Controller Placement Problem resiliency evaluation in SDN-based architectures

Controller Placement Problem resiliency
evaluation in SDN-based architectures
Maurizio D’Arienzo1
, Manfredi Napolitano1
, and Simon Pietro Romano2
1
Dipartimento di Scienze Politiche
Università della Campania ”L.Vanvitelli” - Italy
2
DIETI
Università di Napoli ”Federico II” - Italy
Abstract. The Software-Defined Networking (SDN) paradigm does represent an effective approach aimed
at enhancing the performance of core networks by introducing a clean separation between the routing plane
and the forwarding plane. However, the centralized architecture of SDN networks raises resiliency concerns
that are addressed by a class of algorithms falling under the Controller Placement Problem (CPP) umbrella
term. Such algorithms seek the optimal placement of the SDN controller. In this paper, we evaluate the main
CPP algorithms and provide an experimental analysis of their performance, as well as of their capability
to dynamically adapt to network malfunctions and disconnections.
1 Introduction
Computer networks have developed exponentially over the last twenty years, reaching
important levels of branching across the globe. This has allowed great coverage and conse-
quent interconnection among users, who can nowadays benefit from a plethora of services
offered by the network, whose importance is increasing over time.
The network is currently composed of heterogeneous systems and services provided by
leading IT companies. The increasing complexity of current networks raises a number of
interoperability issues. The capability to modify the configuration of a network system
through direct intervention is impractical except in rare cases, like closed and isolated
environments. Due to these problems, the process of integration of new technologies in
the network is slow, leading to the phenomenon known as “ossification” [1]. This event is
expressed through a high rigidity of the current infrastructure, which is poorly elastic in
adapting to dynamic requirements. This rigidity is linked to the devices that compose the
network itself, i.e., routers, switches and middleboxes. These elements are frequently closed
systems with proprietary interfaces provided by the manufacturers and limited control over
the devices. Sometimes the issue stems from the network protocols. These considerations
led to the introduction of the Software Defined Network (SDN) paradigm, providing a
network that can be updated with simplicity, is easy to maintain, and has high flexibility
and adaptability to new services and technologies. SDN divides the control plane from the
data plane, and its resiliency is of primary importance.
The resilience of a network consists of the ability to recover logical control after the
detection of a failure within a specific time window [2]. In computer networks, resilience
is a key factor (a failure of a few milliseconds in a high-speed connection leads to a packet
loss in the order of terabytes). In traditional networks, where data and control packets
are sent on the same link, control and data information incur the same damage in case
of network failures. Since the control plane and the data plane have the same resilience
qualities, the prior work on evaluating the resilience of a network has assumed an in-band
control model. However, this model does not apply to the divided architecture of SDN. For
instance, control packets in SDN networks can be transmitted on paths different from data
packets (or even in separate networks). For this reason, the reliability of the control plane
International Journal of Computer Networks & Communications (IJCNC) Vol.14, No.5, September 2022
101
DOI: 10.5121/ijcnc.2022.14507

in this type of network is not linked to that of the forwarding plane. On the other side,
cutting the two planes off could render the forwarding plane utterly useless. For example,
when a switch is cut off from the control plane, it is unable to receive any instructions on
how to forward fresh incoming packets, thus going offline [3].
In this study, we compare the primary strategies for resolving the software-defined
networking challenge of controller placement. We reproduce the execution of the algorithms
on an emulated testbed, and select the controller node according to the chosen algorithm.
The algorithms are executed on several network topologies. We then analyze the resiliency
of the algorithms in the case of link failure. We create a dynamic critical situation through
a progressive link disconnection to check the algorithm resiliency, up to a completely
unrecoverable state.
In Section 2 we present state-of-the-art solutions to improve SDN resiliency. After the
presentation (Section 3) of a general SDN resiliency model, in Section 4 we focus our
attention on the controller placement problem (CPP). In Section 5 we survey a selection
of algorithms that can determine the optimal placement of the controller, especially in
large network systems. In Section 6 and Section 7 we compare the behaviour of these
algorithms through a series of experiments that emulate link failures. Section 8 contains
some concluding remarks.
2 Background and Related work
The requirements for improving network properties that led to the introduction of the SDN
paradigm, are not easy to meet. In fact, they impose numerous structural changes that
hard to implement. The main IT companies have hence focused on controlling the network
in a direct way in order to provide it with the required elasticity and simplicity [4]. The
Internet Engineering Task Force (IETF) took the first step towards the SDN paradigm’s
practical implementation in 2004 by defining a standard interface called Forwarding and
Control Element Separation (ForCES) . The network structure was completely redesigned,
with a sharp separation between the physical level of the control (routing) and forwarding
functions. Among the first implementations of this new paradigm, the most relevant ones
were the Ethane project [5] and its direct predecessor, called SANE [6]. The mentioned
projects designed a logically centralized, flow-level solution for access control in enterprise
networks. Ethane introduces the concept of flow tables, ad hoc defined data structures
that are populated only by the controller based on high-level security policies. It paved
the ground to the creation of OpenFlow, as the simple switch design became the basis of
the original OpenFlow API.
In 2008, the first version of the OpenFlow protocol was created, which relied on the
structure described by ForCES. NOX, an operating system for networks [20], was released
the same year. The introduction of these protocols aroused the interest of manufacturers,
who began to develop the first networks based on OpenFlow switches at both academic
and prototype level. In 2011, the Open Networking Foundation (ONF) was established,
officially endorsing the introduction of the SDN idea and its protocol-level implementation
through OpenFlow.
In a nutshell, Software Defined Networking (SDN) is a new approach to network man-
agement. In an SDN architecture, the routing (Control Plane) and forwarding (Data Plane)
processes are decoupled, the intelligence and the network status are logically centralized,
and the underlying network infrastructure is transparent to applications.
The SDN paradigm structure can be divided into three layers (Planes) in order to allow
for an easy understanding of the operation and the components involved: Data, Control
and Application Plane.
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Terminologies and components that are useful to describe the role of such layers are
briefly described below:
– SDN Application: an SDN Application is a program that explicitly, directly, and pro-
grammatically communicates to the SDN controller the requests and behaviors that
the network must take. This happens through the so-called North Bound Interface
(NBI). In addition, applications can use an abstract view of the network to make in-
ternal decisions. Thus, an SDN application consists in an application logic and one or
more NBI interfaces.
– SDN Controller: the process of translating SDN application requests to the SDN dat-
apath and giving the application an abstract picture of the network is performed by
the SDN Controller, a logically centralized device (together with relevant statistics and
events). An SDN controller is composed of one or more NBI agents, a SDN control
logic, and a so-called Control to Data-Plane Interface (CDPI).
– Datapath SDN: The Datapath SDN is a logical network device that provides visibility
and undisputed control over its public forwarding and data processing capabilities.
The underlying physical resource may be entirely or partially included in the logical
representation. The datapath consists of a CDPI agent and a group of one or more traf-
fic forwarding engines and may or may not have traffic processing functions. Engines
and functions can incorporate simple forwarding between external datapath interfaces,
traffic processing functions, or termination functions. Datapaths might exist on a sin-
gle physical network node or across multiple ones. Similarly, a single datapath can be
defined through the use of multiple network elements. The logical definition does nei-
ther prescribe nor preclude implementation details such as physical mapping, shared
physical resources management, virtualization, or datapath partitioning.
– Control to Data-Plane Interface (CDPI): The CDPI is a defined interface between a
controller and a datapath, that provides at least a code-level control of all forwarding
operations. It also has capabilities for publishing, reporting statistics, and event noti-
fications. A feature of SDN is the expectation that the CDPI will be implemented in
an open, manufacturer-neutral, and interoperable manner.
– NorthBound Interface (NBI): NBIs are interfaces that stand between applications and
controllers and generally provide abstract views of the network and enable direct ex-
pression of network behavior and requests. This can happen at any level of abstrac-
tion and through different groups of features. In this case, the SDN can express itself
through a free and open implementation of the interface.
Each presented interface is implemented through a coupling with an agent, where the
agent represents the lower side, i.e., the side facing the infrastructure, and the interface
represents the upper side, i.e., the side facing the application. We now examine the inter-
action among these components through the three planes of operation.
At the bottom layer, the Data Plane is composed of network elements whose dat-
apaths display their capabilities through the CDPI agent. On top of that there are the
SDN applications within the Application Plane, which communicate their requests through
NBI interface drivers. In the center, the controller translates these requests and exercises
low-level controls through datapaths, while providing information to SDN applications.
However, network management is required by an administrator, who is responsible for
configuring the network elements, assigning SDN datapaths within the controller, and
configuring the policies that define the purpose of the control or the application that uses
it. This type of network can coexist with a non-SDN infrastructure, especially when mi-
grating to a network that is fully enabled. The operation described may lead us to think
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that the network is based on the use of physically centralized controllers. Actually, the
description does not imply that the controller is implemented as a single device. For per-
formance, scalability, and/or availability issues, a centralized SDN controller can be used
at the logic level. This way, the controller benefits from a multitude of instances that co-
operate for network control and proper application operation. With the SDN, the control
plane behaves as a single network operating system logically centralized both from the
point of view of resource management and its conflicts and as an abstraction of the details
of low-level devices (such as electrical and/or optical transmission). In addition, there is
a fourth layer, called Management Plane, which provides coverage of tasks that are more
easily manageable outside of the other plans. Some examples can be the management of
the economic relationships between providers and clients, the allocation of resources to
the client, or the setup of physical equipment [7]. Of course, every economic entity has
management bodies. In any case, communication between them goes beyond the purposes
of the SDN architecture. One of the solutions is the convolution of the known management
tasks from the traditional network within the CDPI. However, the description of the SDN
paradigm remains very generic, leaving developers with many possibilities for implementa-
tion at the physical level. In fact, it is possible that two totally different networks interact
with each other from a component/structural point of view, maintaining and exploiting
SDN functionality without incurring any issues.
The Controller Placement Problem (CPP) was first introduced in 2012 [8] and it was
soon clear that the CPP is NP-hard, even if a simple k-center algorithm is adopted [9].
A review of the proposed algorithms is presented in [10] and provides a taxonomy of
the CPP proposals. The authors claim the importance of relying on multiple controllers
to manage large networks. The CPP can determine the minimum number of controllers
required to manage a system, as well as the optimal position. The algorithms are divided
into two classes, namely uncapacitated and capacited controllers. As the term suggests,
the former class is characterized by controllers having unlimited capacity (the opposite
being true for the latter class). Another conclusion of this survey is the importance of
CPP algorithms capable of managing network fractioning in the event that large networks
require the division into sub-systems to reduce complexity. Many studies classify CPP on
the basis of several factors or metrics related to SDN performance and QoS. The analysis
in [11] analyzes factors including delay, cost, and reliability, but offers no information
into resilience. In [12], the resilience is dealt with and modeled in accordance with the
controller, link, or switch failure. The sudies in [13] [14] also reported from a comparison
of the key ideas and improvement strategies dealing with the link failures. In [15], the
resilience depends on many controllers connected to a switch in order to meet specific
quality of service standards. To handle the switch traffic load, the system accounts for
the switch-controller, inter-controller, and controller capacity latency requirements. The
work presented in [16] offers an optimization model for installing controllers and mapping
switches to controllers while ensuring full resilience against a predetermined number of
controller failures, which is similar to our approach.
3 SDN resiliency model
Before delving into the resilience property of an SDN network, it is necessary to first
introduce the SDN network model [17].
Let us consider a single SDN domain where switches are grouped under a single con-
troller. The physical network can be represented as a graph, G(V, E), where V is the
collection of switches (or nodes), and E is the collection of network links. The link be-
tween two nodes u and v is represented as (u, v). We represent the controller as Vc (where
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Vc ∈ V ). Let us consider a control network composed of multiple switches and one con-
troller, already selected to allow the transmission of control signals. The active connections
are in-band, which means there are no links dedicated to controlling traffic. In addition,
there is no load balancing on the controlling traffic, so all nodes will only have one single
route to the controller. Hence, the controlling traffic is sent to and from the controller
through a tree, where the controller is configured as the root. This structure is called
Controller Routing Tree. Once the control network structure is defined, the switches on
the network are divided in two types: protected
¯
and unprotected
¯
. To differentiate between
these two types of switches, we first define a node j as upstream
¯
for a node i if j provides
a path for i to the controller Vc.. Otherwise, j is a downstream
¯
node for i. Then, a switch
is protected against failure if it can use an outbound backup link for the control traffic
towards the controller. More specifically, a switch A is secured if and only if the network
contains a switch B that satisfies these two requirements: 1) Switch B is not a downstream
switch (or it is a parent for A, where parent of A denotes the set of A’s upstream switches).
2) There is a link between A and B which is not part of the controller routing tree. If an
output link or an upstream node of a protected switch fails, it can immediately change
the path towards the controller and use the backup link to reconnect to the controller as
soon as the failure is detected. Hence, the re-routing of control traffic happens with local
change and without affecting the connections of the other switches.
In this type of network, the switches can not inform the downstream switches about
faults. As a result, even if all downstream nodes are protected, if a switch is disconnected
from the controller, all downstream nodes will also be disconnected. This means that
evaluating the resilience of a network requires assigning a weight to switches that have
multiple downstream nodes (generally, the assigned weight is equal to the number of
downstream nodes). We define the weight of the routing tree as the sum of the weights of
all unprotected nodes. This number is used to assess a network’s degree of security (the
lower this value, the more protected the network). For a given routing tree T, we will refer
to this weight with Γ(T).
4 Controller Placement Problem (CPP)
The Controller Placement Problem is a key issue in building a resilient and reliable net-
work. This problem is related to the positioning of the the controller so to provide max-
imum protection of the network nodes, as well as a reduction in propagation delays [9].
The weight of the network can be significantly reduced with the right controller posi-
tioning, which forms the basis for selecting the routing tree method. In this section we
will consider a general routing policy (in order to focus mainly on CPP), which, given a
controller position, will select the primary network paths with the least number of nodes
crossed in order to send control traffic. Let’s see with an example how the positioning of
the controller affects the resilience of the network:
As the network is a complex system, it is important to define a parameter like the
centrality to define the importance of a node within a network. A node with the best
centrality is the candidate to play the role of the controller [18]. The Closeness Centrality
Theorem makes it possible to implement a resilient network. For a given graph G(V, E),
the closeness centrality of a node Vk can be defined as: Cc(Vk) = 1
d′(Vk) , where: d′(Vk) =
1
(n−1)
P
vk̸=vj
d(k, j)
The value d(k,j) is the minimum distance between k and j [19]. The equation can then
be written like this: Cc(Vk) = (n − 1)/(
P
vk̸=vj
d(k, j))
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As a result, we may state that a node’s proximity centrality Vk is the inverse of the
average distance that separates it from other nodes in the network.
Another important factor in choosing the controller location is the latency of the
network. We introduce two types of latency, which we will consider in the evaluation of
the responsiveness of the network. Average-case latency is the average propagation latency
for a given controller location. Given two nodes v, sinV and a number of nodes n = |V |,
the propagation latency for a controller positioning S’ is as follows:
Lavg =
1
n
X
v∈V
min
s∈S′
d(v, s) (1)
Once more, the shortest distance between the two nodes is represented by d(v, s). The
objective is to locate a site S′ where the latency Lavg(S′) and cardinality |S′| = k are as
low as feasible.
An alternative approach is worst-case Latency, i.e., measuring the maximum propa-
gation latency from the node to the controller: Lwc(S′) = maxv∈V mins∈S′ d(v, s) In this
instance, we are also searching for the lowest S′subseteqS. There are of course other met-
rics for measuring latency. These two evaluation techniques were chosen because they
consider the distance to each node, providing a more general view of the network.
5 CPP algorithms
In this section, we will analyze the main algorithms used to solve the Controller Placement
Problem.
5.1 Optimized Placement Algorithm (OPA)
To search for the optimal controller location, OPA looks for the node that maximizes
resilience across all possible network locations [18]. The algorithm outputs the controller
position based on the entire G(V, E) network topology with |V | = n:
Fig. 1. Pseudo-code of the Optimized Placement Algorithm (OPA)
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As presented in the pseudo code OPA implementation in Fig. 1, at line 5, the protection
of a specific switch is evaluated according to the definitions presented in the previous
section. The last step of the algorithm (line 10) is to seek the position that minimizes
the weight of the network, thus maximizing its robustness. The nature of this algorithm
entails that the optimal position in a network is always chosen (hence the name), making
it the best placement algorithm in theory. However, in big networks, scanning across all
potential nodes might become prohibitively expensive and time-consuming. As the network
grows in size, the result of takeover process becomes less and less effective. The algorithm
described in the next section is based on heuristics that allow good positioning of the
controller without making major compromises.
5.2 Greedy Control Placement Algorithm
The Greedy Controller Placement Algorithm is a heuristic method to find the best candi-
date position for the controller. This algorithm can be conveniently executed if we represent
the network graph through an adjacency matrix. This is a square binary matrix, the size
N being the number of nodes. Each matrix element (i, j)can be 1if and only if there is an
edge in the graph connecting vertex i to vertex j; otherwise, there is a 0. The adjacency
matrix is slightly modified to include a weight w(i, j) instead of a simple binary value to
indicate the cost of the link (i, j).
The algorithm starts by sorting the nodes in descending order according to their degree,
that is, the number of siblings a node is directly connected to. To do this, a table T of
size N is created to contain the list of nodes sorted according to their degree. In the
second step, each switch is inspected from the top of the table T to evaluate the number
of protected nodes. The element with the highest number of protected nodes is then the
candidate controller.
5.3 K-Median Algorithm
The K-Median Algorithm employs a modified version of the adjacency matrix, which
contains the latency value for each link (i, j) if the link exists, otherwise it is set to 0. If
such a path exists, the Floyd-Warshall algorithm can calculate it for each pair of nodes in
V . This step leads to the evaluation of the average propagation delay from all the nodes
towards a candidate node. Hence, it is possible to compute the average latency from each
node to a candidate controller placed in S′:
Lmin =
1
n
X
v∈V
min
s∈S′
d(v, s) (2)
The position that minimizes such an average is selected for the placement of the con-
troller.
6 Comparison between OPA and GPA
For a first comparison between OPA and GPA, a network consisting of 30 nodes has been
created in Mininet, as depicted in Fig. 2
If we compute the optimal position through the Optimal Placement Algorithm, the
output of the process is reported as candidate node s1. On the other hand, with the Greedy
Placement Algorithm, the given output position is s2. In the first case, the controller is
directly connected to 3 protected nodes, while in the second case, the controller is directly
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Fig. 2. The node circled in green (s1) is the selected node for the controller according to the OPA. The
node circled in red (s2) is the position chosen by the GPA.
connected to 6 protected nodes. The total number of links is 52. The first test gauges how
resilient the network actually is in the face of link failures. We expose the network to a
sequence of disconnections to evaluate the effective communication capability among all
switches. A ping connection between node 3 and node 18 is kept active throughout all
the experiments. We present the results in the following graphs. In each graph, the x axis
reports mainly the ICMP sequence number of the stream between node 3 and node 18. On
the same axis it is reported the progressive number of disconnected links. The measured
delay is reported on the y axis. For better readability, the disconnections are pointed out
with red X
¯
, and the subsequent ICMP lost packets are not reported on the graphs 1 .
1
Experimental raw data are available at https://github.com/maudarie/SDN cpp/
108

Fig. 3. Greedy Placement Algorithm resiliency test
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Thus, the first ICMP packet after a disconnection is the first successful reply. Fig. 3
presents the results of four experiments, in all of which the GPA is enabled. In each of the
experiments, the disconnected links are randomly selected.
Similarly, Fig. 4 presents the results of four experiments in which the OPA is en-
abled. Also, in this latter case, the disconnected links are randomly selected in each of the
experiments.
Fig. 4. OPA resiliency test
As a final result of our experiments, the maximum number of links that can be discon-
nected is 23 for the OPA and 18 for the GPA. Such figures ensure full operation with 44.2%
and 34.6% disconnected nodes, respectively. As expected, the OPA has greater network
robustness than the GPA. Greedy Placement, on the other hand, has better computational
simplicity, especially when evaluating positioning in more complex networks.
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7 Comparison between GPA and the K-median algorithm
We compared the implemented GPA and K-median algorithms in an emulated network
topology. For implementing the K-median algorithm, an algorithm was developed that
creates a single cluster containing all of the network nodes. This is done on purpose, in
order to allow for a fair comparison with the Greedy Control Placement. The testing
framework is based on the Mininet environment. The sample topologies are composed of
Open vSwitch virtual switches, configured and managed through the OpenFlow Protocol.
The CPP algorithms are compared on several network topologies configured in a mininet
environment. The first topology in Fig. 5 is composed of 15 nodes and 27 connections.
Fig. 5. The node circled in green (s3) is the controller according to GPA. The node circled in yellow (s13)
is the position chosen by the K-Median algorithm
In this topology, the GPA output is reported as candidate node s3. By applying the
K-Median algorithm we instead observe that the given output position is in s13. In the
first case, the controller is directly connected to 4 protected nodes, while in the second
case, the controller is directly connected to 3 protected nodes. The average propagation
latency referred to node 3 is 2, 066ms, while that referred to node 13 is 1, 733ms.
A second topology is composed of 20 nodes and 40 connections, as depicted in Fig. 6
In this case, the outcome of both algorithms indicates the candidate node s10 for the
placement of the controller. The controller is directly connected to 5 protected nodes. The
average propagation latency referred to node 10 is: 1.75ms.
The third topology in Fig. 7 is composed of 32 nodes and 63 connections.
By computing the optimal position through the Greedy Placement Algorithm, the
output of the process reports the node s4 as candidate. If we apply the K-Median approach,
the given output position is instead s10. In the former case, the controller is directly
connected to 7 protected nodes, while in the latter, the controller has no connection with
protected nodes. The average propagation latency for node 4is2.4375ms, while for node
10itis2.0625ms.
The fourth topology is presented in Fig. 8 and is composed of 30 nodes and 52 con-
nections.
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Fig. 6. The red circled node (s10) is the selected node for the controller according to both GPA and K-
Median
Fig. 7. The node circled in green (s4) is the selected controller node according to GPA. The one circled in
yellow (s10) is instead chosen by K-Median
The optimal position reported by the Greedy Placement Algorithm is node s2. By
applying the K-Median approach, the resulting position is instead node s22. In the former
case, the controller is directly connected to 6 protected nodes, while in the latter case it
has direct connections with 3 protected nodes. The average propagation latency referred
to node 2 is 3.3ms, while that referred to node 22 is 2, 566ms.
The fifth topology, depicted in Fig. 9, is composed of 9 nodes and 15 connections.
The optimal position computed through the Greedy Placement Algorithm is node s7.
By applying the K-Median approach, we find the selected candidate is instead node s3.
In the former case, the controller is directly connected to 4 protected nodes, while in
the latter case it has 3 direct connections with protected nodes. The average propagation
latency referred to node 7 is 1.33ms, while that referred to node 3 is 1.22ms.
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Fig. 8. The node circled in green (s2) is the selected node for the controller according to GPA. The one
circled in yellow (s22) is instead chosen by K-Median
Fig. 9. The node circled in green (s7) is the selected node for the controller according to GPA. The one
circled in yellow (s3) is instead chosen by K-Median
We finally provide a comparative view of two parameters of interest associated with the
controllers selected by the two algorithms, namely: (i) the number of adjacent protected
nodes; (ii) the average propagation latency.
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Fig. 10. Number of adjacent protected nodes of the controllers chosen by the two algorithms
Fig. 11. Average propagation latency of the controllers chosen by the two algorithms. A low value corre-
sponds to higher network performance.
Fig. 12. Interpolation of the number of adjacent nodes and the average propagation latency. High values
mean better performance and reliability for the network.
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As a result of the experiments carried out, we notice that there is a considerable
discrepancy between GPA and K-Median with regard to the number of protected nodes; if
we analyze instead the average propagation latency, we do not find substantial differences
between the two algorithms. These findings lead us to the conclusion that the Greedy
Placement Algorithm is the most suitable option to locate the controller in a network
where rapid response times and high speed are desired.
8 Conclusions
In this work we have evaluated the resiliency of the main algorithms for the Controller
Placement Problem in SDN networks. We compared the Optimal Placement Algorithm,
the Greedy Placement Algorithm and the K-Median algorithm. We tested their resiliency
on emulated network topologies, by gradually increasing the number of link disconnections.
From the presented experimental results carried out on several topology configurations,
we were able to derive interesting considerations about the respective performance of
the analyzed approaches. We were particularly interested in investigating the different
reactions to the disconnection test. Based on the results of the testing campaign, we argue
that, depending on parameters like the type of the network, its structure, and density, it
is possible to select the algorithm that strikes an optimal balance between reliability and
performance.
Conflicts of interest
The authors declare no conflict of interest.
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