Review of SDN-based load-balancing methods, issues, challenges, and roadmap

The development of the Internet and smart end systems, such as smartphones and portable laptops, along with the emergence of cloud computing, social networks, and the Internet of Things, has brought about new network requirements. To meet these requirements, a new architecture called software-defined network (SDN) has been introduced. However, traffic distribution in SDN has raised challenges, especially in terms of uneven load distribution impacting network performance. To address this issue, several SDN load balancing (LB) techniques have been developed to improve efficiency. This article provides an overview of SDN and its effect on load balancing, highlighting key elements and discussing various load-balancing schemes based on existing solutions and research challenges. Additionally, the article outlines performance metrics used to evaluate these algorithms and suggests possible future research directions

Optimal Power Flow Solution in Smart Grid Environment Using SVC and TCSC

Flexible AC transmission system devices (FACTS) are most promising controllers in present day scenario when it comes to power transmission in long distances in smart grids. FACTS devices provide system stability, midpoint voltage support and reactive power control in grid interconnections. Conventionally, power flow algorithm was used to evaluate the rating of FACTS devices by taking consideration of magnitude of voltage and phase angle as independent variables. Nowadays, FACTS device rating is evaluated with a new framework called optimal power flow. This chapter provides a comparison for optimal power flow, with or without FACTS devices such as static VAR compensator (SVC) and thyristor controlled series capacitor (TCSC), in terms of cost saving and loss reduction in smart grid scenario

Exploring Path Computation Techniques in Software-Defined Networking: A Review and Performance Evaluation of Centralized, Distributed, and Hybrid Approaches

Software-Defined Networking (SDN) is a networking paradigm that allows network administrators to dynamically manage network traffic flows and optimize network performance. One of the key benefits of SDN is the ability to compute and direct traffic along efficient paths through the network. In recent years, researchers have proposed various SDN-based path computation techniques to improve network performance and reduce congestion. This review paper provides a comprehensive overview of SDN-based path computation techniques, including both centralized and distributed approaches. We discuss the advantages and limitations of each approach and provide a critical analysis of the existing literature. In particular, we focus on recent advances in SDN-based path computation techniques, including Dynamic Shortest Path (DSP), Distributed Flow-Aware Path Computation (DFAPC), and Hybrid Path Computation (HPC). We evaluate three SDN-based path computation algorithms: centralized, distributed, and hybrid, focusing on optimal path determination for network nodes. Test scenarios with random graph simulations are used to compare their performance. The centralized algorithm employs global network knowledge, the distributed algorithm relies on local information, and the hybrid approach combines both. Experimental results demonstrate the hybrid algorithm's superiority in minimizing path costs, striking a balance between optimization and efficiency. The centralized algorithm ranks second, while the distributed algorithm incurs higher costs due to limited local knowledge. This research offers insights into efficient path computation and informs future SDN advancements. We also discuss the challenges associated with implementing SDN-based path computation techniques, including scalability, security, and interoperability. Furthermore, we highlight the potential applications of SDN-based path computation techniques in various domains, including data center networks, wireless networks, and the Internet of Things (IoT). Finally, we conclude that SDN-based path computation techniques have the potential to significantly improvement in-order to improve network performance and reduce congestion. However, further research is needed to evaluate the effectiveness of these techniques under different network conditions and traffic patterns. With the rapid growth of SDN technology, we expect to see continued development and refinement of SDN-based path computation techniques in the future

An Optimised Shortest Path Algorithm for Network Rotuting & SDN: Improvement on Bellman-Ford Algorithm

Network routing algorithms form the backbone of data transmission in modern network architectures, with implications for efficiency, speed, and reliability. This research aims to critically investigate and compare three prominent routing algorithms: Bellman-Ford, Shortest Path Faster Algorithm (SPFA), and our novel improved variant of Bellman-Ford, the Space-efficient Cost-Balancing Bellman-Ford (SCBF). We evaluate the performance of these algorithms in terms of time and space complexity, memory utilization, and routing efficacy, within a simulated network environment. Our results indicate that while Bellman-Ford provides consistent performance, both SPFA and SCBF present improvements in specific scenarios with the SCBF showing notable enhancements in space efficiency. The innovative SCBF algorithm provides competitive performance and greater space efficiency, potentially making it a valuable contribution to the development of network routing protocols. Further research is encouraged to optimize and evaluate these algorithms in real-world network conditions. This study underscores the continuous need for algorithmic innovation in response to evolving network demands

Carrier Transport in High Mobility InAs Nanowire Junctionless Transistors

Ability to understand and model the performance limits of nanowire transistors is the key to design of next generation devices. Here, we report studies on high-mobility junction-less gate-all-around nanowire field effect transistor with carrier mobility reaching 2000 cm2/V.s at room temperature. Temperature-dependent transport measurements reveal activated transport at low temperatures due to surface donors, while at room temperature the transport shows a diffusive behavior. From the conductivity data, the extracted value of sound velocity in InAs nanowires is found to be an order less than the bulk. This low sound velocity is attributed to the extended crystal defects that ubiquitously appear in these nanowires. Analyzing the temperature-dependent mobility data, we identify the key scattering mechanisms limiting the carrier transport in these nanowires. Finally, using these scattering models, we perform drift-diffusion based transport simulations of a nanowire field-effect transistor and compare the device performances with experimental measurements. Our device modeling provides insight into performance limits of InAs nanowire transistors and can be used as a predictive methodology for nanowire-based integrated circuits.Comment: 22 pages, 5 Figures, Nano Letter

Effect of cryogenic treatment on drill tool for enhancing metal cutting operation of aluminium alloy IS737.Gr19000

Drilling is the hole making process on the component face with the aid of a twisted drillbit. Normal drill bits easily wear out through penetration of drill bit into the workpiece material due to force generated in the drilling operation. So this work tries to investigate the machining parameters with cryogenically treated drill bits on various responses. Cryogenic treatment is one of the thermal engineering processes, which is used to cool the material from the temperature of −150 °C to −273 °C. This research work utilizes cryogenically treated drill tools for investigating the drilling performance on aluminium alloy (IS737.Gr19000) workpiece material. The independent variables and dependent variables are studied in this experimental analysis are spindle speed, feed rate and machining time, entry and exit burr dimensions, thrust force, torque, Ovality, surface roughness, respectively. The theoretical investigation is also carried out with statistical analysis. The response surface methodology with Box Behnken design the 17 experimental runs with 9 different treated drill tools are carried out. The cryogenically treated drill bit gave good results on burr dimensions, Ovality, surface roughness on drilled hole quality. The tool wear performance was also studied with drill tool geometry measurements with the tool makers microscope. The cryogenically treated drill bits gave the best results than the normal drill bit

Analysis of Power Quality Issues of Different Types of Household Applications

Most of the loads used in our day-to-day life are non-linear in nature. To investigate the performance of various non-linear loads, a laboratory setup on different load combinations such as light, fan and drive system in this work. The unbalance in supply voltages, supply current and frequency, reduction in power factor and total harmonic distortion for current and voltages are monitored through this lab setup and also the results obtained from these systems are discussed in this paper. To check the nature of voltage, current, reactive power and power factor in real-time systems, a turret punch machine incorporated with several single-phase AC servo motors is considered. The variations in the parameters are recorded using the fluke analyzer. Finally, it is observed that current harmonics at the source side are dominant in both the laboratory setup and the real-time system. Next, shunt active power filters are applied to mitigate the current harmonics. The simulation for the system with compensations is conducted in the Matlab platform and the hardware implementation validates the same

A Hybrid Machine Learning Technique For Feature Optimization In Object-Based Classification of Debris-Covered Glaciers

Object-based features like spectral, topographic, and textural are supportive to determine debris-covered glacier classes. The original feature space includes relevant and irrelevant features. The inclusion of all these features increases the complexity and renders the classifier’s performance. Therefore, feature space optimization is requisite for the classification process. Previous studies have shown a rigorous exercise in manually selecting the best combination of features to define the target class and proven to be a time consuming task. The present study proposed a hybrid feature selection technique to automate the selection of the best suitable features. This study aimed to reduce the classifier’s complexity and enhance the performance of the classification model. Relief-F and Pearson Correlation filter-based feature selection methods ranked features according to the relevance and filtered out irrelevant or less important features based on the defined condition. Later, the hybrid model selected the common features to get an optimal feature set. The proposed hybrid model was tested on Landsat 8 images of debris-covered glaciers in Central Karakoram Range and validated with present glacier inventories. The results showed that the classification accuracy of the proposed hybrid feature selection model with a Decision Tree classifier is 99.82%, which is better than the classification results obtained using other mapping techniques. In addition, the hybrid feature selection technique has sped up the process of classification by reducing the number of features by 77% without compromising the classification accuracy

Interconnection and damping assignment passivity-based non-linear observer control for efficiency maximization of permanent magnet synchronous motor

The permanent magnet synchronous motor (PMSM) has several advantages over the DC motor and is gradually replacing it in the industry. The dynamics of the PMSM are described by non-linear equations; it is sensitive to unknown external disturbances (load), and its characteristics vary over time. All of these restrictions complicate the control task. Non-linear controls are required to adjust for non-linearities and the drawbacks mentioned above. This paper investigates an interconnection and damping assignment (IDA) passivity-based control (PBC) combined with a non-linear observer approach for the PMSM using the model represented in the dq-frame. The IDA-PBC approach has the inherent benefit of not canceling non-linear features but compensating them in a damped manner. The suggested PBC is in charge of creating the intended dynamic of the system, while the non-linear observer is in charge of reconstructing the recorded signals in order to compel the PMSM to track speed. The primary objective of this study is to synthesize the controller while accounting for the whole dynamic of the PMSM and making the system passive. It is performed by restructuring the energy of the proposed strategy and introducing a damping component that addresses the non-linear elements in a damped instead of deleted way, so providing a duality concept between both the IDA-PBC and the observer There are three methods for computing IDA-PBC: parametric, nonparametric, and algebraic. The parameterized IDA-PBC method is used to control the speed of the PMSM. This method uses the energy function in parameterized closed-loop in terms of some functions depending on the system’s state vector, such that the energy formation step is satisfied. Then, the original port-controlled Hamiltonian (PCH) dynamics in open-loop (OL) are equalized with the desired one in closed-loop (CL). The equalization process allows obtaining a set of solutions of the partial differential equations. The latter must be solved in terms of the parameters of the energy function of the closed-loop. Finally, the stability properties are studied using the Lyapunov theory. Generally, the proposed candidate offers high robustness, fast speed convergence, and high efficiency over the conventional benchmark strategies. The effectiveness of the proposed strategy is performed under extensive numerical investigation with MATLAB/Simulink software