SDN-BASED NETWORK

Project Title

SDN-BASED NETWORK

Project Area of Specialization Cyber SecurityProject Summary

Software defined WiFi network (SD-WiFi) is a new paradigm that addresses issues such as mobility management, load management, route policies, link discovery, and access selection in traditional WiFi networks. Due to the rapid growth of wireless devices, uneven load distribution among the network resources still remains a challenging issue in SD-WiFi. In this project, we design a novel four-tier software defined WiFi edge architecture (FT-SDWE) to manage load imbalance through an improved handover mechanism, enhanced authentication technique, and upgraded migration approach. In the first tier, the handover mechanism is improved by using a simple AND operator and by shifting the association control to WAPs. Unauthorized user load
is mitigated in the second tier, with the help of base stations (BSs) which act as edge nodes (ENs), using elliptic ElGamal digital signature algorithm (EEDSA). In the third tier, the load is balanced in the data plane among the OpenFlow enabled switches by using the whale optimization algorithm (WOA). Moreover, the load in the fourth tier is balanced among the multiple controllers. global controller (GC) predicts the load states of local controllers (LCs) from the Markov chain model (MCM) and allocates packets to LCs for processing through a binary search tree (BST). 'e performance evaluation of FT-SDWE is demonstrated using extensive OMNeT++ simulations. 'e proposed framework shows effectiveness in terms of bandwidth, jitter, response time, throughput, and migration time in comparison to SD-WiFi, EASM, GAME-SM, and load information strategy schemes.

Project Objectives

The proposed FT-SDWE has the following contributions:

(i) The handover mechanism is improved in the first tier by using a simple AND operator. Handover decisions are made by WAPs themselves.

(ii) Unauthorized devices that enter into the network and contribute towards unnecessary load are mitigated in the second tier. Edge nodes are responsible for authenticating devices using the elliptic ElGamal digital signature algorithm.

(iii) Load at data plane is balanced among the OpenFlow enabled switches using a whale optimization algorithm, which migrates packets from overloaded switch to an optimal switch. Switches are equipped to route real and non-real-time traffic patterns according to their port numbers.

(iv) Markov chain model is applied to estimate load information of local controller.  The fourth tier maintains a binary search tree in the global controller to allocate packets to a least loaded local controller. The global controller prioritizes packets according to packet types.

Project Implementation Method

The proposed FT-SDWE is developed in this project to overwhelm the problem of load imbalance in each tier. FTSDWE is a combination of SDN, WiFi network, and edge computing which is split into a four-tier architecture. Load balancing is the major focus in each tier designed in FT-SDWE. Tier 1 is a WiFi network in which the wireless devices get access via WAPs. Tier 2 comprises edge nodes that are base stations (BSs) which authenticate devices to mitigate load from unauthorized devices. Tier 3 is a data plane with OpenFlow switches and tier 4 consists of multiple SDN controllers.

Load at each tier is significantly focused and minimized effectively. In tier 1, AND operator-based handover decision is made to balance the number of wireless devices connected to each WAP, tier 2 reduces the load of unauthorized devices by authenticating authorized devices using EEDSA, tier 3 involves selecting an optimal switch using WOA subjected to overloading of flow entries. In tier 3, the packets are also classified into real-time (RT) and non-real-time (NRT) classes. 'e packets classified by switches in the data plane reach GC where they are placed into corresponding queues for further processing by LCs in the control plane. Finally, in tier 4, the GC monitors the load of each LC by MCM and allocates the packets to a least loaded controller using the binary search tree. algorithm. GC manages two queues for serving both RT and NRT packets from mobile devices. The RT packets are given higher priority as compared to NRT packets. Load balancing in individual tier exhibits better performance for different significant metrics.

Benefits of the Project

The previous research works for load balancing have been discussed on core SDN, SDN with edge computing,

and SDN with WiFi (SD-WiFi). Factors such as security, traffic priorities, load balancing in data and control plane, and optimal route policies are not studied in a hybrid software defined WiFi edge architecture. To tackle the aforementioned shortcomings, we develop a novel load balancing architecture that integrates WiFi, SDN, and edge computing to manage and balance load at each tier.

We propose a four-tier software defined edge architecture (FT-SDWE). In the first tier, the handover mechanism is improved by using a simple AND operator and by shifting the association control to WAPs. Unauthorized user load is mitigated in the second tier, with the help of base stations (BSs) which act as edge nodes (ENs), using elliptic ElGamal digital signature algorithm (EEDSA). In the third tier load is balanced in data plane among the OpenFlow enabled switches by using whale optimization algorithm (WOA). The load in the fourth tier is balanced among the multiple controllers. The global controller (GC) predicts the load states of local controllers (LCs) from Markov chain model (MCM) and allocates packets to LCs for processing through a binary search tree (BST). The performance evaluation of FT-SDWE is demonstrated using extensive OMNeT++ simulations. The proposed framework shows effectiveness in terms of bandwidth, jitter, response time, throughput and migration time in comparison to SD-WiFi, efficiency aware switch migration (EASM), game switch migration (GAME-SM) and load information strategy schemes.

Technical Details of Final Deliverable

FT-SDWE is developed in OMNeT++ 4.6 simulator and JDK 1.8 that is installed on Windows 10 (64-bit). 'e proposed scheme is designed with OpenFlow switches, global controller, local controllers, wireless access points, edge nodes, and mobile devices. INET is employed in OMNeT++ supporting the OpenFlow standards. POX

controller is used to performs load balancing in the control plane and data plane. The significant parameters involved to develop FTSDWE are listed in Table. The proposed scheme is not limited to only these parameters.

The simulation topology of FT-SDWE designed in OMNeT++ is depicted in Figure 5. The data packets from 80 mobile devices are aggregated and processed at WAPs.

We have reported the balanced load at controllers for three time periods T1, T2, and T3. Table 7 illustrates the controller ID and its corresponding load which is obtained from MCM. The following entities are managed by the global controller for constructing a binary tree to balance the incoming data packets. The load of each LC is initially calculated using its CPU, memory, and disk values. While these entities are being used by the LC during the packet processing and according to the number of packets being processed, the load values of LCs vary.

Let m be the total number of controllers; Ci(CPU), Ci(Mem), Ci(Disk) are the CPU, memory, and disk capacity of controller i, respectively. In this evaluation, the number of incoming packets is varied in each time period such that, in T1, nearly 2000 packets arrive per second; in T2, 4000 packets arrive per second; and T3 deals with 6000 packets per second. However, in FT-SDWE, if the controllers are overloaded initially, they are still capable enough of processing the packets. 'e overloaded controllers are taken into consideration by BST which helps in load balancing among all LCs in the control plane.

Final Deliverable of the Project HW/SW integrated systemCore Industry TelecommunicationOther Industries IT , Security Core Technology Artificial Intelligence(AI)Other Technologies Internet of Things (IoT), Big Data, Clean TechSustainable Development Goals Affordable and Clean Energy, Decent Work and Economic Growth, Industry, Innovation and InfrastructureRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	80000
USB Universal WNDA Devices	Equipment	10	5000	50000
Wi-Fi testing Unit	Equipment	1	10000	10000
Connecting Cables OpenFlow Standards	Equipment	10	1000	10000
Printing, Stationary	Miscellaneous	1	10000	10000