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Accurate network traffic classification is an essential and challenging issue for wireless network management and survivability. Existing network traffic classification algorithms, on the other hand, cannot meet the required specifications of real networks' in terms of user privacy control overhead, latency, and above all, classification speed. For...
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The centralized power grid bears a heavy burden in a time when consumers expect an uninterrupted reliable power supply, a reduction in carbon emissions, increased efficiency within the national grid, and power supplied to remote communities. One such structure is used to implement this type of control that is based on small- scale Distributed Gener...
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... The separation of planes inculcates flexibility and avoids network ossification which implies the prevention of network additions and upgrades. Thus separation of planes prevents the higher operational and capital costs incurred in the network [14]. NFV is a promising way to virtualize the functions of the network leveraging the proprietary network hardware. ...
5G technology is regarded as the pioneer technology handling the massive increase in the number of connections; however, with higher frequency demands and greater transmission rates used in smart cities, 6G comes into action because of the higher futuristic demands. Transportation is considered a fundamental pillar of the smart city, and it utilizes Internet of Vehicles (IoV). The higher stringent demands of network and computing in IoV are met with the help of novel paradigms of software-defined networking (SDN), cloud computing, fog computing, and mist computing. Traffic engineering is the core issue in the centralized controller in SD-IoV (software-defined-IoV) because of the huge generation of data and stringent network requirements. This chapter discusses the use case of SD-IoV, which utilizes the myriad of computing paradigms, flexibility of SDN, and network function virtualization to improve performance and discusses some open issues.
... The authors used NN with swarm optimization for dataset normalization to improve the quality of three chosen classifiers: DT, LR, and FFNN. Pradhan et al. [36] proposed a traffic classification system for wireless SDN. The authors used the feed-forward NN as the main classifier with the aid of swarm optimization to training the NN. ...
Due to the exponential increase of internet applications and network users, network traffic classification (NTC) is a crucial study subject. It successfully improves network service identifiability and security concerns of the traffic network and provides a way that improves the Quality of services (QoS). Recently, with the emergence of software-defined networking (SDN) and its ability to get the entire network overview using a centralized controller, machine learning (ML) has been used for NTC. In this paper, an SDN QoS guarantee framework with machine learning traffic classification has been proposed. The framework includes a classification system with two stages, the offline stage, where the classifier was trained and tested, and the online stage, where dealing with the flows and testing the classifier speed is simulated using spark streaming. The result shows that the classifier successfully identifies the specific traffic application with an accuracy of 100% on the “IP-network-traffic-flows-labeled-with-87-apps” dataset and identifies the traffic type with an accuracy of 99.95% on the “ISCX-VPN-NONVPN” dataset. In addition, the classifier speed is proven to be a round 3500 record/sec and a patch duration of 917.3 ms on average with 3210 flows/Trigger.
... Since leveraging compression reduces the size of the data to be shuffled so a single time slot is required to complete it. After completion of shuffle is done, reduce task will be executed and finally the job gets completed at t 19 . The reduced tasks in Hadoop need to be in waiting as long as the shuffle gets completed. ...
... The gathering of network statistics at the controller must be an ideal case for the controller to develop proper solutions for improving the network performance (classifying traffic, minimizing energy consumption, and improving link utilization). Applications using network resources need to be allocated proper network resources for avoiding the QoS degradation which requires traffic classification at the controller [18,19]. Also, lowering the cost of the network is essential for increasing the profit of the enterprises where often networking devices do not operate at their full capacity. ...
The evolution and advancements in Information and Communication Technologies (ICT) have enabled large scale distributed computing with a huge chunk of applications for massive number of users. This has obviously generated large volumes of data, thus severely burdening the processing capacity of computers as well as the inflexible traditional networks. State-of-the-art methods for addressing datacenter level performance fixes are yet found wanting for sufficiently addressing this huge processing, storage, and network movement with proprietary protocols for this voluminous data. In this chapter the works have focused on addressing the backend server performance through effective reducer placement, intelligent compression policy, handling slower tasks and in-network performance boosting techniques through effective traffic engineering, traffic classification, topology discovery, energy minimization and load balancing in datacenter-oriented applications. Hadoop, the defacto standard in distributed big data storage and processing, has been designed to store data with its Hadoop Distributed File System (HDFS) and processing engine MapReduce large datasets reliably. However, the processing performance of Hadoop is critically dependent on the time taken to transfer data during the shuffle generated during MapReduce. Also, during concurrent execution of tasks, slower tasks need to be properly identified and efficiently handled to improve the completion time of jobs. To overcome these limitations, three contributions have been made; (i) Compression of generated map outputs at a suitable time when all the map tasks are yet to be completed to shift the load of network onto the CPU; (ii) Placing the reducer onto the nodes where the computation done is highest based on a couple of counters, one maintained at the rack level and another at node level, to minimize the run-time data copying; and (iii) Placing the slower map tasks onto the nodes where the computation done is highest and network is handled by prioritizing. Software defined networking (SDN) has been a boon for next generation networking owing to the separation of control plane from the data plane. It has the capability to address the network requirements in a timely manner by setting flows for every to and fro data movement and gathering large network statistics at the controller to make informed decisions about the network. A core issue in the network for the controller is traffic classification, which can substantially assist SDN controllers towards efficient routing and traffic engineering decisions. This chapter presents a traffic classification scheme utilizing three classifiers namely Feed-forward Neural Network (FFNN), Logistic Regression (LR), Naïve Bayes and employing Particle Swarm Optimization (PSO) for improved traffic classification with less overhead and without overlooking the key Quality of Service (QoS) criterion. Also lowering energy minimization and link utilization has been an important criterion for lowering the operating cost of the network and effectively utilizing the network. This issue has been addressed in the chapter by formulating a multi-objective problem while simultaneously addressing the QoS constraints by proposing a metaheuristic, since no polynomial solution exists and hence an evolutionary based metaheuristic (Clonal Selection) based energy optimization scheme, namely, Clonal Selection Based Energy Minimization (CSEM) has been devised. The obtained results show the efficacy of the proposed traffic classification scheme and CSEM based solution as compared with the state-of-the-art techniques. SDN has been a promising newer network paradigm but security issues and expensive capital procurement of SDN limit its full deployment hence moving to a hybrid SDN (h-SDN) deployment is only logical moving forward. The usage of both centralized and decentralized paradigms in h-SDN with intrinsic issues of interoperability poses challenges to key issues of topology gathering by the controller for proper allocation of network resources and traffic engineering for optimum network performance. State-of-the-art protocols for topology gathering, such as Link Layer Discovery Protocol (LLDP) and Broadcast Domain Discovery Protocol (BDDP) require a huge number of messages and such schemes only gather link information of SDN devices leaving out legacy switches’ (LS) links which results in sub-optimal performance. This chapter provides novel schemes which unearth topology discovery by requiring fewer messages and gathering link information of all the devices in both single and multi-controller environments (might be used when scalability issue is prevalent in h-SDN). Traffic engineering problems in h-SDN are addressed by proper placement of SDN nodes in h-SDN by utilizing the analyzing key criterion of traffic details and the degree of a node while lowering the link utilization in real-time topologies. The results of the proposed schemes for topology discovery and SDN node placement demonstrate the merits as compared with the state-of-the-art protocols.KeywordsBig dataSDNHybrid SDNLink discoveryTraffic classificationTraffic engineering and energy minimization
... NNs have been widely applied to data processing Hasib, Towhid & Islam, 2021;Pradhan et al., 2022), pattern classification and recognition (Chiang et al., 2022;Hammad et al., 2021;Yen, Moh & Moh, 2021), and blockchain technology (Nguyen et al., 2021) owing to their strong learning and approximation capability. For an unknown continuous nonlinear function g K ( ) : , , ] , , , 1 2 refers to the centre of the curve on the x-axis and x s is the width related to the full width at half peak. ...
The topic about consensus target track seeking for high-order nonlinear multi-agent systems (MASs) with unmodeled dynamics, dynamic disturbances, and dead-zone input is considered in the paper. Using the strong nonlinear map characteristic of radial basis function neural networks (RBFNNs), the complex functions arising from recursive procedure are simplified. Also, inspired by input-to-state practical stability (ISpS), the authors construct a dynamical signal in order to counteract the impact of unmodeled dynamics and dynamic disturbances. The bounded inequality expression has been applied to tackle the unknown input of dead zone. Based on this, consensus control protocol suitable for nonlinear constraints has been constructed by using the recursive backstepping technique and adaptive neural network method. Theoretical analysis indicates not only the uniform boundary of all signals in the closed-loop under the neuro-based consensus controller, but uniform ultimate convergence of consensus tracking errors. The final simulations also confirmed the correctness of the theoretical analysis.
As a promising architecture of next‐generation network, software defined‐information centric network (SD‐ICN) inherits the advantages of software defined network (SDN) and information‐centric network (ICN) to enable flexible and fast content retrieval, especially in the current era of artificial intelligence. However, the existing researches mainly focus on a single respective in this field, which motivates in comprehensively providing a forward‐looking guidance and development direction for scholars and engineers. To this end, the latest developments of SD‐ICN is presented. First, the widely‐accepted concepts and impacts on traditional networks are introduced. Second, the shortcomings of SDN and ICN over conventional networks are respectively analyzed to illustrate the necessity of SD‐ICN. Third, based on extensive analysis and deep deliberation, a methodical taxonomy for existing combination studies is proposed. They are divided into SDN over ICN, ICN over SDN, and mutual immersive pattern. Fourth, the performances of three integration categories are compared and the limitations of related works are highlighted. Fifth, the maturity index from six development indicators are evaluated. Further, the maturity and practicality of these schemes are generalized. Based on the above studies and comparisons, the lessons learned by SDN and ICN developments are concluded. Finally, future research directions and opportunities are discussed for the readers.
The current advent of new technological advancements like the internet of things, artificial intelligence, and big data have not only ventured in applications management like regular transactional systems but also large applications like managing cities. The main objective of a “smart city” is the improvement in the living conditions of the people of all strata in the city. The city ideally should provide technological, innovative, and sustainable solutions by utilizing the resources. The smart city concept is providing a higher quality of life (QoL) for all its citizens. The areas which are mainly covered in the purview of smart city are governance, efficient mobility and public transport facility, healthcare facility, affordable housing for all citizens, digitization, and robust IT connectivity; along with focus on good sanitization, sufficient water supply, solid waste management, and electricity supply. In this chapter, the authors investigate the possibility and feasibility of the concept of digital twin in the applications of smart city.
All over the world, smart city projects and related initiatives have reached new technological heights thanks to the enormous organization of the internet of things (IoT). One of the newest methods for integrating ICT solutions and sensors is the internet of things. Numerous smart city projects have been implemented during this time—roughly as a proof-of-concept, nevertheless a rising number as everlasting, production-level dispositions, enhancing the metropolis's operations and citizens' eminence of life cycle. The IoT by means of the empowering expertise for the smart city is the motivation of this chapter. The chapter scrutinizes the recent state of IoT- empowered smart cities by examining pertinent experiences and metropolitan initiatives those have incorporated IoT hooked on their amenities and the ways in which these initiatives have had an impact. The most important skills that have been industrialized besides how they are serving the Smart City life have been represented in this chapter.
Traffic in the 5G network is growing exponentially and is predictable to grow in the future. Providing a high quality of service with ever-increasing traffic volumes is challenging in 5G networks. Software defined networking (SDN) along with emerging cloud technologies plays a significant responsibility in enhancing the performance of 5G networks. The 5G SDN paradigm is designed to support real-time and latency-sensitive applications. This chapter aims to summarize the existing technologies, benefits, and challenges of the 5G network. Also, a novel multivariate traffic analysis framework using time series analysis is proposed to enhance traffic management and its performance. Evaluation is performed on open traffic flow datasets and the analysis results show that the proposed framework performs better despite the inherent uncertainty in terms of classification and forecast accuracy.
Recently, the concept of smart cities has been linked to artificial intelligence (AI) technologies, which greatly help in improving the efficiency of various applications related to smart cities and the green environment. Machine learning (ML) and deep learning (DL) techniques play an important role in upgrading the design of control and management systems at various levels in many smart city applications such as transportation, public safety, smart energy, and building automation. This chapter introduces the concept of artificial intelligence technologies and their uses in smart cities and their methods in many related applications, in addition to challenges and future directions for using artificial intelligence to provide smart city services.
Recent years have seen a significant increase in the use of artificial intelligence (AI) and machine learning (ML) concepts across a variety of academic domains. AI's major objective is to build intelligent systems and give machines human intelligence. Artificial intelligence is a tool for creating systems, making judgements, solving problems, learning, and linguistic intelligence, as well as for imitating human conduct. Electrical and computer engineers are at the forefront of intellectual creativity as they participate in the planning, creation, evaluation, and production processes for newer generations of gadgets and technology. Even if these professionals want to grow, their objectives might conflict with the consequences of artificial intelligence, which are continually expanding. The practice of Artificial Intelligence (AI) and Machine Learning (ML) applications in industrial industries that have a significant influence on sustainability and the environment, such as renewable energy, smart grids, the catalyzed industry, and power storage and distribution The main popular approaches are artificial neural networks and Machine learning. Demand for energy is skyrocketing at a higher pace than production in the industry between 2004 and 2017, implying a decline in energy efficiency (EE). Under the premise of steady future output, an explicit energy efficiency enhancement target of 26% from 2017 and 2050 is set.
