aggregation/composition is applied at the physical level, in general, the objective is to create a more compact representation in the virtual context. The main advantage is the reduction of control overhead, since fewer software layers will be involved. With a proper design, this compressed abstraction can be useful for control, monitoring, and management. Preferably, a VNB or an NA would be allowed to specify how the virtualization in an RP takes place, i.e., VNB/NA could provide information to RP about slicing and composition/aggregation in order to steer the virtual resource creation. Finally, the virtualization of infrastructure resources can be nested or have multiple layers, i.e., slicing and composition/aggregation can occur at multiple levels, with a single underlying physical substrate. Next, we study the cost reduction and user satisfaction improvement achieved by sharing and virtualization by formulating and evaluating different resource allocation problems. IV. R ESOURCE M ANAGEMENT In this section, we illustrate our resource allocation optimization problem that is solved at the VNB to determine the optimal set of resources to be leased from one or multiple RPs and sliced and allocated to various SPs. We consider a geographic area that consists of a set of locations L and is covered by N RPs. Each RP has a set of BSs, and the union of these sets is denoted by S. The capacity of BS s ∈ S is denoted by rs . In our resource allocation models, a BS can be sliced between multiple SP users. The cost of BS s ∈ S is denoted by cs . We assume that there is a def set M = {1, 2, . . . , M } of SPs. We adopt a stochastic resource allocation model, in which the requested rate by SP m at location l, denoted by d˜ml , is assumed to be a discrete random variable, which has a known distribution. The goal is to meet the SP demands with probability β ∈ (0, 1). Due to the uncertainty in the SP user demands,

the feasibility region of the resource allocation problem is uncertain. Different stochastic optimization approaches have been proposed in the literature to deal with the uncertainty of the feasibility region of an optimization problem. Here, we adopt a chance constraint approach [11]–[13]. In many cases, the available resources might not be sufficient to meet all SP demands. Suppose our aim is to maximize the level of SP user satisfaction by providing them with the closest rate to their demand, while minimizing the cost of the required BSs. To explore the tradeoff between these two optimization goals, we formulate our resource allocation problem as a two-stage sequential optimization problem. In the first-stage problem, the goal is to maximize the total rate that can be supported by the leased resources, while restricting the probability that the rate exceeds the SP demands to be ≤ (1 − β). We introduce  ∈ [0, 1] to represent the maximum allowed deviation from the first-stage optimal solution. If  = 0, the purpose of the second-stage problem is to select from the first-stage optimal solutions (if there are multiple) the one that minimizes the cost of the BSs. If  > 0, the secondstage problem tries to reduce the cost of the cheapest optimal first-stage solution, by allowing the level of user satisfaction to be degraded within an  from the optimal satisfaction level. The larger the value of , the more impact the BSs cost has on the allocation decisions. Our optimal resource allocation problem can be stated as follows: Two-stage Stochastic Resource Allocation Problem (

) X XX

S TAGE 1: ( maximize ) δmls m∈M l∈L,s∈S

uls δmls

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∀m ∈ M, ∀l ∈ L X X

δmls ≤ rs , ∀s ∈ S

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(7)

where uls ∈ [0, 1] represents the normalized capacity (with respect to rs ) of BS s at location l, i.e., the normalized

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maximum rate that a user can receive at location l. uls = 0 when l is outside the coverage area of BS s and uls = 1 when l is within a small distance of BS s. In our resource allocation model, a BS s can be sliced between multiple SP users, and δmls ∈ [0, rs ] represents the rate of BS s that is allocated to ∗ SP m at location l. δmls , m ∈ M, l ∈ L, s ∈ S, is the optimal solution of the first-stage problem. Knowing the distribution of d˜ml , the chance constraint in (2) can be reformulated as follows: X uls δmls ≤ Fd˜−1 (1 − β) (8)

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ml

s∈S

where is the inverse CDF of d˜ml . The price charged for the use of a BS is fixed, regardless of the fraction of the BS that is effectively needed by the virtual networks (because one BS can serve more than one SP). Other pricing approaches can also be incorporated in our resource allocation formulation by modifying the objective function of the second-stage problem. Fd˜−1 ml

V. P ERFORMANCE E VALUATION In this section, we evaluate the outcome of the optimization problem described in Section IV. We study the gains achieved by having a common pool of resources from which virtual networks are built. We express this in short as ‘sharing.’ A. Evaluation Setup We consider N RPs. Each independently deploys a set of BSs, following a Poisson point process (PPP) [14], in a common geographical area. The SP demand locations are also distributed according to a PPP, i.e., these locations are distributed randomly and uniformly throughout the considered geographical area. All BSs have the same capacity and cost. The SP demand is based on the model proposed by Lee et al. [15]. This model represents the spatial distribution of the mobile network traffic as a log-normal, which was identified as a satisfactory match to the data collected by the authors. The evaluation area is divided into M × N square pixels, which have the same size as the ones in the measurement. The traffic density matrix whose elements are log-normally distributed is computed as proposed in [15], but we employ a scaled version in order to reduce the number of pixels. The average number of SP demand locations is enough to guarantee that at least 95% of the pixels have at least one demand location. When there are multiple demand locations in a pixel, the pixel’s demand is evenly divided between the locations. Unless stated otherwise, we use the following parameter values: BS capacity (rs , ∀s ∈ S) = 1 Gbps; Average number of available BSs per RP = 35; Average number of locations per SP = 150; βm = 0.9, ∀m ∈ M;  = 0. We used MATLAB to generate the stochastic network deployments and CPLEX to solve the optimization problems. Our results are averaged over 30 simulation runs, each corresponding to one realization of the stochastic network deployment and the SP demands. The 95% confidence intervals are shown in all simulation figures.

We use the COST 231-Walfisch-Ikegami propagation model with parameterization for small cells range [16]. COST 231Walfisch-Ikegami model provides a satisfactory approximation to the path loss experienced when small cells, with a radius of less than 3 km, are employed in urban environments. Because the COST 231-Walfisch-Ikegami model is recommended for distances > 20 m, we use the free-space path loss model for any shorter distance. We set the carrier frequency to 1800 MHz, the BS transmission power to 6.31 Watts (38 dBm), and the bandwidth to 25 MHz. B. Effect of the Number of RPs/SPs (N ) In this section, we evaluate the BS cost reduction and the improvement in the resource usage brought by virtualization as functions of N . First, we gradually increase the number of RPs/SPs and plot in Figure 3 the cost and idle capacity in the cases of sharing (or virtualization) and no-sharing (or novirtualization). Both metrics are computed over the number of used BSs, i.e., we removed the BSs that were not selected as part of the solution. The reason is an RP would not deploy a BS that is never necessary. As shown in Figure 3, the traditional operators (no-sharing) keep a lot of idle capacity due to the coverage demand. Thus, the cost of no-sharing is much higher than sharing and the difference increases as the number of RPs/SPs increases. By using virtual resources, sharing allows for selecting the best positioned BSs and so cover the area at the lowest cost. Actually, the BSs are best positioned to most of the demand, which minimizes the cost. The capacity is important only to satisfy the demand. This explain why the idle capacity does not decrease linearly as the number of RPs/SPs increases. C. Effect of β In Figure 4, we show the impact of the target demand satisfaction probability β on the BSs cost. In the case of nosharing, almost all BSs are assigned under any requested value of β. This is common in real networks that aim to guarantee coverage for all users that are distributed across the network. In the sharing case, increasing β increases the requirements on the infrastructure to service the network load, and hence the cost. As illustrated in Figure 4, the idle capacity of the no-sharing case decreases faster than the sharing case as β increases. This would be expected, since sharing employs a much smaller

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wireless resources. The results show that the resource sharing promoted by virtualization can significantly reduce costs. Virtualization and programmability are approaches to improve resource sharing in mobile wireless networks. They open opportunities for sharing in heterogeneous networks with diverse models of ownerships. On the other hand, the isolation between virtual mobile networks created over a common pool or substrate is an open issue, mainly with respect to the wireless resources. Service providers may have very different demands, which can be an opportunity for a VNB due to the ability of multiplexing the virtual networks over the physical substrate. However, this multiplexing also poses a challenge on how to efficiently assure the different QoS levels.

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Fig. 5: Cost and idle capacity (both in log-scale) as functions of . Number of RPs/SPs: 4.

set of BSs. Even in virtualized infrastructures, some time is consumed to allocate and setup new additional resources, such as BSs. Thus, the resource allocation scheme could be modified to guarantee higher idle capacity, mainly in extreme scenarios, such as β = 0.95. This increases the network reliability and potentially the user satisfaction. D. Relationship between User Satisfaction and Cost We study the relationship between the user satisfaction and cost by adjusting the value of . Setting  above zero may reduce the cost, but also reduces the user satisfaction. In our configuration, the user satisfaction has a direct relationship with , e.g., the average user satisfaction is 92% when  = 0.08. The VNB may accept some level of user dissatisfaction in order to alleviate the network load or make room for more users, for example. In Figure 5, we illustrate the effects of  on the cost and idle capacity. Since the resource allocation schemes prioritize the cost reduction while seeking to satisfy the user demands, a small reduction in user satisfaction can produce a significant decrease in cost. While the percentage of cost reduction is similar in both sharing and no-sharing, there is a large difference between the idle capacity of each regime. The cost reduction of sharing comes from severe reduction on idle capacity, which is possible thanks to the well-positioned BSs and the ability to serve users using any nearby BS. VI. C ONCLUSION In this article, we extended the Network without Borders paradigm by investigating its architecture, entities, and relationships. We discussed how the VWN architecture applies virtualization to improve resource sharing of infrastructure and spectrum. We also presented an initial investigation about resource allocation schemes that use a common pool of virtual

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