This paper presents an overview of our learning-based orchestrator for intelligent Open vSwitch that we
present this using Machine Learning in Software-Defined Networking technology. The first task consists of
extracting relevant information from the Data flow generated from a SDN and using them to learn, to
predict and to accurately identify the optimal destination OVS using Reinforcement Learning and QLearning Algorithm. The second task consists to select this using our hybrid orchestrator the optimal
Intelligent SDN controllers with Supervised Learning. Therefore, we propose as a solution using Intelligent
Software-Defined Networking controllers (SDN) frameworks, OpenFlow deployments and a new intelligent
hybrid Orchestration for multi SDN controllers. After that, we feeded these feature to a Convolutional
Neural Network model to separate the classes that we’re working on. The result was very promising the
model achieved an accuracy of 72.7% on a database of 16 classes. In any case, this paper sheds light to
researchers looking for the trade-offs between SDN performance and IA customization
Improving Network Security in MANETS using IEEACKijsrd.com
This document discusses improving network security in mobile ad hoc networks (MANETs) using an improved version of the EAACK intrusion detection system called IEEACK. IEEACK aims to address some of the weaknesses of EAACK related to link breakage, malicious sources, and partial packet dropping. The document describes the components of IEEACK, including ACK, S-ACK, MRA, digital signatures, and a new trust-based quality of service model. Simulation results show that IEEACK can prevent attacks from malicious nodes and improve security performance metrics like packet delivery ratio and detection of malicious nodes.
A Trust-Based Predictive Model for Mobile Ad Hoc Networkspijans
The Internet of things (IoT) is a heterogeneous network of different types of wireless networks such as wireless sensor networks (WSNs), ZigBee, Wi-Fi, mobile ad hoc networks (MANETs), and RFID. To make IoT a reality for smart environment, more attractive to end users, and economically successful, it must be compatible with WSNs and MANETs. In light of this, the present paper discusses a novel quantitative trust model for an IoT-MANET. The proposed trust model combines both direct and indirect trust opinion in order to calculate the final trust value for a node. Further, a routing protocol has been designed to ensure the secure and reliable end-to-end delivery of packets by only considering trustworthy nodes in the path. Simulation results show that our proposed trust model outperforms similar existing trust models.
Irrational node detection in multihop cellular networks using accounting centereSAT Journals
Abstract In multihop cellular networks mobile nodes typically transmit packets during intermediate mobile nodes for enhancing recital. Stingy nodes typically don't collaborate that incorporates a negative result on the network fairness and recital. A fair, inexpensive and best incentive mechanism by Selfish Node Detection (FESCIMbySND) has been projected to stimulate the mobile node’s cooperation. Hashing operations area unit employed in order to extend the safety. Trivial Hash perform has been wont to improve end-to-end delay and outturn. Additionally Cyclic Redundancy Check Mechanism has been used to spot the ridiculous nodes that involve themselves in sessions with the intention of dropping the in sequence packets. Moreover, to cut back the impact at the Accounting Center a Border node has been commend the task of propose the checks employing a digital signature. Keywords: Border Node Mechanism, Cyclic Redundancy Check, Selfish nodes, Trivial Hash Function
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This document contains information about several M.Phil Computer Science Cloud Computing projects written in C# and NS2. It provides the titles, languages, links, and short abstracts for each project. The projects focus on topics related to cloud computing including secure cloud storage, data integrity verification, privacy-preserving auditing, and keyword search over encrypted cloud data.
A Proposal for End-to-End QoS Provisioning in Software-Defined NetworksIJECEIAES
This paper describes a framework application for the control plane of a network infras- tructure; the objective is to feature end-user applications with the capability of requesting at any time a customised end-to-end Quality-of-Service profile in the context of dynamic Service-Level-Agreements. Our solution targets current and future real-time applications that require tight QoS parameters, such as a guaranteed end-to-end delay bound. These applications include, but are not limited to, health-care, mobility, education, manufacturing, smart grids, gaming and much more. We discuss the issues related to the previous Integrated Service and the reason why the RSVP protocol for guaranteed QoS did not take off. Then we present a new signaling and resource reservation framework based on the cutting-edge network controller ONOS. Moreover, the presented system foresees the need of considering the edges of the network, where terminal applications are connected to, to be piloted by distinct logically centralised controllers. We discuss a possible inter-domain communication mechanism to achieve the end-to-end QoS guarantee.
Improving Network Security in MANETS using IEEACKijsrd.com
This document discusses improving network security in mobile ad hoc networks (MANETs) using an improved version of the EAACK intrusion detection system called IEEACK. IEEACK aims to address some of the weaknesses of EAACK related to link breakage, malicious sources, and partial packet dropping. The document describes the components of IEEACK, including ACK, S-ACK, MRA, digital signatures, and a new trust-based quality of service model. Simulation results show that IEEACK can prevent attacks from malicious nodes and improve security performance metrics like packet delivery ratio and detection of malicious nodes.
A Trust-Based Predictive Model for Mobile Ad Hoc Networkspijans
The Internet of things (IoT) is a heterogeneous network of different types of wireless networks such as wireless sensor networks (WSNs), ZigBee, Wi-Fi, mobile ad hoc networks (MANETs), and RFID. To make IoT a reality for smart environment, more attractive to end users, and economically successful, it must be compatible with WSNs and MANETs. In light of this, the present paper discusses a novel quantitative trust model for an IoT-MANET. The proposed trust model combines both direct and indirect trust opinion in order to calculate the final trust value for a node. Further, a routing protocol has been designed to ensure the secure and reliable end-to-end delivery of packets by only considering trustworthy nodes in the path. Simulation results show that our proposed trust model outperforms similar existing trust models.
Irrational node detection in multihop cellular networks using accounting centereSAT Journals
Abstract In multihop cellular networks mobile nodes typically transmit packets during intermediate mobile nodes for enhancing recital. Stingy nodes typically don't collaborate that incorporates a negative result on the network fairness and recital. A fair, inexpensive and best incentive mechanism by Selfish Node Detection (FESCIMbySND) has been projected to stimulate the mobile node’s cooperation. Hashing operations area unit employed in order to extend the safety. Trivial Hash perform has been wont to improve end-to-end delay and outturn. Additionally Cyclic Redundancy Check Mechanism has been used to spot the ridiculous nodes that involve themselves in sessions with the intention of dropping the in sequence packets. Moreover, to cut back the impact at the Accounting Center a Border node has been commend the task of propose the checks employing a digital signature. Keywords: Border Node Mechanism, Cyclic Redundancy Check, Selfish nodes, Trivial Hash Function
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This document contains information about several M.Phil Computer Science Cloud Computing projects written in C# and NS2. It provides the titles, languages, links, and short abstracts for each project. The projects focus on topics related to cloud computing including secure cloud storage, data integrity verification, privacy-preserving auditing, and keyword search over encrypted cloud data.
A Proposal for End-to-End QoS Provisioning in Software-Defined NetworksIJECEIAES
This paper describes a framework application for the control plane of a network infras- tructure; the objective is to feature end-user applications with the capability of requesting at any time a customised end-to-end Quality-of-Service profile in the context of dynamic Service-Level-Agreements. Our solution targets current and future real-time applications that require tight QoS parameters, such as a guaranteed end-to-end delay bound. These applications include, but are not limited to, health-care, mobility, education, manufacturing, smart grids, gaming and much more. We discuss the issues related to the previous Integrated Service and the reason why the RSVP protocol for guaranteed QoS did not take off. Then we present a new signaling and resource reservation framework based on the cutting-edge network controller ONOS. Moreover, the presented system foresees the need of considering the edges of the network, where terminal applications are connected to, to be piloted by distinct logically centralised controllers. We discuss a possible inter-domain communication mechanism to achieve the end-to-end QoS guarantee.
Delay Tolerant Networks (DTNs) have high end-to-end latency, which is often faces disconnection, and unreliable wireless connections. It does not mean a delay service instead DTNs provides a service where network imposes disruption or delay. It operates in challenged networks with extremely limited resources such as memory size, CPU processing power etc. This paper presents an efficient trust managing mechanism for providing secure environment. The proposed dynamic trust management protocol uses a dynamic threshold updating which overcomes the problems with time changing dynamic characteristics by dynamically updating the criteria in response to changing network conditions. This reduces overheads and increases the efficient use of routing network even in conditions change. Also the dynamic threshold update reduces the false detection probability of the malicious nodes. To show the effectiveness of the proposed system, a detailed simulation in the presence of selfish and malicious nodes is performed with ONE simulator. Finally a comparative analysis of our proposed routing with previous routing protocols is also performed. The results demonstrate that presented algorithm deals effectively with selfish behavior with providing significant gain on effective delivery ratio in trade off with message overhead and delay
On the latency and jitter evaluation of software defined networksjournalBEEI
Conventional networking devices require that each is programmed with different rules to perform specific collective tasks. Next generation networks are required to be elastic, scalable and secured to connect millions of heterogeneous devices. Software defined networking (SDN) is an emerging network architecture that separates control from forwarding devices. This decoupling allows centralized network control to be done network-wide. This paper analyzes the latency and jitter of SDN against a conventional network. Through simulation, it is shown that SDN has an average three times lower jitter and latency per packet that translate to improved throughput under varying traffic conditions.
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.
M phil-computer-science-wireless-communication-projectsVijay Karan
List of Wireless Communication IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Wireless Communication for M.Phil Computer Science students.
This document discusses service management strategies for next generation networks. It proposes a distributed algorithm using a token ring approach. The algorithm establishes a logical ring within the core network consisting of all Access Network Servers. When a mobile host submits a request, its local Access Network Server adds it to a request queue. When the token visits that server, pending requests are moved to a grant queue and serviced. If the mobile host has migrated, search strategies are used to locate and deliver the token to the mobile host at its current Access Network Server. Alternatively, the network can be partitioned into areas each managed by a proxy server, with the token circulating among proxies to service requests from mobile hosts in their respective areas.
A New Scheme of Group-based AKA for Machine Type Communication over LTE Netwo...IJECEIAES
Machine Type Communication (MTC) is considered as one of the most important approaches to the future of mobile communication has attracted more and more attention. To reach the safety of MTC, applications in networks must meet the low power consumption requirements of devices and mass transmission device. When a large number of MTC devices get connected to the network, each MTC device must implement an independent access authentication process according to the 3GPP standard, which will cause serious traffic congestion in the Long Term Evolution (LTE) network. In this article, we propose a new group access authentication scheme, by which a huge number of MTC devices can be simultaneously authenticated by the network and establish an independent session key with the network respectively. Experimental results show that the proposed scheme can achieve robust security and avoid signaling overload on LTE networks.
SBGC provides IEEE projects for students in various domains including Java, J2ME, J2EE, .NET and MATLAB. It offers projects in categories 1) with new ideas/papers and 2) selecting from their project list. They ensure projects are fully implemented and students understand all aspects. SBGC provides latest IEEE projects for students in many engineering and technology fields as well as business and science. It has training and R&D divisions to help students become job ready. Project deliverables include abstracts, papers, materials, presentations, reports, procedures, explanations and certificates.
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed
This document proposes a new networking paradigm called Knowledge-Defined Networking (KDN) that combines Software-Defined Networking (SDN), Network Analytics, and Machine Learning techniques. The key aspects of KDN are:
1) It uses SDN to provide centralized network control and network analytics to provide a rich view of the network.
2) A Knowledge Plane applies machine learning to the network analytics data to build models of network behavior and make automated decisions.
3) The decisions are expressed through an intent-based language and translated by the SDN controller into specific configuration actions for network devices.
1) The document presents a conceptual framework for standardizing and virtualizing the Internet of Things infrastructure through deploying OpenFlow technology.
2) The framework consists of 4 layers and aims to provide context-aware e-services based on context information collected from IoT devices, while leaving the existing infrastructure unchanged.
3) It allows heterogeneous devices and protocols to collaborate actively and form a common platform to share resources and establish multi-operational sensor networks.
CONTAINERIZED SERVICES ORCHESTRATION FOR EDGE COMPUTING IN SOFTWARE-DEFINED W...IJCNCJournal
As SD-WAN disrupts legacy WAN technologies and becomes the preferred WAN technology adopted by corporations, and Kubernetes becomes the de-facto container orchestration tool, the opportunities for deploying edge-computing containerized applications running over SD-WAN are vast. Service orchestration in SD-WAN has not been provided with enough attention, resulting in the lack of research focused on service discovery in these scenarios. In this article, an in-house service discovery solution that works alongside Kubernetes’ master node for allowing improved traffic handling and better user experience when running micro-services is developed. The service discovery solution was conceived following a design science research approach. Our research includes the implementation of a proof-ofconcept SD-WAN topology alongside a Kubernetes cluster that allows us to deploy custom services and delimit the necessary characteristics of our in-house solution. Also, the implementation's performance is tested based on the required times for updating the discovery solution according to service updates. Finally, some conclusions and modifications are pointed out based on the results, while also discussing possible enhancements.
This document summarizes a student's paper on using reinforcement learning for anomaly detection in software defined networks. The student aims to use machine learning techniques, specifically reinforcement learning, to make network traffic control decisions given certain network attack scenarios. The student's methodology involves using network statistics collected from an OpenFlow switch to define states for a reinforcement learning algorithm. The algorithm is deployed on the application plane of an SDN architecture and aims to identify anomalous traffic flows based on features like flow size and packet counts, then take actions through the controller to stop anomalous traffic from affecting the network. Initial testing of the approach showed potential for detecting ping flood and SYN flood attacks on the simulated network.
TRUST BASED ROUTING METRIC FOR RPL ROUTING PROTOCOL IN THE INTERNET OF THINGSpijans
While smart factories are becoming widely recognized as a fundamental concept of Industry 4.0, their implementation has posed several challenges insofar that they generate and process vast amounts of security critical and privacy sensitive data, in addition to the fact that they deploy IoT heterogeneous and constrained devices communicating with each other and being accessed ubiquitously through lossy networks. In this scenario, the routing of data is a specific area of concern especially with the inherent constraints and limiting properties of such devices like processing resources, memory capacity and battery life. To suit these constraints and to provide the required connectivity, the IETF has developed several standards, among them the RPL routing protocol for Low powerand Lossy Networks (LLNs). However, and even though RPL provides support for integrity and confidentiality of messages, its security may be compromised by several threats and attacks. We propose in this work TRM-RPL, a Trust based Routing Metric for the RPL protocol in an IIoT based environments. TRM-RPL uses a trust management mechanism to detect malicious behaviors and resist routing attacks while providing QoS guarantees. In addition, our model addresses both node and link trust and follows a multidimensional approach to enable
an accurate trust assessment for IoT entities. TRM-RPL is implemented, successfully tested and compared with the standard RPL protocol where its effectiveniness and resilience to attacks has been proved to be better.
This document discusses security protocols for position-based routing in vehicular ad hoc networks (VANETs). It first provides background on VANETs and the need for secure routing protocols. It then reviews several existing security protocols for VANET routing, including those using digital signatures, anonymous keys, and group signatures. The document proposes an enhanced secure position-based protocol (SPBR) to address attacks like black hole attacks. It also discusses two specific security methods - hybrid signatures and an efficient scheme using HMAC and digital signatures. The document evaluates the performance of these methods through network simulation.
Analysis Of Wireless Sensor Network Routing ProtocolsAmanda Brady
This document proposes using Border Gateway Protocol (BGP) and inter-domain packet filters (IDPFs) to limit IP spoofing on the internet. IDPFs would be constructed using information from BGP route updates and deployed on border routers. IDPFs aim to minimize IP spoofing without requiring global routing information. The framework is designed so that it does not incorrectly discard packets with valid source addresses. With even partial deployment, IDPFs could reduce the level of IP spoofing on the internet.
A CLASS-BASED ADAPTIVE QOS CONTROL SCHEME ADOPTING OPTIMIZATION TECHNIQUE OVE...IJCNCJournal
This work investigates the QoS provisioning of various traffic classes on an SDN-enabled network. We
propose and implement the class-based adaptive QoS control scheme on SDN-enabled network for various
traffic classes, namely VoIP, Video Streaming and File Transfer. The effectiveness of our proposed scheme
is validated by simulation using Mininet and Ryu controller. The procedure to create the simulation
platform and all details relevant to all software used are described step-by-step in detail. The main
performance evaluation metric is the Maximum Number of Traffic Flows admittable with QoS while
Average Throughput, Latency, Jitter, and Packet Loss Rate are maintained at the comparable level of the
existing work in the literature called JMABC [11]. Our simulation results are illustrated with 95%
confidence interval. According to the simulation results, it is obvious that our proposed class-based
adaptive QoS control scheme adopting the optimization technique significantly outperforms the existing
similar QoS provision scheme in terms of the maximum number of the high priority traffic flows (VoIP)
admittable with QoS while the other evaluation metrics are maintained at the same level.
A Class-based Adaptive QoS Control Scheme Adopting Optimization Technique ove...IJCNCJournal
This work investigates the QoS provisioning of various traffic classes on an SDN-enabled network. We propose and implement the class-based adaptive QoS control scheme on SDN-enabled network for various traffic classes, namely VoIP, Video Streaming and File Transfer. The effectiveness of our proposed scheme is validated by simulation using Mininet and Ryu controller. The procedure to create the simulation platform and all details relevant to all software used are described step-by-step in detail. The main performance evaluation metric is the Maximum Number of Traffic Flows admittable with QoS while Average Throughput, Latency, Jitter, and Packet Loss Rate are maintained at the comparable level of the existing work in the literature called JMABC [11]. Our simulation results are illustrated with 95% confidence interval. According to the simulation results, it is obvious that our proposed class-based adaptive QoS control scheme adopting the optimization technique significantly outperforms the existing similar QoS provision scheme in terms of the maximum number of the high priority traffic flows (VoIP) admittable with QoS while the other evaluation metrics are maintained at the same level.
IRJET- Virtual Network Recognition and Optimization in SDN-Enabled Cloud Env...IRJET Journal
This document summarizes a research paper on virtual network recognition and optimization in an SDN-enabled cloud environment. The paper proposes using SDN and cloud computing technologies to increase the functionality and capacity of wireless networks. It formulates an online routing problem to maximize traffic flow over time while meeting constraints. A fast approximation algorithm is developed based on time-dependent duals. Extensive simulations show the algorithm outperforms heuristics by enabling end-to-end optimization and awareness of congestion and budgets. The paper concludes SDN is still emerging but highlights areas of expanding its scope and applications.
An Investigation into Convergence of Networking and Storage Solutions Blesson Babu
This document discusses convergence of networking and storage solutions through technologies like software-defined networking (SDN) and network function virtualization (NFV). It investigates SDN and networked storage, how they can improve efficiency by reducing wasted capacity and simplifying management. The document also examines using SDN in a cloud computing environment to increase operational efficiency of data centers.
Delay Tolerant Networks (DTNs) have high end-to-end latency, which is often faces disconnection, and unreliable wireless connections. It does not mean a delay service instead DTNs provides a service where network imposes disruption or delay. It operates in challenged networks with extremely limited resources such as memory size, CPU processing power etc. This paper presents an efficient trust managing mechanism for providing secure environment. The proposed dynamic trust management protocol uses a dynamic threshold updating which overcomes the problems with time changing dynamic characteristics by dynamically updating the criteria in response to changing network conditions. This reduces overheads and increases the efficient use of routing network even in conditions change. Also the dynamic threshold update reduces the false detection probability of the malicious nodes. To show the effectiveness of the proposed system, a detailed simulation in the presence of selfish and malicious nodes is performed with ONE simulator. Finally a comparative analysis of our proposed routing with previous routing protocols is also performed. The results demonstrate that presented algorithm deals effectively with selfish behavior with providing significant gain on effective delivery ratio in trade off with message overhead and delay
On the latency and jitter evaluation of software defined networksjournalBEEI
Conventional networking devices require that each is programmed with different rules to perform specific collective tasks. Next generation networks are required to be elastic, scalable and secured to connect millions of heterogeneous devices. Software defined networking (SDN) is an emerging network architecture that separates control from forwarding devices. This decoupling allows centralized network control to be done network-wide. This paper analyzes the latency and jitter of SDN against a conventional network. Through simulation, it is shown that SDN has an average three times lower jitter and latency per packet that translate to improved throughput under varying traffic conditions.
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.
M phil-computer-science-wireless-communication-projectsVijay Karan
List of Wireless Communication IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Wireless Communication for M.Phil Computer Science students.
This document discusses service management strategies for next generation networks. It proposes a distributed algorithm using a token ring approach. The algorithm establishes a logical ring within the core network consisting of all Access Network Servers. When a mobile host submits a request, its local Access Network Server adds it to a request queue. When the token visits that server, pending requests are moved to a grant queue and serviced. If the mobile host has migrated, search strategies are used to locate and deliver the token to the mobile host at its current Access Network Server. Alternatively, the network can be partitioned into areas each managed by a proxy server, with the token circulating among proxies to service requests from mobile hosts in their respective areas.
A New Scheme of Group-based AKA for Machine Type Communication over LTE Netwo...IJECEIAES
Machine Type Communication (MTC) is considered as one of the most important approaches to the future of mobile communication has attracted more and more attention. To reach the safety of MTC, applications in networks must meet the low power consumption requirements of devices and mass transmission device. When a large number of MTC devices get connected to the network, each MTC device must implement an independent access authentication process according to the 3GPP standard, which will cause serious traffic congestion in the Long Term Evolution (LTE) network. In this article, we propose a new group access authentication scheme, by which a huge number of MTC devices can be simultaneously authenticated by the network and establish an independent session key with the network respectively. Experimental results show that the proposed scheme can achieve robust security and avoid signaling overload on LTE networks.
SBGC provides IEEE projects for students in various domains including Java, J2ME, J2EE, .NET and MATLAB. It offers projects in categories 1) with new ideas/papers and 2) selecting from their project list. They ensure projects are fully implemented and students understand all aspects. SBGC provides latest IEEE projects for students in many engineering and technology fields as well as business and science. It has training and R&D divisions to help students become job ready. Project deliverables include abstracts, papers, materials, presentations, reports, procedures, explanations and certificates.
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed
This document proposes a new networking paradigm called Knowledge-Defined Networking (KDN) that combines Software-Defined Networking (SDN), Network Analytics, and Machine Learning techniques. The key aspects of KDN are:
1) It uses SDN to provide centralized network control and network analytics to provide a rich view of the network.
2) A Knowledge Plane applies machine learning to the network analytics data to build models of network behavior and make automated decisions.
3) The decisions are expressed through an intent-based language and translated by the SDN controller into specific configuration actions for network devices.
1) The document presents a conceptual framework for standardizing and virtualizing the Internet of Things infrastructure through deploying OpenFlow technology.
2) The framework consists of 4 layers and aims to provide context-aware e-services based on context information collected from IoT devices, while leaving the existing infrastructure unchanged.
3) It allows heterogeneous devices and protocols to collaborate actively and form a common platform to share resources and establish multi-operational sensor networks.
CONTAINERIZED SERVICES ORCHESTRATION FOR EDGE COMPUTING IN SOFTWARE-DEFINED W...IJCNCJournal
As SD-WAN disrupts legacy WAN technologies and becomes the preferred WAN technology adopted by corporations, and Kubernetes becomes the de-facto container orchestration tool, the opportunities for deploying edge-computing containerized applications running over SD-WAN are vast. Service orchestration in SD-WAN has not been provided with enough attention, resulting in the lack of research focused on service discovery in these scenarios. In this article, an in-house service discovery solution that works alongside Kubernetes’ master node for allowing improved traffic handling and better user experience when running micro-services is developed. The service discovery solution was conceived following a design science research approach. Our research includes the implementation of a proof-ofconcept SD-WAN topology alongside a Kubernetes cluster that allows us to deploy custom services and delimit the necessary characteristics of our in-house solution. Also, the implementation's performance is tested based on the required times for updating the discovery solution according to service updates. Finally, some conclusions and modifications are pointed out based on the results, while also discussing possible enhancements.
This document summarizes a student's paper on using reinforcement learning for anomaly detection in software defined networks. The student aims to use machine learning techniques, specifically reinforcement learning, to make network traffic control decisions given certain network attack scenarios. The student's methodology involves using network statistics collected from an OpenFlow switch to define states for a reinforcement learning algorithm. The algorithm is deployed on the application plane of an SDN architecture and aims to identify anomalous traffic flows based on features like flow size and packet counts, then take actions through the controller to stop anomalous traffic from affecting the network. Initial testing of the approach showed potential for detecting ping flood and SYN flood attacks on the simulated network.
TRUST BASED ROUTING METRIC FOR RPL ROUTING PROTOCOL IN THE INTERNET OF THINGSpijans
While smart factories are becoming widely recognized as a fundamental concept of Industry 4.0, their implementation has posed several challenges insofar that they generate and process vast amounts of security critical and privacy sensitive data, in addition to the fact that they deploy IoT heterogeneous and constrained devices communicating with each other and being accessed ubiquitously through lossy networks. In this scenario, the routing of data is a specific area of concern especially with the inherent constraints and limiting properties of such devices like processing resources, memory capacity and battery life. To suit these constraints and to provide the required connectivity, the IETF has developed several standards, among them the RPL routing protocol for Low powerand Lossy Networks (LLNs). However, and even though RPL provides support for integrity and confidentiality of messages, its security may be compromised by several threats and attacks. We propose in this work TRM-RPL, a Trust based Routing Metric for the RPL protocol in an IIoT based environments. TRM-RPL uses a trust management mechanism to detect malicious behaviors and resist routing attacks while providing QoS guarantees. In addition, our model addresses both node and link trust and follows a multidimensional approach to enable
an accurate trust assessment for IoT entities. TRM-RPL is implemented, successfully tested and compared with the standard RPL protocol where its effectiveniness and resilience to attacks has been proved to be better.
This document discusses security protocols for position-based routing in vehicular ad hoc networks (VANETs). It first provides background on VANETs and the need for secure routing protocols. It then reviews several existing security protocols for VANET routing, including those using digital signatures, anonymous keys, and group signatures. The document proposes an enhanced secure position-based protocol (SPBR) to address attacks like black hole attacks. It also discusses two specific security methods - hybrid signatures and an efficient scheme using HMAC and digital signatures. The document evaluates the performance of these methods through network simulation.
Analysis Of Wireless Sensor Network Routing ProtocolsAmanda Brady
This document proposes using Border Gateway Protocol (BGP) and inter-domain packet filters (IDPFs) to limit IP spoofing on the internet. IDPFs would be constructed using information from BGP route updates and deployed on border routers. IDPFs aim to minimize IP spoofing without requiring global routing information. The framework is designed so that it does not incorrectly discard packets with valid source addresses. With even partial deployment, IDPFs could reduce the level of IP spoofing on the internet.
A CLASS-BASED ADAPTIVE QOS CONTROL SCHEME ADOPTING OPTIMIZATION TECHNIQUE OVE...IJCNCJournal
This work investigates the QoS provisioning of various traffic classes on an SDN-enabled network. We
propose and implement the class-based adaptive QoS control scheme on SDN-enabled network for various
traffic classes, namely VoIP, Video Streaming and File Transfer. The effectiveness of our proposed scheme
is validated by simulation using Mininet and Ryu controller. The procedure to create the simulation
platform and all details relevant to all software used are described step-by-step in detail. The main
performance evaluation metric is the Maximum Number of Traffic Flows admittable with QoS while
Average Throughput, Latency, Jitter, and Packet Loss Rate are maintained at the comparable level of the
existing work in the literature called JMABC [11]. Our simulation results are illustrated with 95%
confidence interval. According to the simulation results, it is obvious that our proposed class-based
adaptive QoS control scheme adopting the optimization technique significantly outperforms the existing
similar QoS provision scheme in terms of the maximum number of the high priority traffic flows (VoIP)
admittable with QoS while the other evaluation metrics are maintained at the same level.
A Class-based Adaptive QoS Control Scheme Adopting Optimization Technique ove...IJCNCJournal
This work investigates the QoS provisioning of various traffic classes on an SDN-enabled network. We propose and implement the class-based adaptive QoS control scheme on SDN-enabled network for various traffic classes, namely VoIP, Video Streaming and File Transfer. The effectiveness of our proposed scheme is validated by simulation using Mininet and Ryu controller. The procedure to create the simulation platform and all details relevant to all software used are described step-by-step in detail. The main performance evaluation metric is the Maximum Number of Traffic Flows admittable with QoS while Average Throughput, Latency, Jitter, and Packet Loss Rate are maintained at the comparable level of the existing work in the literature called JMABC [11]. Our simulation results are illustrated with 95% confidence interval. According to the simulation results, it is obvious that our proposed class-based adaptive QoS control scheme adopting the optimization technique significantly outperforms the existing similar QoS provision scheme in terms of the maximum number of the high priority traffic flows (VoIP) admittable with QoS while the other evaluation metrics are maintained at the same level.
IRJET- Virtual Network Recognition and Optimization in SDN-Enabled Cloud Env...IRJET Journal
This document summarizes a research paper on virtual network recognition and optimization in an SDN-enabled cloud environment. The paper proposes using SDN and cloud computing technologies to increase the functionality and capacity of wireless networks. It formulates an online routing problem to maximize traffic flow over time while meeting constraints. A fast approximation algorithm is developed based on time-dependent duals. Extensive simulations show the algorithm outperforms heuristics by enabling end-to-end optimization and awareness of congestion and budgets. The paper concludes SDN is still emerging but highlights areas of expanding its scope and applications.
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Learning-based Orchestrator for Intelligent Software-defined Networking Controllers
1. International Journal of Software Engineering & Applications (IJSEA), Vol.11, No.6, November 2020
DOI: 10.5121/ijsea.2020.11602 17
LEARNING-BASED ORCHESTRATOR FOR
INTELLIGENT SOFTWARE-DEFINED
NETWORKING CONTROLLERS
Imene Elloumi Zitouna
University of Tunis El Manar, École Nationale d'Ingénieurs de Tunis, Laboratoire
Systems de Communications, Tunisia
ABSTRACT
This paper presents an overview of our learning-based orchestrator for intelligent Open vSwitch that we
present this using Machine Learning in Software-Defined Networking technology. The first task consists of
extracting relevant information from the Data flow generated from a SDN and using them to learn, to
predict and to accurately identify the optimal destination OVS using Reinforcement Learning and Q-
Learning Algorithm. The second task consists to select this using our hybrid orchestrator the optimal
Intelligent SDN controllers with Supervised Learning. Therefore, we propose as a solution using Intelligent
Software-Defined Networking controllers (SDN) frameworks, OpenFlow deployments and a new intelligent
hybrid Orchestration for multi SDN controllers. After that, we feeded these feature to a Convolutional
Neural Network model to separate the classes that we’re working on. The result was very promising the
model achieved an accuracy of 72.7% on a database of 16 classes. In any case, this paper sheds light to
researchers looking for the trade-offs between SDN performance and IA customization
KEYWORDS
Open vSwitch OVS, Artificial Intelligence, Machine Learning, Supervided Learning, Reinforcement
Learning, Hybrid Orckestrator, Openflow, QoS, QoE, Real time, User Behavior, User Engagements,
Intelligent Software-Defined Networking ISDN.
1. INTRODUCTION
With the development of Software-Defined Anything, software is already penetrating into the
Internet and even controlling the network. For example, a software developer can make a
service/application at the application layer, and directly obtain the original data of the network
device in the physical layer using southbound APIs [1]. Software technologies are already
changing traditional network services that are typically provided by servers via application layer
protocols into new network services that are provided from the network infrastructure (e.g.,
firewall service, QoS service). The network infrastructure also can provide services through open
APIs, allowing network services to benefit operationally by enabling automated provisioning of
network applications with different characteristics. This also helps to ensure that specific
applications are getting the proper network resources that are dynamically allocated to meet
service requirements (QoS, QoE, encryption, etc.) [1]. Due to these advances, many new service
models are emerging in the field of networking (SDN/NFV/SD-WAN).
Artificial Intelligence (AI) and Machine Learning (ML) approaches have emerged in the
networking domain with great promise. They can be clustered into AI/ML techniques for network
engineering and management, network design for AI/ML applications, and system aspects.
AI/ML techniques for network management, operations and automation improve the way we
2. International Journal of Software Engineering & Applications (IJSEA), Vol.11, No.6, November 2020
18
address networking today [10] [11] [12]. They support efficient, rapid, and trustworthy
management operations. Machine learning (ML), is a key feature of future networks mainly the
SDN paradigm.
Our work and interest in softwarization and network programmability fuels the need for
improved network automation, including edge and fog environments. Moreover, network design
and optimization for AI/ML applications address the complementary topic of supporting AI/ML-
based systems through novel networking techniques. Including new architectures and
performance models with great predictive ability in order to guarantee the continuity of QoS/QoE
[2] [3].
In this paper, we elaborate an alternate method and enhancements provided. For the purpose of
tackling the current difficulties, we propose an innovative solution based on the application of
Artificial Intelligence and supervised and reinforcement Machine Learning techniques in SDN.
Through SDN network virtualization, its layered architecture and its programmable interfaces,
overall network control is therefore ensured by the logical centralization of the control function in
the SDN controllers. Network flows are thus controlled using the OpenFlow protocol. In this
work we present, a new reinforcement learning model in an SDN infrastructure. Which adds
"learning" to the control function for better intelligent decision-making at the level of SDN
controllers [7] [8].
These results will ultimately bring us a step closer to overcoming the problem of over
exploitation of network resources, and how they can be deployed more efficiently.
One of the challenges that every machine learning algorithm is faced with is scalability and
validity to large datasets. Our research devoted to applying, hybrid machine learning, which use
an orchestrator and a set of intelligent controllers SDN combine supervised and reinforcement
learning techniques, can improve the key performance metric as well as QoS of different real
time applications and the QoE. Furthermore, prediction methods for behavior and engagements
user combining classification techniques have the potential for creating more accurate results
than the individual methods, particularly for large datasets.
The remainder of this paper is structured as follow: section II discusses a challenge, section III
ISDN and orchestrator quality requirements, section IV describes our approach, section V
presents design of reinforcement learning framework of ISDN, section VI describes the
experimental results of supervised learning of the hybrid orchestrator, testbed, model context,
dataset, data preparation, model selection and training, model creation and fine-tuning. Finally,
section VII conclude the paper.
2. CHALLENGE
Adoption of the Service-Oriented Architecture principle in networking has enabled the Network-
as-a-Service paradigm that is expected to play a crucial role. Network services significantly
impact the performance of higher layer services/applications. The behavior of an end-to-end
network service is the result of the combination of the individual network function behaviors as
well as the behaviors of the network infrastructure composition mechanism.
However, this emerging network services are usually are usually compositions of multiple service
components, APIs or network functions, based on new mechanisms, such as service mesh,
internet of services, and service function chaining, running on the network layer and/or
application layer. Meanwhile, machine learning (ML) has seen great success in solving problems
3. International Journal of Software Engineering & Applications (IJSEA), Vol.11, No.6, November 2020
19
from various domains. In order to efficiently organize, manage, maintain and optimize
networking systems, more intelligence needs to be deployed.
In this case, can we apply the machine learning algorithms in the realm of SDN, from the
perspective of traffic classification, routing optimization, quality of service/quality of experience
prediction and resource management ? How can SDN/ML brings us new chances to provide
intelligence inside the networks ?
The logically centralized control, global view of the network, software-based traffic analysis, and
dynamic updating of forwarding rules, of SDN make it easier to apply our solution of machine
learning techniques in the SDN: "The Intelligent Software defined networking, ISDN".
It is believed that ML has high potential the aforementioned challenges in SDN-based networks,
dynamic service provisioning and adaptive traffic control, as ML is a technology that can
effectively extract the knowledge from data, and then accurately predict future resource
requirements of each virtualized software-based appliance and future service demands of each
user. Therefore, our research tends towards the proposal of a new hybrid orchestrator which
learns the optimal ISDN controller in a multi-ISDN environment.
In another context, in the absence of a mechanism for evaluating the best SDN controller and best
OVS in terms of response time in network congestion situations, centralization at the SDN
controller level operated by OpenFlow increases these response times. Consequently, this will
allow any attacker who has gained access to the administration network to act at any time on the
configuration of the OVS.
It is also conceivable for an attacker to place himself in a man-in-the-middle in order to filter the
commands of a legitimate administrator as well as the feedback of information from the OVS.
Finally, listening to unencrypted or encrypted OpenFlow messages passively provides a great
deal of information about the networks maintained by the controller.
Since the memory resources of the controller are likely to be greater than those of the OVS. This
attack model presupposes the possibility of recording flows on the controller. There is a higher
risk compared to the system master-slaves.
Therefore, without an intelligent prediction mechanism, it is simple for an opposing SDN
controller to usurp the role of master, or to prevent a legitimate SDN controller who wishes to be
master can configure the OVS, or at least have processing times that increase with the traffic rate.
On the other hand, we can measure even longer processing times for large data flows. The goal of
our work is to be able to integrate intelligence into SDN controllers through the application of
machine learning algorithms in terms of routing optimization, traffic classification and resource
management.
3. ISDN AND ORCHESTRATOR QUALITY REQUIREMENTS
The QoS refers to technologies that manages network traffic in order to reduce packet loss,
latency and jitter. They control and manage network resources by defining priorities for specific
forms of information on the network. The QoE is a measurement that determines user behavior
and user engagements and how satisfied the end user is network services [15]. Contrary to QoS,
QoE takes considers the end-to-end connection and applications presently running over that
network connection and how multimedia elements are meeting the end user’s demands.
4. International Journal of Software Engineering & Applications (IJSEA), Vol.11, No.6, November 2020
20
• QoS metrics of OVS Flow table [2] [3] [4]:
- PacketIn: this event is raised whenever the controller receives an OpenFlow packet-in
message from the OVS. This indicates that a packet has arrived at the OVS port and the
OVS either does not know how to handle this packet or the matching rule action implies
sending the packet to the controller. This is where the controller performs the overhead
calculation by incrementing the overhead when the OVS forwards a packet.
- UpStream: this event is triggered in response to the establishment of a new control
channel with a OVS.
- DownStream: this event is fired when a connection to a OVS is terminated, either
explicitly or because of OVS rebooting. We utilize this event handler to output the final
value of measured control overhead.
- TCAM: the OVS support the wire-rate access control list (ACL) and QoS feature with
use of the ternary content addressable memory (TCAM). The enablement of ACLs and
policies does not decrease the switching or routing performance of the OVS as long as the
ACLs are fully loaded in the TCAM. If the TCAM is exhausted, the packets may be
forwarded via the CPU path, which can decrease performance for those packets. Therefore,
our learning can be done with the (QoS and ACL) TCAM percentage of the switches. This
value can be used to determine the reward of the learning agent for the OVS 1.
- Throughput: we aim to measure the maximum flow setup rate a controller can handle per
period of time. Being aware of the amount of packet-in a controller can process ease the
choice of SDN controllers appropriate to master network control charge.
- Latency: represents the time consumed by a controller to process and reply an
outstanding flow request from the OVS.
- Scalability: represents the capacity of a controller to handle a large network with an
increased number of connected hosts without degrading throughput and latency
performance.
- ICMP Round Trip Time (RTT): it is important to evaluate the influence of additional
delay due to communications between OVS and SDN controller for the first ICMP Echo
packet we target the selection of a controller that shows the lower delay.
- TCP and UDP measurements: the purpose of TCP and UDP measurements is to estimate
the impact of delay introduced by the communication between controller and OVS on the
TCP transfer time and UDP packet losses. TCP and UDP evaluation are performed over
multiple network topologies by Mininet. Single, linear, tree and data center network (DCN)
topologies.
• User behavior [5] [6] [9]: is the tracking, collecting and assessing of user data and activities
using monitoring systems. Our purpuse is to determine a baseline of normal activities
specific to the organization and its individual users. They can also be used to identify
deviations from normal. We propose to uses machine learning algorithms to assess these
deviations of user behavior in near-real time.
5. International Journal of Software Engineering & Applications (IJSEA), Vol.11, No.6, November 2020
21
• User engagements [5] [6]: is a process comprised of four distinct stages: 1) point of
engagement, 2) period of sustained engagement, 3) disengagement, and 4) reengagement.
The period of Engagement Attributes are: Aesthetic and Sensory Appeal, Attention,
Awareness, Control, Interactivity, Novelty, Challenge, Feedback, Interest and Positive
Affect. Also we propose to uses machine learning algorithms for specification the new
SLA of user engagement
4. APPROACH OF LEARNING ORCHESTRATOR
Our intention would be to design an SDN architecture composed of multiple controllers where
the choice of the suitable and optimal controller and OVS to be implemented in a specific domain
depends on the controller performance and efficiency to respond to QoS and QoE requirement
imposed by the service application with machine learning environment.
We extract relevant information from the Data flow generated from a SDN (PacketIn, UpStream,
DownStream, TCAM, etc.) and using them to learning, to predicting and to accurately identify
the optimal destination OVS using Reinforcement Learning. Also we extract relevant information
from the Data flow generated from the optimal Intelligent Software-Defined Networking
controllers (CPU Throughput, CPU Latency, Scalability, ICMP Round Trip Time (RTT), TCP
and UDP measurements, etc) with Supervised Learning run in the Hybrid Orchestrator (Figure 1).
Our performance evaluation considers two well-known centralized controllers and multi OVS.
The evaluation in terms of throughput, latency, and scalability is performed with Cbench. With
Mininet and Iperf we evaluate TCP, UDP and ICMP flow over several network topologies. The
main purpose is to predict and to select the ISDN controller that exhibits the highest throughput,
scalability, lowest latency, delay, packet loss, TCP transfer time and most rich in features and
also to predict the optimal OVS with lowest TCAM percentages. This value can be used to
determine the reward of the learning agent for the session.
We extend this hybrid orchestrator by adding the code to install the selected forwarding North
Rules (R1(RRD), R2(SRD), R3(RTPD), etc) created by the learning agent prior to handling any
outcoming traffic.
At the beginning of each emulation experiment, traffic flows are generated into the network
topology, the state of the flow table in the OVS, and the state of the ISDN controllers is observed.
To test the controller’s performance in terms of latency, OVS sends an asynchronous message
toward controller and waits for a reply. Hence, the number of acknowledgments gathered in a test
period of time is used to calculate the average latency. For the throughput measurements, each
OVS transfers a burst of packet-in without waiting for a response to estimate maximum flow
requests a controller can handle per second. Emulated hosts have access to Linux file system
commands. Like “Iperf”. Therefore, network design can easily move to the real system with
minor deployment [5].
6. International Journal of Software Engineering & Applications (IJSEA), Vol.11, No.6, November 2020
22
Figure 1. Hybrid Learning Orchestrator for multi-ISDN proposed
5. DESIGN OF REINFORCEMENT LEARNING FRAMEWORK OF ISDN
In our approach, the configuration of rules can be defined by two decisive parameters, flow
match frequency and flow recentness. Therefore, the RL algorithm can be modeled to obtain the
network configuration that minimizes the long-term control plane overhead [7]. This approach is
modeled using a Markov Decision Process (MDP), which can be represented as the tuple <S, A,
P, R>, where S = {s1, s2, . . . , si } is the state-space, A = {a1, a2, . . . , aj } is the action-space, P
defines the transition probability from state i to state j, and R defines the reward associated with
various actions a ∈ A. The goal of this MDP is to develop a policy π : S → A that maximizes the
cumulative rewards obtained in the long term [4][6]. In the problem that we address in this
research, we can know all the forwarding rules that need to be processed for all tra c ows
transported on the network. For our problem, the state-space, action-space, and reward function
are de ned as follows:
In our approach, we utilize the following RL algorithms:
5.1. Reinforcement Learning Framework of ISDN.
In our design, the Q-Learning algorithm is utilized in our emulation experiments. The decision to
utilize the QLearning algorithm is motivated by the fact that it does not need a model for the
environment, and it updates the Q-values at each time step based on the estimation of action-
value function. This iterative learning process is conducted in discrete time steps in which the
agent interacts with the environment. At each step, the agent interacts with the environment. At
each step, the agent selects an action at ∈ A in the given state st.The agent makes its own
7. International Journal of Software Engineering & Applications (IJSEA), Vol.11, No.6, November 2020
23
decisions to choose an action based on the action selection policy with the goal of maximizing
the expected reward. The notation Q(s, a) denotes the average Q-value of an action a at state s.
While the immediate reward is collected, Q(s, a) can be further refined as:
Q(st,at)= r + Q(st,at) - εQ(st,at)+ γ maxQ(st+1,at+1)
where ε (0 < ε ≤ 1) is the learning rate that determines to what degree the newly obtained
information overrides the old one and γ (0 ≤ γ ≤ 1) is the reward factor that defines the
importance of future rewards and guarantees convergence of the accumulated reward. In our
experiments, we use a discount factor of 0.85 as these values are commonly used in practice.
ALGORITHM : Q-Learning Algorithm
Preconditions:
Initialize Q-table random
TCAM percentage Initialize
state st
Initialize goal
Procedure:
01: improvement measures = 0
02: repeat
03: for (step = 0; step < learning_iteration;
step++) 04: Get action at from st using
05: Get parameter from st using
06: = – *(step / learning_iteration)
07: Take action at on the parameter and receive reward r, control overhead C
08: Sample new state st +1 after applied action at
09: Update Qt←r+ Qt −Qt +γ∗maxQt+1
10: Update the corresponding parameter of Qt
11: improvement measures = get_improvement measures
(bestt ++, bestt - - )12: end for
13: until improvement measures >
The Q-Learning algorithm can have a chance to explore the action space in the process of
searching for a better action that produces a better reward. β is used as the action selection policy
in which the action with the highest Q- value is always picked while the probability of picking
some other action at random is small.
If the action is chosen to be performed due to its Q-value, the associated parameter value will be
updated as well. ε represents the terminal objective of the algorithm. Where this algorithm keeps
track of the chosen parameter corresponding to the Q-value of each action.
This value interval can be divided into four different states as follows:
- State 1: If the value of the TCAM belongs to [0..30]: it is a weak state
- State 2: If the value of the TCAM belongs to [30..50]: it is an ideal and appreciated state.
- State 3: If the value of the TCAM belongs to [50..80]: it is a critical state.
- State 4: If the value of the TCAM belongs to [80..90]: it is a mediocre state
To facilitate the understanding of the different actions of the agent, we can first represent the
states as follows:
8. International Journal of Software Engineering & Applications (IJSEA), Vol.11, No.6, November 2020
24
Table 1. The different states available for each controller
State 1 State 2
State 4 State 4
The agent's actions are modelled as follows:
Action 1 (go up): transition from state 4 to state 1 or from state 3 to state 2 Action 1 (go up):
transition from state 4 to state 1 or from state 3 to state 2 Action 3 (turn right): transition from
state 1 to state 2 or from state 4 to state 3 Action 4 (turn left): transition from state 2 to state 1 or
from state 3 to state 4
6. EXPERIMENTAL RESULTS
6.1. Testbed Setup
The testbed consists of a single machine with (Intel Core i5-7200U CPU @ 3.1 GHz), 8 GB of
memory available. The system runs Ubuntu 16.04 LTS-64 bit with VMware Workstation Pro
version 14.1.1 installed. Controllers, Cbench and Mininet are installed in separate VMs the
allocated memory is respectively (4GB, 2GB, 2GB).
When it comes to Machine learning, there are four main steps that need to be followed which are:
1. Looking at the big picture
2. Getting The Data
3. Preparing the Data for Machine Learning algorithms
4. Selecting and training, and fine-tuning a model The machine learning libraries used are :
1. TensorFlow : is well known for its flexible architecture which allows for the deployment of
computation across a variety of platforms like CPUs or GPUs on a desktop, a server, or a
mobile device.
2. Keras : is a python-based Deep Learning. It works in a different way than other Deep
Learning frameworks. Keras does not support low-level computation like TensorFlow
which is why it uses a tool named Backend.
3. Scikit-learn : is a Machine Learning Library. It includes a wide array of classification,
regression, and clustering algorithms. It was conceived in a way to interoperate with other
mathematical python libraries like Scipy, and NumPy.
4. Jupyter Notebook : is a Web application allowing the creation and sharing of doc- uments
containg live code, visualizations, and narrative text.
5. Anaconda : is a Python and R programming languages distribution which conveniently
installs them additionally to other packages commonly used in Data science and scientific
computing.
We have succeded in training a model which is to be implemented on an SDN Controller. its task
is intelligent control and routing. We will go through these steps one by one while explicitly
explaining the choices we have made throughout the development process [13] [14].
6.2. Model Context
For this model, we used Mininet, a network emulator to generate a Network OVS, controllers,
hosts, and links. in figure .2. The OVS support Openflow protocol for high flexibility when it
comes to customized routing and SDN. Figure 3, 4 and 5 presents the iteration of our algorithm.
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6.3. Q-Learning Algoritm Outputs
Starting from the database containing the ternary memory consumption rate of a OVS as a
function of time, we applied the Q-learning algorithm and we have it trained several times so that
it converges towards an optimal result and informs us the best OVS in terms of QoS. At each
iteration, the agent stores the corresponding values (Q-Values) resulting from Bellman's equation
in a dynamic array named Q-table. Since all of the study and testing is done on a database of
seven OVS, such a procedure allows us to get two OVS in order to decide which of these two is
the most efficient.
The different rates of memory consumption existing in our database are quite large. The figure. 2
below shows part of the database imported into the working environment.
The three previous cases show us different iterations of the algorithm, which ends up deciding in
the Xth iteration, with X is the length of the database, that the OVS R2 is optimal and we can also
retain it.
6.4. The Dataset
In order to train a model, we need a Dataset. In our case, we used the emulated network on
Mininet to generated Data flows and recuperate them using Wireshark, a free opensource packet
sniffer. A sample of the recuperated data is shown in Table II.
Figure 2. Percentage of ternary memory consumption as a function of time
Figure 3. 1st iteration of the algorithm
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Figure 4. 5th iteration of the algorithm
Figure 5. Xth iteration of the algorithm
6.5. Data preparation
To prepare our Data, we first start by eliminating useless information from it. As is shown in the
Table II., our Dataset contains many columns that our model’s has no use for, which is the
descriptive information in the last column. Another constraint when it comes to exploiting
datasets is that Machine Learning algorithms can’t handle String input, for that we had to
transform Source IP address, Destination IP address and Protocol columns into integers. This
process can be easily done via simple Python functions. The following Table.I illustrates the
prepared Dataset.
Following this step, we proceed to dividing our Data into two parts: a Training set and a
validation set.
Table 2. A Glimpse of the Raw Dataset
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Table 3. A Glimpse of the Processed Dataset
6.6. Model Selection and Training
As our Data is labeled, it is natural to choose a Supervised Learning al- gorithm, all the more so
due to Supervised learning being customizable and frequently used in problems relating to self-
healing networks. This is particularly the case for the OpenDayLight project, a modular open
platform for the customization and the automatization of networks of all sizes. It is a Project that
took birth out the SDN movement, clear focusing on Network programmability.
In the following section, we introduce the libraries used from realization of our model.
6.7. Model Creation and Fine-tuning
When it comes to Creating and Fine-tuning Convolutional Neural Network models comes down
to optimizing the Hyperparameters of the model (e.g. Number de filters, size of each du filter,
activation function of each layer, etc.). There are many ways to do this. One can try to fiddle with
them manually until a great combination is found, which can be quite time-consuming, or he can
try to use pre-installed Scikit-learn search methods like the Randomized search.
In our case, and considering the fact that our Data is not complexe, we used the simplest fine-
tuning method which is Grid search. the resulting optimized CNN structure had the following
architecture in figure .6.
The following is a break-out of the models layers:
• The Input Layer: this layer contains our input values which we want to predict and train the
model with, where each neuron in that layer contains a value took from the sample. In our
case, our dataset contains 6 columns, thus the 6 neurons at the top layer.
• The Output Layer: is the layer will give us an information about the sample we gave to our
neural network. In our case, it would be predicting the IP destination address. As our Data
flow simulation engaged 16 IP addresses, the number of neurons in this layer is 16.
• The Hidden Layer: The hidden layers can be composed of one or many sublayers whose
numbers and hyperparameters where determined by the Grid Search. In our case it contains
two layers.
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Figure 6. Model Architecture
At this point, we have built our model and pre-processed our Data. All that is left is to compile
the model, train it, and save it using the previously defined functions compile(), model.fit(), and
model.save() respectively.
The evaluation of our model using the validation Data gives an accuracy ratio of 72.7%.
6.8. Problems and limitations
When it comes to Machine Learning, the main tasks are the selection of the algorithm and the
Data, and latter is what we encountered.
• Insufficient Quantity of Data is a Major problem when it comes to training a model. Even
for very simple problems like ours, we need thousands of examples. For more complex
problems like sound recognition or image classification, we would need millions of
examples [16].
• Irrelevant Features: As they say: garbage in, garbage out. Our model will only learn
correctly if the training Data contains many relevant features and not too many
insignificant ones. These problems, more often than not, lead data scientists to over-fitting
their Data to the training set, Optimizing the hyperparameters in search for a better
accuracy ratio at the training without taking into account the global, dynamic context in
which the mode l will be deployed, which usually leads to bad overall performance after
deployment.
CONCLUSIONS AND FUTURE WORKS
Supervised machine learning methods, such as neural network (NN), convolutional neural
network (CNN), and recurrent neural network (RNN) can apply to prediction and classification.
Furthermore, reinforcement learning methods, are tools for generative networks and
discriminative networks.
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These methods contribute substantially to improving prediction and classification in relevant
applications, but there remain issues and limitations that require further attention from the
research community.
The testing process of Machine Learning on SDN controllers has long been considered critical
for ISPs around the world. Fortunately, our trained-model confirmed the viability and the
potential of the combination of the two aforementioned technologies.
For our case, we were implementing a supervised learning algorithm for intelligent routing and a
reinforcement learning algorithm for to choice the optimal controller. This hybrid learning
solution is being improved constantly as it self adjusts itself to all the customers.
In this paper, we trained a supervised-learning CNN to predictively routing data frames while
arguing the choices taken in the process. We additionally presented the used frameworks
followed by the experimental results. We expend the New Supervised Machine Learning
Methods to the business layer and try to implemented it in an hybrid orchestrator which will have
to efficiently choose which controllers ISDN to use in real-time to maximize performance
indicators with Novel Reinforcement Learning Method where an agent has to take real-time
decisions which is true for our problem. Despite the fact that the machine learning-based
approach performs well in our experiments, further development is needed to expand the
capabilities of our emulation scope. The rest of this work will be the application of Q-learning
algorithm that we proposed. Currently, implementing the user behavior and the user engagements
in the learning labels and model training more thoroughly is the focus of our ongoing research.
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