Cross-Layer Software Defined Networks: A Survey

Cross-Layer Software Defined Networks: A Survey I. Al Shiab, Student Member, IEEE, Carleton University  Abstract—Cross-Layer designs has been successfully implemented in many wireless networks with significant performance improvement compared to the traditional layered architecture. Such designs allow more information sharing between different layers in the same system or even between different related systems. Software Defined Networks (SDN) has recently gained a lot of momentum due the flexibility it offers by creating re-programmable networks that adapt quickly and efficiently to the changing demands of today networks. SDN decouples the control and data planes and assign the control task to a logically-centralized controller that has a global overview of the whole network. Motivated by the great benefits of both paradigms, many researchers have recently tackled the idea of creating cross-layer SDN architectures to solve challenges in different applications. This paper surveys the state-of-the-art efforts that has been done in this direction with emphasis on the benefits gained and the future research areas in this field. Index Terms—Cross-layer design, software defined networks, software defined radio, 5G, control plane, data plane.

To solve the challenges faced with traditional layered models and explore the benefits gained by cross-layer design, many valuable efforts have been done. Such efforts are surveyed and classified in [1] and [2]. Additionally, a lot of work has been recently done to explore the different and variant applications of SDN in the different areas. Such work has been surveyed and classified in [3]-[5]. However, and to the best of our knowledge, there were no dedicated efforts that has been done in studying the combination and applications of cross-layer and SDN together. Such a study would give an idea about the benefits of the combined architectures and open the door for future efforts. The remaining of the pare is organized as follows: in Section II, a background information is presented about Crosslayer and SDN. Section III classifies the different efforts in Cross-Layer SDN architectures per the application and highlights the benefits gained. Section IV discusses the issues and future research areas in the field and we conclude in Section V.

I. INTRODUCTION

I

nformation hiding between the different layers in classical TCP/IP protocol stack is considered a main feature that guarantees the abstraction between the different layers[1]. Only minimal information is shared and just between the adjacent layers. Despite that layer strict encapsulation creates easy deployable networks, it hides some important information from being messaged between the different layers [2]. Strict abstraction does not necessarily yield the best performance in all applications and may causes side effects [1]. Cross-Layer designs have been implemented widely in wireless networks and intensively in cellular networks. Successful implementations proved that the traditional layered architecture is not always desirable as it hides important information from other layers. Such information can be used to optimize the overall system performance rather than restricting the optimization in a single layer. Software Defined Networks (SDN) is an emerging and promising network architecture that make networks easy reconfigurable and allow the network administrators to apply network level policies [3]-[5]. In SDN, a physically or logically cartelized controller can be programmed with policies that are translated into actions and rules by the controller and communicated with the networking devices.

II. BACKGROUND To procced with the remaining parts of the paper, we start with a background about the main concepts in Cross-Layer design and Software Defined Networks. A. Cross-Layer Design The layered protocol architecture in wireless communication systems offers a huge simplicity and a lot of advantages. To achieve the system's end-to-end goal, each layer is assigned one or more roles to play. In general, layers of a stack are designed independently such that no interactions are found between different layers. For example, in the TCP/IP protocol stack and the OSI reference model [6], each layer is given a set of roles to play without a regard to information provided from other layers. Each layer only deals with the protocol data units (PDUs) from the other layers as a black box. This solution simplifies the design and offers compatibility across different communication systems. However, the performance of these systems will not be optimal. To enhance the performance, a more complex cross-layer designs are needed. In cross-layer design, the dependence found across different layers’ roles is exploited [7]. Moreover, a layer design parameters may depend entirely on the information available from other layers. Fig.1 shows sample cross-layer design.

Fig. 1: Cross-Layer Design

A cross-layer design for different systems is studied extensively in the literature. In [2], authors studied the different cross-layer designs in wireless communication systems. The cross-layer designs may be classified based on the network organization to centralized and distributed methods or based on the way the information is exchanged between layers into non-manager and manager methods. In the non-manager methods, the data is exchanged between layers directly. A layer in this method can send some information or parameters for another layer, and that layer uses this information to adapt to changes (like wireless channel conditions) in the network [31]. However, in manager methods, the data is not exchanged between layers directly, but it is shared between some layers and a vertical plane. In centralized method, a central node, like the base station in cellular network, or hierarchical tires must exist. The centralized nodes manage the data sharing between different layers in different nodes. In distributed methods, there is no need for centralized nodes or hierarchical tires. The data is shared between layers in different nodes directly or in a multihop path. In [8], some cross-layer designs regarding the mobile wireless communication systems are surveyed. Four coordination planes are targeted through literature; security, quality-of-service (QoS), mobility, and wireless link adaptation. Cross-Layer designs are classified depending on the place of entity performing actions. If the entity resides in the affected node protocol stack, then it is called an internal cross-layer design. However, if the entity resides in different node, then it is referred to as an external cross-layer design. Fig.2 shows different classifications for cross-layer design approaches. Cross-Layer design may contribute in system improvement from many different angles. For example, in [9], the author discusses using cross-layer design for routing in ad hoc network. The interaction between the physical, the data link, and the network layer is exploited to make to develop an energy-efficient routing. In [10], the author study a cross-layer design to enhance the handover process in critical applications like in train control. The interaction of the physical layer on the handover decisions are studied to improve the train control performance.

Fig. 2: Classification of Cross-Layer Designs

In [11], the interaction between the application layer and handover decisions are studied to enhance video delivery by controlling the handover. In [12], the CDMA wireless mobile communication network utilization is enhanced using a new approach for admission control regarding variable bit rate multimedia traffic. The approach exploits effect of physical layer parameters, such as outage probability, on the admission process. In [13], the TCP performance for cognitive radio networks, [32], is optimized using cross-layer designs. In such work, many factors, like spectrum sensing, access decision, physical layer modulation and coding scheme, and data link layer frame size, are considered to maximize the TCP throughput. B. Software Defined Networks (SDN) Networks are usually designed based on a predefined policies and protocols. It is not possible to make changes as a response for any network failures, which is usually needed in some cases. Software Defined Network (SDN) is a paradigm that separate the control from the data plane in the networking devices, like routers and switches, and centralizing the control in an SDN controller. Having this solution, the network can be flexible to respond and adapt to any network failure or changing demands in the underlying network parts. Having a centralized controller in SDN gives the ability for the network operator to make changes and modifications so easily without having to make these changes on each part of the network separately [3] - [5]. In [3], the authors provide a detailed comprehensive survey about SDN concepts, challenges, and opportunities. In [5], the authors discuss a variety of advantages for SDN paradigms, such as intelligence, performance enhancement, and easy management. Moreover, the idea of SDN can be generalized for other applications in the network filed like cloud computing and network virtualization. In SDN, the control plane is separated from the data plane using application programming interfaces (APIs).

Fig. 3: Data and Control in Traditional Networks

The centralized controller is able to control the data plane elements [30]. OpenFlow is a well-known example of an API that is used for this purpose. In [4], authors conduced a full review regarding the early programmable networks like Open Signaling, Active Networking, DCAN, 4D Project, NETCONF, and Ethane. SDNs have some important characteristics. First, control and data planes are separated. Second, the data packets move through switches based on the flow tables and not based on their destinations. Third, an external utility manage the control and it is referred to as SDN controller. Finally, SDNs are programmable networks via software based applications. Hence, these characteristics determine the architecture of the SDNs. An SDN consist of three main parts; SDN controller, switching devices, and flow-entries. A switch is based on the flow not on the destinations, i.e., acts like a filter. Moreover, it consists of three elements; flow tables, communication channels, and OpenFlow protocol [3] - [5]. The architecture of the traditional network compared to the new SDN paradigm is shown in Fig.3 and Fig.4. The controller manages and controls all the switches in the network and can make modifications to the flow-entries in the flow tables. Each flow-entry consist of one or more network operation per a flow item. There two types of control in SDNs [4]; centralized and distributed control. In centralized control, the OpenFlow switches are controlled via one physical SDN controller. However, in the distributed solution, an OpenFlow switch are controlled via multiple controllers that still form a logically centralized controller. Having the logically-centralized solution prevent from having a single point of failure, and hence, add robustness to the SDN.

III. CROSS-LAYER SOFTWARE DEFINED NETWORKS APPLICATIONS

Recently there has been a lot of research in SDN networks. Also, there is a lot of research in cross-layer designs and mainly in the wireless and cellular networks. However, research combining the two fields together is recent and not easy to classify. In this section, we classify the state-of-the art research done in the combination of these two fields based on the application. A. Indoor Wireless Communication Cross-Layer SDN architectures can help in providing the increasing demands for indoor high data rate and strict QoS

Fig. 4: SDN Architecture

requirements. In [14], the authors propose cross-layer software defined architecture to be employed with the HetNets, [34], and high frequency waves (30-300 GHz) known as mmWave, [33], in indoor applications. The new proposed design aims to solve the challenges of the mmWave networks like the interference management, blockage problems, strict QoS requirements and load balancing. This opens the door for such architectures to be employed in the future 5G networks, [35], for indoor applications. In their architecture, they decouple the control functions for all layers from PHY to the Network layer and assign it to logically centralized software-defined controller. The architecture defines four interfaces between the controller and the data plane/network: Measurement Interface that used by the controller to collect network states and application information, The Control Interface used by the data plane devices ,like access points and gateways, to apply the control decisions from the controller, the Network Layer Interface responsible of network level functionalities like handover, resource allocation, and user association with different access points, and finally the Physical Interface in charge of selecting the physical layer parameters like the modulation and coding schemes based on different factors like channel conditions, beamforming, power control and other physical layer conditions. The Evaluation of the proposed architecture in [14] was conducted in real network scenario. The throughput was calculated for the Access Points, as an indication of the aggregate network throughput for the whole network, and for hosts downlink flows, as an indication of the user performance enhancement and always compared with traditional. As per the authors, results showed significant enhancement in the networks and host throughput. This can be reasoned to the intelligence added by the new design that was able to detect cases of traffic reduction/blockage and adapt to it accordingly using handover or other mechanisms. Despite this, we see that the authors’ architecture needs to be tested in more scenarios and different home structures with variety of obstacles. The testing was done in a pre-designed environment and to have more accurate results, we suggest to re-apply the study in different environments and compare the results and maybe do some tuning for the system. In [15], Authors propose the first cross-layer scheme between the application and the MAC layers in the Li-Fi, [37], and Wi-Fi, [36], networks.

SDN controller and SDN agents communicate to decide and configure the best parameters for Li-Fi and Wi-Fi MAC layers that best support the different user traffic demands and guarantee high quality. SDN platform make it possible to integrate the different technologies with different multiple access techniques represented by CSMA/CA in WiFi 802.11 standard and OFDMA in Li-Fi. User traffic can be bandwidth and strict QoS demanding as high definition real-time video, streaming and Video on-demand, or voice, and even normal Internet traffic. The proposed approach in [15] employs Flow Admission Control (FAC) to the different flows from different applications using the cross-design between the application and MAC layers and SDN paradigm to apply differentiated admission control that guarantees QoS requirements or at least doesn’t exceed upper limits. The design, including WiFi and Li-Fi access points and SDN implementation, is simulated with NS-3 and results were post-processed in MATLAB. The simulation was conducted in different scenarios. Results show that for both Voice and video flows, the per-flow throughput became almost independent on the network utilization compared to the traditional method that doesn’t apply Flow admission control where the throughput showed sudden decrease after certain level of network utilization. The authors conclude with the necessity of applying MAC Layer admission control to support real-time flows and maintains an acceptable level of QoS for such applications and in the same time, offering acceptable data rates to the normal traffic flows. We see that the proposed work is very innovative and utilize the SDN to integrate different technologies for indoor applications and achieve diversity with apparent efforts in the simulation. However, we propose to conduct real implementations and evaluate the results. B. Applications in Software Defined Radio(SDR) and 5G Software Defined Radio and Software Defined Networks are getting more importance as the solution to solve the big challenge of high demand on spectrum and bandwidth in future 5G networks [16]-[18]. Combining these two paradigms grant high flexibility in generating and re-allocating Physical and MAC layer resources in a software basis without the need for dedicated complex additional hardware [18]. Cross-layer design is needed to integrate the SDR and SDN together. Both SDN and SDR allow reconfiguration of parameters such as frequency bands, in SDR, and selecting different paths, in SDN, which finally creates adaptive environment that best react based on the channel and network conditions [16]. In [16], the authors propose a hybrid architecture that combines SDR and SDN. The architecture is composed of the SDR Layer, SDN Layer, and the a cross-layer controller that spans across the previously mentioned layers. Their work aims to support the admission and resources allocation in the future crowded 5G networks. Also, the work aims to efficiently utilize the precious spectrum. As per the authors and to the best of their knowledge at the paper writing time, there was no 5G simulator to evaluate the proposed architecture. As of this, they conducted MATLAB simulation to trade-off between results when using SDR only, using SDN only, using SDR with SDN (proposed architecture), and without using SDN or

Fig. 5: Hybrid architecture SDN and SDR in CrossFlow

SDR. Results showed that without the network monitoring and administration achieved by the SDR and SDN, the bandwidth utilization was very poor. Also, the insertion time needed by a service to enter the network and the latency became shorter with the proposed architecture. Additionally, increasing the number of users and the interference resources had much less effect on the total throughput with the new design. This can be attributed to the new design that has overall view of the flow conditions and available resources that enables it to grant better/less occupied channels to the new users or services. That is done after referring to the SDN flow data to originally grant access to new flows. In [17], authors introduce initial SDN architecture that support mobility toward 5G networks. The architecture introduced focuses on achieving Coordinated Scheduling and Beamforming functions using the SDN global view and control over the network. Additionally, it separates the radio access from the network functions by implementing two logically centralized controllers one for the radio access and another for the network. Authors revealed that the radio access controller needs a lot of efforts to become practical implementation while the network controller is similar to the ones implemented in data-centers. However, the framework presented is a valuable one and supports cooperative radio. In [18], authors introduced CrossFlow as a real implementation cross-layer design that combines SDR and SDN together. The proposed design used GNU Radio toolkit [27], which provides basic DSP building blocks and can run on a host or embedded computer, and CPqD SoftSwitch [28] with OpenFlow, and the RYU SDN controller. Both the CPqD and the GNU were installed on the USRP E310 model from Ettus Research with the needed messaging between the USRP and the SoftSwitch. The SoftSwitch and the SDN controller communicated through the OpenFlow protocol. The purpose of the proposed architecture is a proof-of-concept for the ability to manage configurable SDRs through extending existing SDN models. Fig.5 shows the hybrid architecture of SDN and SDR. The work done in [18] proved the ability to control heterogonous networks of wireless, Software Defined Radios and Wired using the principles of SDN. The concept of SDN helped in creating the needed abstractions. Authors plan to extend their work by implementing a complete system of GNU SDRs managed by SDN.

C. Applications in Cloud Computing & Data Centers The flexibility and features introduced by SDN made it a great target to enterprises, data centers and cloud service providers [38]. For example, SDN can help in detecting security issues like Denial of Service attacks i9n cloud computing [39]. Also, it helps in load balancing in Data centers ,[40], and in traffic steering in multitenant virtualized networks [41]. Here, we focus on the cross-layer implementations in cloud and data centers. Reconfigurable applications are becoming increasingly important in cloud computing and enables service providers to offer services ondemand basis [19]. In [20], authors propose an innovative idea that coordinate the scheduling between the SDN scheduler and the Application/Task scheduler using cross-layer design between the application and network layers. The proposed idea is tested in a real cluster scenario Hadoop and Storm at the application and Fat-tree and Jellyfish at the network layers. Also, the idea is tested in simulation mode and in different cloud topologies. In their implementation, Authors modified the Nimbus and YARN cloud schedulers to contact the SDN controller to obtain network topology information and use this information in doing better orchestration between the application level scheduling and network level routing. Results showed benefits for both SDN and application schedulers. Also, the proposed method improved the throughput by about 26% 34% based on the topology with better robustness in case of link failure. In [21], authors introduce a cross-layer design framework for control coordination between the application and network layers for resource orchestration. Despite that similar previous work has been conducted in research project like EuQos and GEYSERS, the authors proposed framework relies on NETCONF and RSVP protocols rather than the less supported and less common protocols between network devices represented by SIP, NSIS, and COPS protocols. The framework was evaluated using test-bed with realistic cloud-based examples. The authors work revealed that the router configuration time has a significant effect on the performance. However, this effect can be minimized by relying on anycast routing in clouds. The integration of SDN and cross-layer made it doable to conduct resource provisioning across different network domains like clouds and data centers. In [22], a cross-layer design with SDN is emulated using Mininet prototype. The idea is to support the increasing demand of Multicast in Datacenters with Optical multicast communication. Everything is emulated inside a Mininet VM using Electrical switch to abstract the traditional datacenter and emulated optical switch with optical power splitters. Additionally, the prototype employs SDN controller on host machine and NAT to allow all servers including the nonroutable ones to communicate with the controller. Authors could calculate the response time for requests to be less than 20ms. In addition to the fast response time, they were able to break down the latency time. Despite that the work done was emulation only and not real implementation, the results shows that the proposed architecture enhances the response time.

D. Network Monitoring & Management SDN is becoming a common approach for network management while Mininet is a strong SDN emulator [23]. In [23], Authors uses both SDN and Mininet with CrossLayer to buildup on a previous work conducted in [24]. The idea behind both works [23] and [24] is reducing the amount of broadcast in Data centers that adopts high speed Layer 2 switching. While authors in [24] Introduced the PMAC as a hierarchical pseudo MAC address with proposing a Manager to the addresses, authors in [23], builds up on it and apply the Fabric manager in SDN controller and OpenFlow protocol. The integration of SDN controller helped a lot in reducing the broadcast and specially the ARP broadcast, and eliminates the need for STP protocol at layer 2. Authors in [23], are planning to complete another work with SDN and OpenFlow and measure the performance in flow basis. Network management and data gathering is essential to enhance the total performance of a network and achieve load balancing by updating the flow tables of certain OpenFlow switches to reduce the load on congested paths which needs SDN and cross-layer to be doable. In [25] the authors utilize the flexibility offered by the innovative new designs of coherent transceivers and employ it in their proposed cross-layer design named ORCHESTRA. These coherent transceivers can adapt to a good range of physical layer parameters like modulation, FEC, different center wavelengths, symbol rate, and others forming a software defined optical performance monitors(Soft-OPMs). In their architecture, a cycle of Observe, Decide, and then Act working on enhancing the efficiency of the Optical network by collecting detailed information about the physical layer (observe phase) and adapt the actions accordingly. The authors recommend such structures to achieve cross-layer optimization that leads to better adaptive and scalable networks. E. Network Resilience & Fault-Tolerance One advantages for using SDN is that the network can be reconfigured to adapt, overcome, or prevent actions that lead to network failures. This special characteristic of a network is referred to by resilience. In [26], some strategies to ensure resilience are discussed. In SDN, it is convenient to adapt and to reconfigure the network since this all can be done through the centralized SDN controller. In [27], the authors discuss the traffic resilience in communication networks. Usually, the network and transport layers handle the traffic. This cross-layer functionality may degrade system overall performance. Hence, using SDN networks to expose the routing planes to the upper transport layer enhances the performance. This adds the ability to change the routing plane to another in order to adapt an endto-end failure in delivery. Due to increasing deployments of SDN networks, multicast services are getting harder to be delivered. In [28], the authors propose an architecture that functions at application, transport, and data-link layers. The proposed architecture is referred to by Multicast push unicast Pull (MPUP). The studied mechanism offers the ability to use multicast, such as OpenFlow Multicast (OFM), for reliable data delivery. The

mechanism is based on using reliable file multicast transfer protocol (FMTP), where multicast push with user datagram protocol (UDP) and unicast pull with TCP are used. In [29], the author studies resource provisioning based on the user demand, which will enhance costumers' experience. This idea needs a cross-layer design between the application and the network layers. The cross-layer orchestrator (CLO) studied will provide the applications running at the server and the client sides the ability to control network resources. In other words, network resources are dynamically assigned based on user demands. During session establishment, the cross-layer interface communicates the requirements of the application to the network control layer. The main requirement for network resilience is having a complete overview of the network and fast response to any fail-over in the network paths. This can’t be done without the SDN controller and cross-layer design. The SDN controller has the overall network overview while the cross-layer design achieves the needed messaging between the network and transport layers to provide end-to-end path failure recovery.

layer design with SDN has been surveyed in this paper. In all the surveyed efforts, the cross-layer helped in providing the SDN controller with a detailed and better overview of the network and the network-devices with its different layers. This helped in having better decisions from the SDN controller and faster and more robust adaptation to the dynamic networking conditions. On the other hand, SDN decouples the control and the data plane and gives the needed abstractions for effective cross-layer designs. Integrating both paradigms together could solve challenges and achieve optimizations that was not doable without both technologies and even with individual implementation of any of the paradigms. However, there is a lack of simulation and emulation tools for some applications, like 5G, for example, and a less amount of real experiments. There is a need to have more testing and real implementations for the proposed architectures with testing in a variety of reallife scenarios to have more accurate evaluation and discover any tuning needed.

REFERENCES IV. OPEN RESEARCH AREAS As the new paradigm integrates cross-layer design and SDN together, all the challenges and future research in every field individually is part of the research areas for this topic. In cross-layer design this includes the cost of cross-layer signaling, the coexistence of cross layer design without a unified platform, more of application-based, the complexity arises because of destructing the layered architecture. In SDN, this research areas include having more intelligent flow tables and rules management, the scalability of the controller and weather it should be centralized physically or logically, the need for highly flexible language of abstraction, and the security issues. In addition to that, new research areas arise from the combination of the two fields including having more simulation and emulation tools to test any new architectures and evaluate the actual performance enhancement rather than only having proof-of-concept. The added cost, signaling and control by the new design need also to be researched deeply for a fair trade-off with the solutions that doesn’t apply any of the new paradigms. Applications in fields like mmWaves, SDRs, Virtualization and Network Slicing, are very helpful in supporting the 5G technology and more research and real experiments are needed to explore the complete benefits of such paradigms on developing 5G. With Internet of Things(IoT) we are approaching more to cloud computing and Fog computing and Bigdata. Research in implementing SDN with cross-layer may add a lot in this field.

V. CONCLUSION Cross-layer design has been implemented in multiple systems and yields in enhancing the system performance. SDN is a very promising architecture that grants the flexibility and re-programmability of future networks. Implementing Cross-

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