Cognitive routing in software defined networks using learning models with latency and throughput constraints

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 13, No. 1, March 2024, pp. 756~763
ISSN: 2252-8938, DOI: 10.11591/ijai.v13.i1.pp756-763  756
Journal homepage: http://ijai.iaescore.com
Cognitive routing in software defined networks using learning
models with latency and throughput constraints
Nagaraju Tumakuru Anadanaiah1,2
, Malode Vishwanatha Panduranga Rao3
1
Electronics Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru, India
2
Department of Electronics and Communication Engineering, Government Engineering College, Ramanagara, India
3
Computer Science & Engineering, Faculty of Engineering & Technology, JAIN (Deemed-to-be University), Bengaluru, India
Article Info ABSTRACT
Article history:
Received Sep 20, 2023
Revised Oct 8, 2023
Accepted Oct 21, 2023
To address latency and throughput challenges in software defined networks
(SDNs), the research investigates cognitive routing's revolutionary
implications. In today's data-driven world, network performance optimisation
is crucial. Cognitive routing is a dynamic and potentially disruptive network
management technology. Cognitive routing, strengthened by reinforcement
learning and adaptive decision-making, is crucial to network efficiency and
responsiveness, according to our study. The results show that cognitive
routing optimises performance by limiting delay and maximising throughput.
SDN application cognitive routing engine (CRE) driving forces, design, and
preliminary assessment are described in this article. The CRE finds almost
optimal paths for a user's quality of service (QoS) need while minimising
monitoring overhead. Instead of global monitoring to find optimal paths, local
monitoring achieves this. In ad-hoc networks, finding a trustworthy path
reduces latency and ensures network stability. The proposed system was
simulated utilising many parameters. Compared to previous SDN-based
systems, end-to-end latency and ping round-trip time were better.
Keywords:
Cognitive routing
Cognitive routing engine
Reinforcement learning
Round trip time
Software defined networks
This is an open access article under the CC BY-SA license.
Corresponding Author:
Nagaraju Tumakuru Anadanaiah
Department of Electronics & Communication Engineering, Government Engineering College
Ramanagara, Karnataka, India
Email: nagarajuta76@gmail.com
1. INTRODUCTION
In the task of managing and running computer networks, the introduction of software defined
networks (SDNs) marks a significant shift in strategy. SDN is a design approach that isolates the control plane
(which decides on routing) from the data plane (which sends packets) [1]. Because of this split, network
managers may more easily monitor and control network resources in a centralised, programmable fashion,
which has several advantages. With SDNs, the duties of controlling the network are separated into a single,
logical component called the controller or SDN controller. This consolidation offers a bird's-eye perspective
of the network, letting admins make informed choices depending on the status of the system and the volume
of traffic. SDNs increase a network's adaptability and flexibility. Software applications can specify and modify
network policies and configurations by communicating with the SDN controller through well-defined
application programming interfaces (APIs) [2]. The capacity to modify and roll out new services is simplified
by this programmability.
With SDNs, traffic can be engineered and optimised at a granular level. Network performance, congestion,
and resource utilisation can all be enhanced by SDNs' ability to dynamically reroute traffic depending on real-time
conditions and requirements. The scalability of SDNs is built in. The SDN controller can effectively manage the
network even when the number of connected devices develops in tandem with the network's traffic load [3]. SDNs

Int J Artif Intell ISSN: 2252-8938 
Cognitive routing in software defined networks using learning models … (Nagaraju Tumakuru Anadanaiah)
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allow for more effective distribution of network assets. SDNs can prevent wasteful over-provisioning of network
resources by the dynamic modification of network pathways and optimisation of traffic flow.
Applications that depend on real-time responsiveness and quick data transfer necessitate networks with
low latency and high throughput. However, there are several obstacles that must be overcome in SDNs before
low latency and high throughput can be achieved concurrently. The first is network congestion management.
Congestion in a network happens when a certain connection or portion of the network is overloaded with data.
The result may be slowed connections and longer wait times [4]. Congestion management techniques, used by
SDNs to identify and relieve congestion in real time by rerouting traffic to less crowded pathways or modifying
quality of service (QoS) policies on the fly, are a necessary component of any successful SDN.
The second is QoS enforcement. To guarantee low latency and high throughput, QoS standards must
be strictly enforced to give some traffic types more priority than others. It can be difficult to strike a balance
between these two goals. For SDNs to function, QoS mechanisms must be implemented to distribute network
resources in accordance with specified criteria, giving priority to latency-sensitive traffic without depriving
other traffic types of enough bandwidth [5].
The third is path selection and optimization. It might be difficult to figure out where data packets
should go in a network if they need to achieve both low latency and high throughput. These considerations
may not be considered sufficiently by common routing systems. To dynamically route traffic for best
performance, SDNs require the use of sophisticated path selection algorithms that take into consideration
variables such as latency, available bandwidth, and network load [6].
The fourth is latency-sensitive applications. Real-time video conferencing and online gaming are two
examples of applications that are very sensitive to latency. It's possible that these programmes can't find the
low-latency links they need using conventional routing [7]. SDNs may prioritise latency-sensitive traffic by
determining which applications are most affected by delays and allocating resources accordingly.
The fifth is network slicing for diverse traffic types. Different types of traffic need different network
characteristics, such as latency and throughput. It might be difficult to guarantee top performance for every
sort of traffic. Network slicing is a feature of SDNs that enables the construction of separate, optimised network
segments for various types of traffic. You may adjust the latency and throughput of each slice separately [8].
SDNs require careful consideration of network congestion, QoS policies, dynamic traffic patterns, path
optimisation, application sensitivity, hardware capabilities, security, and the capacity to manage varied traffic
types if low latency and high throughput routing are to be achieved. SDNs offer the adaptability and
programmability essential for overcoming these obstacles and maximising network performance.
Using cutting-edge technology like machine learning (ML) and artificial intelligence (AI), cognitive
routing takes an intelligent and adaptable approach to network routing to improve routing decisions and overall
network performance [9]. It surpasses conventional routing algorithms by constantly analysing network data and
making real-time adjustments to routing choices to accommodate user-defined requirements. Cognitive routing is
a form of intelligent routing that mainly utilises machine learning algorithms and other forms of artificial
intelligence [10]. These algorithms consider the status of the network, past traffic patterns, and other factors to
determine the most efficient routes to take. Cognitive routing relies heavily on its ability to learn and adapt over
time. With time and experience, the system learns to make better routing decisions. It can quickly adjust to new
traffic patterns and evolving network circumstances because of its flexibility. Decisions made by cognitive routing
systems can be dynamically modified to meet user-defined goals and limitations. Low latency, high throughput,
and QoS needs may all be met because to this flexibility [11].
In the context of today's networked world, the suggested study on "Cognitive routing in software defined
networks using learning models with latency and throughput constraints" is of great importance. Optimising
network performance is essential due to the ever-increasing need for low-latency, high-throughput network
services. Cognitive routing's flexibility to respond to changing conditions and limits holds great promise for
enhancing both network performance and user satisfaction. Intelligent routing solutions are more important than
ever with the rise of 5G networks, edge computing, the internet of things (IoT), and data-intensive applications.
Cognitive routing can assist networks in adjusting to the peculiar demands and difficulties of such innovations.
As network infrastructures grow, scalable routing solutions become increasingly important. Because of its
flexibility and capacity to learn, cognitive routing is suitable for use in networks of varying sizes. The potential
for the proposed works to advance network technology and enhance the overall quality of network services stems
from their applicability across a wide range of industries.
2. COGNITIVE ROUTING IN SOFTWARE DEFINED NETWORKS
Most existing approaches to SDN traffic engineering work on the assumption that, in a logically
centralised controller, SDN provides an overview of the network's current status and topology, allowing
network optimisation via various global traffic engineering techniques such as constrained shortest path first.
In this research, we claim it is inefficient to collect complete and up-to-date status data for the entire network

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at the frequency needed for optimal traffic optimisation because of how vast networks might be. The present
study utilises the cognitive routing engine (CRE) within the context of cognitive packet networks (CPNs) [12],
[13]. In this framework, individual routers employ distinct routing and learning algorithms for each source-
destination pair and QoS specification. Additionally, the network status is gathered through the utilisation of
intelligent packets and acknowledgement packets. As opposed to CRE, which operates decentralized on top of
the SDN controller and relies solely on OpenFlow (OF) methods to ascertain network status, OF is a
conceptually centralised layer that handles all network state gathering [14]–[16]. Figure 1 illustrates the
integration of the CRE application inside the SDN architecture and its interaction with various components in
a standard deployment scenario.
Figure 1. Network architecture
The steps in message exchange are:
- The network forwarding element (NFE) N1 receives a fresh flow F1,3 that must reach the NFE N3.
- The network function element N1 is unable to locate a corresponding rule inside its internal flow tables
for the initial packet of flow F1,3. N1 encapsulates the packet within an OF Packet-In message and sends
it to the master controller. This action is triggered by the NFE's default behaviour in response to a table-
miss event, which is to forward the packet.
- To enhance the interpretability of the OF Packet-In message for the many applications that function on top
of the SDN controller, it will be subjected to dissection.
- The data format delivered by the controller allows the apps that were notified to determine whether they
wish to take any action.
- After the Link Discovery programme has uncovered the topology of the network, CRE will install a route
for flow F1,3 by determining the shortest route between NFE N1 and N2. The network monitoring module
(NMM's) job in CRE is to collect data on the network's status so that cognitive routing algorithm module
(CRAM) can function.
- For the CRAM to compute the numerical value of the objective function of every route it has chosen, it
sends requests to the NMM for specific characteristics of connections in the network and the NMM
responds by updating the recurrent neural networks (RNNs) with return loss (RL) so that the CRAM can
select more suitable routes in future attempts. Because of its intelligence, NMM doesn't need to constantly
poll the network for fresh information; instead, it uses its cache of previously collected data to respond to
CRAM's queries.
- After receiving input from the NMM, CRAM uses RL to update its RNNs and then outputs the optimal
routes. The “Path-to-of translator module” (PTM) is then given the routes to update if necessary.
- The PTM must alter the flow table(s) inside national fire equipment (NFEs) to implement the routes such
that it prevents packet loss and forwarding loops.
2.1. Reinforcement learning in SDNs
The concepts at the heart of ML are not very complicated; they are essentially a model of how humans
learn and develop. To put it another way, the ML approach relies more on induction and synthesis than
deduction to arrive at its conclusions as shown in Figure 2. Different criteria may be used to categorise ML
approaches. The ML models may be broken down into several categories depending on the type of task being
performed, including regression, classification, and structured learning. Many classification and prediction
issues have been successfully solved using ML algorithms [17]. Here we look at ML algorithms from a
classification standpoint and demonstrate a traditional ML approach used in SDNs.

759
Figure 2. A comparison of ML reasoning with that of humans
An agent learns to reward guided behaviour through trial and error using RL, in which the agent
receives reinforcement from its interactions with the environment. To maximise the reinforcement signal, an
RL system (RLS) would constantly fine-tune its settings. The environment's reinforcement signal is an
appraisal of the outcome, good or poor, rather than instruction on how to generate the desired behaviour.
According to our findings, RL is typically employed to promote robustness and scalability [18], [19], and it
enables choosing routes or route optimisation in SDNs [20], [21]. When delay minimise and throughput
enhancement are used as the operation and maintenance approach for deterministic policy gradient (DDPG)
routing optimization mechanism (DROM) [22], the network's performance is enhanced with reliable and
superior routing services, and convergence and effectiveness are boosted. By detecting and learning from the
internet of vehicles (IoV) environment, software defined cognitive routing (SDCoR) [23] is the first research
to present an optimum routing strategy that outperforms multiple standard IoV protocols. Having bigger packet
bucket sizes and reducing the number of consecutive packets traversing various pathways are both effective
solutions for dealing with the primary problem posed by a high level of jitter [24].
Some cutting-edge RL study proposals propose combining RL with additional technologies for
enhanced performance. For instance, RNNS with RL is designed to discover the best overlay pathways with
little maintenance [25]. Research into auto-scaling policy decisions led researchers to SRSA [26], a decision
mechanism based on RL. In addition, we explore RL with architectural modifications due to the complexity
and variability of the network environment. To effectively manage networks enabled by Self-Organizing
Networks, [27] presented a scalable solution based on distributed RL. Combining DL with RL, deep
reinforcement learning (DRL) boosts the efficiency and accuracy of RL programmes. DRL has made
tremendous theoretical and practical advances.
3. COGNITIVE ROUTING ALGORITHM
Routing methods for mobile ad hoc networks have proliferated in recent years. To carry out routing
tasks, they rely mostly on real-time rather than forecasted factors. They have no idea the parameters have
changed over time. The channel conditions and link load are ignored by most conventional routing systems. In
this scenario, it is presumed that all links experience the identical channel conditions and load levels.
In this approach, RNN and RL are the basis of CRAM, a routing method. The shortest path for a
particular flow is then used to train a new RNN at each NFE along that path. Each neuron in an RNN is like an
open port on an NFE, and the entire network works together to make predictions. The RNN might use an
experimental or exploitative approach when deciding which output to utilise for the next layer. In exploration
mode, the RNN picks an output port at random; in exploitation method, it picks the neuron with the greatest
probability. In the absence of an existing RNN for this flow at the next hop NFE, a new RNN is trained to
represent it.
During the exploitation phase of the RNN, the selection of the next node in the NFE process is influenced
by the individual excitation levels of each neuron. To determine neuron i's potential (qi), we can use (1).
𝑞𝑖 =
𝜆𝑖
+
𝑟𝑖+𝜆𝑖
− (1)
The collective positive and negative potentials originating from all interconnected neurons in the RNN and
affecting neuron 𝑖 are represented as 𝜆 + and 𝜆 −, respectively. These global potentials may be estimated as
follows, considering both the individual neuronal potentials and the connection weights,

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𝜆𝑖
+
= ∑𝑗∈𝑁 𝑞𝑗𝑤𝑗,𝑖
+
+ Λ𝑖
+
where 𝑗 ≠ 𝑖 (2)
𝜆𝑖
−
= ∑𝑗∈𝑁 𝑞𝑗𝑤𝑗,𝑖
−
+ Λ𝑖
−
where 𝑗 ≠ 𝑖 (3)
The constant rates at which external positive and negative pulses reach a neuron are denoted by Λ𝑖
+
and Λ𝑖
−
.
The normalisation factor 𝑟𝑖 is determined by (4):
𝑟𝑖 = ∑𝑗∈𝑁 [𝑤𝑖,𝑗
+
+ 𝑤𝑖,𝑗
−
] where 𝑗 ≠ 𝑖 (4)
Each link's positive 𝑤𝑖,𝑗
+
weight and negative 𝑤𝑖,𝑗
−
weight is calculated via RL. The following expression
determines the exponential average value of the objective function 𝑂𝑠𝑑[𝑡] of an entire route.
𝑂𝑠𝑑[𝑡] = 𝛼𝑂𝑠𝑑[𝑡 − 1] + (1 − 𝛼)(𝑜𝑠𝑑[𝑡]) where 0 < 𝛼 < 1 (5)
Where 𝑂𝑠𝑑[𝑡 − 1] is the objective function's value at time step t1, 𝑂𝑠𝑑[𝑡] is the objective function's value at
time step t and α is an exponential average parameter that defines how much weight t1 should be given to t1's
value in the objective function. The link weights W are renormalized such that they don't keep growing forever
by first determining the revised ri according to the revised W values in (3). Furthermore, CRAM effectively
preserves a record of the most recently identified pathways for each route query. If the current route has been
in operation for a duration beyond the minimal threshold, the optimal path from the available options is
employed for the actual transmission of traffic.
3.1. Latency monitoring
The collection of connection latency information is a crucial aspect of our approach. We adopted the
method described in [28], which generates fabricated Ethernet packets of type 0 to 07c3. The payload of the
transmitted data includes the data path ID and timestamp from the transmitting device. To adhere to the
prescribed Network packet size of 64 bytes, the packets undergo a process known as "padding," wherein zeros
are added to the packets. Every second, the packets are transmitted to all nearby switches. The advantage of
this method is that the measured latency coincides with the latency experienced by the flows.
We are aware that packet reordering may occur during the process of changing from one routing
configuration to another. As a result, it is preferable to reduce the frequency with which routing configurations
are altered. Given that a learning strategy necessitates making routing configuration updates, it is desirable to
minimise the time it takes for the network to converge. Most of the convergence traffic remains unrerouted.
Therefore, the decision is between encountering significant traffic congestion, experiencing extended delays
and lengthy processing times, or opting for frequent adjustments in routing with shorter processing times and
occasional packet disarray. Congestion, which increases latency and decreases throughput, may be detected by
measuring the network's latency, making it a helpful indication of the network's performance.
4. PERFORMANCE EVALUATION
Using the SDN emulator Mininet and NS-3, we tested the suggested method. With this setup, nodes
were moving in three distinct groups, each with a 100-meter communication range in a 2000-by-2000-meter
target area. All the nodes in a cluster travelled at the same rate. However, the rates at which the individual
clusters travelled ranged from around 0 to 25 m/s. The spectrum band was split into M = 5 channels, with each
channel being able to house one of two licenced primary users (PUs) and their respective 500 m communication
ranges. These land-based PU nodes were modelled as having an on/off activity pattern exponential in shape,
with a rate parameter of 0.05. End-to-end delay performance using NS-3 is depicted in Figure 3 as a function
of the total number of nodes and various PU idleness probabilities. The end-to-end latency is the total amount
of time it takes for a packet to travel from its initial source node to its destination node, including all
intermediate hops. As the number of connected devices rises in the testing environment, the average network
latency reduces. The latency lowers across the board as network density and the chance of the PU being in an
idle state are both raised. This is because of the enhanced connectedness brought about by higher network
densities. In contrast, the second scenario results in more available channels due to the PU's increased
likelihood of being in an idle state. All the suggested and reference systems have a large end-to-end delay when
the network density is low, meaning the network is sparse. This is because the requesting node experiences a
significant delay since it is unable to locate any other relay nodes with which to establish a stable connection.
Cluster heads (CHs) in our proposed approach, however, make it possible for a querying node to establish a
reliable connection with a gateway that acts as a relay node, even in sparsely populated networks. Therefore,
the suggested scheme's CH selection further reduces the time.

761
Figure 3. Performance comparison with modified settings
Mininet is an open-source SDN emulator that allows researchers and developers to create virtual SDN
networks for testing and experimentation. It provides a platform for emulating network topologies, hosts,
switches, and controllers, enabling users to simulate complex network environments without the need for
physical hardware. Mininet is employed for the purpose of detecting delays and facilitating route switching
within the network shown. Using the topology shown in Figure 1, this simulation demonstrates how CRE may
locate, monitor, and switch pathways in a network where the latency on links might fluctuate. Upon entering
the network, Ping establishes a connection between source 1 and destination 2. The CRE proceeds to install
two paths: 𝑃1,2 = 𝐻1 → 𝑁1 → 𝑁2 → 𝐻2 and 𝑃2,1 = 𝐻2 → 𝑁2 → 𝑁1 → 𝐻1. The routes that
exhibit the minimum hop count also provide the shortest RTT of 130 ms. The initial Ping has a greater RTT
compared to subsequent Pings. The act of informing the controller about newly generated flows, calculating
optimal routes, and implementing regulations inside NFEs results in additional operational burden.
Subsequently, the CRE commences network monitoring utilising RNN in conjunction with RL. At the 10-
second mark of the trial, there was a noticeable increase in delay for the connections connecting N1 to N2,
namely L1,2 and L2,1. The delay rose from 20ms to 200ms, thus causing the Ping RTT to rise to 430 ms. The
rise in path delays is identified by the CRE system, which monitors the network. The initial path that undergoes
modification is P2,1. An alternate path, 𝑃2,1 = 𝐻2 → 𝑁2 → 𝑁3 → 𝑁1 → 𝐻1, is discovered, resulting in
a reduction of the RTT to 260 ms at the 18th Ping iteration.
Additionally, the path denoted as P1,2 is modified to new P1,2, which represents the route 𝐻1 →
𝑁1 → 𝑁3 → 𝑁2 → 𝐻2. This alteration results in the RTT to 145 ms by the 20th ping as shown in
Figure 4. The two recently discovered pathways identified by CRE have been determined to be the most
optimum options for the new network circumstances.
Figure 4. Delay monitoring and path switching

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According to the findings presented in Table 1, it is evident that the utilisation of CRE can
significantly decrease the amount of monitoring necessary to identify an optimal path. Specifically, the
reduction can reach up to 8.6 times the original requirement. Moreover, the average deviation from the optimal
RTT when using CRE is only 2.0%. However, it is worth noting that the process of identifying the best path
with CRE does require additional time, approximately 37.63 seconds more, when compared to the standard
approach. This time difference is based on a minimum update frequency of 5 seconds.
Table 1. Analysis of CRE ping RTTs
Experiment
iterations
Amount of optimal
monitoring
Number of CRE
monitoring
CRE time (s) Increased CRE
ping RTTs (%)
1 350 30 33.5 3.4
2 350 60 38.6 0.4
3 290 40 43.4 2.0
4 350 25 35 2.2
Average 335 38.75 37.63 2.0
5. CONCLUSION
This research effort has explored the domain of cognitive routing in SDNs, providing insight into its
crucial function in tackling the significant obstacles posed by latency and throughput limitations. The study
conducted emphasises the revolutionary nature of cognitive routing, which leverages machine learning and
adaptive decision-making to greatly improve network performance. The primary results underscore the
significance of cognitive routing in enhancing network performance while concurrently achieving a harmonic
equilibrium between the sometimes-opposing objectives of minimising latency and maximising throughput.
The system's ability to respond in real-time guarantees that there are minimum delays in transmitting data
packets, making it well-suited for applications that need low latency. Additionally, it maximises network
efficiency for operations that involve large amounts of data.
In this study, we demonstrate that the CRE algorithm can identify pathways that closely approximate
the best solutions, while minimising the additional monitoring costs. The performance may be achieved by CRE
without necessitating any alterations to the pre-existing SDNs that operate on OpenFlow. The management of
the network is overseen by the primary controller, while the local controllers, known as CHs, reduce the quantity
of control messages and network latency. In the end, it is important to emphasise once again that the
incorporation of learning models into cognitive routing algorithms has great potential for enhancing network
performance on an ongoing basis. The use of these models allows for cognitive routing to emerge as a
transformative force in the quest for efficient, responsive, and dependable networks in the modern day. This is
made possible by the predictive abilities, ongoing learning, and tailored prioritisation facilitated by these models.
There are several limitations that arise while using the SDN strategy. For the protocol to function properly, it is
imperative that the centralised controller remains in its active state. Failure to do so would result in the protocol
relying solely on localised views of the network, which would provide challenges in identifying alternative
pathways. The performance of a system is contingent upon the connection of several nodes. This study serves
as a strong appeal to both the academic and industrial sectors to go deeper into the investigation and use of
cognitive routing to fully harness its capabilities in improving network operations and user experiences.
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BIOGRAPHIES OF AUTHORS
Nagaraju Tumakuru Anadanaiah obtained bachelor of engineering in
electronics and communication. He has completed master of technology in computer network
engginering from V.T.U, Belgavi. He is currently working as an Assistant Professor in Govt.
Engg. College Ramanagara, Karnataka. He can be contacted at: nagarajuta76@gmail.com.
Malode Vishwanatha Panduranga Rao obtained his Ph.D. degree in
computer science from National Institute of Technology Karnataka, Mangalore, India. He
has completed a master of technology in computer science and bachelor of engineering in
electronics and communication engginering. He is currently working as professor in Jain
(Deemed to be University) Bengaluru, India. His research interests are in the field of real-
time and embedded systems - internet of things. He has published various research papers
in journal and conferences across India, also in the IEEE international conference in
Okinawa, Japan (visited) 2008. He has authored two reference books on Linux Internals.
He is the Life member of Indian Society for Technical Education and IAENG. Now from
past three years, he has published 12 indian patents and three patents are stepping towards
grant status. One research scholar under his guidance was awarded a Ph.D. degree. He can
be contacted at email: r.panduranga@jainuniversity.ac.in.

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