Problem Of Network Congestion Research Articles

Compression-based Aggregation and Clustering Techniques for XML Enhanced Performance

This paper has presented a comprehensive literature study and discusses the primary problem of network congestion and bottlenecks in XML applications. The explosive growth of Web services has caused a significant problem in network traffic due to the largely repetitive XML textual structures. The challenge of XML message size reduction in web services has been examined, focusing on new approaches such as compression methods and message clustering for Aggregation and compression of XML messages efficiently. The goal is to examine how different strategies affect network traffic. This overview discusses twenty-five publications categorized into four: compression techniques, aggregation approaches, clustering for web services aggregation approaches, and other web services clustering models. In comparison to all models, evaluation has shown that web service clustering solutions efficiently reduce traffic. Technically, the web service clustering for aggregation models has achieved an excellent average compression ratio of up to (14.12).

Machine Learning-based fixed access Network Interface Congestion Prediction

Network interface congestion refers to a situation in which network interfaces or communication channels within a network experience high levels of traffic, leading to performance degradation or potential network failures. In modern networking, this issue remains relevant and can manifest in various contexts due to a surge in data traffic, Internet of Things (IoT) devices can generate substantial data, increased use of VPNs for remote work and when multiple users or applications concurrently access and transfer data in and out of the cloud. Efforts to forecast and address network interface bottlenecks are crucial to sustaining the performance and consistency of modern networks. In the PTCL fixed access network infrastructure, network blocking is a serious problem that necessities to be addressed. The main objective of this study is to overcome network congestion problems that occur in PTCL's fixed access network through the use of machine learning techniques. Despite having real-time monitoring tools such as SolarWinds, there remains a dearth of software capable of predicting imminent bottlenecks in the network interface. The primary aim is to build an analytical model which can predict potential blockages within 30 days and facilitate proactive management strategies for better administration. Top of Form The study classifies the most effective machine learning technique for this purpose and evaluates its performance against current methods. To achieve the goal, a diagnostic dataset was created, leveraging appropriate configurations, software tools, and hardware resources. Data was sourced from SolarWinds, consisting of information from over 23,000 interfaces across 11,000 access network nodes. This data was analyzed in PTCL's Data Warehouse, where a historical aggregated dataset was constructed. Exploratory data analysis was performed using Python libraries, including Matplotlib, NumPy, Pandas, and Seaborn. This investigation sets the foundation for proactive network management and enhanced user experiences within PTCL's fixed access network, addressing the critical essential for advanced predictive tools to combat congestion and enhance network competence.

Burst traffic: Congestion management and performance optimization strategies in heterogeneous cognitive radio networks

AbstractIn order to solve the network congestion problem caused by burst service flows in heterogeneous cognitive radio networks (Het‐CRN), a two‐stage dual‐threshold random early detection (TD‐RED) control mechanism is proposed here. Meanwhile, a three‐state Markov modulated Poisson process (MMPP‐3) is used to model the burst service to analyze its arrival in depth. Moreover, a performance evaluation system (PES) is proposed to quantify the interference of burst services to different classes of services and their quality of service, which can comprehensively evaluate the performance enhancement of the TD‐RED control mechanism for each service. The system examines performance metrics such as packet loss rate, average delay, average captain etc. PES simulation results show that TD‐RED significantly reduces the packet rejection rate and transmission delay compared to the no congestion control (NCC) and random early detection (RED) methods, thus providing superior quality of service to all services. thereby providing a superior quality of service to cognitive users. Its lower average captain fully proves that TD‐RED can effectively alleviate congestion.

Experimental and rheological modelling on the stabilization of iron ore slurry using a biosurfactant extracted from Aloe barbadensis Miller

The pipeline transportation of iron ore slurry (IOS) is an eco-friendly alternate mode to transport the raw materials to longer distance and reduce the transportation network congestion problems. Proper selection of surfactant could modify mineral’s viscosity, dispersion and wetting characteristics effectively as may be desired, which may further be monitored. A surfactant alters surface characteristics after being adsorbed onto minerals by altering the interfacial behaviour. This work is first report of the influence of a biosurfactant sourced from Aloe barbadensis Miller on the rheological behaviour of a high-grade iron ore (IO) sample at 45–72% by mass slurry concentrations range. The observed rheological parameters confirmed the non-Newtonian flow behaviour of the slurry. The collected IO and dried IOS samples were subjected to UV-Vis, XRF, PSD, XRD, SEM and EDS analyses. Experimental rheological data at varying concentrations was tested on various models to ascertain the best-fit rheological model for IOS transport. The Bingham model was found to be the most suitable to model the rheology of IOS. Experimental rheological behaviour of IOS was found to be linear with the yield stress. The study shall help to ascertain the suitability of a biosurfactant to stabilise high grade iron ores for pipeline transportation.
 KEY WORDS: Iron ore slurry, Aloe barbadensis, Rheological measurements, Rheological modelling
 Bull. Chem. Soc. Ethiop. 2024, 38(3), 615-629. 
 DOI: https://dx.doi.org/10.4314/bcse.v38i3.6

Detecting and Predicting Models for QoS Optimization in SDN

Recently, deep learning algorithms and software‐defined networking technologies enabled traffic management to be more controllable in IP networking and mobile Internet to yield quality services to subscribers. Quality of service (QoS) needs more effort to optimize QoS performance. More specifically, elephant flow management is a critical task that needs further research since its heavy hit behavior leads to high CPU utilization, packet drops, high latency, packet overflow, and network congestion problems. For this purpose, we focused on elephant flow management since elephant flows are big flows that hinder good service delivery (QoS) on demand. Hence, elephant flow detection and early prediction techniques optimize QoS. In this regard, we employed DNN and CNN deep learning models to detect elephant flows, and the random forest model predicts elephant flows in the SDN. As a result of our experiments, the findings reveal that deep learning algorithms within the Ryu controller significantly outperform in detecting and predicting performance in order to yield good network throughput. This solution proves to be significant for QoS optimization in data centers.

Mobile agents-based modelling for the vehicular network congestion problem resolution

Mobile agents-based modelling for the vehicular network congestion problem resolution

Business Sensitivity Options of Carrier Aggregation Strategies: A Simulation Model for 4G Deployment in Emerging Countries

Indonesia’s growing population increasingly relies on 4G networks, presenting challenges for the Ministry managing mobile broadband adoption. There are severe data network congestion problems in metropolitan areas, particularly Jakarta, Indonesia’s capital city. Urgent action is required to address infrastructure and capacity limitations and maintain reliable connectivity. Carrier aggregation (CA) may alleviate bottlenecks by giving users higher bandwidth, throughput, and data rates. This research examined the techno-economic aspects of using 2.1 GHz CA versus non-CA in an urban region of North Jakarta. CA required fewer eNodeB and performed better on key performance indicators like received signal power, signal-to-noise ratio, and throughput. Cellular operators also saw faster investment returns with CA. CA’s performance and economic benefits make it a promising solution for Indonesia’s 4G network congestion.

Improving Traffic Efficiency in a Road Network by Adopting Decentralised Multi-Agent Reinforcement Learning and Smart Navigation

In the future, mixed traffic flow will consist of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs). Effective traffic management is a global challenge, especially in urban areas with many intersections. Much research has focused on solving this problem to increase intersection network performance. Reinforcement learning (RL) is a new approach to optimising traffic signal lights that overcomes the disadvantages of traditional methods. In this paper, we propose an integrated approach that combines the multi-agent advantage actor-critic (MA-A2C) and smart navigation (SN) to solve the congestion problem in a road network under mixed traffic conditions. The A2C algorithm combines the advantages of value-based and policy-based methods to stabilise the training by reducing the variance. It also overcomes the limitations of centralised and independent MARL. In addition, the SN technique reroutes traffic load to alternate paths to avoid congestion at intersections. To evaluate the robustness of our approach, we compare our model against independent-A2C (I-A2C) and max pressure (MP). These results show that our proposed approach performs more efficiently than others regarding average waiting time, speed and queue length. In addition, the simulation results also suggest that the model is effective as the CAV penetration rate is greater than 20%.

Lattice-Based Threshold Signcryption for Blockchain Oracle Data Transmission

Threshold signature can solve the problems of high cost and network congestion during the execution of smart contracts. However, the information transmitted in public links is vulnerable to eavesdropping, tampering and other network attacks, it is essential to ensure the confidentiality and integrity during data transmission. However, traditional threshold signcryption is not suitable for blockchain setting and cannot withstand the quantum computing attacks. In view of these reasons, one lattice-based threshold signcryption for blockchain oracle data transmission (BCODT-LTSC) is devised in this article. BCODT-LTSC satisfies the existential unforgeability, confidentiality and threshold characteristic; it has very low computation cost and is suitable for the application in blockchain scenario.

SHM-NDN: A seamless hybrid mobility management scheme for named data mobile ad hoc networks

Named Data Networking (NDN), a counterpart of address-centric networking resolves consumer mobility issues to some extent, however, it confronts excessive bandwidth consumption and network congestion problems in Mobile Adhoc Networks (MANET) due to an inefficient default request re-transmission mechanism. The producer mobility in NDN-based MANET, on the other hand, poses even worse challenges owing to the continuous flooding of location advertisement messages in the network. This paper aims to address the aforementioned producer and consumer mobility issues in MANET and proposes SHM-NDN, a seamless hybrid mobility management scheme for NDN-based MANETs. The proposed scheme features new packet types and extends the functionality of wireless mobile FIB data structure to support efficient mobility management. Moreover, the SHM-NDN proposes a path recovery mechanism to deal with the network partitioning problem caused by a low signal-to-noise ratio and high congestion rate. Through an extensive simulation study, we demonstrate that SHM-NDN achieves on average 12.2% less bandwidth and energy consumption compared to CBILEM. Similarly, SHM-NDN also attains on average 25% less bandwidth and energy consumption compared to EEIF.

Dynamic Output Feedback Quantization Control of a Networked Control System with Dual-Channel Data Packet Loss

In this paper, the design of a dynamic output feedback controller for a networked control system with dual-channel data packet loss and special discrete-time delay is studied, in which the data packet loss is described by the Markov process. In order to effectively alleviate the problem of network congestion, a quantizer was added to the sensor-to-controller channel. The transition probabilities of the Markov process are uncertain, but they exist in the convex sets of known convex polyhedron types. The mode-dependent Lyapunov function was constructed, and a sufficient condition was given to make the closed-loop system stochastically stable and satisfy the performance index. The parameters of the controller were solved by the linear matrix inequality method. Finally, an example of aircraft shows the validity of the proposed approach. A numerical example is compared with other literature, showing the superiority of the proposed approach.

Efficient and Secure Certificateless Signcryption Without Pairing for Edge Computing-Based Internet of Vehicles

The emergence of the vehicular ad-hoc network (VANETs) could meet the need for an efficient and safe transportation. Vehicles wirelessly interact with nearby road-side units (RSUs) and send the data to the cloud servers (CS), but this approach has limitations (i.e. network congestion and privacy leakage). Edge computing can effectively solve problems of network congestion, feedback delay and privacy leakage during transmission caused by CS. Thus, this paper proposes a certificateless aggregate signcryption with online/offline encryption (CLOASC) based on edge computing (EDC). Correspondingly, certificateless signcryption (CLSC), online/offline encryption (OOE) and aggregate signcryption technology solve the key escrow problem, shorten the encryption time and realize the quick verification of multiple users, respectively. Furthermore, it is worth mentioning that the scheme we propose does not have any bilinear pairings or scalar multiplications in the online encryption stage, which greatly reduces the computational cost in the online stage. In the random oracle model, our scheme was proved to be existential unforgeability under adaptive chosen message attacks (EUF-ACM) and adaptive indistinguishability under chosen-ciphertext attack (IND-CCA2) security. Simulation results show that our scheme is computationally and communication based lightweight as compared to the related.

Intersection-Based Unicast Routing Using Ant Colony Optimization in Software-Defined Vehicular Networks

A vehicular ad hoc network (VANET) is a mobile ad hoc network composed of communication between vehicles, between vehicles and roadside units, and between vehicles and pedestrians, in order to achieve traffic safety and entertainment services. The design of the routing protocol is very important for the realization of the service function of VANET. Local optimum and network congestion problems are restraints of traditional geographic routing protocols for VANET. In this paper, a software-defined network (SDN) based unicast routing scheme in an urban traffic environment is proposed, which uses Dijkstra’s algorithm to find a global optimal anchor path. The RSU neighbor discovery protocol is proposed, through which each RSU can discover its neighbor RSUs, and then each RSU periodically sends ant packets to its neighbor RSUs, evaluates the communication connection quality of each street segment according to the statistical data of the ant packets received, and sends the evaluation value to the SDN server in time. The SDN server has the connection quality evaluation values of all street segments in the global scope, from which an optimal anchor path can be calculated. The simulation results show that the proposed scheme has better packet delivery ratio than other related schemes.

A vertical and horizontal crossover sine cosine algorithm with pattern search for optimal power flow in power systems

Most of the energy consumption is now being used to supply human demand for electricity, which has increased the burden of power system planning to some extent, and thus, researchers proposed solutions to the optimal power flow (OPF) problem. In such a background, flexible AC transmission system (FACTS) devices are widely used in modern power systems to alleviate human power demand and improve network congestion problems. This research proposes a better-quality sine cosine algorithm (CPSCA) based on a crossover mechanism and pattern search algorithm for optimizing multiple objectives, such as system generation cost and transmission losses when we have multiple types of FACTS devices in the power system. The proposed method uses sine and cosine theory and evolutionary strategy to continuously sample and update the framework and improve the quality of the solutions. The method uses a class of vertical and horizontal crossover operators and pattern-shifting techniques to find the optimal solution set. In addition, the Weibull probability density function was used in this study to model uncertainty in wind energy, while direct costs, penalty costs, and standby costs were considered in constructing the target model. Various types of FACTS devices, such as thyristor-controlled series compensators, thyristor-controlled phase shifters, and static reactive power compensators, are also inserted into the model as power in the target optimization problem. The proposed algorithm has experimented on an IEEE30 bus test system. Based on the experimental results, CPSCA achieves an optimal total cost of 806.0225 $/h. In terms of power loss of the bus system, CPSCA reduces the total power loss value from 2.042 MW to 1.41 MW compared to the original sine cosine algorithm, which is a 31% improvement and has an optimal power loss value. Therefore, the simulation results demonstrate that the proposed algorithm is an effective technique for optimizing the optimal power flow of the entire power system and FACTS equipment.

Mobile agents-based modelling for the vehicular network congestion problem resolution

Mobile agents-based modelling for the vehicular network congestion problem resolution

Optimization of Network paths in congested SDN using Genetic Algorithm

Computer network and Communication over such networks become integral part of human life from the very beginning of twenty first century. From the very first radio communication in the beginning of 19th Century to the latest wireless and Software Defined Network (SDN) communication over the satellite and local allied computer networks, caused increase in more and more traffic over the computer network. Internet was not only provided over the copper, fiber optics cables, but rather it becomes a commercial service sector over the low earth orbit satellites across the globe. Majority of the contents flowing over such networks are either academic, research, corporate data bases like banking, finance, IT services, social media, and also specially of the entertainment industry. Every small to large organization in corporate and research and development organization faced problems of computer network congestion over the time. As the demand for more data access is required hence the congestion over the network started to increase. Every nation and individual organization have their own policy to control domestic network traffic as bandwidth is limited and commercial aspects are involved. Various algorithms have focused to regulate network, but very few algorithms exist which focuses on providing alternative paths for better network traffic management. This paper focuses on using modern Genetic Algorithms like Ants colony optimization to first identify congestions over the network and then using such insights to find alternative paths through mutation and cross over. The proposed solution was executed, and generated result comparatively proved that use of Genetic Algorithm has helped to find alternative paths more effectively over the SDN.

Quantum network coding via time-bin multiplexing

<p indent="0mm">Quantum network coding is used to overcome the congestion problem in quantum networks, which can effectively improve the transmission efficiency and total throughput of quantum networks. Here, we propose a time-bin complexing network coding method for crossly transmitting two arbitrary single-qubit states over the butterfly network. To solve the congestion problem, a single photon with polarization and time-bin degrees of freedom acts as a bridge to entangle the two-dimensional quantum memories of two intermediate nodes in a heralded way. Finally, quantum 2-pair unicast communication is completed based on quantum teleportation and the heralded double entangled channels. Concerning dishonest intermediate nodes being concerned, a security-enhanced quantum 2-pair unicast communication protocol is presented. Security and performance are analyzed, and the result shows that the improved protocol is secure against the general entangle-measure attack of intermediate nodes. It can be extended to multiple unicast communication over a more general quantum network by exploiting the time-bin complexing technique.

A Comparative Study of the Application of Glowworm Swarm Optimization Algorithm with other Nature-Inspired Algorithms in the Network Load Balancing Problem

Vast amounts of data are transferred through communication networks resulting in node congestion, which varies according to peak usage times. The Glowworm Swarm Optimization (GSO) algorithm is inspired by the rummaging and courtship behavior of glowworms. The glow intensity of glowworms is a measure of fitness that attracts other glowworms in its neighborhood. This work applies the GSO algorithm to the computer network congestion problem in order to lessen the network burden by shifting loads to the fittest neighborhood nodes, thereby enhancing network performance during peak traffic times, when the response of systems on the network would go down. The proposed solution aims to alleviate the burdened nodes, thereby improving the flow of traffic throughout the network, improving the users’ experience and productivity, and efficiency. In this paper, three swarm algorithms, namely Particle Swarm Optimization (PSO), Cuckoo Search (CK), and GSO have been employed to solve the network load balancing problem. The results produced by GSO show improvement of 71.17%, 74.14%, and 84.15% in networks consisting of 50, 100, and 200 nodes in peak hour load, while PSO shows 13.87%, 11.75%, and 23.72%, and CK 10.61%, 3.19%, and 6%. The results prove the superior performance of GSO.

Impact of Dynamic Thermal Rating on optimal siting and sizing of energy storage systems under Renewable Portfolio Standards requirements

Renewable Portfolio Standards (RPS), enforced as part of environmental regulations, are taking precedence worldwide to promote investments in renewable energy sources (RES). From the grid perspective, one of the bottle-neck factors that limits large-scale RES integration is the transmission network congestion. Dynamic Thermal Rating (DTR) is a smart grid technology that utilizes line condition and real-time weather conditions to alleviate network congestion by exploiting the transmission network inherent flexibility. Due to the intermittency of the RES, energy storage systems (ESS) have emerged as an essential component of power system that stores the excess generated energy by RES and pumps it back into the network at peak hours. However, despite the benefits of these technologies, the coordination of these two technologies with RPS regulations are not studied together. This paper presents a co-optimized model to evaluate the role of DTR in optimal siting and sizing of ESS units in connection with RPS requirements. The modeling framework co-optimizes bulk ESS expansion and DTR investment planning subject to constraints on RPS regulations and is formulated as a two-stage stochastic mixed integer linear programming (MILP) optimization problem. The model utilizes DTR to overcome the problem of network congestion and ESS to mitigate RES curtailment by saving the extra generated energy in non-peak hours. The efficacy of the proposed model in realizing impact of the DTR on ESS (location, energy capacity and power rating) in achieving high RPS targets is investigated on the modified IEEE 24-Bus system. Results demonstrate that DTR complements ESS under different RPS constraints by reducing the total system cost and ESS size significantly.

An Intelligent Congestion Control Strategy in Heterogeneous V2X Based on Deep Reinforcement Learning

High mobility and the complexity of mobile behavior are the main characteristics of nodes in Vehicle to Everything (V2X). Furthermore, these characteristics entail that resource deployment cannot effectively meet the demands of users for differentiated service quality. Due to this significance, the main objective of this study is to propose an intelligent congestion control strategy based on deep reinforcement learning (ICCDRL) in heterogeneous V2X, which can meet the diverse service needs of vehicles to some extent, so as to solve the problem of network congestion effectively. The proposal is implemented through three aspects: Firstly, the paper establishes a congestion control model based on DRL. Secondly, a large amount of QoS data is used as the training set to optimize the model. Finally, the congestion sensitivity factor is used to select the size of the congestion window for the next moment, resulting in an intelligent congestion control strategy based on QoS on-demand drive. For verification, a series of simulation experiments are designed on the ns-3 simulation platform. The results show that the proposed ICCDRL outperforms the traditional algorithm in terms of throughput, convergence, friendliness and fairness, and can effectively guarantee real-time, reliable information interaction in V2X.