Flow-Aware Elephant Flow Detection for Software-Defined Networks

Software-defined networking (SDN) separates the network control plane from the packet forwarding plane, which provides comprehensive network-state visibility for better network management and resilience. Traffic classification, particularly for elephant flow detection, can lead to improved flow control and resource provisioning in SDN networks. Existing elephant flow detection techniques use pre-set thresholds that cannot scale with the changes in the traffic concept and distribution. This paper proposes a flow-aware elephant flow detection applied to SDN. The proposed technique employs two classifiers, each respectively on SDN switches and controller, to achieve accurate elephant flow detection efficiently. Moreover, this technique allows sharing the elephant flow classification tasks between the controller and switches. Hence, most mice flows can be filtered in the switches, thus avoiding the need to send large numbers of classification requests and signaling messages to the controller. Experimental findings reveal that the proposed technique outperforms contemporary methods in terms of the running time, accuracy, F-measure, and recall

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Review of Path Selection Algorithms with Link Quality and Critical Switch Aware for Heterogeneous Traffic in SDN

Software Defined Networking (SDN) introduced network management flexibility that eludes traditional network architecture. Nevertheless, the pervasive demand for various cloud computing services with different levels of Quality of Service requirements in our contemporary world made network service provisioning challenging. One of these challenges is path selection (PS) for routing heterogeneous traffic with end-to-end quality of service support specific to each traffic class. The challenge had gotten the research community\u27s attention to the extent that many PSAs were proposed. However, a gap still exists that calls for further study. This paper reviews the existing PSA and the Baseline Shortest Path Algorithms (BSPA) upon which many relevant PSA(s) are built to help identify these gaps. The paper categorizes the PSAs into four, based on their path selection criteria, (1) PSAs that use static or dynamic link quality to guide PSD, (2) PSAs that consider the criticality of switch in terms of an update operation, FlowTable limitation or port capacity to guide PSD, (3) PSAs that consider flow variabilities to guide PSD and (4) The PSAs that use ML optimization in their PSD. We then reviewed and compared the techniques\u27 design in each category against the identified SDN PSA design objectives, solution approach, BSPA, and validation approaches. Finally, the paper recommends directions for further research

Security Enhanced Applications for Information Systems

Every day, more users access services and electronically transmit information which is usually disseminated over insecure networks and processed by websites and databases, which lack proper security protection mechanisms and tools. This may have an impact on both the users’ trust as well as the reputation of the system’s stakeholders. Designing and implementing security enhanced systems is of vital importance. Therefore, this book aims to present a number of innovative security enhanced applications. It is titled “Security Enhanced Applications for Information Systems” and includes 11 chapters. This book is a quality guide for teaching purposes as well as for young researchers since it presents leading innovative contributions on security enhanced applications on various Information Systems. It involves cases based on the standalone, network and Cloud environments

Conserve and Protect Resources in Software-Defined Networking via the Traffic Engineering Approach

Software Defined Networking (SDN) is revolutionizing the architecture and operation of computer networks and promises a more agile and cost-efficient network management. SDN centralizes the network control logic and separates the control plane from the data plane, thus enabling flexible management of networks. A network based on SDN consists of a data plane and a control plane. To assist management of devices and data flows, a network also has an independent monitoring plane. These coexisting network planes have various types of resources, such as bandwidth utilized to transmit monitoring data, energy spent to power data forwarding devices and computational resources to control a network. Unwise management, even abusive utilization of these resources lead to the degradation of the network performance and increase the Operating Expenditure (Opex) of the network owner. Conserving and protecting limited network resources is thus among the key requirements for efficient networking. However, the heterogeneity of the network hardware and network traffic workloads expands the configuration space of SDN, making it a challenging task to operate a network efficiently. Furthermore, the existing approaches usually lack the capability to automatically adapt network configurations to handle network dynamics and diverse optimization requirements. Addtionally, a centralized SDN controller has to run in a protected environment against certain attacks. This thesis builds upon the centralized management capability of SDN, and uses cross-layer network optimizations to perform joint traffic engineering, e.g., routing, hardware and software configurations. The overall goal is to overcome the management complexities in conserving and protecting resources in multiple functional planes in SDN when facing network heterogeneities and system dynamics. This thesis presents four contributions: (1) resource-efficient network monitoring, (2) resource-efficient data forwarding, (3) using self-adaptive algorithms to improve network resource efficiency, and (4) mitigating abusive usage of resources for network controlling. The first contribution of this thesis is a resource-efficient network monitoring solution. In this thesis, we consider one specific type of virtual network management function: flow packet inspection. This type of the network monitoring application requires to duplicate packets of target flows and send them to packet monitors for in-depth analysis. To avoid the competition for resources between the original data and duplicated data, the network operators can transmit the data flows through physically (e.g., different communication mediums) or virtually (e.g., distinguished network slices) separated channels having different resource consumption properties. We propose the REMO solution, namely Resource Efficient distributed Monitoring, to reduce the overall network resource consumption incurred by both types of data, via jointly considering the locations of the packet monitors, the selection of devices forking the data packets, and flow path scheduling strategies. In the second contribution of this thesis, we investigate the resource efficiency problem in hybrid, server-centric data center networks equipped with both traditional wired connections (e.g., InfiniBand or Ethernet) and advanced high-data-rate wireless links (e.g., directional 60GHz wireless technology). The configuration space of hybrid SDN equipped with both wired and wireless communication technologies is massively large due to the complexity brought by the device heterogeneity. To tackle this problem, we present the ECAS framework to reduce the power consumption and maintain the network performance. The approaches based on the optimization models and heuristic algorithms are considered as the traditional way to reduce the operation and facility resource consumption in SDN. These approaches are either difficult to directly solve or specific for a particular problem space. As the third contribution of this thesis, we investigates the approach of using Deep Reinforcement Learning (DRL) to improve the adaptivity of the management modules for network resource and data flow scheduling. The goal of the DRL agent in the SDN network is to reduce the power consumption of SDN networks without severely degrading the network performance. The fourth contribution of this thesis is a protection mechanism based upon flow rate limiting to mitigate abusive usage of the SDN control plane resource. Due to the centralized architecture of SDN and its handling mechanism for new data flows, the network controller can be the failure point due to the crafted cyber-attacks, especially the Control-Plane- Saturation (CPS) attack. We proposes an In-Network Flow mAnagement Scheme (INFAS) to effectively reduce the generation of malicious control packets depending on the parameters configured for the proposed mitigation algorithm. In summary, the contributions of this thesis address various unique challenges to construct resource-efficient and secure SDN. This is achieved by designing and implementing novel and intelligent models and algorithms to configure networks and perform network traffic engineering, in the protected centralized network controller

Optimizing Flow Routing Using Network Performance Analysis

Relevant conferences were attended at which work was often presented and several papers were published in the course of this project. • Muna Al-Saadi, Bogdan V Ghita, Stavros Shiaeles, Panagiotis Sarigiannidis. A novel approach for performance-based clustering and management of network traffic flows, IWCMC, ©2019 IEEE. • M. Al-Saadi, A. Khan, V. Kelefouras, D. J. Walker, and B. Al-Saadi: Unsupervised Machine Learning-Based Elephant and Mice Flow Identification, Computing Conference 2021. • M. Al-Saadi, A. Khan, V. Kelefouras, D. J. Walker, and B. Al-Saadi: SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning, Network, 3(1), pp.218-238, 2023.The main task of a network is to hold and transfer data between its nodes. To achieve this task, the network needs to find the optimal route for data to travel by employing a particular routing system. This system has a specific job that examines each possible path for data and chooses the suitable one and transmit the data packets where it needs to go as fast as possible. In addition, it contributes to enhance the performance of network as optimal routing algorithm helps to run network efficiently. The clear performance advantage that provides by routing procedures is the faster data access. For example, the routing algorithm take a decision that determine the best route based on the location where the data is stored and the destination device that is asking for it. On the other hand, a network can handle many types of traffic simultaneously, but it cannot exceed the bandwidth allowed as the maximum data rate that the network can transmit. However, the overloading problem are real and still exist. To avoid this problem, the network chooses the route based on the available bandwidth space. One serious problem in the network is network link congestion and disparate load caused by elephant flows. Through forwarding elephant flows, network links will be congested with data packets causing transmission collision, congestion network, and delay in transmission. Consequently, there is not enough bandwidth for mice flows, which causes the problem of transmission delay. Traffic engineering (TE) is a network application that concerns with measuring and managing network traffic and designing feasible routing mechanisms to guide the traffic of the network for improving the utilization of network resources. The main function of traffic engineering is finding an obvious route to achieve the bandwidth requirements of the network consequently optimizing the network performance [1]. Routing optimization has a key role in traffic engineering by finding efficient routes to achieve the desired performance of the network [2]. Furthermore, routing optimization can be considered as one of the primary goals in the field of networks. In particular, this goal is directly related to traffic engineering, as it is based on one particular idea: to achieve that traffic is routed according to accurate traffic requirements [3]. Therefore, we can say that traffic engineering is one of the applications of multiple improvements to routing; routing can also be optimized based on other factors (not just on traffic requirements). In addition, these traffic requirements are variable depending on analyzed dataset that considered if it is data or traffic control. In this regard, the logical central view of the Software Defined Network (SDN) controller facilitates many aspects compared to traditional routing. The main challenge in all network types is performance optimization, but the situation is different in SDN because the technique is changed from distributed approach to a centralized one. The characteristics of SDN such as centralized control and programmability make the possibility of performing not only routing in traditional distributed manner but also routing in centralized manner. The first advantage of centralized routing using SDN is the existence of a path to exchange information between the controller and infrastructure devices. Consequently, the controller has the information for the entire network, flexible routing can be achieved. The second advantage is related to dynamical control of routing due to the capability of each device to change its configuration based on the controller commands [4]. This thesis begins with a wide review of the importance of network performance analysis and its role for understanding network behavior, and how it contributes to improve the performance of the network. Furthermore, it clarifies the existing solutions of network performance optimization using machine learning (ML) techniques in traditional networks and SDN environment. In addition, it highlights recent and ongoing studies of the problem of unfair use of network resources by a particular flow (elephant flow) and the possible solutions to solve this problem. Existing solutions are predominantly, flow routing-based and do not consider the relationship between network performance analysis and flow characterization and how to take advantage of it to optimize flow routing by finding the convenient path for each type of flow. Therefore, attention is given to find a method that may describe the flow based on network performance analysis and how to utilize this method for managing network performance efficiently and find the possible integration for the traffic controlling in SDN. To this purpose, characteristics of network flows is identified as a mechanism which may give insight into the diversity in flow features based on performance metrics and provide the possibility of traffic engineering enhancement using SDN environment. Two different feature sets with respect to network performance metrics are employed to characterize network traffic. Applying unsupervised machine learning techniques including Principal Component Analysis (PCA) and k-means cluster analysis to derive a traffic performance-based clustering model. Afterward, thresholding-based flow identification paradigm has been built using pre-defined parameters and thresholds. Finally, the resulting data clusters are integrated within a unified SDN architectural solution, which improves network management by finding the best flow routing based on the type of flow, to be evaluated against a number of traffic data sources and different performance experiments. The validation process of the novel framework performance has been done by making a performance comparison between SDN-Ryu controller and the proposed SDN-external application based on three factors: throughput, bandwidth,and data transfer rate by conducting two experiments. Furthermore, the proposed method has been validated by using different Data Centre Network (DCN) topologies to demonstrate the effectiveness of the network traffic management solution. The overall validation metrics shows real gains, the results show that 70% of the time, it has high performance with different flows. The proposed routing SDN traffic-engineering paradigm for a particular flow therefore, dynamically provisions network resources among different flow types