Bellavista et al. EURASIP Journal on Wireless Communications and Networking
(2018) 2018:103
https://doi.org/10.1186/s13638-018-1119-0
RESEARCH
Open Access
Multi-domain SDN controller federation
in hybrid FiWi-MANET networks
Paolo Bellavista1, Carlo Giannelli2* , Thomas Lagkas3 and Panagiotis Sarigiannidis4
Abstract
Traditionally, mobile ad hoc networks (MANET) and hybrid optical-wireless networks, also known as fiber-wireless (FiWi),
have been considered disjointly and independently, with no synergic management solutions. The former has primarily
focused on dispatching packets among mobile nodes in infrastructure-less and very dynamic environments, the latter
on offering high-bandwidth and low-latency access to cellular-equipped mobile nodes. The recent advancements and
penetration of software-defined networking (SDN) techniques have stimulated the adoption of SDN-based flexible
monitoring and control also for the MANET and FiWi domains. In this perspective, the paper originally proposes a
model and an architecture that loosely federate MANET and FiWi domains based on the interaction of MANET and
FiWi controllers: the primary idea is that our MANET and FiWi federated controllers can seldom exchange monitoring
data and control hints the one with the other, thus mutually enhancing their capability of packet management over
hybrid FiWi-MANET networks. Our model is applied to several relevant use cases to practically point out the benefits of
the proposal in terms of both load balancing and fairness improvements.
Keywords: Software-defined networking, Multi-domain federation, MANET, Fiber-wireless
1 Introduction
To increase the quality of service (QoS) perceived by
mobile users, many industrial and academic research activities are pushing for two primary goals: high performance and dynamic management. In particular, mobile
and wireless networking is achieving relevant results towards two directions, each one considering specific
goals: on the one hand, large bandwidth and wide coverage by providing Internet access via infrastructure-based
hybrid optical-wireless networks; on the other hand, dynamic and flexible connectivity by supporting peer-topeer services via (partially) infrastructure-less multi-hop
mobile networks, e.g., mobile ad hoc networks
(MANETs) and vehicular ad hoc networks (VANETs),
also based on spontaneous sharing of computing/storage/networking resources provided by mass market offthe-shelf smartphones/devices.
The two directions are typically considered as separated domains and managed in a silos-based fashion.
Hybrid optical-wireless networks, also known as fiber* Correspondence: carlo.giannelli@unife.it
2
Department of Mathematics and Computer Science, University of Ferrara,
Ferrara, Italy
Full list of author information is available at the end of the article
wireless (FiWi) networks, aim at maximizing the offered
bandwidth to cellular-equipped mobile devices, considering as a block-box anything beyond the identity of
connected mobile devices, e.g., without any awareness of
the applications running on mobile devices or even of
the presence of other mobile devices that may potentially interact, also in a multi-hop spontaneous way. On
the contrary, spontaneous MANETs [1] aim at supporting multi-hop dispatching of packets among devices that
join/leave the network very dynamically, as well as the
flexible offering/invoking of peer-to-peer services, by
considering as a black-box the access network that provides them with Internet connectivity. In fact, mobile devices can traditionally gather very little information
about the access network status, with no possibility to
directly configure/tune it.
This silos-based approach has facilitated the development and management of the associated networks. In
addition, since FiWi and MANET domains are typically
managed in a different way (via traffic engineering teams
of network operators the former, in a completely decentralized and individual fashion the latter), considering
one another as a black-box was a sound and simplifying
assumption for integrated network management.
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
However, nowadays, this sharp separation does not allow
to fully exploit the potential of some novel scenarios demanding for both performance and flexibility, such as
the following:
Edge networks in Internet of things (IoT), where
several (usually fixed) devices with sensing and
actuation capabilities are inter-connected, by forming a multi-hop local network to dispatch and refine
raw data as well as to deliver commands among
nodes. In addition, some nodes are cellular-equipped
to dispatch packets back and forth to cloud-based
software components via the Internet. In this
scenario, FiWi-MANET federated management may
allow to dynamically reroute traffic among MANET
nodes based also on the current load of the
employed FiWi access network(s);
Sparse Smart Cities [2], characterized by users
willing to collaborate by allowing new applications
to be deployed on their smartphones to remotely
monitor and control fixed/mobile devices. For
instance, notable contributions in the recent
literature exploit node collaboration to support
better content delivery [3] and better distribution of
computational tasks [4]. In this case sparse nodes
collaborate with one another while moving; users
access different IP subnets, by increasing the
probability of spreading packets towards their
destinations. In this perspective, MANET and FiWi
domains can benefit from coordinating one another
to minimize performance degradations generated by
frequent mobile node movements (and related
disconnections/reconnections).
In addition, let us note that in such scenarios, there is
the need to consider end-to-end capabilities and requirements in line with recent 5G guidelines. According to
the latest specifications published by 3GPP about the
service requirements for the 5G system [5], the user
equipment (UE) should be able to support one or both
of the following connectivity models: traditional direct
network connection and/or indirect network connection
based on other UEs used as relays. Involved UEs can be
anything from legacy mobile devices to various types of
smart devices and sensors that operate in the context of
IoT. The relay devices are expected to be able to access
the network over 3GPP, through a wireless local area
network (WLAN) or via personal area network (PAN).
Under the indirect network connection scheme, a UE
should support discovery of relays in proximity, forming
that way a MANET. Moreover, the specific connectivity
model needs to allow connections in a visited public
land mobile network (VPLMN), when remote and relay
UEs are subscribed to different PLMNs with a roaming
Page 2 of 19
agreement. Further related considerations that have to
be made include battery optimization and subscriber
identity hiding of the relay UE. Hence, it becomes evident that the 5G vision dictates holistic management of
all involved network domains, from the core optical and
the wireless access segments to the interconnected
MANETs formed by end-users in an ad hoc manner.
The recent embracing of the software-defined networking (SDN) approach has vividly changed how the
two domains are managed. For example, the availability
of SDN controllers for FiWi networks [6] enables management mechanisms that can be used even from outside the access network itself, with proper techniques for
ensuring privacy of mobile users and correct management of network capabilities. At the same time, the
availability of an SDN controller in spontaneous networks allows to achieve a centralized management point
of view of the decentralized multi-hop network as well
as to more easily control it by adopting some mechanisms typical of traffic engineering [7].
By originally considering SDN techniques plus hybrid
FiWi-MANET networks, this paper proposes a novel
model to maximizing the QoS perceived by final users in
terms of load balancing and fairness, by considering the
state and requirements of both FiWi and MANET domains and by managing/controlling them accordingly. In
particular, we claim that to take full advantage of both
networks, there is the need to foster the interaction of
the two domains by supporting the loosely federation of
SDN controllers. In fact, while we agree that the two domains should be managed by different SDN controllers
with typically differentiated and specific optimizations,
we believe that enabling the exchange of information
and control data between them can relevantly improve
their management effectiveness, while keeping low the
needed growth of complexity.
In particular, the proposed model is based on distributed and different MANET and FiWi SDN controllers.
The former appropriately reroutes packets among
mobile nodes by considering the current MANET state,
application requirements, and the access network load
advertised by the FiWi SDN controller. The latter
detects eventually overloaded cellular base stations (BSs)
and reroutes traffic towards less loaded ones, by also
informing the MANET SDN controller to appropriately
manage the MANET data plane to dispatch packets to
the destination mobile node.
In addition, also to clarify the exact positioning of the
proposal if compared with the existing literature in the
field, the paper provides an original contribution in
terms of a novel taxonomy of state-of-the-art SDN approaches, by classifying them on the basis of their possible distribution, controller integration, management
openness, and domain heterogeneity. This taxonomy
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
also allows to clarify the need for further research activities in the area of multi-domain SDN controller federation in hybrid FiWi-MANET networks.
The remainder of the paper is structured as follows.
Section 2 describes our novel taxonomy for SDN controllers, while Section 3 sketches the background needed for
the full and easy understanding of our proposed model for
federated SDN controllers for hybrid FiWi-MANET networks (Section 4). Application use cases and traffic management modeling show how the proposed model and
architecture can apply to some practical scenarios of
growing relevance. Some practical implementation insights and challenges, as well as conclusive remarks and
directions of ongoing work end the paper.
2 A novel taxonomy for state-of-the-art SDN
controllers
At first, SDN has emerged primarily to manage switches
of closed and geographically centralized environments
such as datacenters and department networks via the
OpenFlow protocol [8], the de facto standard supported
by networking industrial-grade devices. Readers interested in a general presentation of the SDN architecture,
together with a thorough survey of SDN solutions applied to optical networks, are encouraged to refer to [9].
In particular, the centralized nature of the SDN approach makes it the natural choice for managing networks of small-to-medium size related to a single
organization. However, the adoption of the SDN approach has quickly proven its benefits also in different
scenarios with more relaxed requirements in terms of
closeness and geographical centralization. For instance,
SDN is exploited in wide area networks to efficiently
interconnect different datacenters [10, 11], eventually
based on a multi-controller SDN architecture [12]. In
addition, the SDN approach has been proposed also in
scenarios differing from traditional datacenters, such as
MANETs [13, 14], vehicular networks [15], naval systems [16], and access/transport networks [6, 10, 17].
Based on these considerations and with the main purpose of providing a wider point of view of the state-of-the-
Page 3 of 19
art, we propose to model the literature along four primary
directions: location distribution, controller integration,
management openness, and domain heterogeneity.
Location distribution refers to the geographical spreading of managed devices, deployed either in a relatively
small area such as a single datacenter or scattered in
multiple location such as different and geographically
distant departments of the same organizations.
Controller integration focuses on how multiple SDN
controllers, e.g., each one specialized for a different location, interact on each other to take proper decisions. For
instance, SDN controllers can be federated in a tight
fashion with a hierarchical architecture or loosely
coupled in a peer-to-peer fashion.
Management openness identifies if there is only one
organization in charge of managing the target environment (typically delving into close scenarios), such as a
single university, or if multiple operators collaborate to
provide the service, for instance multiple datacenters of
different vendors (with higher degrees of openness).
Note that in the latter case, it is required that each
organization provides some sort of controllability of
their own environments to other organizations.
Domain heterogeneity refers to the environments that
are managed within the same SDN-based solution that
can be of the same or different types. For instance, in
case of multiple datacenters federated via SDN, there is
no domain heterogeneity, while in case of an access network and a datacenter jointly managed by the same operator, there is domain heterogeneity.
Based on the four directions above, it is possible to
identify some primary research activity directions (see
Table 1):
Single datacenter: this is the simplest case where a
single datacenter is managed in an SDN-based
approach to optimize the intra-datacenter traffic,
either by adopting a single centralized SDN
controller or multiple SDN controllers;
Multiple datacenters with a single centralized
controller: in this case, there are multiple
datacenters in different locations, typically managed
Table 1 Primary clusters of the SDN literature based on the proposed model
Location distribution
Controller integration
Management openness
Domain heterogeneity
Single datacenter
No
No/tight
No
No
Multiple datacenters,
centralized controller
Yes
No
No
No
Multiple datacenters,
distributed controllers
Yes
Tight
No
No
Federated datacenters
Yes
Loosely
Yes
No
Inter-domain operator
Yes
Tight
No
Yes
Federated domains
Yes
Loosely
Yes
Yes
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
by a single cloud provider, and a single SDN
controller monitoring and optimizing the whole
inter-datacenter traffic. Note that there could be
multiple intra-datacenter SDN controllers, each one
managing the traffic of a single datacenter, but the
traffic among datacenters is engineered by only one
SDN controller;
Multiple datacenters with distributed controllers:
similarly to the previous case, there are multiple
datacenters in different locations managed by the
same cloud provider. However, each datacenter has
its own SDN controller managing the interdatacenter traffic. SDN controllers are tightly integrated, coordinating one each other to optimize
inter-datacenter traffic, e.g., either in a peer-to-peer
fashion or adopting a hierarchical architecture, in
the latter case with an SDN controller taking final
control decisions;
Federated datacenters: multiple cloud providers
manage their own datacenters in an independent
fashion, but the traffic among datacenters of
different cloud providers is engineered by adopting
an SDN approach, typically by loosely coupling SDN
controllers in a peer-to-peer fashion. In this case,
there is the need of opening the borders of datacenters by allowing external cloud vendors to control
(or at least to influence) how incoming and outgoing
traffic is managed;
Inter-domain operator: only one operator, e.g., a
telco company, is in charge of managing
heterogeneous domains, e.g., both a datacenter and
an access network. Typically, multiple SDN
controllers (each one dedicated to a single domain)
are tightly integrated and interact to optimize the
end-to-end QoS;
Federated domains: multiple actors are in charge of
managing heterogeneous domains with different
loosely integrated SDN controllers interacting to
optimize the end-to-end QoS. For instance, telco operators, service providers, and users manage datacenters, access networks, MANETs/VANETs
respectively with different SDN controllers interacting in a peer-to-peer fashion.
The traditional adoption of SDN in datacenters is
clearly evidenced by the many solutions already available
in the literature. For instance, considering the network
optimization of single datacenters, Cziva et al. [18] provide an SDN-based solution optimizing intra-datacenter
virtual machine (VM) migration by jointly considering
server and network resources. Son et al. [19] adopt the
SDN approach to optimize the energy consumption of a
datacenter by dynamically consolidating traffic to fully
exploit only part of network resources. Lu and Zhu [20]
Page 4 of 19
exploit SDN in datacenters to optimize TCP based on
information provided by OpenFlow-enabled switches.
Xie et al. [21] consider a more articulated scenario characterized by multiple SDN controllers in charge of virtualizing a single datacenter, by addressing the three
primary issues related to minimal coverage, minimal
fault-tolerant coverage, and minimal communication
overhead among controllers.
Considering the adoption of SDN to improve the QoS
of inter-datacenter traffic, some solutions adopt a centralized architecture with a unique controller. For instance, software-driven WAN (SWAN) [22] improves
inter-datacenter network exploitation by centrally tuning
when and how much traffic services generate and reconfiguring the data plane in relation to actual traffic demand. Wu et al. [11] address the issue of managing bulk
data transfers among geo-distributed datacenters hosted
by a single cloud provider. To this purpose, they propose
that a central SDN controller gathers information from
distributed gateways to optimally schedule interdatacenter transfers based on per-chunk routing choices.
However, many solutions adopt multiple SDN controllers to manage inter-datacenter traffic, such as multiple
datacenters with distributed controllers. To this purpose,
Ahmed and Boutaba [12] present multiple possible architectures, e.g., with either horizontal or vertical multicontroller architecture and based on either in-band or
out-of-band communication.
The federation of datacenters based on distributed SDN
controllers deployed and managed by different operators
is considered by Levin et al. [23], identifying as main requirements supporting the capability of inter-operating
among heterogeneous SDN controllers, VM migration
among clouds in a seamless fashion, and cross-cloud virtual networks that allow tenants to transparently exploit
multiple datacenters. CHIEF [24] supports the interoperability of community networks based on a controller farm
managing the federation of SDN controllers, with the goal
of enabling very large horizontal scalability, tenant isolation, and additional services such as billing. It is also interesting to note that the research activity in the SDN field is
also pushing for the deployment and joint management of
large testbeds, eventually distributed in multiple locations
[25]. Finally, Risdianto et al. [26] present how the adoption
of open-source software can leverage the federation of
SDN and cloud environments under separate administrative domains.
More recently, the state-of-the art literature is moving
towards the adoption of SDN in other domains. Alvizu
et al. [17] present a survey modeling the state-of-the-art
literature about SDN-based solutions managing heterogeneous transport networks based on monolithic,
hierarchical, and flat or mesh control plane architectures. Considering general purpose wireless networks,
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
Abolhasan et al. [27] propose to extend the SDN approach towards a centralized/distributed mixed architecture, with a centralized SDN controller gathering and
pre-processing information and several distributed nodes
(typically BSs providing connectivity) performing
decision-making and configuring the data plane of mobile nodes. Focusing on wireless sensor networks, Zhou
et al. [28] exploit the SDN approach to efficiently manage cooperative communication and task execution
among nodes. Some solutions in the wireless sensor network domain not only adopt the SDN approach, but also
exploit the OpenFlow-like protocols. For instance, SDNWISE [29] extends OpenFlow to optimize the communication among sensor nodes and the controller and to
program nodes as finite state machines. Anadiotis et al.
[30] adopt SDN-WISE to optimize the deployment of
MapReduce tasks among nodes, with the controller in
charge of actuating the data plane of nodes to better
route traffic from mappers to reducers. Luo et al. [31]
propose an extension (but backward compatible) of
OpenFlow to improve its flexibility, making it more appropriate for the inherent dynamicity of wireless networks. Lai et al. [32] and Fontes et al. [15] exploit SDN
to optimize vehicular networks, e.g., to deliver multimedia streams.
Even the SDN-based federation of heterogeneous domains is recently emerging, but usually in specific environments. For instance, Yu et al. [10] consider both
datacenter and optical network domains, by adopting a
multi-controller collaboration framework in charge of
managing not only network devices but also cloud-based
storage and computing resources. Similarly, Zhao et al.
[33] jointly manage datacenter resources, optical networks, and IP-based networks in a unified control system providing available capabilities based on a unified
resource description model. Finally, let us stress that we
envision that SDN-based federation of heterogeneous
domains will gain additional attention, based on the easier management of heterogeneous resources the SDN
approach leverages.
3 Background on FiWi and MANET
As already stated, this paper originally focuses on the challenging issues raised by the integration of hybrid FiWi and
MANETs through federated SDN controllers. In particular, we believe that providing (sub-)optimal QoS management by jointly considering the capabilities of access
networks and MANET nodes represents the primary aspect that has to be addressed, while we intend to integrate
the management of federated datacenters in our future research activity. Before presenting our proposed framework
in Section 4 and to facilitate its full and easy understanding, the section outlines the main characteristics of the
two target FiWi and MANET domains.
Page 5 of 19
3.1 Hybrid optical-wireless access networks
The integration of optical and wireless networks provides a cost-effective and flexible access network, which
combines the huge bandwidth potential of the optical
domain, in the backhaul, and the advantageous characteristics of the wireless networks, in the fronthaul, such
as mobility, reachability, roaming, and mobile services
provisioning. In essence, the integration of optical and
wireless domains in a single access network defines a
FiWi access network, which is divided into two main
categories based on the level of integration, namely
Radio over Fiber (RoF) and Radio and Fiber (R&F).
While the RoF concept has low practical value since it
entails complex PHY operations such as converged
modulation, coding, and transmission, R&F seems to be
much more functional. R&F paradigms allow flexible architectures without imposing serious modifications in
each domain. As a result, efficient and cost-effective topologies are feasible allowing an effective way of converging multiple types of optical with various wireless or
cellular technologies [34].
The R&F architecture comes with two main paradigms
in the literature: optical-wireless mesh networking and
optical-wireless (broadband) access networking (or hybrid optical-wireless access networking). In the former
case, several wireless routers and a number of gateways
are connected to an optical unit, e.g., to the optical network unit (ONU) in the case that a passive optical network (PON) is used as the main technology for the
backhaul of the network, and thus to the network backbone and the Internet. It is worth mentioning that this
kind of hybrid network introduces a routing subnetwork at the edges, where multiple wireless nodes
(smartphones, sensors, IoT devices, vehicles, and anything that is considered mobile and is distinguished with
an IP address) are indirectly connected to the optical
backhaul through multiple gateways and relay wireless
links. In the latter case, optical-wireless access networks
employ multiple users and nodes that are connected to a
hybrid BS which is equipped with two interfaces, the optical interface that terminates the optical fiber and the
wireless interface that provides a 4/5G radio interface
(cell, macrocell, or picocell). Details about the components of a hybrid optical-wireless FiWi architecture are
provided in [35].
In the context of this paper, the former paradigm is
adopted where multiple mobile users (or nodes) are connected with each other in an ad hoc basis. The optical
domain in the fronthaul is a PON infrastructure where
various PON technologies could be used, i.e., Ethernet
PONs, Gigabit PONs, or multi-wavelength PONs. In the
central office (CO) premises is deployed the optical line
terminal (OLT), the main decision-making component
of the optical domain. Next, the OLT is connected
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
directly with the passive splitter/combiner via optical
fiber, thus, single or multiple wavelength light-paths are
created between the CO and the edge of the optical network. As a result, a cost-effective topology, mostly a tree
topology, is realized that engages several merits such as
low maintenance, protocol transparency, and low operation cost. At the edge of the optical network, the conventional ONU, which is used in pure PONs, is replaced
by the enhanced ONU-BS which consists of two interfaces, namely an optical interface that interconnects the
ONU-BS with the OLT through optical fiber and a radio
interface, e.g., a long-term evolution (LTE) radio access
network. For instance, the architecture proposed in [6]
introduces an ONU-eNB, where the optical interface
supports an XG-PON system, while the radio interface
supports an LTE network. The evolved packet core
(EPC), as part of the LTE radio technology, is located at
the CO. Its architecture separates the user data (user
plane) and the signaling (control plane) to make the
scaling independent. Thus, telecom providers and operators could handle the channel and (cellular) network
configurations easily. In this way, two directions are defined, the downstream direction supporting 9.95328 Gbps
from the CO to the ONU-eNB and the upstream direction which supports either 2.48832 Gbps or even 9.
95328 Gbps in a symmetrical fashion.
Effective traffic engineering in FiWi networks is crucial
for the provision of advanced quality of experience
(QoE) to users of hybrid next-generation networks. A lot
of interest was recently attracted to resource allocation
techniques both at the optical as well as at the wireless
domains. Most of the related research endeavors adopt
the dynamic bandwidth allocation (DBA) approach to
improve QoS and energy efficiency in FiWi networks
[36]. Another relevant factor, which has a high impact
on traffic engineering, is resource allocation fairness,
which is the focus of the work in [37], while network
performance is maintained at high levels. Balancing fairness in bandwidth distribution with network efficiency is
also the primary aim of the DBA scheme proposed in
[38], which targets XG-PONs. At the wireless access domain, a number of techniques of issues for the provision
of QoS in heterogeneous wireless networks are presented in [39]. Game theory has arisen as a promising
approach for fair resource allocation to mobile stations,
as shown in [40] which introduced a related algorithm.
Energy efficiency is apparently of high importance for
the autonomy of mobile devices and a major consideration of modern bandwidth distribution schemes, such
as the medium access control protocol proposed in [41].
Finally, the integration of traffic engineering techniques traversing across the optical core and the wireless
access domains renders as an important challenge,
which is currently being addressed by the research
Page 6 of 19
community. Authors in [42] have devised a holistic resource allocation solution in optical-wireless networks,
focusing on balancing fairness and efficiency across
WiMAX and 10-EPON sectors. A key aspect of this
work is mapping service classes between optical and
wireless domains in an effort to provide end-to-end QoS
support. In more detail, unsolicited grant service (UGS),
real-time polling service (rtPS), and best effort (BE) traffic services of WiMAX are mapped to expedited forwarding (EF), assured forwarding (AF), and BE classes of
10G-EPON, respectively. The bandwidth distribution
process between the OLT and ONU-BSs is realized via
the multi-point control protocol (MPCP), which involves
the GATE and the REPORT control messages. The
former is used by the OLT to assign transmission opportunities to ONU-BSs, whereas the latter is used by
ONU-BSs to inform the OLT about its buffers’ size and
to ask for bandwidth allocation in the following frame.
The algorithm efficiently balances fairness and performance through an optimization scheme.
3.2 Spontaneous MANETs
The most relevant and specific property of multi-hop
spontaneous networks is that they originate from the
willingness of social interactions of people via impromptu interconnections of the personal devices they
carry, e.g., smartphones, tablets, and laptops [43]. In
spontaneous networks, devices discover and interact
with one another opportunistically and without any prior
mutual knowledge, by exploiting all supported connectivity opportunities, e.g., Wi-Fi or Bluetooth ad hoc links
and LTE infrastructure-based ones [1, 44]. In particular,
group-related behavior and the ever-increasing willingness to share rich user-generated contents, also pertaining to the personal sphere, calls for a user-centric
communication paradigm shift, where the ad-hoc interconnection of portable devices plays a central role. On
the one hand, the user-centric nature of spontaneous
networking partially relaxes the constraint of having
infrastructure-based communication support (e.g., anywhere cellular coverage, which is often expensive). On
the other hand, it naturally yields to very heterogeneous,
uncoordinated, and dynamic networking environments
where, for instance, any node can create its selfadministered layer2 links. In addition, spontaneous networking nodes are expected to be able to take advantage
of simultaneous exploitation of different communication
interfaces to join/create multiple IP networks (via either
ad hoc or infrastructure connectivity); these networks
are autonomously created, configured, and destroyed by
collaborating users in a completely decentralized way.
It is worth noting that, traditionally, spontaneous network nodes take management decisions based on their
limited scope visibility and without a global knowledge
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
of network topology/conditions, by typically reacting to
modifications in local resource availability. In fact, also
because of its general purpose and collaborative nature,
spontaneous networking has usually focused on simplifying the dispatching of packets at multi-hop distance,
eventually aiming at improving the QoS with a perapplication view [45]. From a wider point of view, the efficient application and optimization of traffic engineering
techniques have not been a primary topic in the MANET research area. While state-of-the-art literature recognizes the importance of improving QoS in MANET
scenarios, traditional traffic engineering solutions based
on strictly enforced resource allocation can be hard to
adopt, in particular because of the general-purpose nature and the limited resources available over MANET
nodes. In other words, MANET QoS has been mainly
addressed so far by only considering localized visibility
and decisions, e.g., based on link and path performance
[46]. Pease et al. [47] proposed a middleware solution to
support timely MANET communications based on an
adaptive approach where their solution reacts to dynamic network conditions by switching the employed
channels to ensure their optimal and robust exploitation.
Abuashour and Kadoch [48] explored VANET QoS issues: in particular, they aim at increasing path stability
and throughput, while reducing delay, by selecting cluster heads based on vehicles’ lifetime. Li and Shen [49]
focused on hybrid networks (MANET nodes plus a wireless infrastructure), by exploiting anycast communication and by modeling packet routing issues as resource
scheduling problems.
Differently from what already available in state-of-theart literature, we claim that the SDN approach very well
fits the dynamic and heterogeneous nature of spontaneous
networking. On the one hand, since spontaneous networking nodes interact to offer and access services in a
collaborative manner, there is no a priori knowledge of
service availability. Thus, it is suitable to have a centralized
point of view with full visibility, able to take proper control decisions. On the other hand, spontaneous networking nodes are willing to further cooperate to improve QoS
by better exploiting the currently available networking opportunities. In fact, based on their limited visibility of the
network, competing applications/nodes may exploit the
same (apparently best) multi-hop path, while erroneously
neglecting alternative paths that could be preferred because of more limited load. In other words, we claim that
the adoption of the SDN approach in spontaneous networking allows to gain deeper knowledge of the available
topology and of its state, as well as to consider application
requirements and to adapt packet dispatching mechanisms accordingly. Additional information on our mechanisms and strategies for SDN-based management of
spontaneous networks can be found at [7].
Page 7 of 19
4 Proposed methodology
As already anticipated, our target environment consists of
a FiWi domain (based on fixed hybrid optical-wireless access networks) and a MANET domain (based on multihop spontaneous networks). The former provides Internet
wireless access via cellular technology while ensuring huge
bandwidth, whereas the latter enables infrastructure-less
multi-hop communication among mobile nodes via, e.g.,
Wi-Fi Direct and Bluetooth. Mobile nodes exploit the
multi-hop MANET to interact with one another by
offering and invoking services in a peer-to-peer fashion
without any need of Internet connectivity, e.g., to stream
multimedia content among MANET participants. In
addition, some of the mobile nodes have Internet connectivity via cellular technology and can share it with other
neighbors, e.g., without cellular capabilities. The generated
traffic can be either complete within the MANET, i.e., involving only mobile nodes and not reaching the FiWi access network, or traversing the FiWi, i.e., generated/
received by a mobile node but also traversing the fixed
FiWi access network.
We claim that, to the purpose of effective QoS management, there are significant benefits in deploying two
different but cooperating/federated SDN controllers, i.e.,
the FiWi SDN controller and the MANET SDN controller, each one in charge of managing only a specific domain. Each SDN controller, by default, separately
manages traffic either within the MANET or in the FiWi
network. In this manner, it is possible to ensure QoS
while limiting the associated complexity/overhead, since
each SDN controller is in charge of collecting information and managing nodes of only a subset of the whole
network. Thus, usually SDN controllers manage their
own domain in an uncoordinated and independent
fashion, each one with silos-based visibility. On the one
hand, the FiWi SDN controller evaluates uplinkdownlink traffic in an aggregated fashion, without distinguishing among packets related to different mobile
nodes or different flows of the same mobile node, with
full compliance with 5G communication standards. On
the other hand, the MANET SDN controller manages
traffic in its multi-hop wireless domain by taking advantage of the greater flexibility that the MANET overlay
provides, e.g., by allowing to fully control how mobile
nodes dispatch packets/flows to their neighbors and also
by easily permitting to modify/upgrade the adopted
management mechanisms and policies.
4.1 Federated FiWi-MANET architecture
Figure 1 presents an overview of our proposed FiWiMANET architecture. The FiWi domain is divided into
three parts: the central office (CO), the backhaul/
fronthaul domain, and the radio access domain. Note
that the exact structure of the backhaul/fronthaul
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
Page 8 of 19
Fig. 1 FiWi and MANET domains combined into our federated architecture
domain depends on the location where the backhaul
data are decoded, so it is technology-specific. At the
edge of the network, mobile nodes form a multi-hop
spontaneous network (denoted as MANET) that can be
regarded as an independent domain, locally able to
dispatch packets without exploiting the cellular
infrastructure.
The FiWi domain consists of the OLT (located at the
CO), the powerless passive splitter/combiner, and hybrid
optical-wireless units at the fronthaul. Hybrid opticalwireless units are the evolution of the integration of the
traditional ONU, which terminates the PON by providing optical interface to the final users (realizing the Fiber
To The x—FTTx paradigm), and the BS which can be a
traditional 4G BS (a WiMAX BS or an LTE evolved
NodeB) or a 5G cloud radio access network (C-RAN) BS
with remote radio head (RRH). In the former case, various integration methods of the ONU and BS can be applied such as independent, hybrid, combined, unified,
and microwave over fiber (MoF) architecture [34].
In the context of this work, a simple integration
method, such as the independent architecture, is
adopted to form an integrated framework that allows
ONUs and BSs to be connected independently, e.g., by
using a single (bridging) cable. For example, assuming
that both devices apply a common protocol such as
Ethernet as the common interface protocol, no special
requirements are needed and no additional complex
hardware is necessary. Moreover, the recent proliferation of the C-RAN concept [50, 51], which was first introduced by IBM [52] and is based on the concept of
distributed wireless communication systems, established the separation of the BS in a radio unit, called
RRH, and a signal processing unit, called baseband unit
(BBU). While RRH offers the interface to the fiber and
performs digital processing, e.g., digital to analog conversion and vice versa, the BBU equipment can be
placed in a more flexible way, meaning that it is not
required to be co-located with the BS. As a result, a
cost-effective solution is provided where rental and
maintenance costs are reduced. In this way, traditional
ONU-BS devices are replaced by modern RRH and
BBU units towards the integration of the optical backhaul network with latest 5G standards. In addition,
traditional C-RAN with RRHs is further evolved by upgrading the concept of BBU deployment. While RRH is
placed together with the antenna at the fronthaul of the
FiWi network, BBUs that serve many RRHs are colocated in a pool. This pool can be located far away
from the RRH location such as the CO premises. As a
result, energy and cost savings are permitted while the
control and data planes can be smoothly separated. In
the light of the aforementioned aspects, Fig. 1 shows
how traditional (e.g., 4G LTE) and the evolved (e.g., 5G
C-RAN) technologies can be co-located in the same
federated network.
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
4.2 Application use cases and primary technical
challenges for FiWi-MANET federation
This section presents how the joint management of FiWi
and MANET domains based on federated SDN controllers can improve the overall QoS by achieving better
load balancing and higher fairness.
4.2.1 Suitability of federation for load balancing purposes
Considering load balancing, Fig. 2 outlines the case of the
detection and management of a traffic flow coming from
the Internet and directed to a mobile node. At first, the
traffic flow is dispatched to the destination node towards
the shortest path but involving the already heavily loaded
RRH unit on the center. After a while, the FiWi SDN controller detects it and informs the MANET SDN controller
that the traffic flow will be shortly redirected towards the
bottom RRH unit. Then, the MANET SDN controller selects an LTE-equipped mobile node connected to the bottom RRH, e.g., based on current traffic and CPU load,
appropriately manages the MANET data plane from the
selected mobile node to the destination one, and replies to
the FiWi SDN controller with the network address of the
LTE-equipped mobile node connected to the bottom RRH
unit in charge of receiving the flow from the access network. Finally, the FiWi SDN controller reroutes the traffic
flow towards the longer but less loaded bottom path.
In this example, it is possible to note that FiWi and MANET SDN controllers interact and collaborate by sharing
some control plane information and (indirectly) triggering
Fig. 2 Load balancing scenario
Page 9 of 19
data plane modifications in each other’s control plane. In
fact, the former selects the less loaded RRH unit based on
information only available in the FiWi domain. Then, the
latter selects the less loaded LTE-equipped mobile node
(among the ones connected to the given RRH unit) based
on control plane information only available in the MANET domain. Finally, the MANET SDN controller appropriately manages its own data plane and notifies the FiWi
SDN controller about the LTE-equipped mobile node it
has to reroute the traffic flow to.
Delving into finer details, we identify two primary usecases where the federation and collaboration of MANET
and FiWi SDN controllers can relevantly increase the
QoS by achieving load balancing: (i) an application ontop-of a mobile node makes available its applicationspecific requirements and (ii) an unusually huge traffic
flow traverses the FiWi network. These cases can be addressed with either a proactive application-driven or a
reactive access network-driven approach (Figs. 3 and 4).
In case of proactive application-driven inter-domain
management, an application running on top of a mobile
node proactively declares that it is going to generate a
traffic flow, i.e., before packets are actually transmitted.
To this purpose, the application:
1. Informs the local control agent (CA) about the
type of traffic it is going to generate, e.g.,
multimedia stream with a given bitrate or file
transfer of a given size;
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
Fig. 3 Proactive application-driven sequence diagram
Fig. 4 Reactive network-driven sequence diagram
Page 10 of 19
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
2. The local CA informs the MANET SDN controller;
3. The MANET SDN controller informs the FiWi
SDN controller, the latter replying with information
about the best (e.g., since less loaded) RRH
(ONU-BS) unit to exploit;
4. Then, the MANET SDN controller manages the
MANET data plane, e.g., to route the forthcoming
traffic flow towards an LTE equipped, not currently
overloaded mobile node currently served by the
indicated RRH (ONU-BS) unit;
5. At the same time, the FiWi SDN controller manages
the backhaul to maximize the QoS of the
forthcoming traffic flow, e.g., by making the
appropriate bandwidth slots reservations in the PON,
which interconnects the gateway RRH (ONU-BS) to
the central OLT, as well as efficiently allocating
OFDMA (orthogonal frequency division multiple
access) resources at the corresponding LTE eNB.
It is worth noting that, since applications explicitly indicate their own requirements, MANET and FiWi SDN
controllers are able to finely tune the data plane. In fact,
by specifying application requirements of single flows,
the management of forthcoming packets can be optimized. For instance, in case of long lasting multimedia
streams with limited bitrate it is better to exploit low latency and reliable paths. On the contrary, to transfer
huge files it would be better to exploit paths with broad
bandwidth, even if available only for a relatively small
time period [53].
In case of reactive access network-driven inter-domain
management, a traffic flow is reactively detected by the
FiWi SDN controller, i.e., the traffic is not advertised by
any mobile node. In this case,
1. The FiWi SDN controller detects that some notable
traffic with a given flow id has just started
traversing the access network towards the MANET;
2. The FiWi SDN controller informs the MANET
SDN controller about the traffic flow also specifying
the destination mobile node;
3. Then, the MANET SDN controller notifies the
FiWi controller about one or more LTE equipped
mobile nodes that could efficiently receive the
traffic and dispatch it towards the destination
mobile node;
4. Finally, the FiWi SDN controller reroute the traffic
towards one of the LTE-equipped mobile nodes
suggested by the MANET SDN controller.
Let us stress that in this approach the destination mobile node is completely unaware of the data plane management, since every traffic engineering procedure is
done by MANET and FiWi SDN controllers in a
Page 11 of 19
completely transparent fashion. However, since in this
case there is no a priori awareness of application
requirements, it is possible to manage traffic only in a
reactive fashion.
4.2.2 Suitability of federation for fairness purposes
The scenario in Fig. 5 tries to highlight the benefits of
our proposed model and architecture to the purpose of
fairness. Assuming that each RAN defines an area of
mobile and ad hoc nodes, an interesting challenge
emerges in case it is required to fairly distribute bandwidth among the different areas, given that the
allocated bandwidth is controlled by the backhaul
(optical) network. This challenge is also in line with the
latest recommendations of the Telecommunication
standardization sector of the International Telecommunication Union (ITU-T) regarding the next-generation
PONs (NG-PONs), where the provisioning of fairness
in allocating bandwidth to the various ONUs is of paramount importance. In other words, the provisioning of
fairness in the different MANET areas, as marked in
Fig. 1, highly depends on the bandwidth allocation decisions of the OLT. Recent advances in that direction
[42]indicate that the fairness provisioning is strongly
influenced by the number of mobile devices that are
connected in each cell. Hence, considering that MANET nodes are either directly or indirectly connected
to cells, the fair bandwidth distribution in MANET
areas is highly affected by the bandwidth allocated to
each cell. As a result, SDN controllers at MANETs
could monitor the traffic received and inform corresponding FiWi SDN controllers for keeping a fair
bandwidth distribution among MANET nodes (see
Section 5 for details about our MANET SDN controller
architecture).
Cooperation between RRHs (ONU-BSs) and
MANET nodes is also needed to support QoS in
terms of high-throughput, low latency, and dynamic
traffic scheduling. Figure 6 depicts a case where the
uplink-downlink ratio of the RRH (ONU-BS) devices
is configurable, allowing traffic engineering capabilities
in the underlying FiWi-MANET federated network.
Considering that both WiMAX and LTE technologies
allow the dynamic configuration of the uplink-todownlink ratio, dynamic decisions on adjusting the
ratio in RRH (ONU-BS) devices enable better resource allocation in cells and MANET areas based on
the ongoing data traffic conditions. For example, [6]
introduces an optical-wireless access architecture
where SDN controllers monitor the traffic conditions
in both optical and wireless domains to dynamically
adjust the uplink-to-downlink configuration of the
LTE framing in ONU-eNB. This is beneficial for the
whole network, since it can determine the most
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
Page 12 of 19
Fig. 5 Fair bandwidth allocation scenario among various MANET areas
appropriate uplink-to-downlink ratio, e.g., by using a
machine learning algorithm. The specific concept is
also beneficial for the MANET network in the
introduced FiWi-MANET architecture since traffic
conditions in MANET nodes could be also recorded
using SDN controllers. Therefore, the MANET traffic
condition information can be also used in RRHs as a
feedback towards adjusting the uplink-to-downlink
ratio, in a different way in each RRH based on the
ongoing downlink and uplink traffic loads.
Fig. 6 Traffic engineering paradigm by adjusting the uplink-to-downlink ratio in the fronthaul network to support traffic engineering in the
MANET domain
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
5 Results on modeling and managing traversing
traffic in our architecture
To enable the proper management of the traffic related
to different nodes/applications, packets related to the
same content, e.g., different packets carrying parts of
the same file or of the same audio/video stream, are
grouped into a given traffic flow. MANET and FiWi
data planes are able to detect that different packets
belong to the same application flow by exploiting a flow
identifier. To this purpose, packets dispatched by the
MANET overlay network are managed by the Real Adhoc Multi-hop Peer-to-peer (RAMP) middleware and
tagged with specific flow ids at the application layer, i.e.
, exploiting a RAMP packet header field (additional details in [7]). Instead, the FiWi data plane exploits the
IPv6 Flow Label header field to identify packets related
to the same flow. Mobile nodes equipped with cellular
capabilities managing traversing traffic between FiWi
and MANET domains are in charge of modifying
packets to maintain flow id information, i.e., forging
IPv6 packets with the appropriate Flow Label in case of
traffic from the MANET to the FiWi and specifying the
appropriate application layer flow id in RAMP packets
for traffic from the FiWi to the MANET domain.
By specifically focusing on the traffic traversing the
access network, we adopt differentiated management
for regular and special traffic. The former is managed
in a best-effort but very dynamic way, e.g., by adopting machine learning to improve network utilization
(like the DIANA solution [6]). In this case, traffic
from/to mobile nodes is monitored in an aggregated
fashion and the FiWi SDN controller does not need
to differentiate among flows to/from different nodes.
In fact, in this case, since applications have not indicated any specific requirement, the goal is to correctly
dispatch packets without any differentiated management. The latter is managed in a quality-aware way,
since either an application has specified requirements
that the forthcoming traffic flow should fulfill or the
traffic flow greatly differs from the regular pattern
and thus should be appropriately managed to reduce
its (potentially) negative impact on the remainder of
the traffic. The guideline is to manage only “elephant
flows,” with the associated overhead, but not “mice
flows.” In particular, elephant flows consist of relatively huge packets and/or last for a long-time period,
thus can impose a relevant load to the network and
their appropriate management can improve overall
QoS in a very significant way. On the contrary, mice
flows are composed by small packets and are usually
short lived; their impact on network performance is
singularly limited and, moreover, the overhead due to
control message exchange can impose an overhead
greater than the traffic flow itself.
Page 13 of 19
Let us stress that even if FiWi and MANET SDN
controllers are logically centralized for each domain
(abstracted as a single point of control in an interdomain perspective), their implementation may be centralized or distributed (see Fig. 7). On the one hand, the
FiWi SDN controller typically consists of multiple controllers, i.e., a centralized OLT SDN controller and multiple ONU-BS SDN controllers. However, the former is
in charge of taking final traffic engineering decisions,
while the latter are in charge of collecting information,
sending them to the OLT SDN controller, and receive/
apply decisions of the former. On the other hand, multiple distributed SDN controllers may be employed in
MANET as well. In fact, the dynamic nature of spontaneous networks (if compared with traditional datacenters)
prevents from the adoption of a unique truly centralized
MANET SDN controller in charge of monitoring and
managing hundreds or thousands of nodes. Each distributed MANET SDN controller has the primarily goal of
enforcing QoS in its spontaneous island, i.e., a subset of
nodes residing at relatively small multi-hop distance and
sharing common interests (thus, with a relevant difference if compared with Open Network Operating System—ONOS [54], where a logically centralized SDN
controller is distributed mainly to increase scalability
and fault tolerance). Moreover, distributed SDN controllers of neighbor spontaneous islands eventually coordinate one each other to also support (as a secondary goal)
QoS of inter-island traffic flows, generated by sender/receiver nodes residing in different spontaneous islands
(additional details in [7]). For instance, Fig. 7 outlines
three MANET SDN controllers, one for each ONU-BS,
deployed on-top-of an LTE-equipped mobile node.
6 Discussion on paving the way towards
implementation: practical considerations and
technical challenges
Enrolling SDN in integrated hybrid domains, such as the
FiWi-MANET scenario, is still an early stage research
field, hence, it is necessary to thoroughly consider several practical aspects and to discuss some open technical
challenges related to efficient implementation. In fact,
towards the full implementation and experimentation of
our proposal over deployment environments of industrial interest, as Table 2 points out, several open issues
call for further investigation:
OpenFlow compatibility. OpenFlow has been
developed to demonstrate the feasibility of shaping a
unified control and management framework.
Currently, many OpenFlow functionalities are
available, e.g., load monitoring, finding the shortest
way in a routing network, and providing per-flow
control in a network. Nevertheless, new extensions
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
Page 14 of 19
Fig. 7 Distributed SDN deployment and implementation in the FiWi-MANET architecture
of OpenFlow are needed to enable advanced capabilities, as discussed in Section 4, for example advanced
bandwidth allocation strategies based on traffic prediction schemes;
Global vision. SDN controllers should have a global
vision of the MANET topology (lightweight
coordination among MANET islands). Also, they
should be able to monitor traffic flows in the
MANET domain in real-time;
SDN-based traffic monitoring in the MANET
domain. Smartphones and tablets consume 40% of
the data traffic and generate 99% of the signaling
traffic [55]. Such high levels of traffic entail efficient
bandwidth allocation in the CO. Hence, SDN
capabilities have to be enhanced in order to support
real-time traffic monitoring in MANET networks so
as to provide a global and accurate traffic status in
the backhaul domain (optical network);
RAN topology information. SDN controllers should
be able to acquire a global network view in terms of
topology, uplink/downlink traffic flows and
requirements, granted and required traffic per
domain, and throughput and latency monitoring. In
fact, in this manner, they would be able to acquire
full knowledge about the current network status as
well as about the forthcoming traffic generated by
applications, thus allowing to take proper traffic
management decisions. In [56], a fine-grained network resource allocation framework is provided;
however, this framework is limited to LTE cellular
networks. A wider cooperation of SDN controllers is
needed to ensure that a global view in terms of
Table 2 Open implementation issues for federated FiWi-MANET deployments
Scenario
Covered by OpenFlow
Features
What is needed to be addressed
Evolving
Impact
Load balancing
Yes
High-throughput
Flow tables installed in devices
Fast
MANET: high
FiWi: high
Fair bandwidth
allocation
No
Fairness
Hardware extensions to support SDN
deployment in MANET devices and
OpenFlow extensions to provide fairness
monitoring
Not so fast
MANET: high
FiWi: medium
Traffic engineering
No
Low latency,
low jitter
OpenFlow extensions to support more
complicated decisions
(e.g., LTE framing characteristics adjustment)
Slowly
MANET: high
FiWi: medium
Energy management
No
Energy savings
Hardware abstractions to support power
management
Very slowly
MANET: high
FiWi: medium
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
bandwidth allocation is feasible for the whole network. By achieving this, fairness could be ensured by
monitoring and adjusting fairness indices either per
cell or per MANET domain;
Additional resources. MANET devices and EPC
servers are likely to need more resources to store
the required information about the networking state
under its domain of control. These resources may
include flow entries, data traffic indices, traffic
history, and bandwidth allocation matrices.
Consequently, appropriate memory and CPU
capabilities are required to store such information
and to calculate the adequate handling for each
session [57];
Hardware abstractions. To support advanced PHY
modifications based on information acquired by
SDN controllers, new hardware abstractions are
needed. Devices that should be adjusted to provide
sophisticated capabilities, e.g., dynamic bandwidth
allocation and uplink-to-downlink modifications,
have to be enhanced with additional hardware abstractions that SDN controllers may use and exploit.
Page 15 of 19
The realization of the FiWi-MANET federated architecture requires advanced collaboration between the respective SDN controllers. Figure 8 presents an
architectural block diagram of the FiWi-MANET SDN
inter-connection network: all SDN controllers are interconnected through an inter-controller interface; the
manager can abstract all technological domains in the
underlying FiWi-MANET network and can configure
network elements by using flow tables and advanced
OpenFlow extensions for providing inter-controller policies. In finer detail, network management takes place
centrally and is performed by the network operator, such
as a telecommunications company or an Internet service
provider (ISP), which has control over the whole infrastructure, including the optical core and the wireless access domain. Representative involved technologies are
variants of PONs (e.g., XG-PON, NG-PON, 10G-EPON,
25G-EPON) as well as variants of cell standards (e.g.,
LTE, LTE-A, LTE-A Pro). In the MANET side, different
versions of ad hoc mesh networking technologies can be
used, such as schemes based on IEEE 802.11 or IEEE
802.15. Depending on the tenants’ real-time
Fig. 8 An SDN controller management network. A management policy is applied to the SDN network through a management computer located
at the CO
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
requirements and/or predefined service-level agreements
(SLAs), the manager applies end-to-end policies over the
FiWi-MANET architecture. These policies are mainly
enforced through targeted resource allocations for traffic
engineering in various parts of the involved network domains, enabling the formation of agile data paths. The
establishment, maintenance, and adaptation of routes
are realized through SDN commands in SDN-capable
networking devices, issued by the FiWi SDN controller
in collaboration with the MANET SDN controller.
The use of OpenFlow extensions for the implementation of the introduced inter-domain federated SDN control is the most dominant approach, due to the growing
interest attracted by the OpenFlow standard. In the following, we present three related solutions. NETCONF
[58] is a promising protocol for network management
defined by the Internet Engineering Task Force (IETF).
It relies on remote procedure calls (RPCs) to allow the
configuration of various networking devices, such as
routers, switches, and firewalls. It has been shown to
produce less control overhead, but also to achieve lower
bandwidth utilization, if compared to OpenFlow [59].
The Open vSwitch Database Management Protocol
(OVSDB) is a network configuration solution based on
OpenFlow designed for managing Open vSwitch [60].
The latter constitutes a virtual switch that allows the
automation
of
networking
processes
through
Fig. 9 Federated tasks performed by FiWi and MANET SDN
controllers (SCs) to provide load balancing
Page 16 of 19
management protocols, such as NetFlow [61]. According
to OVSBD, each SDN controller can manage multiple
virtual switches, while each one of the latter can be
managed by multiple controllers. The OpenFlow Configuration and Management Protocol (OF-CONFIG) has
been lately defined by the Open Networking Foundation
(ONF) as the fundamental mechanism for configuring
vSwitches through OpenFlow SDN controllers [62]. A
detailed review of SDN management approaches can be
found in [63].
Here, we present some implementation fundamentals
of the functions demonstrated in Section 5 in the context of specific scenarios. Specifically, the flow charts in
Figs. 9, 10, and 11 show the course of tasks that need to
be implemented to provide load balancing, fairness, and
traffic engineering respectively, as they were illustrated
in the corresponding scenarios. Specifically, Fig. 9 corresponds to the load balancing scenario depicted in Fig. 2
and presents the course of actions to be followed to
avoid collided paths and to select less loaded ones from
the CO to the MANET through the FiWi domain.
Figure 10 presents the involved flow of actions for fairly
allocating bandwidth among multiple RRHs, in line with
the scenario illustrated in Fig. 5. Lastly, the traffic engineering scenario presented in Fig. 6 can be realized
through the execution of the sequence of tasks presented in Fig. 11, allowing the adaptation of the uplinkto-downlink sub-frames ratio at the RRH, according to
the respective traffic requirements.
Fig. 10 Federated tasks performed by FiWi and MANET SDN
controllers (SCs) to provide Fairness
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
Page 17 of 19
head; rtPS: Real-time polling service; SDN: Software-defined networking;
SLAs: Service-level agreements; SWAN: Software-driven WAN; UE: User
equipment; UGS: Unsolicited grant service; VANET: Vehicular ad hoc network;
VM: Virtual machine; VPLMN: Visited public land mobile network;
WLAN: Wireless local area network
Funding
The work by Carlo Giannelli has been partially funded by “Fondo per
l’Incentivazione alla Ricerca” (FIR)—Year 2017—University of Ferrara—Prot. n.
126136 del 30.10.2017.
Authors’ contributions
All the authors have equivalently contributed to the manuscript, with a
strong focus of PB and CG on related work modeling and MANET and a
prevalent contribution of TL and PS on FiWi and use cases. All authors read
and approved the final manuscript
Fig. 11 Federated tasks performed by FiWi and MANET SDN
controllers (SCs) to conduct Traffic Engineering
7 Conclusions
The paper presents a novel architecture allowing to improve end-to-end QoS in hybrid MANET-FiWi environments by loosely integrating SDN controllers in charge
of monitoring and controlling each domain. In particular, by allowing to share monitoring information among
MANET and FiWi SDN controllers, we have shown the
potential to take better control decisions, e.g., to achieve
better load balancing and/or fair exploitation of resources based on requirements specified by applications
running on top of mobile nodes. The encouraging preliminary results already achieved are stimulating our ongoing research work. We are mainly working on the
development of a proof-of-concept prototype to apply
the proposed architecture in ETSI mobile edge computing (MEC) networks, in particular to allow the efficient
“elephant-oriented” dispatching of multimedia streams
generated by surveillance cameras.
Abbreviations
AF: Assured forwarding; BBU: Baseband unit; BE: Best effort; BS: Base station;
CA: Control agent; CO: Central office; C-RAN: Cloud radio access network;
DBA: Dynamic bandwidth allocation; EF: Expedited forwarding; EPC: Evolved
packet core; FiWi: Fiber-wireless; IETF: Internet Engineering Task Force;
IoT: Internet of things; ITU-T: International Telecommunication Union;
LTE: Long-term evolution; MANET: Mobile ad hoc networks; MoF: Microwave
over fiber; MPCP: Multi-point control protocol; NG-PONs: Next-generation
PONs; OF-CONFIG: OpenFlow Configuration and Management Protocol;
OLT: Optical line terminal; ONF: Open Networking Foundation; ONOS: Open
Network Operating System; ONU: Optical network unit; OVSDB: Open
vSwitch Database Management Protocol; PAN: Personal area network;
PON: Passive optical network; QoE: Quality of experience; QoS: Quality of
service; R&F: Radio and Fiber; RAMP: Real Ad-hoc Multi-hop Peer-to-peer;
RoF: Radio over Fiber; RPCs: Remote procedure calls; RRH: Remote radio
Authors’ information
Paolo Bellavista (paolo.bellavista@unibo.it) is an associate professor of mobile
and distributed systems at the University of Bologna, Italy. His research activities
span from mobile computing to pervasive ubiquitous middleware, from
vehicular sensor networks to big data adaptive stream processing and adaptive
scalable multimedia, from IoT middleware for cloud integration to resource
management in SDN, with specific focus on scalability aspects in wide-scale
smart city deployment environments. He has published more than 70
magazine/journal articles and more than 90 conference/workshop papers in
those fields, reporting results from several national- and EU-funded projects. He
serves on the Editorial Boards of IEEE TNSM, IEEE TSC, Elsevier JNCA, Elsevier
PMC, Springer WINET, and Springer JNSM. See also http://lia.disi.unibo.it/Staff/
PaoloBellavista/ for additional details and the complete publications list.
Carlo Giannelli graduated from the University of Bologna (Italy), where he
received a Ph.D. in computer engineering in 2008. He is now an assistant
professor in computer science at the University of Ferrara (Italy). His
primary research activities focus on software-defined networking,
heterogeneous wireless interface integration, and hybrid infrastructure/ad
hoc and spontaneous multi-hop networking environments based on social
relationships. See also http://docente.unife.it/docenti-en/carlo.giannelli?se
t_language=en for additional details and the complete publications list.
Dr. Lagkas is a full-time lecturer at the International Faculty of the University
of Sheffield, CITY College, since 2012. He is the research director of the
Computer Science Department and leader of the ICT Research Track at the
South-East European Research Centre, since 2017. He serves as the Chair of the
Faculty’s ICT Committee. He has been an adjunct lecturer at the Department of
Informatics and Telecommunications Engineering, University of Western
Macedonia, Greece, from 2007 to 2013. He has also served as Laboratory
Associate and Scientific Associate at the Technological Educational Institute of
Thessaloniki from 2004 to 2012. His research and teaching mainly focus on the
Computer Networks scientific field. He has worked as a freelancer computer
programmer; he is an IEEE and ACM member and fellow of the Higher
Education Academy in UK.
Dr. Lagkas’ research interests are in the areas of wireless communication
networks, QoS in medium access control, mobile multimedia communications,
power saving/fairness ensure for resource allocation in wireless sensorcooperative-broadband networks as well as in hybrid fiber-wireless networks,
IoT distributed architectures, e-health data monitoring, 5G systems, and
computer-based educational technologies with relevant publications at a
number of widely recognized international scientific journals and conferences.
He has edited two books titled “Wireless Network Traffic and Quality of Service
Support: Trends and Standards” and “Evolution of Cognitive Networks and
Self-Adaptive Communication Systems”.
He also participates in the Editorial Boards of the Computer Networks
Journal (published by Elsevier), the Telecommunication Systems Journal
(published by Springer) and the EURASIP Journal on Wireless
Communications and Networking (published by Springer).
Dr. Panagiotis Sarigiannidis is an assistant professor in the Informatics and
Telecommunications Department of University of Western Macedonia,
Kozani, Greece, since 2016. He received the B.Sc. and Ph.D. degrees in
computer science from the Aristotle University of Thessaloniki, Thessaloniki,
Greece, in 2001 and 2007, respectively. He has published over 100 papers in
international journals, conferences, and book chapters. He has been involved
in several national, EU, and international projects His research interests
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
include optical and wireless networks, fiber-wireless networks, traffic
engineering, optimization, scheduling, resource allocation, and development
of analytic frameworks for systems, and applications.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
1
Department of Computer Science and Engineering, University of Bologna,
Bologna, Italy. 2Department of Mathematics and Computer Science,
University of Ferrara, Ferrara, Italy. 3Computer Science Department, The
University of Sheffield International Faculty, CITY College, Thessaloniki,
Greece. 4Department of Informatics and Telecommunications Engineering,
University of Western Macedonia, Kozani, Greece.
Received: 15 January 2018 Accepted: 20 April 2018
References
1. P Bellavista, A Corradi, C Giannelli, “The Real Ad-Hoc Multi-Hop Peer-To-Peer
(RAMP) Middleware: An Easy-To-Use Support for Spontaneous Networking”,
15th IEEE Symposium on Computers and Communications (ISCC'10) (RiccioneRimini, 2010)
2. P Bellavista, C Giannelli, S Lanzone, G Riberto, C Stefanelli, M Tortonesi, A
middleware solution for wireless IoT applications in sparse Smart Cities.
Sensors 17, 11 (2017)
3. L Zhou, D Wu, Z Dong, X Li, When collaboration hugs intelligence: content
delivery over ultra-dense networks. IEEE Communications Mag 55(12), 91–
95 (2017)
4. L Zhou, D Wu, J Chen, Z Dong, When computation hugs intelligence:
content-aware data processing for industrial IoT. IEEE Internet of Things
Journal. https://doi.org/10.1109/JIOT.2017.2785624
5. V16.1.0, “Technical specification group services and system aspects, service
requirements for the 5G system”, Stage 1, Release 16, Sep. 2017.
6. P. Sarigiannidis, A. Sarigiannidis, I. Moscholios, and P. Zwierzykowski, “DIANA:
a machine learning mechanism for adjusting the TDD uplink-downlink
configuration in XG-PON-LTE systems Mobile Information Systems,”. 2017.
7. C Giannelli, P Bellavista, D Scotece, Software defined networking for
quality—aware management of multi-hop spontaneous networks (Int. Conf. on
Computing, Networking and Communications (ICNC, Maui, 2018)
8. Open Networking Foundation: OpenFlow. Available online at https://www.
opennetworking.org/sdn-resources/openflow (last Accessed on 14 Nov 2017).
9. AS Thyagaturu, A Mercian, MP McGarry, M Reisslein, W Kellerer, Software
defined optical networks (SDONs): a comprehensive survey. IEEE
Communications Surveys & Tutorials 18, 4 (2016)
10. Y Yu et al., Field demonstration of multi-domain software-defined transport
networking with multi-controller collaboration for data center interconnection.
IEEE/OSA Journal of Optical Comm. and Networking 7, 2 (2015)
11. Y Wu et al., Orchestrating bulk data transfers across geo-distributed
datacenters. IEEE Trans. on Cloud Computing 5, 1 (2017)
12. R Ahmed, R Boutaba, Design considerations for managing wide area
software defined networks. IEEE Comm. Magazine 52, 7 (2014)
13. P. Bellavista, A. Dolci, and C. Giannelli, “MANET-Oriented SDN: motivations,
challenges, and a solution prototype”, 19th IEEE Int. Symp. On a World of
Wireless, Mobile and Multimedia Networks (WoWMoM 2018), June 2018
(accepted for publication) (Chania)
14. H.C. Yu, G. Quer, and R.R. Rao, “Wireless SDN mobile ad hoc network: from
theory to practice”, 2017 IEEE International Conference on Communications
(ICC), 21–25 2017.
15. R Dos Reis Fontes et al., “From theory to experimental evaluation: resource
management in software-defined vehicular networks”, IEEE Access, vol 5 (2017)
16. K. Lee et al., 2017, “Optimal flow rate control for SDN-based naval systems”,
IEEE Trans. On Aerospace and Electronic Systems, https://doi.org/10.1109/
TAES.2711679 (to be published).
17. R. Alvizu, et al., “Comprehensive survey on T-SDN: software-defined
networking for transport networks”, IEEE Comm. Surveys & Tutorials, https://
doi.org/10.1109/COMST.2017.2715220 (to be published).
Page 18 of 19
18. R Cziva, S Jouët, D Stapleton, FP Tso, DP Pezaros, SDN-based virtual
machine management for cloud data centers. IEEE Trans. Netw. Serv.
Manag. 13, 2 (2016)
19. J Son, AV Dastjerdi, RN Calheiros, R Buyya, SLA-aware and energy-efficient
dynamic overbooking in SDN-based cloud data centers. IEEE Transactions
on Sustainable Computing 2, 2 (2017)
20. Yifei Lu, Shuhong Zhu, “SDN-based TCP congestion control in data center
networks”, 34th IEEE Int. Performance Computing and Communications
Conference (IPCCC), 2015.
21. Junjie Xie, Deke Guo, Xiaomin Zhu, Bangbang Ren, and Honghui Chen,
"Minimal fault-tolerant coverage of controllers in IaaS datacenters", IEEE Trans.
Serv. Comput., https://doi.org/10.1109/TSC.2017.2753260 (to be published).
22. C-Y Hong, S Kandula, R Mahajan, M Zhang, V Gill, M Nanduri, R Wattenhofer,
“Achieving high utilization with software-driven WAN”, SIGCOMM Comput.
Commun. Rev. 43, 4 (2013)
23. A. Levin, K Barabash, Y. Ben-Itzhak, S. Guenender, and L. Schour,
“Networking architecture for seamless cloud interoperability”, 8th IEEE Int.
Conf. On Cloud Computing (CLOUD), 2015.
24. P. Kathiravelu, L. Veiga, “CHIEF: controller farm for clouds of softwaredefined community networks”, 2016 IEEE Int. Conf. On Cloud Engineering
Workshop (IC2EW), 2016.
25. T Huang et al., A survey on large-scale software defined networking (SDN)
testbeds: approaches and challenges. IEEE Comm. Surveys & Tutorials 19, 2
(2017)
26. Aris Cahyadi Risdianto, Teck Chaw Ling, Pang-Wei Tsai, Chu-Sing Yang, and
JongWon Kim, “Leveraging open-source software for federated multisite SDNcloud playground”, 2016 IEEE NetSoft Conf. and Workshops (NetSoft), 2016.
27. M Abolhasan et al., Software-defined wireless networking: centralized,
distributed, or hybrid? IEEE Netw. 29, 4 (2015)
28. J Zhou et al., “SDN-based application framework for wireless sensor and actor
networks”, IEEE Access, vol 4 (2016)
29. L Galluccio et al., “SDN-WISE: design, prototyping and experimentation of a
stateful SDN solution for WIreless SEnsor networks”, IEEE Conf. On Computer
Communications (INFOCOM) (2015), pp. 513–521
30. ACG Anadiotis et al., An SDN-assisted framework for optimal deployment
of MapReduce functions in WSNs. IEEE Trans. on Mobile Computing 15, 9 (2016)
31. T Luo et al., Sensor OpenFlow: Enabling software-defined wireless sensor
networks. IEEE Comm. Letters 16, 11 (2012)
32. C-F Lai, Y-C Chang, H-C Chao, M Shamim Hossain, A Ghoneim, A bufferaware QoS streaming approach for SDN-enabled 5G vehicular networks.
IEEE Commun. Mag. 55, 8 (2017)
33. Yongli Zhao, Jie Zhang, Lingnan Gao, and Hui Yang, “Unified Control
System for Heterogeneous Networks with Software Defined Networking
(SDN)”, 8th Int. ICST Conf. on Communications and Networking in China
(CHINACOM), 2013.
34. AG Sarigiannidis, M Iloridou, P Nicopolitidis, G Papadimitriou, FN Pavlidou,
PG Sarigiannidis, MD Louta, V Vitsas, Architecture and bandwidth allocation
schemes for hybrid wireless-optical networks. IEEE Comm. Surveys &
Tutorials 17, 1 (2015)
35. P Sarigiannidis, T Lagkas, S Bibi, A Ampatzoglou, P Bellavista, Hybrid 5G
optical-wireless SDN-based networks, challenges and open issues. IET
Networks 6, 6 (2017)
36. J Liu, H Guo, H Nishiyama, H Ujikawa, K Suzuki, N Kato, New perspectives on
future smart fiwi networks: scalability, reliability, and energy efficiency. IEEE
Communications Surveys Tutorials 18, 2 (2016)
37. X Bai, A Shami, C Assi, On the fairness of dynamic bandwidth allocation
schemes in ethernet passive optical networks. Comput. Commun. 29, 11
(2006)
38. P Sarigiannidis, G Papadimitriou, P Nicopolitidis, E Varvarigos, K
Yiannopoulos, “Towards a Fair and Efficient Downlink Bandwidth Distribution
in Xg-Pon Frameworks”, 17th IEEE Mediterranean Electrotechnical Conf.
(MELECON) (2014), pp. 49–53
39. T.D. Lagkas, P. Angelidis, and L. Georgiadis, (Editors), “Wireless network traffic
and quality of service support: trends and standards,” IGI Global Inc., ISBN:
9781615207718, (Hershey, 2010)
40. SP Algur, NP Kumar, Novel user centric, game theory based bandwidth
allocation mechanism in wimax. Human-Centric Computing and
Information Sciences 3, 1 (2013)
41. TD Lagkas, GI Papadimitriou, P Nicopolitidis, AS Pomportsis, A new
approach to the design of MAC protocols for wireless LANs: combining QoS
guarantee with power saving. IEEE Comm. Letters 10, 7 (2006)
Bellavista et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:103
42. A Sarigiannidis, P Nicopolitidis, Addressing the interdependence in
providing fair and efficient bandwidth distribution in hybrid optical-wireless
networks. Int. J. Commun. Syst. 29, 10 (2016)
43. LM Feeney, B Ahlgren, A Westerlund, Spontaneous networking: an
application oriented approach to ad hoc networking. IEEE Communication
Magazine 39, 6 (2001)
44. P Bellavista, A Corradi, C Giannelli, Middleware for differentiated quality in
spontaneous networks. IEEE Pervasive Computing 11, 3 (2012)
45. P Bellavista, A Corradi, C Giannelli, Middleware-layer quality-aware
collaborative re-casting of live multimedia in multi-hop spontaneous
networks. J. Netw. Syst. Manag. 23, 3 (2015)
46. P Bellavista, A Corradi, C Giannelli, Differentiated management strategies for
multi-hop multi-path heterogeneous connectivity in mobile environments.
IEEE Trans. on Network and Service Management 8, 3 (2011)
47. A Abuashour, M Kadoch, Performance improvement of cluster-based
routing protocol in VANET. IEEE Access 5, 15354–15371 (2017)
48. SG Pease, IW Phillips, L Guan, Adaptive intelligent middleware architecture
for mobile real-time communications. IEEE Trans. Mob. Comput. 15, 3 (2016)
49. Z Li, H Shen, A QoS-oriented distributed routing protocol for hybrid wireless
networks. IEEE Trans. on Mobile Computing 13, 3 (2014)
50. A Checko, HL Christiansen, Y Yan, L Scolari, G Kardaras, MS Berger, L
Dittmann, Cloud RAN for mobile networks—a technology overview. IEEE
Comm. Surveys & Tutorials 17, 1 (2015)
51. M Peng, Y Sun, X Li, Z Mao, C Wang, Recent advances in cloud radio access
networks: system architectures, key techniques, and open issues. IEEE
Communications Surveys & Tutorials 18, 3 (2016)
52. Y Lin, L Shao, Z Zhu, Q Wang, RK Sabhikhi, Wireless network cloud:
architecture and system requirements. IBM J. Res. Dev. 5(1), 4:1–4:12 (2010)
53. P Bellavista, A Corradi, C Giannelli, Differentiated management strategies for
multi-hop multi-path heterogeneous connectivity in mobile environments.
IEEE Transactions on Network and Service Management (IEEE TNSM) 8, 3
(2011)
54. P Berde et al., ed. by ONOS: Towards an Open, Distributed SDN OS, Work.
on hot topics in software defined networking (ACM, Chicago, 2014)
55. M.R. Sama, S.B. Hadj Said, K. Guillouard, and L. Suciu, “Enabling Network
Programmability in LTE/EPC Architecture Using OpenFlow”, 12th Int. Symp.
On Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
(WiOpt), 2014.
56. K Samdanis, R Shrivastava, A Prasad, D Grace, X Costa-Pereza, TD-LTE virtual
cells: An SDN architecture for user-centric multi-eNB elastic resource
management. Comput. Commun. 83, 1 (2016)
57. S.B. Hadj Said, M.R. Sama, K. Guillouard, L. Suciu, G. Simon, X. Lagrange, J-M
Bonnin, “New Control Plane in 3GPP LTE/EPC Architecture for On-Demand
Connectivity Service”, IEEE 2nd International Conference on Cloud
Networking (CloudNet), 2013.
58. R Enns, M Bjorklund, J Schoenwaelder, A Bierman, Network configuration
protocol (NETCONF). IETF RFC 6241, 1–113 (2011)
59. T Kunz, K Muthukumar, Comparing OpenFlow and NETCONF when
interconnecting data centers. IEEE 25th Int. Conf. on Network Protocols
(ICNP) (Toronto, 2017)
60. B Pfaff et al., “The Design and Implementation of Open vSwitch”, 12th USENIX
Conf. On Networked Systems Design and Implementation (NSDI ‘15) (USENIX,
Oakland, 2015)
61. J. Suárez-Varela, P. Barlet-Ros, “Towards a NetFlow Implementation for
OpenFlow Software-Defined Networks”, 29th Int. Teletraffic Congress (ITC
29), 2017.
62. T. Čejka, R. Krejčí, “Configuration of Open vSwitch Using OF-CONFIG”, 2016
IEEE/IFIP Network Operations and Management Symposium (NOMS 2016),
Istanbul, 2016.
63. E Rojas et al., Are we ready to drive software-defined networks? A
comprehensive survey on management tools and techniques. ACM
Computing Surveys (CSUR) 51, 2 (2018)
Page 19 of 19