Software Defined Networking Management Research Articles

Real-Time IoV Task Offloading through Dynamic Assignment of SDN Controllers: Algorithmic Approaches and Performance Evaluation

Task offloading in Internet of Vehicles (IoV) is very crucial. The widespread use of IoT applications frequently interacts with the cloud, thereby increasing the load on centralized cloud controllers. Centralized network management in cloud infrastructure is not feasible for the latest IoT trends. Decentralized and decoupled network management in Software Defined Networks (SDN) can enhance IoV services. SDN and IoV coupling can better handle task offloading in ubiquitous and dynamic IoV environments. However, appropriate SDN controller assignment and allotment strategies play a prominent role in IoV communication. In this study, we developed algorithms for SDN controller assignment and allotment namely 1) Next Fit Allotment and Assignment of SDN Controller in IoV (NFAAC), 2) Dynamic Bin Packing Allotment and Assignment of SDN Controller in IoV (DBPAAC), and 3) Dynamic Focused and Bidding Allotment and Assignment algorithm of SDN Controller in IoV (DFBAAC). These algorithms were simulated using open-flow switch controllers. The controllers were modeled as Road Side Units (RSU) that can allocate bandwidth and resource requirements to vehicles on the road. Our results show that our proposed algorithm works efficiently for SDN controller assignment and allocation, outperforming the existing work by a significant improvement of 13.5%. The working of the proposed algorithms are verified, tested, and analytically presented in this study.

Security-aware resource management approaches in software defined networks: Comprehensive analysis, opportunities and challenges

Today, with the fast growth of emerging technologies and applying numerous advantages of 5G communications, there is a critical gap between the supporting security of data transmission and resource management of Software Defined Networks (SDN). To provide a secure communication perspective for Internet of Things (IoT) devices and smart applications, network slicing in resource management is currently an important issue and is widely studied for the SDN. Due to the importance of security-aware resource management in several aspects of the SDN, this research aims to represent a new comprehensive review of existing technical classification and deep-detailed categorization of resource management approaches for SDN communications in the IoT environments. Based on each category, a technical taxonomy is presented to show a variety of security-aware platforms that include resource allocation, service composition, task scheduling, and service offloading in the IoT environments. According to the main state-of-the-art discussed comprehensive technical review, some important evaluation factors, main methodologies, advantages, and disadvantages of each case study are elaborated. Finally, some important new research directions and open challenges are presented for future scientific efforts.

Machine Learning for Management in Software-defined Networks: A Systematic Literature Review

Software-Defined Networking (SDN) has emerged as a new paradigm for managing data networks, and Machine Learning (ML) techniques have become relevant in the scientific community to solve management problems. Research on using these two variables has increased in recent years. Therefore, a systematic literature review based on Kitchenham’s guidelines and PRISMA guidelines is necessary. The review included publications from between 2016 and 2021. The study recorded 21,743 primary articles, and after applying rigorous exclusion and quality criteria, 81 articles were obtained. The results show the most productive authors, such as Julong, as well as the relationships between the most productive authors and the keywords “SDN” and “Machine Learning,” which are the most used among researchers.

Flow Scheduling in Data Center Networks with Time and Energy Constraints: A Software-Defined Network Approach

Flow scheduling in Data Center Networks (DCN) is a hot topic as cloud computing and virtualization are becoming the dominant paradigm in the increasing demand of digital services. Within the cost of the DCN, the energy demands associated with the network infrastructure represent an important portion. When flows have temporal restrictions, the scheduling with path selection to reduce the number of active switching devices is a NP-hard problem as proven in the literature. In this paper, an heuristic approach to schedule real-time flows in data-centers is proposed, optimizing the temporal requirements while reducing the energy consumption in the network infrastructure via a proper selection of the paths. The experiments show good performance of the solutions found in relation to exact solution approximations based on an integer linear programming model. The possibility of programming the network switches allows the dynamic schedule of paths of flows under the software-defined network management.

DPLBAnt: Improved load balancing technique based on detection and rerouting of elephant flows in software-defined networks

Traffic management in software-defined networks (SDNs) is critical for efficient bandwidth utilization and resource provisioning. Recent works on SDN load balancing (LB) have focused on identifying and rerouting elephant flows (EFs) for effective bandwidth usage. These techniques have some limitations, such as using source-to-destination hop count as the primary rerouting metric and not differentiating the types of flow that result in frequent resource conflicts when handling EF with long-lived bandwidth. Besides, current EF detection techniques use predefined bandwidth-use thresholds that cannot adapt to the ever-changing traffic condition. Also, detecting EF on switches results in high controller-switch bandwidth and high EF detection time. This study presents an ant colony optimization-based technique for rerouting EFs while considering load-balancing in the SDN links. This technique, called DPLBAnt, is formulated as a shortest-path problem in SDN that can alleviate the high controller-switch load. The proposed technique first detects EF by using a pair of classifiers on both SDN controller and switches. Most EF candidates are sifted on the switches, resulting in accurate and efficient detection of EF. Then, DPLBAnt obtains the global state of the SDN from which the most optimal paths for congested links are retrieved, and EF are redirected accordingly. The performance of the proposed DPLBAnt has been extensively simulated. Results indicate its superior performance over Equal-Cost Multi-Path (ECMP) and FlowSeer techniques in terms of average end-to-end delay (54% and 7.9% better), average network throughput (3.5× and 1.5× better), and average packet loss (18% and 10% better) respectively. The overall performance indicates that the proposed LB technique based on detection and rerouting of EFs can improve SDN’s overall performance.

Indirect Multi-Mapping for Burstiness Management in Software Defined Networks

Large software defined networks use a cluster of distributed controllers to process flow requests from a massive number of switches. To cope with traffic dynamics, this paper studies a new problem of how to improve the residual capacity available at the controllers to handle request bursts experienced at the switches. While the total residual capacity is a constant under a given total capacity of all controllers and a given total workload from all switches, this paper considers the residual capacity available to each individual switch, which depends on how the switches are mapped to the controllers for management. We focus on how to maximize the minimum residual capacity available to any switch . The prior work either provides poor residual capacity or incurs heavy synchronization overhead by simulation results. This paper proposes a new method called indirect multi-mapping that achieves both high residual capacity and low synchronization cost. We formally define a non-linear integer optimization problem for max-min residual capacity under indirect multi-mapping. We then approximate the problem as two sub-problems: switch-controller mapping selection and weight assignment for each switch-controller mapping. We solve these sub-problems and formally analyze their approximate factor. We implement the proposed solution on an SDN testbed for experimental studies and use simulations for large-scale investigation. Our evaluation shows that indirect multi-mapping improves the minimum residual capacity by 49.8% on average and reduces the synchronization cost by 41.9-60.3% on average when compared with the alternatives.

An approach based on Yet Another Next Generation for software‐defined networking management

SummaryThe lack of a Management Plane specification in the software‐defined networking (SDN) is a fundamental problem for the integrated management of networks that follow this paradigm. Several solutions have addressed this problem, but they do not provide data models intended to support Fault, Configuration, Accounting, Performance, and Security (FCAPS) in SDN from a specialized Management Plane. In this paper, we introduce a technology‐agnostic approach that comprises mainly a collection of YANG data models and their relationships. These models characterize SDN management and handle technology heterogeneity. To evaluate our approach, we developed the proof of concept of the YANG data models in an SDN scenario involving the configuration of devices from different vendors supporting diverse technologies. The evaluation results show the efficiency of our approach regarding response time, composition time, consumption time, and network traffic.

Smart SDN Management of Fog Services to Optimize QoS and Energy.

The short latency required by IoT devices that need to access specific services have led to the development of Fog architectures that can serve as a useful intermediary between IoT systems and the Cloud. However, the massive numbers of IoT devices that are being deployed raise concerns about the power consumption of such systems as the number of IoT devices and Fog servers increase. Thus, in this paper, we describe a software-defined network (SDN)-based control scheme for client–server interaction that constantly measures ongoing client–server response times and estimates network power consumption, in order to select connection paths that minimize a composite goal function, including both QoS and power consumption. The approach using reinforcement learning with neural networks has been implemented in a test-bed and is detailed in this paper. Experiments are presented that show the effectiveness of our proposed system in the presence of a time-varying workload of client-to-service requests, resulting in a reduction of power consumption of approximately 15% for an average response time increase of under 2%.

Emulating Software-Defined Disaggregated Optical Networks in a Containerized Framework

Telecom operators’ infrastructure is undergoing high pressure to keep the pace with the traffic demand generated by the societal need of remote communications, bandwidth-hungry applications, and the fulfilment of 5G requirements. Software-defined networking (SDN) entered in scene decoupling the data-plane forwarding actions from the control-plane decisions, hence boosting network programmability and innovation. Optical networks are also capitalizing on SDN benefits jointly with a disaggregation trend that holds the promise of overcoming traditional vendor-locked island limitations. In this work, we present our framework for disaggregated optical networks that leverages on SDN and container-based management for a realistic emulation of deployment scenarios. Our proposal relies on Kubernetes for the containers’ control and management, while employing the NETCONF protocol for the interaction with the light-weight software entities, i.e., agents, which govern the emulated optical devices. Remarkably, our agents’ structure relies on components that offer high versatility for accommodating the wide variety of components and systems in the optical domain. We showcase our proposal with the emulation of an 18-node European topology employing Cassini-compliant optical models, i.e., a state-of-the-art optical transponder proposed in the Telecom Infrastructure Project. The combination of our versatile framework based on containerized entities, the automatic creation of agents and the optical-layer characteristics represents a novel approach suitable for operationally complex carrier-grade transport infrastructure with SDN-based disaggregated optical systems.

An Experimental OpenFlow Proposal over Legacy GPONs to Allow Real-Time Service Reconfiguration Policies

The integration of Software Defined Networking (SDN) technologies in Passive Optical Networks (PONs) would provide great advantages to Internet Service Providers (ISPs) and Network Operators, since they can optimize the network operation and reduce its complexity. However, some tasks regarding online service and network configuration strategies are difficult to move to external SDN-controllers since they are time-critical operations. However, the control of some of these policies by SDN techniques could lead to better network and management configuration in a centralized and automatic way. As a consequence, we propose and experimentally test the integration of an OpenFlow approach over legacy Gigabit Passive Optical Networks (GPONs), which allows moving some global service configuration policies to an external SDN controller implementing an SDN management layer that adjust these strategies according to dynamic Quality of Service (QoS) requirements of services in residential users. The viability and efficiency of our approach are demonstrated using a GPON testbed and proposing a new business scenario for ISPs and Network Operators.

A Survey on Traffic Management in Software-Defined Networks: Challenges, Effective Approaches, and Potential Measures

Software-defined networks (SDNs), as an emerging paradigm by separating the control plane from the data plane, increases flexibility and network utilization and reduces redundancy and operational cost. Traffic management of software-defined networks can be defined as network traffic monitoring and analyzing measures to improve network performance and quality of service metrics. Traffic management as an effective instrument for optimizing network traffic can offer the appropriate services according to network situation. Due to the inherent characteristics of SDN, special techniques are required to analyze, predict, and adapt the network traffic in order to achieve an efficient traffic management mechanism. This paper surveys traffic management techniques of SDN in four distinct categories including, routing, load balancing, congestion control, and flow control to cover the impressible issues. Moreover, the differences between SDN and traditional networks are analyzed in terms of traffic management necessities across the various groups to further determine the dimensions affecting research in this area. Furthermore, the available algorithms in each group and their role in traffic management are reviewed as well as the research challenges and future trends.

A survey on stateful data plane in software defined networks

Software Defined Networking (SDN), which decouples control plane and data plane, normally stores states on controllers to provide flexible programmability and convenient management. However, recent studies have shown that such configuration may cause frequent and unnecessary interactions between data plane and controllers in some cases. For example, a DDoS detection installed on a controller needs to fetch information from data plane periodically, introducing additional network delay and controller overhead. Thus, stateful data plane is proposed to offload states and operation logics from controller to data plane. Stateful data plane allows switches to perform some operations independently, accelerating packets processing while reducing overhead on both controllers and networks. However, stateful data plane increases the complexity of network devices and imposes many new challenges to the management and schedule of SDN enabled networks. This paper conducts a comprehensive survey on the latest research works and provides insights into stateful data plane. Both stateful data plane platforms and compilers are extensively summarized and analyzed, as well as explicit design of applications based on them. Afterwards, we dwell on the fundamental technologies and research challenges, including implementation considerations of stateful data plane. Finally, we conclude this survey paper with some future works and discuss open research issues.

A System for Source Address Authentication and Anonymous Communication Based on SDN

Based on the idea of SDN (Software Defined Network) management and control separation and the development convenience provided by the programmable interface, we conduct the research on the security during the communication security, and protection for anonymity: (1) For the problem of communication security, we design two source address authentication schemes to detect whether the source host is legal. (2) For the problem of anonymity protection, A series of changes of the IP address in data packets by intermediate SDN switches make it difficult for the attacker to obtain information from both parties in communication. And to ensure that the address generated after the execution of those changes can uniquely identify the current data stream, we proposed the Improved-MAGA algorithm based on MAGA (M-Address Generation Algorithm). (3) For the shortest path routing algorithm, which maybe result in hot-links problems, the measurement link bandwidth utilization is added as a weight vector in the routing process to solve the problem of excessive link load caused by the default routing algorithm.

SDNOFS: Software Defined Networking with Openflow Switches & BCN-ECN with ALTQ for Congestion Avoidance

High-performance computing cluster in a cloud environment. High-performance computing (HPC) helps scientists and researchers to solve complex problems involving multiple computational capabilities. The main reason for using a message passing model is to promote application development, porting, and execution on the variety of parallel computers that can support the paradigm. Since congestion avoidance is critical for the efficient use of different applications, an efficient method for congestion management in software-defined networks based on Open Flow protocol has been presented. This paper proposed two methods; initially, to avoid the congestion problem used by Software Defined Networks (SDN) with open flow switches, this method was originally defined as a communication protocol in SDN environments which allows the SDN controller to interact directly with the forwarding plane of network devices such as switches and routers, both physical and virtual (hypervisor-based), so that it could better adapt to changing business requirements.. Second, to enhance the quality of service and avoid the congestion problem used BCN-ECN with ALTQ. While comparing the existing method, the SDN open flow switches and BCN-ECN with ALTQ provides 98 % accuracy. Usage of these proposed methods will enhance the parameters structures delay time, level of congestion quality time and execution time

Smart routing: Towards proactive fault handling of software-defined networks

In recent years, the emerging paradigm of software-defined networking has become a hot and thriving topic in both the industrial and academic sectors. Software-defined networking offers numerous benefits against legacy networking systems by simplifying the process of network management through reducing the cost of network configurations. Currently, data plane fault management is limited to two mechanisms: proactive and reactive. These fault management and recovery techniques are activated only after a failure occurrence and hence packet loss is highly likely to occur. This is due to convergence time where new network paths will need to be allocated in order to forward the affected traffic rather than drop it. Such convergence leads to temporary service disruption and unavailability. Practically, not only the speed of recovery mechanisms affects the convergence, but also the delay caused by the process of failure detection. In this paper, we define a new approach for data plane fault management in software-defined networks where the goal is to eliminate the convergence process altogether rather than accelerate the failure detection and recovery. We propose a new framework, called Smart Routing, which allows the network controller to receive forewarning signs on failures and hence avoid risky paths before the failure incidents occur. The proposed approach aims to decrease service disruption, which in turn increases network service availability. We validate our framework through a set of experiments that demonstrate how the underlying model runs and its impact on improving service availability. We take as example of the applicability of the new framework three types of topologies covering real and simulated networks.

A Systemic and Secure SDN Framework for NoC-Based Many-Cores

Recent exploration of Software-Defined Networking (SDN) for Many-Core Systems-on-Chip (MCSoCs) showed higher management flexibility and reduced physical complexity compared to other runtime communication management. In SDN, there is a software SDN Controller (control layer) that configures generic routers (data layer). The adoption of SDN makes the path establishment programmable and straightforward, according to different network policies, such as low power, QoS, fault-tolerance. It is also possible to change the path establishment policies at runtime without the need to redesign the NoC. Current works focus on proposing SDN architectures, lacking a systemic framework that describes the steps required to implement SDN into a Many-core environment. Security is an observed gap in SDN designs. A malicious task could configure SDN routers and take control of the NoC. The contribution of this work is to present a systemic and secure SDN framework (SDN-SS), detailing the steps required to support SDN in MCSoCs. This work also describes the iteration between the hardware, operating system, and user's tasks. The SDN-SS manages a Multiple-Physical NoC, with one packet-switching subnet and a set of circuit-switching subnets. The originality of SDN-SS includes (i) a step-by-step framework description addressing the phases required to support a secure SDN management; (ii) a secure SDN router configuration protocol; (iii) a protocol to change the subnet at runtime. Experimental results show the framework's capability to avoid DoS and Spoofing attacks while presents a low SDN router configuration overhead, corresponding up to 2% of a related work delay and a small impact over the user's task communication.

Improving Security for Internet of Things Devices using Software Defined Networking

This theory has recently been expanded to IEEE 802.15.4 wireless networks, which constitute a key element of the Internet of Things (IoT). Nonetheless, the various patterns of traffic needed for SDN management make it difficult to adapt this method to these extremely demanding situations. Software-Defined Networking (SDN) key contribution of this work is the solution to network with IoT devices that enables network because of better functionalities in case of providing interfacesfor the layers. SDN enables significant advantages of applications to be created on the basis of interaction with traffic networks, trustable authentication, or service eminence. This report suggests the use of a SDN gateway as a decentralized platform to track traffic from IoT gadgets. The configured SDN gateway capable of detection the possible abnormal behaviors and provide it particularly valuable applicability for (obstructive, transmission or application of providing best services to the system).

A Software-Defined Management System for IP-Enabled WSNs

Software-defined networking (SDN) offers potential pathways to overcome the management complexity of the Internet of Things (IoT). Previous studies have often been limited to software simulations or general proposals only. In this article, we design and evaluate an SDN-based management system for wireless sensor networks (WSNs) using IPv6 over low-power wireless personal area networks (6LoWPAN). The framework is described in detail covering different data-, control-, and application-plane implementations, and includes a novel addressing scheme and packet format. It also uses a centralized routing protocol, located at the SDN controller, based on the shortest path algorithm. We compare our approach with the routing protocol for low-power and lossy networks (RPL), which uses a distributed routing protocol. Hardware tests were carried out in a dynamic environment, with multiple sources of interference for different payload sizes to evaluate the impacts and practicality of SDN in WSNs. The performance comparison shows that the proposed SDN management system for IP-enabled WSNs using a centralized routing protocol outperforms the RPL protocol in terms of round-trip time, jitter, memory consumption, and packet loss rate (PLR), despite the control overhead introduced.

A semantic data lake framework for autonomous fault management in SDN environments

AbstractFault Management is a vital issue for any network operator since the beginning of the telecommunications era. As networks have become more and more complex, their management systems are crucial for any operator company. In this ecosystem, the Software‐Defined Networking (SDN) approach has appeared as a possible solution for different networking issues. The flexibility provided by SDN to the network management enables a great dynamism in the configuration of network devices. However, this feature introduces the cost of a potential increase in failures because every modification introduced on the control plane is a new possibility for failures to appear and cause a decrement of the quality for offered services. Because of the growing pace of the networks, the classical approach is not feasible to cope that dynamism. Increasing the number of human operators in charge of the fault management process would increase the operation cost dramatically. Thus, this paper presents an approach to apply machine learning over a big data framework for an autonomous fault management process in SDN networks. In this paper, we present a Semantic Data Lake framework for a self‐diagnosis service, which is deployed on top of an SDN management platform. Moreover, we have developed a prototype of the proposed service with different diagnosis models for SDN networks. Models and algorithms have been evaluated showing good results.

FastRule: Efficient Flow Entry Updates for TCAM-Based OpenFlow Switches

With an increasing demand for flexible management in software-defined networks (SDNs), it becomes critical to minimize the network policy update time. Although major SDN controllers are now optimized for rapid network update at the control plane, there is still room for data plane optimization in terms of update time, when using TCAM-based physical SDN commodity-off-the-shelf switches. A slow update directly affects network performance and creates bottlenecks. To minimize the flow entry update time, a dependency graph, a kind of directed acyclic graph (DAG), can be used for the access management of flow entries at the switch. Thanks to the DAG, unnecessary entry movements, which are the main factor slowing down flow entry updates, can be avoided. However, existing algorithms show limitations when updates become very frequent. We propose a new flow entry update algorithm, called FastRule, that exploits a greedy strategy with an efficient data structure to accelerate flow entry update with a DAG approach. Moreover, we also adjust our algorithm for other flow table layouts to make it scalable. We elaborate on the correctness of FastRule and test our algorithm using a hardware switch. Compared with existing algorithms, the evaluation shows that our algorithm is about 100x faster than state-of-the-art solutions with a flow table of 1k size.