Software-Defined Networking (SDN), with segregated data and control planes, provides faster data routing, stability, and enhanced quality metrics, such as throughput (Th), maximum available bandwidth (Bd(max)), data transfer (DTransfer), and reduction in end-to-end delay (D(E-E)). This paper explores the critical work of deploying SDN in large­scale Data Center Networks (DCNs) to enhance its Quality of Service (QoS) parameters, using logically distributed control configurations. There is a noticeable increase in Delay(E-E) when adopting SDN with a unified (single) control structure in big DCNs to handle Hypertext Transfer Protocol (HTTP) requests causing a reduction in network quality parameters (Bd(max), Th, DTransfer, D(E-E), etc.). This article examines the network performance in terms of quality matrices (bandwidth, throughput, data transfer, etc.), by establishing a large–scale SDN-based virtual network in the Mininet environment. The SDN network is simulated in three stages: (1) An SDN network with unitary controller-POX to manage the data traffic flow of the network without the server load management algorithm. (2) An SDN network with only one controller to manage the data traffic flow of the network with a server load management algorithm. (3) Deployment of SDN in proposed control arrangement (logically distributed controlled framework) with multiple controllers managing data traffic flow under the proposed Intelligent Sensing Server Load Management (ISSLM) algorithm. As a result of this approach, the network quality parameters in large-scale networks are enhanced.

For modern power plants to be dependable, safe, sustainable, and provide the highest operational efficiency (i.e., enhance dynamic load distribution with a faster response time at reduced reactive losses), there must be an intelligent dynamic load management system based on modern computational techniques to prevent overloading of power devices (i.e., alternators, transformers, etc.) in grid systems. In this paper, a co-simulation framework (Panda-SDN Load Balancer) is designed to achieve maximum operational efficiency from the power grid with the prime objective of real-time intelligent load balancing of operational power devices (i.e., power transformers, etc.). This framework is based on the integration of two tools: (a) PandaPower (an open-source Python tool) used for real-time power data (voltage; current; real power, P_Real; apparent power, P_Apparent; reactive power, P_Reactive; power factor, PF; etc.) load flow analysis; (b) Mininet used for the designing of a Software-Defined Network (SDN) with a POX controller for managing the load patterns on power transformers after load flow analysis obtained through PandaPower via the synchronization tool Message Queuing Telemetry Transport (MQTT) and Intelligent Electrical Devices (IEDs). In this research article, the simulation is performed in three scenarios: (a) normal flow, (b) loaded flow without the proposed framework, and (c) loaded flow with the proposed framework. As per simulation results, the proposed framework offered intelligent substation automation with (a) balanced utilization of a transformer, (b) enhanced system power factor in extreme load conditions, and (c) significant gain in system operational efficiency as compared to legacy load management methods.

As the cyber threats rise, and IT organization solutions become more complex, the signature-based or the perimeter solutions proved themselves insufficient or ineffective. In this research, Zero-Trust Network Access (ZTNA) deployment is examined in hybrid environments and can be analyzed and discussed within three major contexts: compliance, identity, and security measures. Conducting a quantitative study, the research aimed to assess security performance data from 12 organizations collected over the 24-month period, pre-and post-implementation of ZTNA. The study presented several strengths and increases by showing the effectiveness of the proposed solution: a 46% decrease of access violations, a 44% decline in authentication failures, a 63% decrease of the length of time threats remained undetected, and a 67% increase of response time to an alerted threat. These outcomes clearly depict the utility of ZTNA in minimizing lateral movement, improving the concept of adaptive authentication, and further positioning it as an ideal solution to support micro-segmentation to contain threats before they can spread further. With the federation of identity, behaviour, and contextual access controls, ZTNA presents comprehensive security that meets the requirements of modern enterprises and their systems. This paper not only asserts the efficiency of the Zero-Trust concepts but also outlines practical recommendations for organizations to adopt from traditional security models to more flexible and customizable identity-based approach to network authorization.

In the modern era of 5th generation (5G) networks, the data generated by User Equipments (UE) has increased significantly, with data file sizes varying from modest sensor logs to enormous multimedia files. In modern telecommunications networks, the need for high-end security and efficient management of these large data files is a great challenge for network designers. The proposed model provides the efficient real-time virtual data storage of UE data files (light and heavy) using an object storage system MinIO having inbuilt Software Development Kits (SDKs) that are compatible with Amazon (S3) Application Program Interface (API) making operations like file uploading, and data retrieval extremely efficient as compared to legacy virtual storage system requiring low-level HTTP requests for data management. To provide integrity, authenticity, and confidentiality (integrity checking via an authentication tag) to the data files of UE, the encrypted algorithm 256-bit oriented-Advanced Encryption Standard (256-AES) in Galois/Counter Mode (GCM) is utilized in combination with MinIO. The AES-based MinIO signifies in more secure and faster approach than older models like Cipher Block Chaining (CBC). The performance of the proposed model is analyzed using the Iperf utility to perform the Teletraffic parametric (bandwidth, throughput, latency, and transmission delay) analysis for three different cases namely: (a) light UE traffic (uploading and retrieval) (b) heavy UE traffic (uploading and retrieval) and (c) comparison of Teletraffic parameters namely: bandwidth (Bava), throughput (Tput), data transfer (DTrans), latency (Lms), and transmission delay (TDelay) obtained from proposed method with legacy virtual storage methods. The results show that the suggested MinIO-based system outperforms conventional systems in terms of latency, encryption efficiency, and performance under varying data load conditions.