Shih-Chun Lin

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

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.

5G roadmap

The fifth generation (5G) mobile communication networks will require a major paradigm shift to satisfy the increasing demand for higher data rates, lower network latencies, better energy efficiency, and reliable ubiquitous connectivity. With prediction of the advent of 5G systems in the near future, many efforts and revolutionary ideas have been proposed and explored around the world. The major technological breakthroughs that will bring renaissance to wireless communication networks include (1) a wireless software-defined network, (2) network function virtualization, (3) millimeter wave spectrum, (4) massive MIMO, (5) network ultra-densification, (6) big data and mobile cloud computing, (7) scalable Internet of Things, (8) device-to-device connectivity with high mobility, (9) green communications, and (10) new radio access techniques. In this paper, the state-of-the-art and the potentials of these ten enabling technologies are extensively surveyed. Furthermore, the challenges and limitations for each technology are treated in depth, while the possible solutions are highlighted.

Wireless software-defined networks (W-SDNs) and network function virtualization (NFV) for 5G cellular systems

Cellular network technologies have evolved to support the ever-increasing wireless data traffic, which results from the rapidly-evolving Internet and widely-adopted cloud applications over wireless networks. However, hardware-based designs, which rely on closed and inflexible architectures of current cellular systems, make a typical 10-year cycle for a new generation of wireless networks to be standardized and deployed. To overcome this limitation, the concept of software-defined networking (SDN) has been proposed to efficiently create centralized network abstraction with the provisioning of programmability over the entire network. Moreover, the complementary concept of network function virtualization (NFV) has been further proposed to effectively separate the abstraction of functionalities from the hardware by decoupling the data forwarding plane from the control plane. These two concepts provide cellular networks with the needed flexibility to evolve and adapt according to the ever-changing network context and introduce wireless software-defined networks (W-SDNs) for 5G cellular systems. Thus, there is an urgent need to study the fundamental architectural principles underlying a new generation of software-defined cellular network as well as the enabling technologies that supports and manages such emerging architecture. In this paper, first, the state-of-the-art W-SDNs solutions along with their associated NFV techniques are surveyed. Then, the key differences among these W-SDN solutions as well as their limitations are highlighted. To counter those limitations, SoftAir, a new SDN architecture for 5G cellular systems, is introduced.

SoftWater: Software-defined networking for next-generation underwater communication systems

Abstract Underwater communication systems have drawn the attention of the research community in the last 15 years. This growing interest can largely be attributed to new civil and military applications enabled by large-scale networks of underwater devices (e.g., underwater static sensors, unmanned autonomous vehicles (AUVs), and autonomous robots), which can retrieve information from the aquatic and marine environment, perform in-network processing on the extracted data, and transmit the collected information to remote locations. Currently underwater communication systems are inherently hardware-based and rely on closed and inflexible architectural design. This imposes significant challenges into adopting new underwater communication and networking technologies, prevent the provision of truly-differentiated services to highly diverse underwater applications, and induce great barriers to integrate heterogeneous underwater devices. Software-defined networking (SDN), recognized as the next-generation networking paradigm, relies on the highly flexible, programmable, and virtualizable network architecture to dramatically improve network resource utilization, simplify network management, reduce operating cost, and promote innovation and evolution. In this paper, a software-defined architecture, namely SoftWater, is first introduced to facilitate the development of the next-generation underwater communication systems. More specifically, by exploiting the network function virtualization (NFV) and network virtualization concepts, SoftWater architecture can easily incorporate new underwater communication solutions, accordingly maximize the network capacity, can achieve the network robustness and energy efficiency, as well as can provide truly differentiated and scalable networking services. Consequently, the SoftWater architecture can simultaneously support a variety of different underwater applications, and can enable the interoperability of underwater devices from different manufacturers that operate on different underwater communication technologies based on acoustic, optical, or radio waves. Moreover, the essential network management tools of SoftWater are discussed, including reconfigurable multi-controller placement, hybrid in-band and out-of-band control traffic balancing, and utility-optimal network virtualization. Furthermore, the major benefits of SoftWater architecture are demonstrated by introducing software-defined underwater networking solutions, including the throughput-optimal underwater routing, SDN-enhanced fault recovery, and software-defined underwater mobility management. The research challenges to realize the SoftWater are also discussed in detail.

Distributed Cross-Layer Protocol Design for Magnetic Induction Communication in Wireless Underground Sensor Networks

Wireless underground sensor networks (WUSNs) enable many applications such as underground pipeline monitoring, power grid maintenance, mine disaster prevention, and oil upstream monitoring among many others. While the classical electromagnetic waves do not work well in WUSNs, the magnetic induction (MI) propagation technique provides constant channel conditions via small size of antenna coils in the underground environments. In this paper, instead of adopting currently layered protocols approach, a distributed cross-layer protocol design is proposed for MI-based WUSNs. First, a detailed overview is given for different communication functionalities from physical to network layers as well as the QoS requirements of applications. Utilizing the interactions of different layer functionalities, a distributed environment-aware protocol, called DEAP, is then developed to satisfy statistical QoS guarantees and achieve both optimal energy savings and throughput gain concurrently. Simulations confirm that the proposed cross-layer protocol achieves significant energy savings, high throughput efficiency and dependable MI communication for WUSNs.

QoS-Aware Adaptive Routing in Multi-layer Hierarchical Software Defined Networks: A Reinforcement Learning Approach

Software-defined networks (SDNs) have been recognized as the next-generation networking paradigm that decouples the data forwarding from the centralized control. To realize the merits of dedicated QoS provisioning and fast route (re-)configuration services over the decoupled SDNs, various QoS requirements in packet delay, loss, and throughput should be supported by an efficient transportation with respect to each specific application. In this paper, a QoS-aware adaptive routing (QAR) is proposed in the designed multi-layer hierarchical SDNs. Specifically, the distributed hierarchical control plane architecture is employed to minimize signaling delay in large SDNs via three-levels design of controllers, i.e., the super, domain (or master), and slave controllers. Furthermore, QAR algorithm is proposed with the aid of reinforcement learning and QoS-aware reward function, achieving a time-efficient, adaptive, QoS-provisioning packet forwarding. Simulation results confirm that QAR outperforms the existing learning solution and provides fast convergence with QoS provisioning, facilitating the practical implementations in large-scale software service-defined networks.

Control traffic balancing in software defined networks

To promise on-line and adaptive traffic engineering in software defined networks (SDNs), the control messages, e.g., the first packet of every new flow and network traffic statistics, should be forwarded from software defined switches to the controller(s) in a fast and robust manner. As many signaling events and control plane operations are required in SDNs, they could easily generate a significant amount of control traffic that must be addressed together with the data traffic. However, the usage of in-band control channel imposes a great challenge into timely and reliable transmissions of control traffic, while out-band control is usually cost-prohibitive. To counter this, in this paper, the control traffic balancing problem is first formulated as a nonlinear optimization framework with an objective to find the optimal control traffic forwarding paths for each switch in such a way the average control traffic delay in the whole network is minimized. This problem is extremely critical in SDNs because the timely delivery of control traffic initiated by Openflow switches directly impacts the effectiveness of the routing strategies. Specifically, the fundamental mathematical structures of the formulated nonlinear problem and solution set are provided and accordingly, an efficient algorithm, called polynomial-time approximation algorithm (PTAA), is proposed to yield the fast convergence to a near optimal solution by employing the alternating direction method of multipliers (ADMM). Furthermore, the optimal controller placement problem in in-band mode is examined, which aims to find the optimal switch location where the controller can be collocated by minimizing the control message delay. While it is not widely researched except quantitative or heuristic results, a simple and efficient algorithm is proposed to guarantee the optimum placement with regards of traffic statistics. Simulation results confirm that the proposed PTAA achieves considerable delay reduction, greatly facilitating controllers traffic engineering in large-scale SDNs.

QoS-aware virtualization-enabled routing in Software-Defined Networks

Software-Defined Networking (SDN) has been recognized as the next-generation networking paradigm. It is a fast-evolving technology that decouples the network control plane from the data forwarding plane. A logically centralized controller is responsible for all the control decisions and communication among the forwarding elements. However, current traffic engineering techniques and state-of-the-art routing algorithms do not effectively use the merits of SDNs, such as global centralized visibility, control and data plane decoupling, network management simplification and portability. In this paper, a multi-tenancy management framework is proposed to fulfill the quality-of-services (QoSs) requirements through tenant isolation, prioritization and flow allocation. First, a network virtualization algorithm is provided to isolate and prioritize tenants from different clients. Second, a novel routing scheme, called QoS-aware Virtualization-enabled Routing (QVR), is presented. It combines the proposed virtualization technique and a QoS-aware framework to enable flow allocation with respect to different tenant applications. Simulation results confirm that the proposed QVR algorithm surpasses the conventional algorithms with less traffic congestion and packet delay. This facilitates reliable and efficient data transportation in generalized SDNs. Therefore, it yields to service performance improvement for numerous applications and enhancement of client isolation.

A framework for QoS-aware traffic classification using semi-supervised machine learning in SDNs

In this paper, a QoS-aware traffic classification framework for software defined networks is proposed. Instead of identifying specific applications in most of the previous work of traffic classification, our approach classifies the network traffic into different classes according to the QoS requirements, which provide the crucial information to enable the fine-grained and QoS-aware traffic engineering. The proposed framework is fully located in the network controller so that the real-time, adaptive, and accurate traffic classification can be realized by exploiting the superior computation capacity, the global visibility, and the inherent programmability of the network controller. More specifically, the proposed framework jointly exploits deep packet inspection (DPI) and semi-supervised machine learning so that accurate traffic classification can be realized, while requiring minimal communications between the network controller and the SDN switches. Based on the real Internet data set, the simulation results show the proposed classification framework can provide good performance in terms of classification accuracy and communication costs.

Magnetic Induction-Based Localization in Randomly Deployed Wireless Underground Sensor Networks

Wireless underground sensor networks enable many applications, such as mine and tunnel disaster prevention, oil upstream monitoring, earthquake prediction and landslide detection, and intelligent farming and irrigation among many others. Most applications are location-dependent, so they require precise sensor positions. However, classical localization solutions based on the propagation properties of electromagnetic waves do not function well in underground environments. This paper proposes a magnetic induction (MI)-based localization that accurately and efficiently locates randomly deployed sensors in underground environments by leveraging the multipath fading free nature of MI signals. Specifically, the MI-based localization framework is first proposed based on underground MI channel modeling with additive white Gaussian noise, the designated error function, and semidefinite programming relaxation. Next, this paper proposes a two-step positioning mechanism for obtaining fast and accurate localization results by: first, developing the fast-initial positioning through an alternating direction augmented Lagrangian method for rough sensor locations within a short processing time, and then proposing fine-grained positioning for performing powerful search for optimal location estimations via the conjugate gradient algorithm. Simulations confirm that our solution yields accurate sensor locations with both low and high noise and reveals the fundamental impact of underground environments on the localization performance.