Our journal is official publication of the Utilitas mathematical journal original research articles and aspect of both the pure and applied mathematics. UMJ coverage extends to Operations Research, Mathematical Economics, Mathematics Biology and Computer Science. our Journal has became fully open access Journal.
This document is a term paper on Software Defined Networking (SDN). It discusses how SDN proposes separating the control plane from the data plane in network architecture, making networks programmable. The key points made are:
1) SDN introduces three planes - data, control, and management. The control plane centralizes network intelligence through a controller.
2) Benefits of SDN include simpler network management through centralized control and programming. It also enables network virtualization.
3) The document outlines the layers in the SDN architecture, including the data plane (forwarding devices), southbound interface, network operating system controller, and northbound interface for programming.
The document summarizes the history and principles of software-defined networking (SDN). It discusses how SDN evolved from separating the control plane and data plane in telephone networks. The Internet Engineering Task Force later proposed standards to apply this concept to data networks, including the OpenFlow protocol. SDN aims to centralize network intelligence in a controller to make the network more programmable and flexible. It separates the network into three layers - the application, control, and infrastructure layers connected through APIs. The centralized controller directs traffic flow without touching individual switches, allowing administrators to shape traffic from a software-based control plane.
In software-defined networking (SDN), network traffic is managed by software controllers or application programming interfaces (APIs) rather than hardware components. It differs from traditional networks, which use
switches and routers to control traffic. Using SDN, you can create and control virtual networks or traditional hardware networks. Furthermore, OpenFlow allows network administrators to control exact network behavior
through centralized control of packet forwarding. For these reasons, SDN has advantages over certain security issues, unlike traditional networks.
However, most of the existing vulnerabilities and security threats in the traditional network also impact the SDN network. This document presents the attacks targeting the SDN network and the solutions that protect against
these attacks. In addition, we introduce a variety of SDN security controls, such as intrusion detection systems (IDS)/intrusion prevention system (IPS), and firewalls. Towards the end, we outline a conclusion and perspectives.
Controller Placement Problem resiliency evaluation in SDN-based architecturesIJCNCJournal
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.
Controller Placement Problem Resiliency Evaluation in SDN-based ArchitecturesIJCNCJournal
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.
Software Defined Networking (SDN): A Revolution in Computer NetworkIOSR Journals
Abstract: SDN creates a dynamic and flexible network architecture that can change as the business
requirements change. The growth of the SDN market and cloud computing are very much connected. As the
applications change and the network is abstracted, virtualization become a necessary step and SDN serves as
the fundamental building blocks for the network. Traditional networking devices are composed of an embedded
control plane that manages switching, routing and traffic engineering activities while the data plane forwards
packet/frames based on traffic. In SDN architecture, control plane functions are removed from individual
networking devices and embedded in a centralizedserver. The SDN controller makes all traffic related decisions
in the network without nodes active participation, as opposed to today’s networks.
Keyword-API, cloud computing, IT, middleware, OpenFlow, SDN
This document discusses software defined networking (SDN) and its applications to optical transport networks. It begins with an introduction to the rapid growth of network traffic and need for more programmatic control of networks. It then provides an overview of SDN architecture with separated control and data planes. OpenFlow is discussed as an SDN protocol that can enable programmatic control of optical elements. The document outlines some key characteristics and applications of optical networks, including setting up connection paths and transport virtual private networks (VPNs). It also discusses optical network architectures like ROADMs and different control paradigms like centralized versus distributed control. In summary, the document explores how SDN principles can be applied to optical transport networks to provide more flexible, automated
This document is a term paper on Software Defined Networking (SDN). It discusses how SDN proposes separating the control plane from the data plane in network architecture, making networks programmable. The key points made are:
1) SDN introduces three planes - data, control, and management. The control plane centralizes network intelligence through a controller.
2) Benefits of SDN include simpler network management through centralized control and programming. It also enables network virtualization.
3) The document outlines the layers in the SDN architecture, including the data plane (forwarding devices), southbound interface, network operating system controller, and northbound interface for programming.
The document summarizes the history and principles of software-defined networking (SDN). It discusses how SDN evolved from separating the control plane and data plane in telephone networks. The Internet Engineering Task Force later proposed standards to apply this concept to data networks, including the OpenFlow protocol. SDN aims to centralize network intelligence in a controller to make the network more programmable and flexible. It separates the network into three layers - the application, control, and infrastructure layers connected through APIs. The centralized controller directs traffic flow without touching individual switches, allowing administrators to shape traffic from a software-based control plane.
In software-defined networking (SDN), network traffic is managed by software controllers or application programming interfaces (APIs) rather than hardware components. It differs from traditional networks, which use
switches and routers to control traffic. Using SDN, you can create and control virtual networks or traditional hardware networks. Furthermore, OpenFlow allows network administrators to control exact network behavior
through centralized control of packet forwarding. For these reasons, SDN has advantages over certain security issues, unlike traditional networks.
However, most of the existing vulnerabilities and security threats in the traditional network also impact the SDN network. This document presents the attacks targeting the SDN network and the solutions that protect against
these attacks. In addition, we introduce a variety of SDN security controls, such as intrusion detection systems (IDS)/intrusion prevention system (IPS), and firewalls. Towards the end, we outline a conclusion and perspectives.
Controller Placement Problem resiliency evaluation in SDN-based architecturesIJCNCJournal
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.
Controller Placement Problem Resiliency Evaluation in SDN-based ArchitecturesIJCNCJournal
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.
Software Defined Networking (SDN): A Revolution in Computer NetworkIOSR Journals
Abstract: SDN creates a dynamic and flexible network architecture that can change as the business
requirements change. The growth of the SDN market and cloud computing are very much connected. As the
applications change and the network is abstracted, virtualization become a necessary step and SDN serves as
the fundamental building blocks for the network. Traditional networking devices are composed of an embedded
control plane that manages switching, routing and traffic engineering activities while the data plane forwards
packet/frames based on traffic. In SDN architecture, control plane functions are removed from individual
networking devices and embedded in a centralizedserver. The SDN controller makes all traffic related decisions
in the network without nodes active participation, as opposed to today’s networks.
Keyword-API, cloud computing, IT, middleware, OpenFlow, SDN
This document discusses software defined networking (SDN) and its applications to optical transport networks. It begins with an introduction to the rapid growth of network traffic and need for more programmatic control of networks. It then provides an overview of SDN architecture with separated control and data planes. OpenFlow is discussed as an SDN protocol that can enable programmatic control of optical elements. The document outlines some key characteristics and applications of optical networks, including setting up connection paths and transport virtual private networks (VPNs). It also discusses optical network architectures like ROADMs and different control paradigms like centralized versus distributed control. In summary, the document explores how SDN principles can be applied to optical transport networks to provide more flexible, automated
Web-Based User Interface for the Floodlight SDN ControllerEswar Publications
Software Defined Networking (SDN) was born as a solution for next-generation network design. Due to its flexible architecture, SDN promises to make network devices simpler while giving better centralized control ability over network and improving parameters such as flexibility, resilience, reliability, and security. In this paper, we briefly introduce the SDN architecture and the Floodlight Controller that is one of the popular SDN controllers. We build a web-based user interface for the Floodlight Controller by using REST API. This application is the first program in the Floodlight SDN Controller literature to view the controller upon several properties such as device connections and flow tables.
Software Defined Networking (SDN) is an emerging trend in the networking and communication industry and promises to deliver enormous benefits, from reduced costs to more efficient network operations. It is a new approach that gives network operators and owners more control of the infrastructure, allowing optimization, customization and virtualization that enable the creation of new types of network services. This is done by decoupling the management and control planes that make decisions about where traffic is sent from (the control plane) the underlying hardware that forwards data traffic to the selected destination (the data plane).
Software Defined Networking Attacks and Countermeasures .docxrosemariebrayshaw
Software Defined Networking: Attacks and
Countermeasures
Nada Mostafa Abd Elazim
Computer Engineering Department.
Arab Academy for Science and
Technology, College of Engineering
Cairo, Egypt
[email protected]
Mohamed A. Sobh
Ain Shams University
Cairo, Egypt
[email protected]
Ayman M. Bahaa-Eldin
Misr International University
On leave from Ain Shams University
[email protected]
Abstract —Software defined networking (SDN) is an
emerging network architecture; it differs from traditional
networks as it separates control planes from data planes.
This separation helps the network to be more flexible and
easier to handle and allows faster innovation cycles at both
planes. SDN has benefit over traditional networks in terms
of simplicity, programmability and elasticity. Openflow
protocol is a south-bound API interface; it is the most
popular and common protocol that used to communicate the
controller with the switches. This paper will focus on the
architecture of SDN and provide some challenges faces the
SDN; finally, it will discuss some security threats that face
SDN and their countermeasures.
Index Terms—SDN, Openflow, API interface
I. INTRODUCTION
Traditional networks were very complex and difficult
to manage. They combine the control plane with data
plane that make network management difficult.
On the other hand, software defined networking
(SDN) is a new networking approach to build computer
networks that separates and abstracts elements of these
systems to help building flexible and scalable network.
Advantages of Software defined networking (SDN)
over traditional network [1]:
• It has virtual environment as it uses resources
without caring about where it is located and how
it is orderly.
• Monitor large number of devices by one
command.
• Easy to change behaviour, size, and quantity.
• Minimize downtime, enforcement of policy,
discover the faults and solve them, and add new
devices, resources, sites, and workloads.
• Monitoring of resources.
• Improve the utilization of network device.
• The global vision of the network due to the
centralization of the controller.
Openflow [2] is a protocol found in the southbound
API interface that locates between the control and data
forwarding layer. It is the way to virtualize the network.
openflow is designed to be easy programmed, that helps
the network manager to create new protocols for solving
problems.
SDN has many applications in data centre, WAN,
IoTs, cellular networks, and Wi-Fi network.
Security threats are on the rise, SDN faces many
security threats in each of its layer, for example, in Data
forwarding layer there are man at the end attack, DoS
attack, spoofing attack, intrusion attack, scanning attack,
tampering attack, hijacking attack, side channel attack,
and anomaly attack. In control layer there are DoS/DDoS
attack, intrusion attack, anomaly attack, threats based on
distributed multi-controllers, threats from a.
The document provides an overview of Software-Defined Networking (SDN), including its key components and benefits. SDN allows network administrators to manage network services through abstraction of lower level functionality and control. It separates the system that makes decisions about traffic from the underlying systems that forward traffic. SDN provides benefits like business agility, easier policy implementation, and support for multi-vendor ecosystems. Key considerations for SDN include a focus on applications and open standards.
Software Defined Networking: A Concept and Related IssuesEswar Publications
SDN (Software Defined Networking) is the networking architecture that has gained attention of researchers in recent past. It is the future of programmable networks. Traditional networks were very complex and difficult to manage. SDN is going to change this by offering a standard interface (OpenFlow) between the control plane and the networking devices (data plane). Its implementation is fully supported by software so that we can control the behavior of networking devices through programmatic control. This programmatic control provides various new ways to find breakpoints and failures in networking devices. Today SDN has become an important part of networking, so it is important to emulate its behavior. SDN support virtualization which makes it scalable and flexible. Data traffic resides in the data plane. The main function of intelligent controller is to decide the routing
policy and manage the traffic in data plane. So effectively SDN emerges as a networking architecture that has the ability to solve all problems those were found in traditional architecture In this paper the authors discussed historical perspective of SDN, languages that support SDN, emulation tools, security issues with SDN and advantages that makes SDN suitable choice for today’s network.
The document discusses implementing a hybrid SDN network at RAF Company by introducing SDN functionality into the wireless network segment. A Floodlight controller was installed on a virtual machine to optimize traffic forwarding and provide flexible allocation of wireless resources. Applications were developed to dynamically manage network security and control user throughput. The benefits of SDN for enterprises include network programmability, simplified management, reduced costs, and the ability to accelerate services to meet business needs.
Software-Defined Networking(SDN):A New Approach to NetworkingAnju Ann
This document provides an overview of Software-Defined Networking (SDN). It discusses how SDN decouples the network control plane from the forwarding plane, allowing for centralized control and programmability. The key components of the SDN architecture include OpenFlow switches, an SDN controller, and northbound and southbound APIs. OpenFlow is described as the primary southbound protocol, allowing the controller to program how packets are handled by switches. Example applications of SDN mentioned are network slicing and multi-tenancy in cloud computing. Challenges for SDN adoption are also noted.
As computer network grow larger and more complex, there is a need for a new simple kind of approach to configure them. SDN has emerged as promising network architecture. It takes the control plane away from the individual nodes and centralize the network control by utilizing a flow based traffic management. Mininet is a cost effective and an efficient way to emulate and study SDN.This paper presents a study of programmable networks with basics of Mininet.
Survey of optimizing dynamic virtual local area network algorithm for softwar...TELKOMNIKA JOURNAL
Software-defined network (SDN) is one of the most predominant technologies for networking in the existing and next-generation networks. Therefore, this paper is conducted to introduce a survey for researchers who are interested in exploiting the dynamic tunneling technique to optimize software-defined wide area network (SD-WAN). The main purpose of this survey is not only to investigate the related works of dynamic tunneling with SD-WAN but also to classify this related work according to the aim of each research into the practicable categories and present the most dominated employments for tunneling with SD-WAN, specifically virtual local area network (VLAN). The performed classification accompany dynamic tunneling in SDN can be summarized into four categories as following: exploring VLAN in SDN; management of multi VLAN in SDN; recover link failure of SDN; and development of SDN by using VLAN. Finally,
the intensive study of the literature in this paper discovers that the dominant path of research falls in the class that covers SDN’s link failure. This class takes full advantage of SD-WANs due to offering more robust networking and restoring most communication failures. In the event of a fault, the controller could respond and recover quickly by switching to a pre-computed backup route.
Ericsson Review: Software-Defined-NetworkingEricsson
An architecture based on software-defined networking (SDN) techniques gives operators greater freedom to balance operational and business parameters, such as network resilience, service performance and QoE against opex and capex. With its beginnings in data-center technology, software-defined networking (SDN) technology has developed to the point where it can offer significant opportunities to service providers.
The traditional way of describing network architecture and how a network behaves is through the fixed designs and behaviors of its various elements. The concept of software-defined networking (SDN) describes networks and how they behave in a more flexible way – through software tools that describe network elements in terms of programmable network states.
To maximize the potential benefits and deliver superior user experience, software-defined networking (SDN) needs to be implemented outside the sphere of the data center across the entire network. This can be achieved through enabling network programmability based on open APIs. Service Provider SDN will help operators to scale networks and take advantage of new revenue-generating possibilities.
For more from Ericsson Review visit: http://www.ericsson.com/thinkingahead/technology_insights
A SCALABLE MONITORING SYSTEM FOR SOFTWARE DEFINED NETWORKSijdpsjournal
Monitoring functionality is an essential element of any network system. Traditional monitoring solutions
are mostly used for manual and infrequent network management tasks. Software-defined networks (SDN)
have emerged with enabled automatic and frequent network reconfigurations. In this paper, a scalable
monitoring system for SDN is introduced. The proposed system monitors small, medium, and large-scale
SDN. Multiple instances of the proposed monitoring system can run in parallel for monitoring many SDN
slices. The introduced monitoring system receives requests from network management applications,
collects considerable amounts of measurement data, processes them, and returns the resulting knowledge
to the network management applications. The proposed monitoring system slices the network (switches and
links) into multiple slices. The introduced monitoring system concurrently monitors applications for
various tenants, with each tenant's application running on a dedicated network slice. Each slice is
monitored by a separate copy of the proposed monitoring system. These copies operate in parallel and are
synchronized. The scalability of the monitoring system is achieved by enhancing the performance of SDN.
In this context, scalability is addressed by increasing the number of tenant applications and expanding the
size of the physical network without compromising SDN performance.
Provide a diagram and description of the flow table entries that can.pdfarihantelehyb
Provide a diagram and description of the flow table entries that can be
modified in an OpenFlow Switch.
Provide a diagram and description of an SDN Controller and describe
how the SDN Controller works – OpenDaylight is an appropriate
example that you could use.
Solution
Open Flow Switch:
An Open Flow switch is a software program or hardware device that forwards packets in a
software-defined networking (SDN) environment. Open Flow switches are either based on the
Open Flow protocol or compatible with it.
In a conventional switch, packet forwarding (the data plane) and high-level routing (the control
plane) occur on the same device. In software-defined networking, the data plane is decoupled
from the control plane. The data plane is still implemented in the switch itself but the control
plane is implemented in software and a separate SDN controller makes high-level routing
decisions. The switch and controller communicate by means of the Open Flow protocol.
Flow table entries:
The components of flow table entries and the process by which incoming packets are matched
against flow table entries.
A flow entry consists of header fields, counters, and actions.
Header fields
Counters
Actions
Each flow table entry contains:
1. Header fields to match against packets.
2. Counters to update for matching packet.
3. Actions to apply to matching packets.
These are flow table entries are used to modified in an open flow switch.
Open Flow is an open standard that enables researchers to run experimental protocols in the
campus networks we use every day. Open Flow is added as a feature to commercial Ethernet
switches, routers and wireless access points – and provides a standardized hook to allow
researchers to run experiments, without requiring vendors to expose the internal workings of
their network devices. Open Flow is currently being implemented by major vendors, with Open
Flow-enabled switches now commercially available.
SDN controller (software-defined networking controller):
An SDN controller is an application in software-defined networking (SDN) that manages flow
control to enable intelligent networking. SDN controllers are based on protocols, such as Open
Flow, that allow servers to tell switches where to send packets.
SDN controller is a new paradigm to configure and operate computer networks through a
centralized software controller that dictates how the network behaves. The core of this new
paradigm is the SDN controller.
There are typically two sets of SDN controllers:
The controller is the core of an SDN network. It lies between network devices at one end and
applications at the other end. Any communications between applications and devices have to go
through the controller. The controller also uses protocols such as Open Flow to configure
network devices and choose the optimal network path for application traffic.
SDN controllers works:
Software Defined Networking, as it evolved from prior proposals, standards, and
implementations such as For CES,.
IRJET- Build SDN with Openflow ControllerIRJET Journal
This document summarizes a research paper on building an SDN network using an OpenFlow controller. It discusses how SDN addresses limitations in traditional network technologies by introducing programmability through the OpenFlow protocol. It proposes a firewall system for SDN networks to identify attacks and report intrusion events. The paper also implements a load balancing rule based on SDN specifications using Dijkstra's algorithm to find multiple equal cost paths, helping to scale the network. It describes how SDN can improve common network management tasks through paradigm deployments in the field.
Software-defined Networking (SDN)
It is an approach to computer networking that allows network administrators to programmatically initialize, control, change, and manage network behavior dynamically via:
open interfaces
abstraction of lower-level functionality
SDN is meant to address the fact that the static architecture of traditional networks doesn't support the dynamic, scalable computing and storage needs of more modern computing environments such as data centers.
This is done by decoupling or disassociating the system that makes decisions about where traffic is sent (the SDN controller, or control plane) from the underlying systems that forward traffic to the selected destination (the data plane).
ODL is one of the most reliable and secured controllers in Software Defined Networks. This presentation takes you through the journey of load-balanced switching.
The document summarizes key topics from Chapter 6 of the book "Foundations of Modern Networking" regarding SDN application planes. It discusses the northbound interface that allows SDN applications to access control plane functions without knowing network details. It also describes the network services abstraction layer, which provides an abstract view of network resources to applications and hides low-level device details. Finally, it reviews several SDN application examples, including traffic engineering applications and those for security, data center networking, and cloud networking.
SDN Control Plane scalability research proposalYatindra shashi
The document proposes a hybrid SDN control plane scalability model to handle millions of flows per second with low latency for large networks. It combines hierarchical and distributed control by using local controllers to manage specific switches, a logical central controller with a global view, and distributed physical controllers running ONIX instances with a shared network information base to reduce load on the central controller. The proposed model aims to scale control plane capacity while maintaining SDN principles of separating the control and data planes. It is argued this hybrid approach could improve performance and reliability compared to simple OpenFlow architectures.
Denial of Service Attacks in Software Defined Networking - A SurveyIRJET Journal
This document summarizes a survey on denial of service attacks in software defined networking. It begins with an introduction to software defined networking and how it separates the control plane from the data plane. It then discusses how saturation attacks like denial of service (DoS) and distributed denial of service (DDoS) attacks work in SDNs by overwhelming switches, controller-switch links, and controllers. Various proposals for detecting and mitigating these attacks are overviewed, such as diverting packets, caching packets, classifying packets, and anomaly detection. Challenges in mitigating low rate attacks and securing SDN-based IoT networks are also discussed.
Cloud computing and Software defined networkingsaigandham1
This is my Graduate defense presentation. I have interest in various topics like cloud computing and software defined networking. This slides includes the research of various researchers on cloud computing and SDN, presented their work as my comprehensive exam.
https://jst.org.in/index.html
Our journal has academic journals form a crucial nexus. Educators leverage the latest research findings to enrich their teaching methodologies, ensuring that students are exposed to the most current and relevant information. Simultaneously, these educators contribute to the body of knowledge through their own research, creating a perpetual cycle of growth.
https://ijaast.com/index.html
Our journal has open-access nature of IJAAST fosters global collaboration. Researchers from diverse geographical locations can engage with and build upon each other's work, transcending borders to collectively address the challenges and opportunities in agricultural science and technology.
Web-Based User Interface for the Floodlight SDN ControllerEswar Publications
Software Defined Networking (SDN) was born as a solution for next-generation network design. Due to its flexible architecture, SDN promises to make network devices simpler while giving better centralized control ability over network and improving parameters such as flexibility, resilience, reliability, and security. In this paper, we briefly introduce the SDN architecture and the Floodlight Controller that is one of the popular SDN controllers. We build a web-based user interface for the Floodlight Controller by using REST API. This application is the first program in the Floodlight SDN Controller literature to view the controller upon several properties such as device connections and flow tables.
Software Defined Networking (SDN) is an emerging trend in the networking and communication industry and promises to deliver enormous benefits, from reduced costs to more efficient network operations. It is a new approach that gives network operators and owners more control of the infrastructure, allowing optimization, customization and virtualization that enable the creation of new types of network services. This is done by decoupling the management and control planes that make decisions about where traffic is sent from (the control plane) the underlying hardware that forwards data traffic to the selected destination (the data plane).
Software Defined Networking Attacks and Countermeasures .docxrosemariebrayshaw
Software Defined Networking: Attacks and
Countermeasures
Nada Mostafa Abd Elazim
Computer Engineering Department.
Arab Academy for Science and
Technology, College of Engineering
Cairo, Egypt
[email protected]
Mohamed A. Sobh
Ain Shams University
Cairo, Egypt
[email protected]
Ayman M. Bahaa-Eldin
Misr International University
On leave from Ain Shams University
[email protected]
Abstract —Software defined networking (SDN) is an
emerging network architecture; it differs from traditional
networks as it separates control planes from data planes.
This separation helps the network to be more flexible and
easier to handle and allows faster innovation cycles at both
planes. SDN has benefit over traditional networks in terms
of simplicity, programmability and elasticity. Openflow
protocol is a south-bound API interface; it is the most
popular and common protocol that used to communicate the
controller with the switches. This paper will focus on the
architecture of SDN and provide some challenges faces the
SDN; finally, it will discuss some security threats that face
SDN and their countermeasures.
Index Terms—SDN, Openflow, API interface
I. INTRODUCTION
Traditional networks were very complex and difficult
to manage. They combine the control plane with data
plane that make network management difficult.
On the other hand, software defined networking
(SDN) is a new networking approach to build computer
networks that separates and abstracts elements of these
systems to help building flexible and scalable network.
Advantages of Software defined networking (SDN)
over traditional network [1]:
• It has virtual environment as it uses resources
without caring about where it is located and how
it is orderly.
• Monitor large number of devices by one
command.
• Easy to change behaviour, size, and quantity.
• Minimize downtime, enforcement of policy,
discover the faults and solve them, and add new
devices, resources, sites, and workloads.
• Monitoring of resources.
• Improve the utilization of network device.
• The global vision of the network due to the
centralization of the controller.
Openflow [2] is a protocol found in the southbound
API interface that locates between the control and data
forwarding layer. It is the way to virtualize the network.
openflow is designed to be easy programmed, that helps
the network manager to create new protocols for solving
problems.
SDN has many applications in data centre, WAN,
IoTs, cellular networks, and Wi-Fi network.
Security threats are on the rise, SDN faces many
security threats in each of its layer, for example, in Data
forwarding layer there are man at the end attack, DoS
attack, spoofing attack, intrusion attack, scanning attack,
tampering attack, hijacking attack, side channel attack,
and anomaly attack. In control layer there are DoS/DDoS
attack, intrusion attack, anomaly attack, threats based on
distributed multi-controllers, threats from a.
The document provides an overview of Software-Defined Networking (SDN), including its key components and benefits. SDN allows network administrators to manage network services through abstraction of lower level functionality and control. It separates the system that makes decisions about traffic from the underlying systems that forward traffic. SDN provides benefits like business agility, easier policy implementation, and support for multi-vendor ecosystems. Key considerations for SDN include a focus on applications and open standards.
Software Defined Networking: A Concept and Related IssuesEswar Publications
SDN (Software Defined Networking) is the networking architecture that has gained attention of researchers in recent past. It is the future of programmable networks. Traditional networks were very complex and difficult to manage. SDN is going to change this by offering a standard interface (OpenFlow) between the control plane and the networking devices (data plane). Its implementation is fully supported by software so that we can control the behavior of networking devices through programmatic control. This programmatic control provides various new ways to find breakpoints and failures in networking devices. Today SDN has become an important part of networking, so it is important to emulate its behavior. SDN support virtualization which makes it scalable and flexible. Data traffic resides in the data plane. The main function of intelligent controller is to decide the routing
policy and manage the traffic in data plane. So effectively SDN emerges as a networking architecture that has the ability to solve all problems those were found in traditional architecture In this paper the authors discussed historical perspective of SDN, languages that support SDN, emulation tools, security issues with SDN and advantages that makes SDN suitable choice for today’s network.
The document discusses implementing a hybrid SDN network at RAF Company by introducing SDN functionality into the wireless network segment. A Floodlight controller was installed on a virtual machine to optimize traffic forwarding and provide flexible allocation of wireless resources. Applications were developed to dynamically manage network security and control user throughput. The benefits of SDN for enterprises include network programmability, simplified management, reduced costs, and the ability to accelerate services to meet business needs.
Software-Defined Networking(SDN):A New Approach to NetworkingAnju Ann
This document provides an overview of Software-Defined Networking (SDN). It discusses how SDN decouples the network control plane from the forwarding plane, allowing for centralized control and programmability. The key components of the SDN architecture include OpenFlow switches, an SDN controller, and northbound and southbound APIs. OpenFlow is described as the primary southbound protocol, allowing the controller to program how packets are handled by switches. Example applications of SDN mentioned are network slicing and multi-tenancy in cloud computing. Challenges for SDN adoption are also noted.
As computer network grow larger and more complex, there is a need for a new simple kind of approach to configure them. SDN has emerged as promising network architecture. It takes the control plane away from the individual nodes and centralize the network control by utilizing a flow based traffic management. Mininet is a cost effective and an efficient way to emulate and study SDN.This paper presents a study of programmable networks with basics of Mininet.
Survey of optimizing dynamic virtual local area network algorithm for softwar...TELKOMNIKA JOURNAL
Software-defined network (SDN) is one of the most predominant technologies for networking in the existing and next-generation networks. Therefore, this paper is conducted to introduce a survey for researchers who are interested in exploiting the dynamic tunneling technique to optimize software-defined wide area network (SD-WAN). The main purpose of this survey is not only to investigate the related works of dynamic tunneling with SD-WAN but also to classify this related work according to the aim of each research into the practicable categories and present the most dominated employments for tunneling with SD-WAN, specifically virtual local area network (VLAN). The performed classification accompany dynamic tunneling in SDN can be summarized into four categories as following: exploring VLAN in SDN; management of multi VLAN in SDN; recover link failure of SDN; and development of SDN by using VLAN. Finally,
the intensive study of the literature in this paper discovers that the dominant path of research falls in the class that covers SDN’s link failure. This class takes full advantage of SD-WANs due to offering more robust networking and restoring most communication failures. In the event of a fault, the controller could respond and recover quickly by switching to a pre-computed backup route.
Ericsson Review: Software-Defined-NetworkingEricsson
An architecture based on software-defined networking (SDN) techniques gives operators greater freedom to balance operational and business parameters, such as network resilience, service performance and QoE against opex and capex. With its beginnings in data-center technology, software-defined networking (SDN) technology has developed to the point where it can offer significant opportunities to service providers.
The traditional way of describing network architecture and how a network behaves is through the fixed designs and behaviors of its various elements. The concept of software-defined networking (SDN) describes networks and how they behave in a more flexible way – through software tools that describe network elements in terms of programmable network states.
To maximize the potential benefits and deliver superior user experience, software-defined networking (SDN) needs to be implemented outside the sphere of the data center across the entire network. This can be achieved through enabling network programmability based on open APIs. Service Provider SDN will help operators to scale networks and take advantage of new revenue-generating possibilities.
For more from Ericsson Review visit: http://www.ericsson.com/thinkingahead/technology_insights
A SCALABLE MONITORING SYSTEM FOR SOFTWARE DEFINED NETWORKSijdpsjournal
Monitoring functionality is an essential element of any network system. Traditional monitoring solutions
are mostly used for manual and infrequent network management tasks. Software-defined networks (SDN)
have emerged with enabled automatic and frequent network reconfigurations. In this paper, a scalable
monitoring system for SDN is introduced. The proposed system monitors small, medium, and large-scale
SDN. Multiple instances of the proposed monitoring system can run in parallel for monitoring many SDN
slices. The introduced monitoring system receives requests from network management applications,
collects considerable amounts of measurement data, processes them, and returns the resulting knowledge
to the network management applications. The proposed monitoring system slices the network (switches and
links) into multiple slices. The introduced monitoring system concurrently monitors applications for
various tenants, with each tenant's application running on a dedicated network slice. Each slice is
monitored by a separate copy of the proposed monitoring system. These copies operate in parallel and are
synchronized. The scalability of the monitoring system is achieved by enhancing the performance of SDN.
In this context, scalability is addressed by increasing the number of tenant applications and expanding the
size of the physical network without compromising SDN performance.
Provide a diagram and description of the flow table entries that can.pdfarihantelehyb
Provide a diagram and description of the flow table entries that can be
modified in an OpenFlow Switch.
Provide a diagram and description of an SDN Controller and describe
how the SDN Controller works – OpenDaylight is an appropriate
example that you could use.
Solution
Open Flow Switch:
An Open Flow switch is a software program or hardware device that forwards packets in a
software-defined networking (SDN) environment. Open Flow switches are either based on the
Open Flow protocol or compatible with it.
In a conventional switch, packet forwarding (the data plane) and high-level routing (the control
plane) occur on the same device. In software-defined networking, the data plane is decoupled
from the control plane. The data plane is still implemented in the switch itself but the control
plane is implemented in software and a separate SDN controller makes high-level routing
decisions. The switch and controller communicate by means of the Open Flow protocol.
Flow table entries:
The components of flow table entries and the process by which incoming packets are matched
against flow table entries.
A flow entry consists of header fields, counters, and actions.
Header fields
Counters
Actions
Each flow table entry contains:
1. Header fields to match against packets.
2. Counters to update for matching packet.
3. Actions to apply to matching packets.
These are flow table entries are used to modified in an open flow switch.
Open Flow is an open standard that enables researchers to run experimental protocols in the
campus networks we use every day. Open Flow is added as a feature to commercial Ethernet
switches, routers and wireless access points – and provides a standardized hook to allow
researchers to run experiments, without requiring vendors to expose the internal workings of
their network devices. Open Flow is currently being implemented by major vendors, with Open
Flow-enabled switches now commercially available.
SDN controller (software-defined networking controller):
An SDN controller is an application in software-defined networking (SDN) that manages flow
control to enable intelligent networking. SDN controllers are based on protocols, such as Open
Flow, that allow servers to tell switches where to send packets.
SDN controller is a new paradigm to configure and operate computer networks through a
centralized software controller that dictates how the network behaves. The core of this new
paradigm is the SDN controller.
There are typically two sets of SDN controllers:
The controller is the core of an SDN network. It lies between network devices at one end and
applications at the other end. Any communications between applications and devices have to go
through the controller. The controller also uses protocols such as Open Flow to configure
network devices and choose the optimal network path for application traffic.
SDN controllers works:
Software Defined Networking, as it evolved from prior proposals, standards, and
implementations such as For CES,.
IRJET- Build SDN with Openflow ControllerIRJET Journal
This document summarizes a research paper on building an SDN network using an OpenFlow controller. It discusses how SDN addresses limitations in traditional network technologies by introducing programmability through the OpenFlow protocol. It proposes a firewall system for SDN networks to identify attacks and report intrusion events. The paper also implements a load balancing rule based on SDN specifications using Dijkstra's algorithm to find multiple equal cost paths, helping to scale the network. It describes how SDN can improve common network management tasks through paradigm deployments in the field.
Software-defined Networking (SDN)
It is an approach to computer networking that allows network administrators to programmatically initialize, control, change, and manage network behavior dynamically via:
open interfaces
abstraction of lower-level functionality
SDN is meant to address the fact that the static architecture of traditional networks doesn't support the dynamic, scalable computing and storage needs of more modern computing environments such as data centers.
This is done by decoupling or disassociating the system that makes decisions about where traffic is sent (the SDN controller, or control plane) from the underlying systems that forward traffic to the selected destination (the data plane).
ODL is one of the most reliable and secured controllers in Software Defined Networks. This presentation takes you through the journey of load-balanced switching.
The document summarizes key topics from Chapter 6 of the book "Foundations of Modern Networking" regarding SDN application planes. It discusses the northbound interface that allows SDN applications to access control plane functions without knowing network details. It also describes the network services abstraction layer, which provides an abstract view of network resources to applications and hides low-level device details. Finally, it reviews several SDN application examples, including traffic engineering applications and those for security, data center networking, and cloud networking.
SDN Control Plane scalability research proposalYatindra shashi
The document proposes a hybrid SDN control plane scalability model to handle millions of flows per second with low latency for large networks. It combines hierarchical and distributed control by using local controllers to manage specific switches, a logical central controller with a global view, and distributed physical controllers running ONIX instances with a shared network information base to reduce load on the central controller. The proposed model aims to scale control plane capacity while maintaining SDN principles of separating the control and data planes. It is argued this hybrid approach could improve performance and reliability compared to simple OpenFlow architectures.
Denial of Service Attacks in Software Defined Networking - A SurveyIRJET Journal
This document summarizes a survey on denial of service attacks in software defined networking. It begins with an introduction to software defined networking and how it separates the control plane from the data plane. It then discusses how saturation attacks like denial of service (DoS) and distributed denial of service (DDoS) attacks work in SDNs by overwhelming switches, controller-switch links, and controllers. Various proposals for detecting and mitigating these attacks are overviewed, such as diverting packets, caching packets, classifying packets, and anomaly detection. Challenges in mitigating low rate attacks and securing SDN-based IoT networks are also discussed.
Cloud computing and Software defined networkingsaigandham1
This is my Graduate defense presentation. I have interest in various topics like cloud computing and software defined networking. This slides includes the research of various researchers on cloud computing and SDN, presented their work as my comprehensive exam.
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This document discusses a study that uses GIS techniques to estimate floods in the Upper Sarada River Basin in Visakhapatnam District, India. Daily rainfall data from 23 stations in the region from 1990-2019 and discharge data from a gauge station near Anakapalle are collected. A digital elevation model of the basin is created to extract drainage characteristics. A unit hydrograph is derived from the DEM hydrological processing and used to estimate peak floods for different storms. Eight observed storm events are validated using the unit hydrograph and Thiessen polygon method.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
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The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
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Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
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How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
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Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
journal of mathematics research
1. UtilitasMathematica
ISSN 0315-3681 Volume 120, 2023
368
Smart Dynamic Resource Allocation Controller in Bandwidth Slicing Based
on Spiking Neural Networks
Mohammed Mousa Rashid Al-Yasari1, Nadia Adnan Shiltagh Al-Jamali2
1
Information Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and
Informatics (ICCI), Baghdad, Iraq, phd202020555@iips.icci.edu.iq
2
University of Baghdad, College of Engineering, Computer Engineering Department, Baghdad,
Iraq
Abstract
Software Defined Networking (SDN) is a modern network architectural model that manages
network traffic using software. SDN is a networking scenario that modifies the conventional
network design by combining all control features into a single place and making all choices
centrally. Controllers are the "brains" of SDN architecture since they are responsible for making
control decisions and routing packets at the same time. The capacity for centralized decision-
making on routing improves the performance of the network. SDN's growing functionality and
uses have led to the development of many controller systems. Every SDN controller idea or
design must prioritize the control plane since it is the most crucial part of the SDN architecture.
This paper, present Dynamic Resource Allocation based on the Spiking Neural Network
(DRASR) approach, which is responsible to distribute available resources among all slices justly
according to the type of demand. The simulation results show that the benefit of the network
throughput is about 98.8%.
1. Introduction
Because it is more controllable, dynamic, and cost-effective than traditional architecture, SDN is a
strong network architecture that is best for high-bandwidth applications that change quickly [1]. The
idea that a network's control operations should be kept distinct from its forwarding functions became
the foundation of the SDN design. This would make it simpler to directly program the network control
and abstract forwarding devices for services and network applications [2].
• Easy to program: Since the control function of the forwarding device has been taken away, the
network's control operation can be directly programmed.
• Agile: The network administrator can change network traffic on the fly to meet different
management needs since control is separated from the infrastructure underneath.
• Centralized management: Because the network's brain (controller) is logically centralized and
appears as a single switch to the application and policy engines, it has a global view of the entire
network.
2. UtilitasMathematica
ISSN 0315-3681 Volume 120, 2023
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• Employing dynamic SDN programs, network administrators may rapidly manage, modify, protect,
and enhance network resources with SDN.
SDN is an emerging network paradigm that enables current network architecture constraints to be
overcome; it is characterized as a foursome-pillar network architecture [3]. In a distinct data and
control plane, routing decisions are flow-based rather than destination-based, the control logic is
handled by an external entity known as the SDN Controller, and the network may be programmable
through software applications running on top of the SDN Controller. According to the examination of
the relevant literature, an attempt was made to compare the existing SDN controllers. The SDN
concept and its technical components were thoroughly analyzed in [4]. However, this assessment was
not intended to be a comparative analysis of controllers from a commercial standpoint. Referring to
aspects such as the difficulty of getting started, the Application Program Interface (APIs) that are
supported, the accessibility of documentation, and the version of Openflow, among other factors,
conducted a comparative analysis of SDN controllers based on a systematic study. Unfortunately, this
work does not do a comparative categorization of the offered controllers, nor does it emphasize the
market viewpoint, which is crucial to the acceptability of any technology [5]. Give a comparison of
SDN controllers based on characteristics however, they do not perform a market-oriented comparison,
such as programming language, documentation, modularity, and performance. This paper's primary
contribution is a comparative analysis of the existing SDN controllers and their primary
characteristics, taking into account not only functional and technical aspects, nevertheless, but market
adoption, documentation availability, and OpenFlow support are also all-important factors. To
evaluate the study, we made a comparison between the method based on Spike Response and Integrate
and fire then compared it with other techniques such as traditional neural networks.
2. SDN Architecture
Based on the prior SDN description, SDN components may be characterized as a collection of the
separate data layers, control layers, and application layers that reflect the SDN architecture, as shown
in 'figure 1', each of which has its own functionality and can interact through open standard interfaces.
These layers were then depicted using a bottom-up approach [6].
3. UtilitasMathematica
ISSN 0315-3681 Volume 120, 2023
370
Figure 1. Fundamental SDN components
2.1 Data Plane: This plane provides a description of the forwarding devices, which include switches
and routers, in addition to a set of instructions that may be given via an application program interface
(API). SDN network devices function similarly to traditional network devices, except those packets
are forwarded based on a higher plane decision. This signifies that control is no longer delegated to an
external party and is now logically centralized. The data plane and the control plane are connected
through a standard interface (OpenFlow). In other words, open and standard interfaces are used to
build the network brain (control) and applications (conceptually). The controller may use this interface
to dynamically setup various forwarding devices. For traffic processing logic in SDN data plane
forwarding devices, an API for interacting with the controller, an abstraction layer, and a traffic
(packet) processing function will be implemented as software in virtual switches and as hardware in
physical switches [7]. The abstraction layer is made up of one or more flow tables, and its main
function is to enable the device to decide what to do with the next packet based on its contents. The
packet may be routed to a particular switch port, flooded to all ports, or dropped entirely [8]. A flow
table in an open flow switch is a data structure placed in a high-speed data plane data structure It
provides information about the forwarding and packet handling behavior of the open flow switch.
There are one or more flow entries in an open flow table, each with a number of components. A flow
table with three entries (match fields, action, and priority), as well as a counter and timer [9].
2.2 Control Plane: SDN controller, also known as Network Operating System (NOS), is the name
given to the control plane in SDN architecture. Due to the fact that the controller is connected to all
devices that perform forwarding in the bottom plane, management of the network exchange moves
from distributed to centralized [2]. The controller's primary functions are as follows:
4. UtilitasMathematica
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• Provide the applications plane with an abstraction view of the underlying infrastructure so they
can link with devices that use the SDN (switches, routers).
• Execute the directives of the administration (load balancing, forwarding, and routing).
• Command and control all the devices that make up the network's data plane [10].
Because malfunctioning nodes are linked to the controller, the controller's logical location assists in the
resolution of many distributed issues, such as quicker reactions to node or link failures. Because the
controller has a full picture of the whole network, loop avoidance is substantially easier. Depending on
the programming language used for implementation, there are several kinds of SDN controllers, such
as the pox controller, which is implemented in the Python language, the flooding light controller,
which is developed in the Java programming language, and even the NOX controller, which could use
the C programming language. They're all open-source controllers, however, there are also commercial
ones like HP and NEC [11].
2.3 SDN Application Layer is a programmable platform provided by SDN technology that enables
users to build SDN applications for routing management and resolving critical network issues.
Network applications communicate with the controller using an API known as the northbound
interface in SDN architecture. These applications' primary function is to manage traffic within network
devices by modifying flow entries via the southbound interface [12].
3. Related Work
For dispersed 5G-based SDN/NFV networks, the best workload distribution has been presented [13].
A network slicing architecture from end to end is made to enable a variety of services, including
URLLC and eMBB. Additionally, by splitting client requests in the integrated environment of SDN,
NFV, and edge computing, the network operating cost may be decreased. Network slicing was carried
out by the authors in a large-scale Internet of Things (IoT) context (long-range wide area network) in a
prior research [14]. The network is divided into three slices: the best-effort slice, the reliability-aware
slice, and the urgency and reliability-aware slice. First, one-to-many matching is used to execute
cooperative slicing, with the number of IoT devices allocated to virtual slices serving as the
determining factor. Then, using a one-to-one matching game, resources are assigned for each slice
(inter-slice resource allocation). The coalitional multigame theory generates significant computational
complexity and requires a lot of processing time.
Resource sharing and allocation in 5G slice networks are thought to be improved by packet-based data
traffic scheduling [15]. Static sharing resources (SSR) and dynamic sharing resources (DSR) are the
two operational modes that are utilised. The given capacity weight is calculated for each slice and
distributed in accordance with resource allocation. In the end, it is calculated how fairly resources are
distributed across slices. A global network controller is necessary to manage vast varieties of slices,
including popular, heavy, and sensitive slices. A slice management plan that allocates resources
according to priority has also been presented by researchers [16]. Lower priority slices are sent
through different pathways, whereas requests for high priority slices are sent along the shortest paths.
200 nodes are used for the experiments, and they are set up in a grid network architecture. In the end,
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the delays of slices are reduced by up to 11–14% while the average throughput slices up to 6, 13, and
7%. The shortest route is used for flow (high priority) forwarding in the data plane. The quickest route
is not always accessible, however. Therefore, the relevant flow must be put in the controller when the
flow does not match a flow table. This causes customers with static resource allocation to have a
bandwidth shortage issue. Network slicing has been accomplished via service function chaining [17].
Every slice has its own set of service function chains that handle various types of traffic. The trade-offs
between slicing and execution runtime are then examined using a greedy-based heuristic approach.
The requisite bandwidth and latency are then achieved using an optimisation model. Network slice
mobility is not taken into account, which lowers QoS. To assign resources for each slice in a network,
a network slicing resource management (NSRM) system has been created [18]. In this research, an
LTE network is taken into account for varying slice assignments and equitable bandwidth allocation
across slices. The LTE slice controller manages all slices and deploys the controller for each slice. The
radio network resources are distributed through a virtual eNodeB via the LTE slice controller. Slice
request dynamic provisioning is really challenging. For instance, the Industry 4.0 application has to
handle huge slice requests.
4. Machine Learning and Spiking Neural Network
Much effort is put into optimizing and effectively scheduling radio and network resources, but in 5G
networks, resource allocation on the basis of the service being provided is a must-have. The increasing
number of devices and new services offered by 5G networks will add to the already massive quantity
of data traffic that operators now deal with [19]. This stream of traffic may be divided into smaller
chunks and handled separately. Since the service provider may now charge differently for each sliced
piece and even alter the pricing for each slice, the provider can strike a good balance between
company profitability and customer happiness. As a bonus, 5G network slicing enables service
providers to construct not just established but also new applications and services. It will be a "one size
fits all" solution. Each network section may operate autonomously, with its controls and policy
administrators [20].
With ML in place, we can examine any gaps in our knowledge and make any required adjustments.
Machine learning will offer a network analysis of the massive data set, which may be researched
further to adjust any given slice swiftly and cost-effectively. As shown in Figures 1-a and 1-b, SNN
may dynamically activate network automation to adjust resource allocation. Without human
involvement, SNN will be accountable for delivering and processing, and making an intelligent choice
for network resource adaption. To make the best judgments, it will also weigh some different
variables, maybe more than a single person could take into account in a short time [21].
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Figure 1. Spiking Neural Network Architecture
Whenever a new slice is added to the network, SNN will analyze it in real-time to determine how well
it is doing, where it stands in comparison to other networks, what problems could arise, what sections
of the network are functioning normally, and if anything seems out of the ordinary [22]. An easy way
to see this is on a slice for a fixed wireless corporate network, where capacity may be dynamically
added in response to a sudden increase in demand thanks to automation. We could then communicate
more efficiently. This will make it easier to create new services or network slices if they become
required. With automation, we can do all of this in less time with no degradation in the performance of
an ongoing session [20]. Organizational challenges now prevent the widespread use of network
slicing. This is due to the fact that several pieces of hardware and organizations inside a service
provider's network must be interacted with in order for a single modification to be implemented [23].
5G's programmability features will make it possible to create a customized end-to-end solution for
every use case. The data rate, latency, mobility, isolation, power constraints, etc., are typical
parameters that a typical consumer might seek. If the current instance of the network slice does not
have enough resources, then a different network slice type will be made available, and the associated
network services will be activated [24].
Network slicing takes into account a wide variety of factors, such as the kind of slice, bandwidth,
throughput, latency, equipment type, portability, reliability, isolation, power, and many more [25].
Because 5G enables the collection of such large datasets, big data analytics need the use of machine
learning. One of the most important and useful ML-based applications in the wireless sector is the
detection and revival of dormant cellular cells. Other relevant and useful ML-based applications in the
wireless industry include optimizing mobile tower operations, accelerating wireless channel adoption,
facilitating targeted marketing, autonomous decision-making in IoT networks, real-time data analysis,
predictive maintenance, customer churn, sentiment analysis by social networking, fraud detection, e-
commerce, and many more [26]. Since Uber employs real-time differential pricing depending on
demand, the number of available vehicles, the weather, the time of day, and other factors, using ML in
apps that are comparable to Uber will have several advantages. Better accuracy and prediction in the
future may be achieved by using a platform built on machine learning to analyze and process massive
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amounts of historical and real-time data. This is because automatically adjusts to price differences in
real-time.
5. Proposed Controller
using an SNN to calculate an unknown value for the traffic data that changes over time based on the
number of network slices. The variance (Err) between actual and desired buffer occupancy is used to
update the SNN's weights using the Back Propagation (BP) training method. Because the online
training is used to manage the estimating traffic to accommodate it, the SNN must learn the network's
behaviour and control it.
SNN has eight input nodes, a hidden number of layers, a number of neurons in each layer, and a
number of synapses (sub-connections) that are typically determined experimentally. A large number
of hidden layers slows and decreased the training process and increases network complexity. The
improvement of the non-linearity of the solution requires the adoption of a three-layered feed-forward
neural network to achieve the accomplishment of a traffic signal controller.
The structure’s sketch of SNN is shown in Fig (2) and Fig (3), the activation function of the neurons in
the hidden layer is tanh. The number of neurons in each of the three layers’ input, the hidden, and the
output layer are 8,8, and 4 respectively.
Figure 2. Structure of SNN
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Input neurons of the SNN are assigned to network TR features TR(t); the number of packets arriving
at the global slice buffer.
Figure 3. Structure of SNN in detail
The number of sub-connections or synapses k in the interaction between the input and hidden layers,
as illustrated in Fig. (4), is five delayed sub-connections. To determine the number of synapses k, the
trial-and-error approach is applied.
Figure 4. Sub-connection consists of five synapses
As shown in Fig. (4), the weight of each synapse effects the spike-response function ε; denotes the
neuron's activation function. In the encoding process, the actual information TR(t) is encoded
information t_h^act computed using Eq. (1).
𝑡ℎ
𝑎𝑐𝑡
= 𝑡𝑚𝑎𝑥 − 𝑟𝑜𝑢𝑛𝑑 (𝑡𝑚𝑖𝑛 +
(𝑇𝑅(𝑡)− 𝑇𝑅min)(𝑡𝑚𝑎𝑥− 𝑡𝑚𝑖𝑛)
(𝑇𝑅𝑚𝑎𝑥− 𝑇𝑅𝑚𝑖𝑛)
) (1)
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𝑇𝑅𝑚𝑎𝑥 and 𝑇𝑅𝑚𝑖𝑛 that are represent the maximum and minimum values of the real input
information. The 𝑡𝑚𝑎𝑥 and 𝑡𝑚𝑖𝑛 represent the largest and minimum interval T.
The maximum and lowest values of the actual information for input are represented by the
variables 𝑇𝑅𝑚𝑎𝑥 and 𝑇𝑅𝑚𝑖𝑛. Maximum and minimum intervals T are represented by the 𝑡𝑚𝑎𝑥
and 𝑡𝑚𝑖𝑛.
The decoding equation derived from Eq. (1) by Eq. (2)
𝑇𝑅(𝑡𝑗) =
(𝑡𝑚𝑎𝑥−𝑡𝑗− 𝑡𝑚𝑖𝑛)(𝑇𝑅𝑚𝑎𝑥− 𝑇𝑅𝑚𝑖𝑛)
(𝑡𝑚𝑎𝑥− 𝑡𝑚𝑖𝑛)
+ 𝑇𝑅𝑚𝑖𝑛 (2)
The SNN algorithm operates in two modes. The first is known as feed-forward mode, in which each
neuron spikes just once at most during each time interval T when membrane potential m exceeds the
value. Starting from hidden layer I, the feed-forward mode constantly examine neuron i to see whether
it has spiked or not. The algorithm uses the next neuron i+1 when the neuron i is spiked. The
membrane potential m_i (t) is calculated by the SNN algorithm according to Eq. (3) based on input
spikes t_h^f of neuron h at input layer H .
𝑚𝑖(𝑡) = ∑ ∑ 𝑤ℎ𝑖
𝑘
𝐷
𝑘=1
𝑁𝐻
ℎ=1 ɛ(𝑡 − 𝑡ℎ
𝑓
− 𝑑𝑘
) (3)
The neuron i is not allowed to spike anymore through the remaining period of time interval T, when
the threshold is exceeded at a particular instant t. In the next instant t+1, the neuron i will be reset.
When the neurons in the second layer have completed, the algorithm will repeat the same procedure in
the output layer J, and the back-propagation phase will then start.
The connection weights of synapses are updated when the feed-forward mode finishes. In contrast to
feed-forward, back-propagation starts at the output layer and returns to the hidden layer. The synapses
of the hidden layer will be modified in accordance with Eq. (4), Eq.(5), and Eq.(6).
𝑤𝑖𝑗
𝑘
(𝑡 + 1) = 𝑤𝑖𝑗
𝑘
(𝑅) − △ 𝑤𝑖𝑗
𝑘
(𝑅) (4)
Eq.(2.14) , Eq. (3.8) and Eq. (3.9) show how the updating of synapses input layer.
△ 𝑤ℎ𝑖
𝑘
(𝑅) = 𝜂. 𝛿𝑖. 𝑦ℎ
𝑘
(5)
𝑤ℎ𝑖
𝑘
(𝑡 + 1) = 𝑤ℎ𝑖
𝑘
(𝑅) − △ 𝑤ℎ𝑖
𝑘
(𝑅) (6)
If the Root Mean Square Error (RMSE) exceeds the allowable level of error, the two phases are
repeated. The output of the SNN will be used to estimate network traffic for the next time.
the error which can be used to adjust the weights in SNN can be described in Eq. (7).
𝐸𝑟𝑟(𝑡) = 𝑇𝑅(𝑡) − 𝐵𝐷 (7)
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Figure 5. Flowchart of the proposed controller
In order guarantee balance amongst slices, the Estimated Traffic Rate 〖𝑇𝑅𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑒. Controller is then
distributed among the active slices uniformly to rate in the next time. Eq. (8) explains how the rate
adjustment control operates.
𝑇𝑅𝑛𝑒𝑤(𝑆𝑙𝑖𝑐𝑒𝑖(𝑡 + 1)) =
𝑇𝑅𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑒
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑐𝑡𝑖𝑣𝑒 𝑠𝑢𝑏−𝑠𝑙𝑖𝑐𝑒𝑠
(8)
Where the 𝑇𝑅𝑛𝑒𝑤(𝑆𝑙𝑖𝑐𝑒𝑖(𝑡 + 1)) is the new TR for each slice, which depends on it, slice determines
the number of active sub slices in the global slice to avoid congestion in next time
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6. Simulation Setup and Results
The simulation is carried out using the simulation settings specified in Table (1). To evaluate
performance in a high-traffic situation, 100 sub-slice nodes are randomly set up in simulation with
four global slices in the initial position in the coverage region. The amount of active sub slices in each
global slice varies depending on the protocol used, which creates different Traffic (TR) based on the
number of active global slices and active sub slices. The three proposed controllers are designed to
compare the simulation (DRASR which is depend on SNN spike and response, DRAIF which is
depend on SNN integrate and fire, and DRAN that depend on traditional neural network)
Table (1) Simulation parameter
Number of global slices 4 global slices
Number of sub slice 100
Number of sub slices for each
global slice
25
Initial global Buffer size 1500 packets
Initial Buffer size of each sub
slice
150 packets
Data packet size 800 bytes
Simulation time 100 msec.
6.1 Packet Loss Ratio (PLR)
Fig. (5) illustrates the PLR for the network when three approaches based on the neural network are
implemented. These approaches are compared using the PLR parameter of the same network; the
comparison also includes a network without the controller.
Figure 5. Comparison packet loss ratio of the network when three proposed approaches are used and
without
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It is clarified in Fig. (5) that the PLR of the proposed approaches is better than without a controller
because the buffer controller decreases sending rate of the active sub-slice during the transmission
process in high network traffic.
It is also obvious that DRAIF controller which is depend on (SNN Integrate and Fire) performs well
but it hasn’t accuracy as DRASR. The performance DRASR is very good.
6.2 Network Throughput Ratio (NTR)
Fig. (6) represents the proportion of the received packets by the global slice over the simulation in time
in the form of a comparison between networks with proposed approaches controller and networks
without controller.
Figure 6. Comparison of NTR of the network when three proposed approaches are used and without
The network with the proposed approaches controller performs better than the network without a
controller in increasing the proportion of packets received at the global slice for all active sub-slices.
For more explanation, WRA (Without Controller), which means that the network traffic is high due to
the number of active sub slice is high too. Moreover, DRASR performs better along the simulation
time because it is designed to work with any changeable traffic rate to avoid overflow on the buffer of
the global slice.
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7. Conclusion
The SDN is responsible and monitored for the entire network, and the resources are not sufficient for
all slices, a controller must be available on the network to be responsible for a fair and efficient
distribution of resources according to the requirements of each slice based on traffic features. The
results presented the efficiency of the proposed controller spike response (DRASR) compared with the
controller with the Integrate and Fire and Artificial Neural are better in network PLR and throughput.
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