Software-defined wireless mesh networks for internet access sharing
Transcript of Software-defined wireless mesh networks for internet access sharing
Computer Networks 93 (2015) 359–372
Contents lists available at ScienceDirect
Computer Networks
journal homepage: www.elsevier.com/locate/comnet
Software-defined wireless mesh networks for internet
access sharing
Ahmed Abujoda a,∗, David Dietrich a, Panagiotis Papadimitriou a,Arjuna Sathiaseelan b
a Institute of Communications Technology, Leibniz Universität Hannover, Germanyb Computer Laboratory, University of Cambridge, UK
a r t i c l e i n f o
Article history:
Received 15 February 2015
Revised 31 July 2015
Accepted 3 September 2015
Available online 25 September 2015
Keywords:
SDN
Wireless mesh network
Internet access
a b s t r a c t
Universal access to Internet is crucial, and as such, there have been several initiatives to enable
wider access to the Internet. Public Access WiFi Service (PAWS) is one such initiative that takes
advantage of the available unused capacity in home broadband connections and allows Less-
than-Best Effort (LBE) access to these resources, as exemplified by Lowest Cost Denominator
Networking (LCDNet). PAWS has been recently deployed in a deprived community in Notting-
ham, and, as any crowd-shared network, it faces limited coverage, since there is a single point
of Internet access per guest whose availability depends on user sharing policies.
To mitigate this problem and extend the coverage, we use a crowd-shared wireless
mesh network (WMN), at which the home routers are interconnected as a mesh. Such a
WMN provides multiple points of Internet access and can enable resource pooling across all
available paths to the Internet backhaul. In order to coordinate traffic redirections through
the WMN, we implement and deploy a software-defined WMN (SDWMN) control plane in
one of the CONFINE community networks. We further investigate the potential benefits of a
crowd-shared WMN for public Internet access by performing a comparative study between
a WMN and PAWS. Our experimental results show that a crowd-shared WMN can provide
much higher utilization of the shared bandwidth and can accommodate a substantially larger
volume of guest traffic.
© 2015 Elsevier B.V. All rights reserved.
1. Introduction
The Internet has evolved into a critical infrastructure
for education, employment, e-governance, remote health
care, digital economy, and social media. However, the In-
ternet today is facing the challenge of a growing digital
divide, i.e., an increasing disparity between those with and
without Internet access [60]. Access problems often stem
from sparsely spread populations living in physically remote
locations, since it is simply not cost-effective for Internet
Service Providers (ISPs) to deploy the required infrastructure
∗ Corresponding author. Tel.: +49 5117622596.
E-mail address: [email protected] (A. Abujoda).
http://dx.doi.org/10.1016/j.comnet.2015.09.008
1389-1286/© 2015 Elsevier B.V. All rights reserved.
for broadband Internet access in these areas. Coupled with
physical limitations of terrestrial infrastructures (mainly due
to distance) to provide last mile access, remote communities
also incur higher costs for connection between the exchange
and backbone network when using wired technologies, be-
cause the distances are longer. Ubiquitous mobile broadband
coverage is currently not feasible, since direct investment in
local infrastructure is uneconomic [55]. Addressing digital
exclusion due to socio-economic barriers is also important.
The United Nations revealed the global disparity in fixed
broadband access, showing that access to fixed broadband
in some countries costs almost 40–100 times their national
average income [34].
The reluctance of network operators (who are economi-
cally motivated) to provide wired and cellular infrastructures
360 A. Abujoda et al. / Computer Networks 93 (2015) 359–372
to rural/remote areas have led to several initiatives to build
large-scale, self-organized, and decentralized community
wireless networks that use WiFi mesh technology (includ-
ing long distance), due to the reduced cost of using the unli-
censed spectrum [51]. These community wireless mesh net-
works have self-sustainable business models, which provide
more localized communication services, as well as Internet
backhaul support via peering agreements with traditional
network operators who see such networks as a way to ex-
tend their reach at a lower cost. There are also community-
led wireless initiatives such as crowd-shared wireless net-
works, in which home broadband owners share a portion of
their home broadband with friends, neighbors, or other users
either for free or as part of a service offering by the ISP (e.g.,
[6,53]).
Public Access WiFi Service (PAWS) [53] is a community-
led crowd-shared WiFi service that uses a set of tech-
niques that make use of the available unused capacity in
home broadband networks and allowing Less-than-Best Ef-
fort (LBE) access to these resources, based on the Low-
est Cost Denominator Networking (LCDNet) paradigm [58].
PAWS adopts an approach of community-wide participation,
where home broadband subscribers are enabled to donate
controlled but free use of their high-speed broadband Inter-
net to fellow citizens. PAWS was deployed with 20 custom-
made PAWS routers placed in a deprived community in Not-
tingham and was also trialed out in rural Wales. PAWS is
essentially a crowd-shared access network (similar to FON
[5]) for under-privileged users in urban and rural communi-
ties. PAWS has been facing ongoing deployment challenges,
such as limited coverage, stemming from user sharing pat-
terns. In particular, during the PAWS trial deployment, it was
observed that home users did not share their broadband con-
nection over periods at which either the whole bandwidth or
all the ports of the home router were needed (i.e., PAWS uses
an access point connected to the home router for Internet ac-
cess sharing). Essentially, PAWS is a crowd-shared network
with a single point of access per guest, and as such, Internet
access sharing is highly dependent on user sharing policies
(i.e., the periods at which user share their Internet connec-
tion).
To mitigate this problem, we investigate the potential
benefits of extending PAWS or any crowd-shared wireless
network to a wireless mesh network (WMN) by intercon-
necting wireless home routers. As such, a crowd-shared
WMN provides extended coverage via multiple points of ac-
cess for each guest. We particularly consider crowd-shared
WMNs in residential areas, taking advantage of the dense de-
ployment of wireless home routers. The main challenge in
the management of such a WMN lies in the coordination of
guest traffic redirections, such that the shared bandwidth is
efficiently utilized. More precisely, traffic redirection requires
the assignment of guest flows to gateways and the selection
of paths (through the WMN) that provide sufficient capacity
and low delay. Furthermore, decisions for traffic redirections
should be also based on user sharing policies, when these are
disclosed in advance. Given the amount of information that
has to be collected before flow assignments can be made, we
deem a centralized control plane as a more suitable approach
to WMN management for Internet access sharing, since all
information can be conveyed to a centralized controller
facilitating the coordination of traffic redirections. In this re-
spect, we leverage on software-defined networking (SDN)
principles for the control plane design.
Along these lines, our contributions are the following:
• We present the design and implementation of a SDN con-
trol plane for the coordination of guest traffic redirections
through the WMN.
• We develop an algorithm for the assignment of gateways
to flows that require redirection, taking into account the
flow demands, the residual bandwidth in the access links
and the WMN, as well as, the advance knowledge of shar-
ing policies.
• We generate a model for user sharing patterns based on
the router on/off periods captured from the PAWS deploy-
ment in Nottingham.
• We quantify the benefits of a crowd-shared WMN for
Internet access sharing using a deployment of our SDN
control plane in Athens Wireless Metropolitan Network
(AWMN) [4], i.e., one of the community networks of
the CONFINE project [2,3,24]. Community Lab, https://
community-lab.net/ In this respect, we perform a com-
parative study between a WMN and a crowd-shared net-
work with limited coverage (i.e., one access point per
guest).
This paper extends our previous study on software-
defined WMNs, at which the efficiency of a crowd-shared
WMN was assessed using simulations [18]. The remainder of
the paper is organized as follows. In Section 2, we provide
an overview of the software-defined WMN (SDWMN) con-
trol plane. Section 3 elaborates on techniques for guest traf-
fic redirection. In Section 4, we discuss the implementation
of the SDWMN and its deployment in AWMN. In Section 5,
we present our evaluation results and discuss the benefits of
a crowd-shared WMN for Internet access sharing. Section 6
discusses related work. Finally, Section 7 highlights our con-
clusions.
2. Software defined crowd-shared wireless mesh
networks
The underlying problem with PAWS or any crowd-shared
network is that they serve as single point of Internet access to
guests within the coverage of the wireless router and hence,
they have no provision to extend the coverage when no band-
width is being shared. Based on our experience from the trial
PAWS deployment, PAWS routers were not available for cer-
tain periods, because sharers needed all the bandwidth of
their broadband connection or due to other reasons, such as
economic constraints placed on home users in underprivi-
leged areas where they are enforced to conserve energy by
turning off the routers at nights. These observed user behav-
iors entail significant challenges for the successful adoption
of PAWS.
A potential solution to this problem is to extend the PAWS
network as a crowd-shared WMN. Such a network would
allow home network users to share part of their own broad-
band connection with the public for free while also con-
nected to each other as a WMN providing extended cover-
age (Fig. 1). Extending PAWS to a crowd-shared WMN departs
from the norm: multiple users from different ISPs form part
A. Abujoda et al. / Computer Networks 93 (2015) 359–372 361
Fig. 1. Crowd-shared WMN for public Internet access.
Sharer
Community Network
Traffic Redirection
Southbound API (OpenFlow, SNMP)
Monitoring
Northbound API
SD
N C
ont
rol P
lane
Virtual Network Operator
Research Device
Research Device
Research Device
Research Device
Research Device
Fig. 2. Software-defined WMN control plane overview.
of the WMN to provide free Internet connectivity, while most
wireless community WMNs today are operated by a single
organization. This raises important questions regarding the
operation, configuration, and management of crowd-shared
WMNs.
SDN can facilitate the management and operation of
wireless networks at large scale. Leveraging on SDN’s
centralized control and network-wide visibility, the man-
agement and operation of a crowd-shared WMN can be
outsourced to a third party. In [54], we describe a holistic
approach of coupling both social and economic incentives
in designing future networks allowing the extension of
the stakeholder value chain to include more than the two
traditional parties (consumer and Internet Service Provider).
Compared to existing mesh networks for Internet access
sharing (e.g., Freifunk [6]), our approach provides more
opportunities for non-governmental organizations and local
governments (driven by social goals rather than economic)
to become virtual network operators. Enabling a third party
to federate such wireless home networks would reduce the
operating expenditures for network operators as well as
enable new economic models for revenue generation from
currently underutilized infrastructures.
In particular, we rely on SDN to create the notion of
Virtual Public Networks (VPuN), i.e., crowd-shared home
networks created, deployed and managed through an evo-
lutionary SDN control abstraction [54]. Although originally
intended for crowd-shared wireless networks such as PAWS,
VPuN can be also used for crowd-shared WMNs, enabling
resource pooling across multiple home broadband connec-
tions based on the prevailing network conditions and usage
sharing patterns. Based on the notion of VPuN, we have
deployed a SDWMN control plane in AWMN [4]. As part
of the Community-Lab [3], AWMN consists of more than
1000 wireless nodes and 30 research devices (RDs) that
host isolated containers (i.e., so-called slivers) which can be
allocated and controlled by a user, according to the needs of
his experiment [24].
Fig. 2 illustrates an overview of the SDWMN control plane.
The main goal of the control plane is to improve the uti-
lization of shared bandwidth by redirecting guest traffic to
one of the home routers through the WMN, based on user
sharing policies. As such, the control plane exposes an inter-
face to each home user, allowing him to express a sharing
policy (e.g., using the sharing policy expression language
presented in [54]). The control plane functionality mainly
consists of traffic redirection and monitoring. The traffic
redirection module selects the gateway for Internet access
(using the algorithm presented in Section 3) and subse-
quently creates a tunnel between the home router where
the guest is connected and the router assigned as the gate-
way (home routers are deployed in the AWMN RDs). Traffic
is redirected through a tunnel, since the wireless nodes in
AWMN are not under our control (as opposed to the RDs).
A tunnel is dynamically set up when traffic has to be redi-
rected over that path, and it is torn down when it is no longer
needed. The monitoring module collects statistics about the
bandwidth utilization of the WMN and the access links. A de-
tailed description of the implementation of the SDWMN con-
trol plane and the gateway is given in Section 4.
3. Traffic redirection
Assigning flows to network paths with constrained capac-
ity can be formulated as a multi-commodity flow problem
which is known to be NP-hard [33]. Hereby, we present a
heuristic algorithm for the assignment of the gateway and
the path over which the traffic will be redirected through
the WMN. The algorithm aims at maximizing Internet shared
bandwidth utilization by accommodating as large as possible
number of flows. It is executed by a SDN controller which has
knowledge of the WMN topology and utilization, the Internet
access link utilizations, and the user sharing policies. We first
describe the traffic redirection algorithm (Section 3.1) and
then investigate the potential benefits of user sharing poli-
cies (Section 3.2).
3.1. Traffic redirection algorithm
We represent the WMN as a weighted undirected graph
G = (N, L), where N is the set of nodes and L is the set of links
362 A. Abujoda et al. / Computer Networks 93 (2015) 359–372
Algorithm 1 Gateway and path assignment.
Inputs: G = (N, L), D
SORT(D)
for each d ∈ D do
search ← true
M ← {N \ nu} // M : candidate set of routers
while (search AND M �= ∅)
g ← argmaxi∈MC(ni)
for each p ∈ Pug
if C(p) < r then
Pug ← Pug \ p
end if
end for
if Pug = ∅ then
M ← M \ ng
else
gateway ← ng // gateway assignment
path ← SP(Pug) // path assignment
search ← false
end if
end while
end for
C
Fig. 3. Flow redirections.
between nodes of the set N. Each node ni is associated with
an Internet access link whose available shared bandwidth is
denoted by C(ni). Each link lij ∈ L between two nodes ni and
nj is associated with the available bandwidth C(lij). Let Pij de-
note the set of paths in the network G, between the pair of
nodes ni and nj. The available bandwidth C(p) associated to a
path p ∈ Pij is given by the minimum residual bandwidth of
the links along the path:
(p) = minli j∈p
C(li j) (1)
We further represent a flow demand for a guest with the
tuple d = (nu, r), where nu ∈ N is the node where the guest
device has been attached and r denotes the flow rate. We use
D to represent the set of flow demands that have arrived in
the network.
Algorithm 1 assigns a gateway and the shortest path to
each flow demand from the set D. The algorithm is executed
whenever there is insufficient shared bandwidth in the lo-
cal home network. Initially, the flow demands are sorted in
decreasing order. For each flow demand, the algorithm se-
lects the home router with the highest access link bandwidth
(i.e., C(ni)). Subsequently, the algorithm identifies the set of
paths, Pug, between the local home router (nu) and the as-
signed gateway (ng) that satisfy the flow demand (Eq. (1)). In
case there is no such path, the algorithm performs another
iteration for the selection of the gateway, excluding the pre-
viously selected home router. Otherwise, the shortest among
these paths is being identified based on the number of hops
(SP function in the algorithm). This eventually computes the
path for the traffic redirection through the WMN.
This algorithm carries out the gateway assignment based
on the available shared bandwidth of the home routers in
the crowd-shared network. Although this can pool resources
from all home networks where sharing is permitted achiev-
ing efficient utilization of the shared bandwidth, guest traffic
may have to traverse multiple hops in the WMN. This can
lead to latency inflation, delay variation, reordering [56,57]
and in general, performance degradation. To mitigate this,
we further use a variant of this algorithm by introducing a
threshold in the number of hops between the local home
router and the assigned gateway. For example, adjusting the
threshold to 1 restricts the search space to the next-hop
routers with sufficient shared bandwidth.
3.2. User sharing policies
Algorithm 1 assigns flows to gateways taking into ac-
count the amount of shared bandwidth and optionally the
path hop-count. So far, we have not exploited any advance
knowledge of user sharing policies for traffic redirection.
Such information can prevent wasteful traffic redirections
(i.e., forwarding traffic to a router which will shortly become
unavailable for Internet access sharing). Sharing policies
can be specified using a sharing policy expression language
(SPEL), e.g., the XML-based schema presented in our previous
work [54]. SPEL essentially provides the necessary means to
the sharers to specify the amount of shared bandwidth as
well as the period of sharing. This information is conveyed
to the SDN control plane and is used as an additional input
for gateway assignment.
We run a trace-driven simulation of a crowd-shared
WMN to investigate the impact of sharing policies on traf-
fic redirections. In particular, we model the availability of
the home routers using an on-off Markov chain. On and off
times are exponentially distributed with mean values μon =106 min and μof f = 555 min. We parameterize the exponen-
tial distributions using datasets from the PAWS deployment
in UK.
Fig. 3 illustrates the number of traffic redirections for dif-
ferent network loads with and without the knowledge of
sharing policies. When sharing policies are known, the traffic
A. Abujoda et al. / Computer Networks 93 (2015) 359–372 363
path 1
Sink
delay node (tc)
OVSSrc
path 2
Fig. 4. Experimental setup for packet reordering evaluation.
0 100 200 300 400 5000
25
50
75
100
125
150
175
200
225
delay(old_path) − delay(new_path) (ms)
num
ber
of p
acke
ts o
ut−
of−
orde
r
1 Mbps2 Mbps5 Mbps
Fig. 5. Packet reordering.
Fig. 6. SDWMN controller.
is directed to the router with the longest period of availability
(among the routers with sufficient shared bandwidth). Ac-
cording to Fig. 3, using sharing policies leads to fewer traffic
redirections, especially for low and moderate network loads.
For higher loads, the number of routers with sufficient shared
bandwidth diminishes, and as such, the knowledge of sharing
policies does not yield significant gains.
We perform additional tests to investigate potential im-
plications, such as packet reordering and higher communi-
cation overhead, due to traffic redirections. First, we use the
experimental setup depicted in Fig. 4 to quantify the level of
packet reordering. In this setup, UDP traffic is redirected from
path 1 to path 2 at the intermediate node. The redirection is
carried out by inserting flow entries to an OpenvSwitch (OVS)
datapath that has been set up in the intermediate node. We
further employ a delay node, set up in path 2, to adjust the
delay along this path. Fig. 5 shows that the number of out-
of-order packets increases in proportion to the delay differ-
ence between the two paths. Our path delay measurements
in AWMN indicate that a redirection through the WMN in-
creases the RTT with up to 50 ms. This will result in the
out-of-order delivery of a small number of packets, which, in
turn, can lead to congestion control invocations, and, hence,
lower throughput for TCP flows.
Besides packet reordering, traffic redirections generate
additional communication overhead between the SDWMN
controller and the routers. Specifically, for each flow redirec-
tion the router sends a OFPT_PACKET_IN message to the con-
troller, which, in turn, sends an OFPT_FLOW_MOD message to
the router. Taking the respective acknowledgment messages
into account, the communication overhead per flow redirec-
tion is 490 bytes. This overhead can increase substantially
with a large number of guest flows per home router, as the
PAWS router logs indicate. Considering the identified impli-
cations of traffic redirections, we use sharing policies in the
assignment of guest flows to gateways.
4. Implementation
We provide a detailed description of the implementation
of the SDWMN control plane and gateway in Sections 4.1 and
4.2, respectively.
4.1. SDWMN controller
We implement our controller in Python using POX which
provides a framework to implement SDN controllers with
OpenFlow protocol interface [43]. Since OpenFlow supports
only the configuration of switch flow tables, we use XML-RPC
interface to install and configure tunnels between the gate-
ways. Our SDWMN controller consists of the following mod-
ules (Fig. 6):
• Gateway registration: Each new gateway joining the
crowd-shared network is registered with the creation of
a new gateway object in the controller repository. Each
gateway object holds information about the gateway’s:
(i) datapath ID, which is a 64 bit unique identifier for
each OpenFlow switch instance, (ii) IP address of the
WMN port, (iii) ports table, which contains the names and
OpenFlow switch ports numbers (i.e., DSL port, the WMN
port and the guests’ wireless network port), (iv) moni-
toring table, which contains the gateway shared band-
width utilization and WMN path quality, and (v) tunnel
table, which contains the IP addresses, UDP port numbers
(we use UDP-in-IP tunnelling) and OpenFlow switch port
numbers of the installed tunnels. The registration process
is triggered by POX when the gateway establishes a new
control channel with the controller.
• Monitoring: This module collects and processes the mea-
surements of shared bandwidth utilization and the WMN
paths delay and hop counts for each gateway. Bandwidth
(BW) estimation in a WMN is very challenging, due to BW
fluctuations and the low degree of accuracy of available
BW measurement tools (e.g., pathload [39], TOPP [44],
and IGI/PTR [37]) that was also confirmed by our own
tests. However, in our experimental setup, the Internet
access links are the bottleneck (since they have been con-
figured at lower capacity than the WMN), and, as such,
364 A. Abujoda et al. / Computer Networks 93 (2015) 359–372
Fig. 7. SDWMN gateway. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
the aforementioned WMN bandwidth estimation issues
do not affect the accuracy of our measurements. Using
the OpenFlow protocol, the BW measurement module
pulls the network port counters of each home gateway to
acquire the accumulated number of bytes received/sent
in the network port (e.g., the DSL port). In particular,
the controller sends an OpenFlow OFPT_STATS_REQUEST
message with OFPST_PORT type and the port number to
the gateway. The gateway replies with OFPT_STATS_REPLY
message which carries the latest reads of the gateway
rx_bytes and tx_bytes counters. To calculate the shared
bandwidth utilization, the BW measurement module ap-
plies exponential moving average to the counters changes
over time. In addition, the path measurement module
uses XMP-RPC to pull the RTT and hop-count measure-
ments of each WMN path to/from each gateway. All mea-
surements are stored in the monitoring table of the re-
spective gateway object.
• Traffic redirection: Flows are redirected using the follow-
ing modules:
• Gateway selection: The gateway selection module is in-
voked when a gateway sends a OFPT_PACKET_IN mes-
sage to the controller to announce the arrival of a
new flow. Based on the measurements (shared BW
utilization and path length) provided by the moni-
toring module and using the algorithm described in
Section 3, the gateway selection module assigns a
gateway to the new flow. Subsequently, a tunnel is
being set up (see tunnel installation for further de-
tails) between the home gateway (where the flow ar-
rives) and the assigned gateway (where the flow is
redirected). Next, the flow table configuration module
installs the respective forwarding entries in the home
and the assigned gateway such that the guest flow is
redirected accordingly.
• Tunnel installation: Using the XML-RPC interface,
packet encapsulation modules are installed in the
gateway. This consists in sending the IP address of the
assigned gateway to the flow home gateway as well
as the source and destination port numbers of the
UDP header used for encapsulation. Upon successful
installation of the tunneling module, the home gate-
way replies with the switch port number of the new
encapsulation module. This information is stored in
the tunnel table of the home gateway object to be used
later by the flow table configuration module.
• Flow table configuration: This module installs the cor-
responding flow entries in the home gateway and
the assigned gateway using OpenFlow. This consists
in sending a OFPT_FLOW_MOD message to the home
and assigned gateways. This message carries the flow
matching fields [43] (e.g., source/destination IP, port
source/destination) as well as the OpenFlow switch
output port number.
4.2. SDWMN gateway
Our SDN gateway exposes to the SDWMN controller an
OpenFlow and XML-RPC interface to install tunnels and redi-
rect flows (see Fig. 7). Tunnels are created by encapsulating
guest traffic in UDP packets (i.e., IP-in-UDP) using the encap-
sulation module (red line Fig. 7). The destination IP address
of the tunnel header is set to the IP address of the assigned
gateway. For each new tunnel a new encapsulation module is
created. On the other hand, incoming traffic (green line Fig. 7)
is processed by the decapsulation module, which strips the
A. Abujoda et al. / Computer Networks 93 (2015) 359–372 365
Fig. 8. The WMN topology measured using traceroute from a research de-
vice (blue circle) toward the other research devices (red circles). (For inter-
pretation of the references to color in this figure legend, the reader is re-
ferred to the web version of this article.)
outer header before the traffic is delivered to the output port.
Both encapsulation and decapsulation modules are imple-
mented using Click [41].
To steer data traffic between the physical ports and the
modules (encapsulation/decapsulation), we rely on Open-
vSwitch [49]. This switching module also forwards messages
from the SDN controller to the gateway controller module
through the XML-RPC server. To connect modules to Open-
vSwitch, we use Linux TAP devices.
The instantiation of encapsulation/decapsulation mod-
ules is performed by the gateway controller in the following
steps: (i) creation of TAP interfaces, (ii) appending TAP in-
terfaces names, MAC and IP address of the assigned gateway
to Click configuration templates stored on the repository (we
use templates to speed up modules instantiation) and (iii) in-
stallation of the Click configuration.
Furthermore, the gateway controller collects and trans-
fers the measurements generated by the path measurement
module to the SDN controller. The path measurement mod-
ule uses ping and traceroute to collect RTT and hop counts of
the WMN paths. A list of gateways IP addresses is passed to
this module to obtain the measurements. The module sends
10 probing packets every 30 s to avoid flooding the network.
5. Evaluation
In this section, we perform a comparative study of crowd-
shared WMN against PAWS (or any other crowd-shared net-
work with a single point of access). To this end, we run exper-
iments in AWMN (Section 5.1) and further use simulations
with larger WMN topologies (Section 5.2).
Using experimentation and simulations, we initially mea-
sure the utilization level of the shared BW and the accumu-
lated serving rate across time and for different flow arrival
rates. To study the scalability of our control plane, using ex-
perimentation we quantify the control communication over-
head in terms of BW consumption and flow setup delay. We
further use simulations to study the effect of traffic redi-
rection on latency by measuring the shared BW utilization
when applying a threshold to the redirection path length (see
Section 3). Finally, we investigate the scalability of the crowd-
shared WMN in terms of BW utilization and serving rate with
different network sizes using simulations.
5.1. Experimental results
For our experiments, we use 12 research devices (RDs) to
deploy 11 home routers (or gateways) and one SDWMN con-
troller. Fig. 8 illustrates the WMN topology measured from a
single RD. We implemented a tool to analyze and construct
the topology out of traceroute measurements. Using MGEN
[1], we generate guest flows with a rate and lifetime sam-
pled out of a uniform and an exponential distribution, re-
spectively. The flows arrive to the network according to a
Poisson process. For router availability pattern, we use the
model described in Section 3.2. Since running tests for more
than 555 min (the mean value of the off period) is not fea-
sible, we scale down the mean values to μon = 10.6 s and
μof f = 55.5 s. In each gateway, we further implement a timer
to emulate the on and the off period of the router. Upon the
expiration of the timer, the gateway sends a signal to the
controller to announce the beginning of an on or off period.
The time required to generate the signal is negligible (a few
milliseconds). This timer can be also used by the sharers for
sharing policy configurations in their home routers. Due to
occasional reliability issues in the RDs, we restricted the du-
ration of our experiments to 250 s. After downscaling the
router on/off periods, there are several transitions between
the router states during each experiment.
Our main evaluation metrics include: (i) the shared band-
width utilization across all home routers, and (ii) the accu-
mulated serving rate ASR(T) at time T, defined as:
ASR(T) = �Tt sfinished
t(�T
t sfinishedt + �T
t srejectedt
) , (2)
where sf inishedt denotes the total sizes of the flows at time t
which are accepted (i.e., assigned Internet access) and suc-
cessfully served without disruption due to router unavailabil-
ity. sre jectedt denotes the total sizes of the flows at time t which
are rejected (i.e., not assigned Internet access).
We initially measure the shared bandwidth utilization
(Fig. 9) and the serving rate (Fig. 10) with an arrival rate of
60 flows per minute, across 10 runs. Variations in the mea-
surements across the different runs were insignificant. Fig. 9
illustrates a low utilization of the shared bandwidth with-
out a WMN during the whole period, although there is high
demand for Internet access by guests attached to the vari-
ous home networks. In contrast, a WMN allows to capital-
ize the unused capacity and accommodate a larger volume of
guest traffic. More precisely, according to Fig. 9 guest traffic
366 A. Abujoda et al. / Computer Networks 93 (2015) 359–372
Fig. 9. Shared bandwidth utilization.
Fig. 10. Accumulated serving rate.
Fig. 11. Shared bandwidth utilization for diverse flow arrival rates across
250 s experimentation time.
Fig. 12. Accumulated serving rate for diverse arrival rates across 250 s ex-
perimentation time.
redirection through the WMN results in the full utilization
of the shared bandwidth. Furthermore, crowd-shared WMNs
can accommodate substantially larger volume of guest traf-
fic, as depicted in Fig. 10. This stems from the high utilization
of the shared bandwidth.
We further measure the shared bandwidth utilization and
serving rate with a wide range of guest traffic demands. In
this respect, the boxplots in Figs. 11 and 12 illustrate the
shared bandwidth utilization and serving rate with diverse
flow arrival rates, ranging from 20 to 80 flows per minute.1
These results corroborate the efficiency of the WMN for vari-
ous traffic loads, as the shared bandwidth utilization and the
1 All boxplots show measurements across the time period of the exper-
iment, i.e., each point on the graph represents a different time point. For
different runs we calculate the average, the variation is insignificant.
serving rate always remain very high. In Fig. 12, the flow ar-
rival rates of 60 flows per minute and beyond lead to request
rejections, due to the insufficient access link bandwidth. On
the other hand, Figs. 11 and 12 show poor bandwidth utiliza-
tion and serving rate without a WMN, due to the presence
of a single point of Internet access for each guest. Essentially,
our results show the significant benefit that a WMN can bring
into crowd-shared networks, by effectively pooling resources
across all home networks.
To evaluate the scalability of our control plane, we mea-
sure the flow setup time and the average control communi-
cation overhead across a range of flow arrival rates. We define
the setup time as the interval between the flow’s first packet
arrival at the gateway and its transmission to the next hop.
This is essentially the time incurred for gateway selection
and flow entry installation. We also define the control com-
munication overhead as the bandwidth consumed to setup
flows and monitor the shared bandwidth utilization. We run
A. Abujoda et al. / Computer Networks 93 (2015) 359–372 367
Fig. 13. Setup time per flow.
Fig. 14. Control communication overhead.
Fig. 15. Simulation WMN topology.
time (hours)0 5 10 15 20 25 30 35 40 45 50 55 60
shar
ed b
andw
idth
util
izat
ion
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
with WMNwithout WMN
Fig. 16. Shared bandwidth utilization.
our experiments on a single gateway (the results can be eas-
ily extrapolated for more gateways) and generate 100 differ-
ent flows for each arrival rate. Figs. 13 and 14 show that per
flow setup time does not change significantly, while the con-
trol communication overhead increases linearly and is in the
range of tenths of Kbps. According to these results, the perfor-
mance of our controller scales with increasing control loads.
5.2. Simulation results
To run tests at larger scale, we developed a simulator (in
Python) that models the flow-level behavior of the guest traf-
fic in a crowd-shared WMN. The flow parameters that we use
in our simulations are the average flow rate, and their arrival
and departure time. We use the TFA wireless mesh topology
[7] which consists of 21 nodes (Fig. 15). TFA is an operational
network deployed in a densely populated residential area;
that is exactly the network environment we consider for the
proposed crowd-shared WMN. Each node in this topology
represents a home router with shared Internet access band-
width, whereas each edge is a wireless mesh link. Guest flows
arrive randomly at different home routers. Each generated
flow has a rate and lifetime sampled out of a uniform and ex-
ponential distribution, respectively. The guest flows arrive to
the network according to a Poisson process. We simulate the
router availability using our model with the mean values ob-
tained from the PAWS datasets. Guest flows are granted Inter-
net access either through local routers or by redirection to re-
mote routers based on the algorithm presented in Section 3.
Flows which cannot be accommodated are rejected.
Each simulation run comprises 60 h at a time discretiza-
tion of 20 min. For each scenario, we perform 20 simulation
runs. We assume a homogeneous setting where each router
has 16 Mbps ADSL downlink and set the shared bandwidth
per router to 8 Mbps (i.e., in the periods that each router is
active). We run our simulation with mesh link capacity of
200, 54 and 10 Mbps. This does not have any impact on the
simulation results, since the bottleneck is the Internet access
links. Note that our wireless link model does not consider the
impact of interference and distance on the offered link band-
width.
The simulation results are in accordance with the results
from the community network. According to Figs. 16 and 17,
the crowd-shared WMN yields significantly higher utiliza-
tion of the shared bandwidth and accommodates more traffic
in comparison to the crowd-shared network without WMN.
368 A. Abujoda et al. / Computer Networks 93 (2015) 359–372
time (hours)0 5 10 15 20 25 30 35 40 45 50 55 60
accu
mul
ated
ser
ving
rat
e
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
with WMNwithout WMN
Fig. 17. Accumulated serving rate.
flows arrrival rate (flows/min)10 20 30 40 50 60 70 80 90 100
shar
ed b
andw
idth
util
izat
ion
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
with WMNwithout WMN
Fig. 18. Shared bandwidth utilization vs. flow arrival rate across 60 h simu-
lation time.
flows arrival rate (flows/min)20 50 80
shar
ed b
anw
idth
util
izat
ion
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
max number of hops=1max number of hops=2max number of hops=3no limit
Fig. 19. Shared bandwidth utilization for diverse hop-count threshold val-
ues vs. flow arrival rate.
TFA (21) 50 100 150 200
shar
ed b
andw
idth
util
izat
ion
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
topologies with different size (# of nodes)
with WMN
without WMN
Fig. 20. Shared bandwidth utilization with different topologies for 60 h sim-
ulation time.
The serving rate, in particular, drops as guest flows are not
granted Internet access, due to either insufficient bandwidth
(with WMN) or changes in sharing policies (without WMN).
Eventually, in both cases, the serving rate reaches a steady
state. Shared bandwidth utilization for a wide range of guest
traffic demands is depicted in Fig. 18.
In our simulations, redirected guest traffic traverses up to
4 hops in the WMN. We further use a variant of our gateway
assignment algorithm (briefly discussed in Section 3), which
introduces a threshold in the number of hops between the
home router and the assigned gateway. Fig. 19 illustrates the
shared bandwidth utilization for diverse hop-count thresh-
old values ranging from 1 to 3, and without any limit in the
hop count. Restricting the number of hops has a noticeable
impact on bandwidth utilization, especially for a single hop,
since Internet access is permitted only through one of the
next-hop home routers. In essence, there is a trade-off be-
tween coverage extension (and thus effective bandwidth uti-
lization) and latency. This may become more critical in large
WMNs, where long paths can inflate latency.
We further run simulations using synthetic WMN topolo-
gies of diverse size and structure generated using NPART
[45]. NPART is a tool for generating realistic WMN topologies,
based on real-world WMN deployments (Berlin Freifunk[6]).
Using NPART and the Berlin WMN model, we randomly gen-
erate 4 additional WMN topologies with 50, 100, 150 and 200
nodes. We set the wireless link capacity and shared band-
width to 54 Mbps and 8 Mbps, respectively. For all topolo-
gies, we generate flows with arrival rate of 120 flows per
minute, which allows us to saturate the WMN capacity with
all topologies. As shown in Figs. 20 and 21, the shared band-
width utilization and serving rate scale linearly with the in-
crease of the WMN size. This is further confirmed by the
increase in WMN utilization (Fig. 22) and the number of
re-assigned flows (Fig. 23) with larger WMN sizes.
6. Related work
In the following, we discuss related work on WMN rout-
ing, software-defined wireless networks, and Internet access
sharing.
A. Abujoda et al. / Computer Networks 93 (2015) 359–372 369
time (hours)0 10 20 30 40 50 60
accu
mul
ated
ser
ving
rat
e
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1TFA (21 nodes)50 nodes 100 nodes 150 nodes 200 nodes
Fig. 21. Accumulated serving rate with different topologies.
topologies with different size (# of nodes)TFA (21) 50 100 150 200
WM
N u
tiliz
atio
n
0
0.1
0.2
0.3
0.4
0.5
0.6
Fig. 22. WMN utilization with different topologies for 60 h simulation time.
topologies with different size (# of nodes)TFA (21) 50 100 150 200
num
ber
of r
e-as
sign
ed fl
ows
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Fig. 23. Number of re-assigned flows with different topologies for 60 h sim-
ulation time.
Routing in WMN. There is a large body of work on rout-
ing in WMN [20,21,25]. This diversity stems from the differ-
ent optimization goals of routing protocols (e.g., low response
time, low control overhead, scalability, QoS support). More
specifically, WMN routing protocols can be classified into: (i)
proactive (e.g., OLSR [27], WRP [46], DSDV [48], B.A.T.M.A.N.
[38]) where routes are computed in advance leading to lower
response time at the cost of high routing control overhead,
(ii) reactive (e.g., AODV [47], DSR) which provide routes on
demand leading to lower control overhead, but higher re-
sponse time, and (iii) hybrid (e.g., LQSR [32] and SrcRR [19])
where both approaches (i.e., proactive and reactive) are com-
bined to adjust the tradeoff between response time and con-
trol overhead.
Over the years, WMN routing protocols (e.g., AODV-ST,
SrcRR, B.A.T.M.A.N.) have evolved to target particular chal-
lenges or structures of WMNs. In particular, AODV-ST [50]
performs routing assuming a tree-based traffic flow, where a
gateway residing at the root of the tree (Internet access gate-
way) frequently requests routes to other nodes of the net-
work. SrcRR computes routes taking into account the effect
of interference on links quality. B.A.T.M.A.N. is another WMN
routing protocol developed within the Freifunk community
[6] to address the scalability limitations of the OLSR protocol
and account for the static nature of WMNs.
All these protocols can be integrated into our SDN archi-
tecture for the selection of WMN paths between the home
routers or as a fallback in case the connection between a
router and its controller is lost. In our experimental evalu-
ation, we rely on the WMN for the computation of routes be-
tween the local and the remote gateway (i.e., since the WMN
nodes do not support OpenFlow), while we use our algorithm
for gateway selection whenever traffic redirection is needed.
Our approach of coupling WMN routing protocols with SDN
is also employed by authors in [28,29,62].
Software-defined wireless networks. Recent work has been
leveraging on SDN for WMN management. Hasan et al. [36]
discuss the benefits of using SDN to decouple the opera-
tion and management of rural wireless networks from the
physical infrastructure. Our work is in the same direction,
but we focus on the management of crowd-shared WMN in
residential areas and investigate techniques for the efficient
utilization of the shared bandwidth. Authors in [28,29,62]
present OpenFlow-based architectures for WMNs with em-
phasis on mobility management, routing, and load balanc-
ing. Dimogerontakis et al. [30] propose a SDN extension to
WMN community networks for L2 experimentation. Other
applications of SDN to the wireless network domain include
OpenRoads [63] and OpenSDWN [59], which propose solu-
tions for mobility management using OpenFlow. These tech-
niques are complimentary to our work and can be integrated
into our SDN control plane to facilitate mobility management
in crowd-shared WMNs. Furthermore, authors in [52] inves-
tigate and evaluate the SDN control plane distribution for
WMNs with emphasis on master controller selection. This
can complement our work to provide higher resilience and
SDN controller availability.
Internet access sharing.FON [5], Kabel Deutschland (KD)
[17], Deutsche Telekom (DT) [16] and British Telecom (BT)
[8] are among the network operators that provide free
Internet access to their subscribers via a large number
370 A. Abujoda et al. / Computer Networks 93 (2015) 359–372
of shared hotspots. For example, BT and KD use Home
Hub and homespot, respectively, for Internet access sharing
with other subscribers. Furthermore, DT and BT have part-
nered with FON to provide Internet access through shared
home broadband connections. These efforts have also been
supported by several affordable, easy-to-install and open-
firmware wireless routers offered by manufacturers such as
FON [9], openMesh [15], Linksys [13], and Netgear [14]. Our
approach leverages on SDN to bring the following benefits to
Internet access sharing: (i) reduction of operational expenses
for home networks and ISPs, since the deployment, configu-
ration, and operation of crowd-shared networks can be out-
sourced to third-parties (i.e., virtual network operators), and
(ii) better coordination of network configurations for traffic
redirections, due to the global WMN view and the collection
of all monitoring and sharing policy information in a central-
ized controller.
In addition, Link-alike [40] promotes sharing by aggre-
gating the uplink bandwidth of the broadband connections
through WMN, such that home users have access to higher
upstream capacity. On the other hand, ARBOR [61] aggregates
broadband connection bandwidth (both uplink and down-
link) from multiple WiFi networks. We achieve the same goal
(i.e., high utilization of the shared bandwidth) via guest traf-
fic redirections through the WMN, enabling resource pooling
from all shared broadband connections.
7. Conclusions
In this paper, we investigated the benefits of extending
the coverage of any crowd-shared network (e.g., PAWS) by
connecting the home routers as a mesh. A crowd-shared
WMN can mitigate the fundamental problem of any crowd-
shared network, i.e., the presence of a single point of ac-
cess for each guest. We showed that the advance knowl-
edge of user sharing policies can reduce the number of guest
flow redirections (particularly for low and moderate network
loads) avoiding implications, such as packet reordering and
control communication overhead. This leads to a significantly
higher utilization of the shared bandwidth, as opposed to a
crowd-shared network with a single point of access where a
portion of the shared bandwidth is wasted. This, in turn, en-
ables a software-defined crowd-shared WMN to accommo-
date a substantially larger volume of guest traffic.
SDN brings significant benefits to crowd-shared net-
works, as all sharing policy and WMN utilization information
can be conveyed to a centralized controller facilitating the
assignment of guest flows to gateways and the traffic redirec-
tion configurations in the WMN. In contrast, a decentralized
crowd-shared WMN management would require synchro-
nization primitives and could yield slow convergence, espe-
cially at the event of message loss. Our experimental results
show that our SDWMN control plane exhibits low per-flow
setup time and low control communication overhead.
Deploying a SDWMN in the real world raises challenges,
such as the controller placement, control plane distribu-
tion for robustness and load balancing, and SDN support
SDN in home routers. We envision hosting the SDN con-
troller as a virtual machine in micro-datacenters hosted by
large ISPs [35]. Micro-data centers are small DCs deployed
at POP and network aggregation points. By deploying the
controller in micro-DCs we ensure low communication de-
lay with the home routers and scalable processing power
to cope with high loads (scale up the controller computa-
tion capacity by using more servers). We can further leverage
on several frameworks to distribute the control load across
multiple servers, leading to faster response time and provid-
ing a fallback controller in case of failure [22,23,31,42,52]. In
terms of SDN support in home networks, there are already
OpenFlow-based wireless routers (e.g., HP [11], Meru [12]).
Furthermore, OpenFlow has been ported to OpenWRT [9,10],
allowing wireless routers running OpenWRT to be configured
using OpenFlow. This essentially facilitates the extension of
PAWS as a crowd-shared SDWMN, since PAWS already uses
wireless routers with OpenWRT-based firmware.
Content caching and the ever-growing penetration of on-
line social networks (OSN) create for more opportunities
in software-defined crowd-shared networks. More specifi-
cally, popular content can be placed in caches within home
networks, while the SDN controller can redirect guests to
caches, conserving bandwidth in shared broadband connec-
tions. Furthermore, information can be utilized from OSNs
in order to associate and authenticate guests to access points
that belong to their online friends, leveraging on the trust be-
tween two parties with an OSN relationship [26]. In future
work, we plan to investigate any potential benefits by cou-
pling content caching and OSNs with crowd-shared WMNs.
Acknowledgments
This work was partially supported by the EU CONFINE
Project (288535). We thank Amr Rizk for his help in modeling
the home router on/off periods.
References
[1] http://www.nrl.navy.mil/itd/ncs/products/mgen (accessed 18.03.15).
[2] CONFINE Project, http://confine-project.eu/ (accessed 18.03.15).[3] Community Lab, https://community-lab.net/ (accessed 18.03.15).
[4] http://www.awmn.net (accessed 18.03.15).[5] FON, http://corp.fon.com/ (accessed 18.03.15).
[6] Freifunk, http://freifunk.net/ (accessed 18.03.15).
[7] TFA Rice, http://tfa.rice.edu/ (accessed 18.03.15).[8] http://www.btwifi.com/help/about-bt-fon.jsp (accessed 18.03.15).
[9] http://wiki.openwrt.org/toh/start (accessed 18.03.15).[10] http://archive.openflow.org/wk/index.php/Pantou_:_OpenFlow_1.
0_for_OpenWRT (accessed 18.03.15).[11] http://h17007.www1.hp.com/docs/campus-lan/c04219571-hires.pdf
(accessed 18.03.15).
[12] http://www.merunetworks.com/collateral/white-papers/sdn-for-wifi-wp.pdf (accessed 18.03.15).
[13] http://www.linksys.com/gb/c/wireless-routers/ (accessed 18.03.15).[14] http://www.linksys.com/gb/c/wireless-routers/ (accessed 18.03.15).
[15] http://www.open-mesh.com/ (accessed 18.03.15).[16] https://www.telekom.com/media/consumer-products/180150 (acces-
sed 18.03.15).
[17] http://www.kabeldeutschland.de/wlan-hotspots/homespot-service.html (accessed 18.03.15).
[18] A. Abujoda, A. Sathiaseelan, A. Rizk, P. Papadimitriou, Software-definedcrowd-shared wireless mesh networks, in: IEEE International Work-
shop on Community Networks and Bottom-up-Broadband(CNBuB2014), Larnaca, Cyprus, 2014.
[19] D. Aguayo, J. Bicket, R. Morris, 2003, SrcRR: A High-Throughput Rout-
ing Protocol for 802.11 Mesh Networks. !http://pdos.csail.mit.edu/rtm/srcrr-draft.pdf?
[20] I. Akyildiz, X. Wang, W. Wang, Wireless mesh networks: a survey, Com-put. Netw. 47 (4) (2005) 445–487.
[21] E. Alotaibi, B. Mukherjee, A survey on routing algorithms for wirelessad-hoc and mesh networks, Comput. Netw. 56 (2012) 940–965.
A. Abujoda et al. / Computer Networks 93 (2015) 359–372 371
[22] Z. Bozakov, P. Papadimitriou, Autoslice: automated and scalable slicingfor software-defined networks, in: Proceedings of the 2012 ACM Con-
ference on CoNEXT Student Workshop, CoNEXT Student 12, ACM, 2012,p. 34.New York, NY, USA,
[23] Z. Bozakov, P. Papadimitriou, Towards a scalable software-defined net-work virtualization platform, in: Proceedings of the IEEE/IFIP SDNMO
2014, Krakow, Poland, 2014.
[24] B. Braem, et al., A Case for Research with and on community networks,ACM SIGCOMM CCR 43 (3) (2013).
[25] M.E.M. Campista, et al., Routing metrics and protocols for wirelessmesh networks, IEEE Netw. 22 (1) (2008) 6–12.
[26] Z. Cao, J. Fitschen, P. Papadimitriou, Social WiFi: hotspot sharing withonline friends, in: Proceedings of the IEEE PIMRC, Hong Kong, China,
2015.[27] T. Clausen, P. Jacquet, 2003, Optimized Link State Routing Protocol
(OLSR),RFC 3626 (Experimental), IETF.
[28] P. Dely, A. Kassler, N. Bayer, Openflow for wireless mesh networks, in:Proceedings of the IEEE ICCCN, Maui, USA, 2011.
[29] A. Detti, C. Pisa, S. Salsano, N. Blefari-Melazzi, Wireless Mesh softwaredefined networks (wmSDN), in: Proceedings of the CNBuB Workshop
at IEEE WiMob, France, Lyon, 7, 2013.[30] E. Dimogerontakis, I. Vilata, L. Navarro, Software defined networking
for community network testbeds, in: Proceedings of the IEEE WiMob,
2013.[31] A. Dixit, F. Hao, S. Mukherjee, T.V. Lakshman, R. Kompella, Towards
an elastic distributed SDN controller, in: Proceedings of the ACM SIG-COMM Workshop on Hot Topics in Software Defined Networking,
HotSDN, 2013.[32] R. Draves, J. Padhye, B. Zill, Routing in multi-radio, multi-hop wireless
mesh networks, in: Proceedings of the ACM International Conference
on Mobile Computing and Networking (MobiCom), 2004, pp. 114–128.[33] S. Even, A. Itai, A. Shamir, On the complexity of timetable and multi-
commodity flow problems, SIAM J. Comput. 5 (1976) 691–703.[34] J. Fildes, 2010, UN reveals global disparity in broadband access, BBC,
http://www.bbc.co.uk/news/technology-11162656.[35] B. Frank, I. Poese, G. Smaragdakis, A. Feldmann, B. Maggs, S. Uhlig,
V. Aggarwal, F. Schneider, Collaboration opportunities for content de-
livery and network infrastructures, in: H. Haddadi, O. Bonaventure(Eds.), ACM SIGCOMM ebook on Recent Advances in Networking, 2013,
pp. 305–377.[36] S. Hasan, Y. Ben-David, C. Scott, E. Brewer, S. Shenker, Enhancing rural
connectivity with software defined networks, in: Proceedings of theAnnual Symposium on Computing for Development ACM DEV, New
York, NY, USA, 2013.
[37] N. Hu, P. Steenkiste, Evaluation and characterization of available band-width probing techniques, in: Proceedings of the IEEE JSAC, 2003.
[38] D. Johnson, N. Ntlatlapa, C. Aichele, Simple pragmatic approach to meshrouting using batman, in: Proceedings of the 2nd IFIP International
Symposium on Wireless Communications and Information Technologyin Developing Countries, CSIR, Pretoria, South Africa, 6–7 October 2008,
2008.
[39] M. Jain, C. Dovrolis, Pathload: A measurement tool for end-to-end avail-able bandwidth, in: Proceedings of the Passive and Active Measure-
ments (PAM) Workshop, 2002.[40] S. Jakubczak, D. Andersen, M. Kaminsky, K. Papagiannaki, S. Seshan,
Link-alike: using wireless to share network resources in a neighbor-hood, in: MC2R, 2008.
[41] E. Kohler, R. Morris, B. Chen, J. Jahnotti, M.F. Kasshoek, The Click Mod-ular Router, ACM Trans. Comput. Syst. 18 (3) (2000).
[42] T. Koponen, M. Casado, N. Gude, J. Stribling, L. Poutievski, M. Zhu, R. Ra-
manathan, Y. Iwata, H. Inouye, T. Hama, S. Shenker, Onix: a distributedcontrol platform for,large-scale production networks, in: Operating
Systems, Design and Implementation, USENIX association, 2010.[43] N. McKeown, et al., OpenFlow: enabling innovation in campus net-
works, ACM SIGCOMM CCR 38 (2) (2008).[44] B. Melander, M. Bjorkman, P. Gunningberg, A new end-to-end prob-
ing and analysis method for estimating bandwidth bottlenecks, in: Pro-
ceedings of the Global Internet Symposium, 2000.[45] B. Milic, M. Malek, NPART - Node Placement Algorithm for Realistic
Topologies in Wireless Multihop Network Simulation, in: Proceedingsof the 2nd International Conference on Simulation Tools and Tech-
niques, 2009.[46] S. Murthy, J.J. Garcia-Luna-Aveces, An efficient routing protocol for
wireless networks, ACM/Baltzer Journal on Mobile Networks and Ap-
plications, Spec. Issue Routing Mob. Commun. Netw. 1 (2) (1996) 183–197.
[47] C. Perkins, E. Belding-Royer, S. Das, Ad hoc on-demand distance vector(AODV) routing, in: RFC 3561 (Experimental)„ IETF, 2003.
[48] C.E. Perkins, P. Bhagwat, Highly dynamic destination sequenceddistance-vector routing (DSDV) for mobile computers, in: Proceedings
of the Annual ACM Special Interest Group on Data Communications(SIGCOMM94), 1994, pp. 234–244.
[49] B. Pfaff, J. Pettit, T. Koponen, K. Amidon, M. Casado, S. Shenker, Ex-tending networking into the virtualization layer, in: ACM HotNets, New
York, USA, ACM, 2009.
[50] K.N. Ramachandran, M.M. Buddhikot, G. Chandranmenon, S. Miller,E.M. Belding-Royer, K.C. Almeroth, On the design and implementation
of infrastructure mesh networks, in: Proceedings of the IEEE Workshopon Wireless Mesh Networks (WiMesh), IEEE, 2005.
[51] J. Saldana, et al., 2014, Community Networks: Definition and taxonomy,Internet Draft.
[52] S. Salsano, G. Siracusano, A. Detti, C. Pisa, P.L. Ventre, N. Blefari-Melazzi,Controller selection in a Wireless Mesh SDN under network partition-
ing and merging scenarios, submitted paper, available at http://arxiv.
org/abs/1406.2470.[53] A. Sathiaseelan, et al., Public access WiFi service (PAWS), in: Digital
Economy All Hands Meeting, Digital Futures, Aberdeen, 2012.[54] A. Sathiaseelan, C. Rotsos, C.S. Sriram, D. Trossen, P. Papadimitriou,
J. Crowcroft, Virtual public networks, in: Proceedings of the IEEE Eu-ropean Workshop on Software Defined Networks EWSDN, Berlin, Ger-
many, 2013.
[55] A. Sathiaseelan, J. Crowcroft, Internet on the move: challenges and so-lutions, ACM SIGCOMM CCR 43 (1) (2013).
[56] A. Sathiaseelan, T. Radzik, Reorder detecting TCP, in: Proceedings of theIEEE International Conference on HSNMC, Estoril, Portugal, 2003.
[57] A. Sathiaseelan, T. Radzik, Reorder notifying TCP (RN-TCP) with ex-plicit packet drop notification (EPDN), Int. J. Commun. Syst. 19 (6)
(2005).Wiley
[58] A. Sathiaseelan, J. Crowcroft, LCD-Net: lowest cost denominator net-working, ACM SIGCOMM CCR 43 (2) (2013).
[59] J. Schulz-Zander, C. Mayer, B. Ciobotaru, S. Schmid, A. Feldmann,OpenSDWN: programmatic control over home and enterprise WiFi, in:
Proceedings of the ACM SIGCOMM SOSR, Santa Clara, CA, USA, 2015.[60] L. Townsend, A. Sathiaseelan, G. Fairhurst, C. Wallace, Enhanced broad-
band access as a solution to the social and economic problems of the
rural digital divide, J. Local Econ. 28 (6) (2013).[61] X. Xing, S. Mishra, X. Liu, ARBOR: hang together rather than hang sep-
arately in 802.11 wifi networks, in: Proceedings of the IEEE INFOCOM,2010, 2010, pp. 1–9.IEEE
[62] F. Yang, V. Gondi, J.O. Hallstrom, K. Wang, G. Eidson, OpenFlow-basedload balancing for wireless mesh infrastructure, in: Proceedings of the
IEEE Consumer Communications and Networking Conference, CCNC,
Las Vegas, 2014.[63] K. Yap, et al., OpenRoads: empowering research in mobile networks, in:
Proceedings of the ACM SIGCOMM, Barcelona, Spain, 2009.
Ahmed Abujoda graduated in electrical engi-neering from the university of Sana’a, Yemen, in
2004. He also obtained a master of science de-gree in Mechatronics engineering from University
of Siegen, Germany, in 2009. Since 2010, he hasbeen working towards the Ph.D. degree in elec-
trical and computer engineering at the Institute
for Communications Technology at the LeibnizUniversitt Hannover, Germany. His research inter-
ests include software-defined networking, net-work function virtualization and network man-
agement. Ahmed Abujoda is a student member ofIEEE.
David Dietrich graduated in electrical engineer-
ing from the Universitt Kassel, Germany, in 2006.During the years 2006 until 2009 he was a soft-
ware engineer for real-time simulation of auto-motive communications networks. Since 2010, he
has been working towards the Ph.D. degree in
electrical and computer engineering at the Insti-tute for Communications Technology at the Leib-
niz Universitt Hannover, Germany. He also partic-ipated in several research projects, e.g., the EU-
funded T-NOVA project and the national projectG-Lab. His research interests include network vir-
tualization and network management with focus
on algorithms and optimization. David Dietrich is a student member of IEEEand ACM.
372 A. Abujoda et al. / Computer Networks 93 (2015) 359–372
Panagiotis Papadimitriou is an Assistant Pro-
fessor at the Communications Technology Insti-tute of Leibniz Universitt Hannover. He received
a Ph.D. in Electrical and Computer Engineering
from Democritus University of Thrace, Greece, in2008. From 2008 to 2010, Panagiotis was a Post-
Doctoral Researcher at the Computing Depart-ment of Lancaster University, UK, and at Telecom
SudParis, France. Panagiotis received the runner-up Poster Award at ACM SIGCOMM 2009 and the
Best Paper Award at IFIP WWIC 2012. The re-
search activities of his group currently span next-generation Internet architectures, network func-
tion virtualization, software-defined networks, and cloud networking.
Arjuna Sathiaseelan is a Senior Research Asso-
ciate at the Computer Laboratory, University ofCambridge. He leads the Networking for Develop-
ment (N4D Lab). The research group conducts re-
search on novel Internet architectures for improv-ing and reducing the cost of Internet access. He is
the Chair of IRTF Global Access to the Internet forAll (GAIA) research group and a member of the
Internet Research Steering Group (IRSG). He hasa Ph.D. in Networking from Kings College London
(2005), M.Sc. in Computing and Internet Systems
from Kings College London (2001) and Bachelorsin Computer Science and Engineering from NIT,
Trichy, India (2000).