Elsevier

Computer Networks

Volume 85, 5 July 2015, Pages 1-18
Computer Networks

SoftAir: A software defined networking architecture for 5G wireless systems

https://doi.org/10.1016/j.comnet.2015.05.007Get rights and content

Abstract

One of the main building blocks and major challenges for 5G cellular systems is the design of flexible network architectures which can be realized by the software defined networking paradigm. Existing commercial cellular systems rely on closed and inflexible hardware-based architectures both at the radio frontend and in the core network. These problems significantly delay the adoption and deployment of new standards, impose significant challenges in implementing and innovation of new techniques to maximize the network capacity and accordingly the coverage, and prevent provisioning of truly- differentiated services which are able to adapt to growing and uneven and highly variable traffic patterns. In this paper, a new software-defined architecture, called SoftAir, for next generation (5G) wireless systems, is introduced. Specifically, the novel ideas of network function cloudification and network virtualization are exploited to provide a scalable, flexible and resilient network architecture. Moreover, the essential enabling technologies to support and manage the proposed architecture are discussed in details, including fine-grained base station decomposition, seamless incorporation of Openflow, mobility- aware control traffic balancing, resource-efficient network virtualization, and distributed and collaborative traffic classification. Furthermore, the major benefits of SoftAir architecture with its enabling technologies are showcased by introducing software- defined traffic engineering solutions. The challenging issues for realizing SoftAir are also discussed in details.

Introduction

Existing commercial wireless networks are inherently hardware-based and rely on closed and inflexible architectural designs. Such inflexible hardware-based architectures typically lead to a 10-year cycle for a new generation of wireless networks to be standardized and deployed, impose significant challenges into adopting new wireless networking technologies to maximize the network capacity and coverage, and prevent the provision of truly-differentiated services able to adapt to increasingly growing, uneven, and highly variable traffic patterns. In particular, for 5G cellular system requirements, the ultra high capacity should have 1000-fold capacity/km2 compared to LTE, the user-plane latency should be less than 1 ms over the radio access network, and the ultra high data rates should provide 100-fold increase in user-experienced throughput (targeting 1 Gbps experienced user throughput everywhere). The challenges faced by the current network architectures cannot be solved without a radical paradigm shift in the design of next-generation wireless networks. Hence, in this paper, we propose the utilization of Software-Defined Networking (SDN) concept for next generation (5G) wireless networks, introduce a new architecture for wireless software- defined networks, called SoftAir, and present solutions and challenges for related research in this domain.

SDN has been recently introduced primarily for data center networks and for the next-generation Internet [6], [25]. The main ideas are (i) to separate the data plane from the control plane, and (ii) to introduce novel network control functionalities based on an abstract representation of the network. In current instantiations of this idea, these are realized by (i) removing control decisions from the hardware, e.g., switches, (ii) enabling the hardware to be programmable through an open and standardized interface, e.g., Openflow [37], and (iii) using a network controller to define the behavior and operation of the network forwarding infrastructure. SDN makes it easier to introduce and deploy new applications and services than the classical hardware-dependent standards. So far, the majority of SDN developments has concentrated on wired networks [6]. In this paper, we propose the utilization of SDNs for next generation (5G) wireless networks and present a new architecture for wireless SDNs, called SoftAir, and the solutions and challenges for related research in this domain. In our proposed SoftAir architecture, the control plane consists of network management and optimization tools and is implemented on the network servers. The data plane consists of software-defined base stations (SD-BSs) in the radio access network (RAN) and software-defined switches (SD-switches) in the cellular core network. Their control logic, e.g., physical/MAC/network functions, are implemented in software on general purpose computers and remote data centers.

Our proposed SoftAir architecture offers five core properties: (i) programmability, i.e., SDN nodes (e.g., SD-BSs and SD-switches) can be reprogrammed on-the-fly by dynamically associating with different network resources and networking algorithms; (ii) cooperativeness, i.e., SDN nodes can be implemented and aggregated at data centers for joint control and optimization to enhance the global network performance; (iii) virtualizability, i.e., multiple virtual wireless networks can be created on a single SoftAir, each of which operates under its own independent network protocols with network resources allocated based on demand; (iv) openness, i.e., data plane elements (i.e., BSs and switches), regardless of the underlying forwarding technologies and vendors, have unified data/control interfaces, e.g., CPRI and OpenFlow [20], [38], thus significantly simplifying the data plane monitoring and management; and (v) visibility, i.e., centralized controllers have a global view of the network status collected from BSs and switches.

The above five properties provide functionalities that are essential to enable 5G wireless communication networks to possess the following promising features:

  • Evolvability and adaptiveness: Because of the inherent separation between data plane and control plane, in SoftAir, both hardware forwarding infrastructure and software networking algorithms can easily, continuously and independently be upgraded quickly, which allows to timely adopt emerging radio technologies (e.g., millimeter wave (mm Wave), full-duplex, massive MIMO, and TeraHertz [5,10,29]) in the hardware infrastructure, while deploying novel traffic engineering, network management, and network optimization solutions at controllers. Moreover, the programmable data plane allows controllers to dynamically allocate network resources and adopt new networking solutions, according to the highly variable traffic patterns, unexpected network failures, and diverse quality of service (QoS) requirements of traffic flows.

  • Infrastructure-as-a-service: Emerging network services, such as machine-to-machine communications, smart grid applications, mobile virtual network operators (MVNOs), and over-the-top content services (e.g., Netflix video streaming), require highly differentiated networking capabilities to be integrated and deployed over the same network infrastructure. The network virtualizability of SoftAir allows the wireless hardware infrastructure to be offered as a service rather than as a physical asset. Specifically, in SoftAir, the service providers are provided with the ability to control, optimize, and customize the underlying infrastructure without owning it and without interfering with the operations and performance of other service providers, thus leading to more cost-efficient operations and enhanced QoS. Moreover, thanks to the programmable data plane, the network resources, e.g., spectrum, can be dynamically shared among the service providers, e.g., MVNOs.

  • Maximal spectral efficiency: In SoftAir, SD-BSs (e.g., both macro and small-cell base stations) can be implemented and aggregated at a server or a data center. There, they can easily share control information, mobile data and channel state information (CSI) associated with different active users in the system. Therefore, with SoftAir, it is much easier to implement algorithms to mitigate or exploit inter-cell interference towards universal frequency reuse, i.e., achieving frequency reuse factor 1 in the entire network.

  • Convergence of heterogeneous networks: The constant influx of new and amended wireless standards (e.g., small cells, WiFi, WiMAX, LTE, LTE-A, and super-WiFi) has created a rich but chaotic wireless environment in which multiple standards are competing and coexisting. Moreover, the fundamentally different features between wireless RAN and wired core network prevent the deployment of simple and unified network planning and control. By utilizing open and technology-independent interfaces, SoftAir can enable smooth transition and unified management among different wireless standards and between wireless RANs and wired core networks.

  • Low carbon footprints: The high energy-efficiency of SoftAir relies on its software-defined data plane, where the processing capacity of the SD-BSs can be dynamically scaled according to the uneven network traffic patterns in such a way that the number of idle BSs, which consume almost the same amount of energy as the active ones, is reduced. Moreover, with the centralized implementation of SD-BSs at data centers, the number of physical BS sites can be significantly reduced. Thus, air conditioning and other onsite power-hungry equipment can be considerably reduced.

The rest of the paper is organized as follows. Section II provides the related work. Section III introduces the architecture design of SoftAir. Section IV summarizes the essential management tools for SoftAir. Section V presents the software-defined traffic engineering solutions enabled by SoftAir. Section VI concludes this paper.

Section snippets

Related work

In literature, the software-defined architectures are well- studied in wired networks. For example, in data center networks and campus Local Area Networks (LANs) [12,[37], [54]], these architectures mainly support centralized and adaptive manipulation of flow tables at switches and routers. Furthermore, considering wired network virtualization, cloud computing and computer virtualization have maintained strong foothold for the past few years. In particular, the virtualization of routers and

SoftAir architecture design

As shown in Fig. 1, the architecture of SoftAir consists of a data plane and a control plane. The data plane is an open, programmable, and virtualizable network forwarding infrastructure, which consists of software-defined radio access network (SD-RAN) and software-defined core network (SD-CN). The SD-RAN consists of a set of SD-BSs, while the SD-CN is composed of a collection of SD-switches. The control plane mainly consists of two components: (1) network management tools, and (2) customized

Mobility-aware control traffic balancing

Rather than exploiting costly out-band control due to a separate control channel, the in-band mode is favored and adopted gradually in practical SDN implementation. In particular, for SoftAir in Fig. 4, each SD-switch or SD-BS (i.e., BBS) needs to send the control purpose traffic, such as the route setup requests for new flows and real-time network congestion status, to the SDN controller. Based on the continuously received control messages, the controller optimizes the best routes for data

Software-defined traffic engineering for SoftAir

In this section, we propose new traffic engineering solutions designed to leverage the full potential of the SoftAir architecture. Specifically, BS clustering, collaborative scheduling and rerouting solutions proposed in this section are directly supported by the enabling tools discussed in Section 3. In particular, the distributed traffic classification solutions in Section 3.3 provide fine-grained, accurate, and fast QoS classification for incoming traffic. By leveraging this feature,

Conclusion

In this paper, we propose SoftAir as a new paradigm towards next-generation wireless networks. SoftAir provides high flexible architecture, which can accelerate the innovations for both hardware forwarding infrastructure and software networking algorithms through control and data plane separation, enable the efficient and adaptive sharing of network resources through network virtualization, achieve maximum spectrum efficiency through cloud-based collaborative baseband processing, encourage the

Ian F. Akyildiz (M'86-SM'89-F'96) received the B.S., M.S., and Ph.D. degrees in Computer Engineering from the University of Erlangen-Nurnberg, Germany, in 1978, 1981 and 1984, respectively. Currently, he is the Ken Byers Chair Professor in Telecommunications with the School of Electrical and Computer Engineering, Georgia Institute of Technology (Georgia Tech), Atlanta, GA USA; the Director of the Broadband Wireless Networking (BWN) Laboratory and the Chair of the Telecommunication Group at

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    Ian F. Akyildiz (M'86-SM'89-F'96) received the B.S., M.S., and Ph.D. degrees in Computer Engineering from the University of Erlangen-Nurnberg, Germany, in 1978, 1981 and 1984, respectively. Currently, he is the Ken Byers Chair Professor in Telecommunications with the School of Electrical and Computer Engineering, Georgia Institute of Technology (Georgia Tech), Atlanta, GA USA; the Director of the Broadband Wireless Networking (BWN) Laboratory and the Chair of the Telecommunication Group at Georgia Tech. Since 2013, he is a FiDiPro Professor (Finland Distinguished Professor Program (FiDiPro) supported by the Academy of Science) with the Department of Electronics and Communications Engineering, at Tampere University of Technology, Finland, and the founding director of NCC (Nano Communications Center). Since 2008, he is also an honorary professor with the School of Electrical Engineering at Universitat Politcnica de Catalunya (UPC) in Barcelona, Catalunya, Spain, and the founding director of N3Cat (NaNoNetworking Center in Catalunya). Since 2011, he is a Consulting Chair Professor at the Department of Information Technology, King Abdulaziz University (KAU) in Jeddah, Saudi Arabia. He is the Editor-in-Chief of Computer Networks (Elsevier) Journal, and the founding Editor-in-Chief of the Ad Hoc Networks (Elsevier) Journal, the Physical Communication (Elsevier) Journal and the Nano Communication Networks (Elsevier) Journal. He is an IEEE Fellow (1996) and an ACM Fellow (1997). He received numerous awards from IEEE and ACM. His h-index is 88 and the total number of citations is above 69K due to Google scholar as of March 2015. His current research interests are in wireless sensor networks in challenged environments, 5G Cellular systems, nanonetworks, Terahertz Band, and software defined networks.

    Pu Wang (M'05) received the B.S. degree in electrical engineering from the Beijing Institute of Technology, Beijing, China, in 2003, the M.Eng. degree in computer engineering from the Memorial University of Newfoundland, St. Johns, NL, Canada, in 2008, and the Ph.D. degree in electrical and computer engineering from the Georgia Institute of Technology, Atlanta, GA, in 2013. He is currently an Assistant Professor with the Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS. Dr. Wang received the BWN Lab Researcher of the Year award in 2012, Georgia Institute of Technology. He received the TPC top ranked paper award of IEEE DySPAN 2011. He was also named Fellow of the School of Graduate Studies, 2008, Memorial University of Newfoundland. His research interests include wireless sensor networks, cognitive radio networks, software defined networks, nanonetworks, multimedia communications, wireless communications in challenged environment, and cyber-physical systems.

    Shih-Chun Lin (S'08) received the B.S. degree in electrical engineering and the M.S. degree in communication engineering from National Taiwan University in 2008 and 2010, respectively. He is a graduate research assistant in the Broadband Wireless Networking Laboratory (BWN Lab), School of Electrical and Computer Engineering, Georgia Institute of Technology. Currently, he is working toward the PhD degree in electrical and computer engineering under the supervision of Prof. Ian F. Akyildiz. His research interests include wireless underground sensor networks, software defined networking, large machine-to-machine communication, cognitive radio networks, and statistical scheduling in wireless systems.

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