Network Congestion Research Articles

Towards Deterministic-Delay Data Delivery Using Multi-Criteria Routing over Satellite Networks

The satellite Internet can cover up to 70% of the surface of our planet Earth to provide network services for nearly 3 billion people. As such, it is promising to become the building block of future 6G networks. The satellite Internet is capable of providing uniform communication capacity to every part of the Earth’s surface, due to its uniform and symmetrical constellation structure, while the uneven distribution of ground populations leads to globally uneven traffic delivery requests, incurring a mismatch between the capacity and traffic transmission demands. As such, traditional single-criteria (e.g., shortest delay) routing algorithms can lead to severe network congestion and cannot provision delay-deterministic data delivery. To overcome this bottleneck, we propose a multi-criteria routing and scheduling scheme to redirect time-tolerant data, thus preventing congestion for time-sensitive data, based on the spatiotemporal distribution of data traffic. First, we construct a traffic spatiotemporal distribution model, to indicate the network load status. Next, we model the satellite network multi-criteria routing problem as an integer linear programming one, which is NP-hard and challenging to solve within polynomial time. A novel link weight design based on both the link delay and load is introduced, transforming the mathematical programming problem into a routing optimization problem. The proposed correlation scheduling algorithm fully utilizes idle network link resources, significantly improving network resource utilization and eliminating resource competition between non-time-sensitive and time-sensitive services. Simulation results show that compared with traditional algorithms, the proposed method can increase the throughput of time-sensitive data by up to 20.8% and reduce the packet loss rate of time-sensitive services by up to 76.8%.

A Mobility Handover Decision Method Based on Multi-Topology

With the emergence of new applications in mobile networks, users demand higher network stability and lower data transmission delays. When the network address of a mobile user changes, the data transmission path in the wired network may need to be switched to maintain service continuity. Traditional mobility support methods typically rely on a single switching path for all mobile data flows. However, if this path cannot meet the requirements of all the flows, it may lead to network congestion or a decline in user experience. To overcome this limitation, this paper proposes a mobility handover decision method based on multi-topology. It enables the dynamic allocation of mobile data flows across different switching paths within multiple logical topologies. The method models a multi-topology selection problem aimed at minimizing average packet transmission delay and packet loss rate, while considering network conditions and the Quality of Service (QoS) requirements for each flow. By solving the dual problem of the original optimization, a near-optimal solution is achieved. The proposed scheme and algorithm were implemented and tested using the Mininet network simulator. Results show that the proposed approach achieves low average packet transmission delay, low average packet loss rate, and high throughput, compared to traditional single-path switching methods and existing multipath routing methods.

Energy-Efficient Green Information Centric Networking for Future Wireless Communications

Energy-efficient green information-centric networking (EEGICN) is proposed in this paper for advancing future wireless communication networks by addressing the challenge of energy consumption. This model can adapt the power consumption of network nodes to optimized values according to the associated link utilization. The model aimed to reduce energy consumption and increase network performance and stability. The EEGICN model incorporates efficient routing mechanisms, content caching strategies, and energy-intelligent communication protocols to improve resource utilization across the network infrastructure. EEGICN minimizes large data transmission, reduces power consumption, and reduces network congestion. Popular resources are cached in key network locations, and proximity data is exchanged to achieve this. The model also includes dynamic power management algorithms that adapt to changing traffic demand and network conditions to provide consistent performance across a variety of operational scenarios. In comparison to current wireless network systems that employ various forms of cache, the evaluation findings demonstrated that EEGICN can increase network efficiency by dramatically lowering the number of hops and energy consumption. Future networks may find this application to be a quick and easy way to transmit content.

Reinforcement learning-based Tsunami evacuation guidance system

Congestion and crowding are critical issues during indoor and outdoor emergency evacuations. In the 2011 Great East Japan Earthquake and Tsunami, vehicle traffic was one of the causes of the event’s casualties. After this, vehicle evacuation in tsunami events is not advised in Japan. Then, pedestrian evacuation is expected to be the primary mode of mobility in emergencies. However, crowding and congestion may affect the evacuation time of individuals and the overall outcome of the process. In addition, narrow streets and a high preference for the shortest routes may worsen the situation. This study aims to find the best evacuation route for a target population, considering less congestion in the road network and increasing the chances of reaching safe areas on time. We propose using reinforcement learning to train an intelligent network of agents, placed at the intersections, in charge of the evacuation process to fully complying evacuee agents. The model rewards decisions that lead to successful evacuation, considering the dynamics of departure times and street congestion throughout the simulation. We demonstrate the applicability of reinforcement learning to guide tsunami evacuation in a simulation and test this against a non-guided case where evacuees move following the shortest paths. Results show that the reinforcement learning model yields better outcomes than evacuations following the shortest paths.

DFA-mode-dependent stability of impulsive switched memristive neural networks under channel-covert aperiodic asynchronous attacks

This article is concerned with the deterministic finite automaton-mode-dependent (DFAMD) exponential stability problem of impulsive switched memristive neural networks (SMNNs) with aperiodic asynchronous attacks and the network covert channel. First, unlike the existing literature on SMNNs, this article focuses on DFA to drive mode switching, which facilitates precise system behavior modeling based on deterministic rules and input characters. To eliminate the periodicity and consistency constraints of traditional attacks, this article presents the multichannel aperiodic asynchronous denial-of-service (DoS) attacks, allowing for the diversity of attack sequences. Meanwhile, the network covert channel with a security layer is exploited and its dynamic adjustment is realized jointly through the dynamic weighted try-once-discard (DWTOD) protocol and selector, which can reduce network congestion, improve data security, and enhance system defense capability. In addition, this article proposes a novel mode-dependent hybrid controller composed of output feedback control and mode-dependent impulsive control, with the goal of increasing system flexibility and efficiency. Inspired by the semi-tensor product (STP) technique, Lyapunov–Krasovskii functions, and inequality technology, the novel exponential stability conditions are derived. Finally, a numerical simulation is provided to illustrate the effectiveness of the developed approach.

PopFL: A scalable Federated Learning model in serverless edge computing integrating with dynamic pop-up network

With the rapid increase in the number of Internet-of-Things (IoT) devices, the massive volume of data creates significant challenges for traditional cloud-based solutions. These solutions often lead to high latency, increased operational costs, and limited scalability, making them unsuitable for real-time applications and resource-constrained environments. As a result, edge, and fog computing have emerged as viable alternatives, reducing latency and costs by processing data closer to its source. However, managing the flow of such vast and distributed data streams requires well-structured data pipelines to control the complete lifecycle—from data acquisition at the source to processing at the edge and fog layers, and finally storage and analytics in the cloud. To dynamically handle data analytics at varying distances from the source, often on heterogeneous hardware devices with collaborative learning techniques such as Federated Learning (FL). FL enables decentralized model training by leveraging the local data on Edge Devices (EDs), thereby preserving data privacy and reducing communication overhead with the cloud. However, FL faces critical challenges, including data heterogeneity, where the non-independent and identically distributed (non-IID) nature of data degrades model performance, and resource limitations on EDs, which lead to inefficiencies in training and biases in the aggregated models.To address these issues, we propose a novel FL solution, called Pop-Up Federated Learning (PopFL) in edge networks. This solution introduces hierarchical aggregation to reduce network congestion by distributing the aggregation tasks across multiple Fog Servers (FSs), rather than relying solely on centralized cloud aggregation. To further enhance participation and resource utilization at the edge, we incorporate the Stackelberg game model, which incentivizes EDs based on their contribution and resource availability. Additionally, PopFL employs a pop-up ad-hoc network for scalable and efficient communication between EDs and FSs, ensuring robust data transmission in dynamic network conditions. Extensive experiments conducted on three diverse datasets highlight the superior performance of PopFL compared to state-of-the-art FL techniques. The results show significant improvements in model accuracy, robustness, and fairness across various scenarios, effectively addressing the challenges of data heterogeneity and resource limitations. Through these innovations, PopFL paves the way for more reliable and efficient distributed learning systems, unlocking the full potential of FL in real-world applications where low latency and scalable solutions are critical.

Determine a Performance Parameter by Using the IOTA Distributed Ledger Technology Method

This study aims to improve execution time, CPU utilization, network efficiency, and scalability while upholding robust security measures. The IOTA-DLT-based RA-WRW algorithm is developed in Python, considering node resources and transaction weights for optimal tip selection—verification procedures confirming tip authenticity and transaction validity. The algorithm significantly improves IOTA network transaction processing efficiency tips exhibit high authenticity and consistency, affirming the algorithm's effectiveness. The research presents the innovative IOTA-DLT RA-WRW algorithm, which integrates Resource Allocation (RA) and Weighted Random Walk strategies. This novel approach tackles challenges like lazy tip selection, network congestion, and double spending. By performance parameter and the tip selection process, the algorithm improves comparative analyses against existing methods and confirms the superior performance of our model, boasting high accuracy, f-measure, recall, precision, and scalability in distributed ledger transactions, significantly enhancing the IOTA network's transaction processing capabilities.

A Niche Quantum Ant Colony Multifaceted Routing Algorithm for WSN-based IoT Networks in the Emerging Quantum Industry

Quantum computing (QC) is an important area of technological study. The ever-growing network size and complexity in quantum computation research are the cause of evolutionary dynamics in QC. The Internet of Things (IoT) is a paradigm shift in thinking; it advances many forms of communication that rely on WSNs to gather data, and it has the potential to be even more effective with the help of quantum computing. Network congestion results in lost packets, delayed transmissions, and wasted effort during recovery. In an attempt to improve the efficiency and velocity of the route, the proposed method considers a number of objectives. Since quantum computers leverage the characteristics of quantum states to do certain computations at orders of magnitude quicker than classical computers, this is an exciting new direction to investigate. This study introduced the Niche Quantum Ant Colony Multifaceted Routing Algorithm (NQAC-MRA), a method for optimizing wireless distributed networks in IoT routes that combines quantum computation, an optimization function for several objectives, and monitoring. Producing cost-effective routing that is congestion-aware for alarm message transmission over WSN-IoT and for alert message transmission utilizing IoT devices to their respective destinations with the lowest latency is the primary goal of this study. To be more specific, the quantum bits stand in for the node pheromone, and turning quantum gates modifies the search path's pheromone. Nodes' power consumption, communication delay, and degree of network load-balancing serve as fitness metrics to find the best communication path. According to the results of the performance analysis, the suggested scheme is both lightweight and more effective than the alternatives. The performance research showed that the proposed approach outperformed the alternatives while being significantly lighter. The suggested NQAC-MRA beat its competitors in terms of total simulation results thanks to its reduced energy usage and enhanced performance.

A Study Evaluating the Problems and Challenges Faced by the Employees of the Indian Telecom Industry

The nation's economic expansion, connectivity, and digital transformation are all greatly aided by the telecom sector in India. However, workers in this industry frequently face difficulties including fierce competition, interruptions from technology, changes in regulations, and unstable employment. Despite these challenges, the sector is nevertheless vital to India's society, trade, and communication growth. This research paper was conducted with the focus on evaluating the problems and challenges faced by the employees of the Indian Telecom Industry. Text mining technique using the qualitative tool NVIVO 12 has been used in the current study. It is was found that the most important challenges faced by the employees of the Indian Telecom Industry are High workload, Intense competition among companies, Technological advancements, Network congestion, Poor infrastructure, Security concerns and Work-life balance. Whereas, minor challenges faced by the employees of the Indian Telecom Industry are Gender discrimination, Limited career growth, Language barriers, Increasing Customer expectations, and Rapid changes in consumer preferences

HawkEye: An end-host method to detect the Low-rate Denial-of-Service attack of cross-traffic over bottleneck links

The adaptive mechanisms of the Transmission Control Protocol (TCP) address network congestion and other unpredictable network conditions. They ensure the reliability of data transmission and the stability of the network. Unfortunately, the vulnerabilities in these adaptive mechanisms are targeted explicitly by low-rate denial-of-service (LDoS) attacks, which severely degrade network service quality. Only by modifying these protocols and addressing their vulnerabilities can one entirely prevent LDoS attacks. Although various improved TCP algorithms exist, they often fail to identify LDoS attacks accurately and, in some cases, may even reduce TCP performance. Furthermore, traditional LDoS attack detection methods rely on intermediate devices, which do not meet TCP’s end-to-end performance optimization needs. We introduce HawkEye, which moves the detection mechanism to the end hosts to address this issue. HawkEye uses an improved genetic algorithm to fine-tune the parameters of the LightGBM on the sending host, integrating multiple network traffic features to detect LDoS attacks. Experimental results show that our proposed method achieves the high accuracy, high true positive rate, and low false positive rate, successfully addressing the limitations of end-host detection of LDoS attacks and providing an innovative and effective solution for enhancing LDoS attack detection.

VIGOR: Reviving Cloud Gaming Sessions

Cloud gaming is a multi-billion dollar industry. A client in cloud gaming sends its movement to the game server on the Internet, which renders and transmits the resulting video back. In order to provide a good gaming experience, a latency below 80 ms is required. This means that video rendering, encoding, transmission, decoding, and displaying have to finish within that time frame, which is especially challenging to achieve due to server overload, network congestion, and losses. In this paper, we propose VIGOR, a new method to revive cloud gaming sessions by recovering lost or corrupted video frames. Unlike traditional video frame recovery, VIGOR uses game states to enhance recovery accuracy significantly and utilizes partially decoded frames to recover lost portions. We develop a holistic system that consists of (i) efficiently extracting game states, (ii) modifying H.264 video decoder to generate a mask to indicate which portions of video frames need recovery, and (iii) designing a novel neural network to recover either complete or partial video frames. VIGOR is extensively evaluated using iPhone 12 and laptop implementations, and we demonstrate the utility of game states in the game video recovery and the effectiveness of our overall design.

Reducing tail latency for multi-bottleneck in datacenter networks: A compound approach

The effectiveness of network congestion control fundamentally depends on the accuracy and granularity of congestion feedback. In datacenter networks, precise feedback is essential for achieving high performance. Most existing approaches use either Explicit Congestion Notification (ECN) or network delay (e.g., RTT) independently as congestion indicators. However, in multi-bottleneck networks, the limitations of these signals become more pronounced: ECN struggles with large cumulative end-to-end latency, while RTT lacks the precision needed to control queuing delays at individual hops. To address these challenges, we propose Cocktail, a simple yet effective transport protocol for datacenter networks that combines both ECN and RTT congestion signals to more effectively handle multi-bottleneck scenarios. By leveraging the ECN signal, Cocktail bounds per-hop queue lengths, enhancing its ability to control single-hop latency and prevent packet loss. Additionally, by estimating RTT, Cocktail effectively manages end-to-end delay, resulting in lower Flow Completion Time (FCT). Extensive experimental evaluations in Mininet demonstrate that Cocktail significantly reduces the average and 99th-percentile completion times for small flows by up to 20% and 29%, respectively, compared to current practices under production workloads.

Reliability impact of dynamic thermal line rating and electric vehicles on wind power integrated networks

Wind power, as a sustainable clean energy source, has been widely used in power systems. However, the intermittency of wind power poses a threat to the reliability of these systems. One solution to address this issue is the integration of electric vehicle (EV) scheduling. By using EVs with Vehicle-to-Grid (V2G) technology, grid reliability can be improved: EVs can charge when there is excess wind power and discharge when there is a shortage. Another technology that enhances wind power system reliability is Dynamic Thermal Line Rating (DTLR). The DTLR system increases transmission line capacity based on actual weather conditions, helping to alleviate network congestion. Despite the benefits of these technologies, their coordinated operation has not been jointly modeled for reliability purposes before. This study presents, for the first time, a reliability model for the coordinated operation of these two technologies and introduces a new reliability index, the Smart Regulation Reliability Index (SRRI). Simulations were conducted using the IEEE 24-bus reliability test system. The results show that the coordinated operation of these technologies reduces Expected Energy Not Supplied (EENS) by nearly 28.8 % and increases SRRI by about 12.9 %.

Wavelet and VMD enhanced traffic forecasting and scheduling method for edge cloud networks

With the proliferation of Intelligent applications, more and more organizations migrate their applications from cloud to edge cloud network to reduce the latency of applications and alleviate the workload of cloud. When the network congestion occurs, network monitoring and detection system may disable, and edge cloud network will be vulnerable to be attacked. Traffic forecasting can identify the traffic patterns in advance, and dynamically allocate network resources to decrease latency of applications and avoid security risks caused by network congestion. Therefore, we propose a network scheduling framework WVNF (Wavelet VMD Based Network Flow Management) based on traffic prediction, which utilizes neural networks to forecast network traffic and deploys route strategies to optimize network scheduling. Specifically, in order to accurately forecast network traffic, we propose a neural network model, named TSWNet (Traffic Sequence Wavelet Network). TSWNet uses VMD (variational mode decomposition) to decompose time series and extract signal structure information on different time scales, and adopts wavelet transformation to extract the local and global features of the traffic sequence in the time and frequency domain. In addition, we model this traffic scheduling problem and propose a route strategy, which utilizes the result of TSWNet to find the best path. In extensive tests, TSWNet significantly outperformed existing models, reducing MSE and MAE by up to 48.8% and 27.8% respectively, demonstrating its effective traffic prediction and network scheduling capabilities.

Adaptive Random Early Detection Using Deep Reinforcement Learning for Enhanced Congestion Control in Dynamic TCP/IP Networks

AQM is used to meet targets for congestion control and low delay, high throughput in TCP/IP networks. But methods developed for AQM like Random Early Detection (RED) work strictly on the control parameters pre-set on them and thus may not respond very well to changing network conditions. Thus, this paper presents a composite AQM model using DRL based on DQN, and the ability to learn independently regarding the AQM weight parameter of the queue. The fact that DRL is adaptive means the effectiveness of the proposed system is also assured. As it is observed in implementing traditional model-based approach, the stability and performance degrade when tested in different network conditions; however, DRL’s ability to learn independently enables ML to solve network congestion as it happens. Consequently, more stability is achieved, the delay is lesser, more bandwidth is used and the overall packet drop rate is low; the proposed DRL-RED model is ideal for dynamic network environments. The proposed DRL-RED model is compared with the regular RED algorithm for both low density and high-density networks. This proposed model has achieved sustained throughput of up to 49.9 Mbps with 0.949 % reduction on delay and very low PLR of 8.38043%. From the comparative analysis, it is clear that the employment of DRL with RED improves the stock of network throughput, the reduction on packet drop frequency, and the general delay in network situations. There are two main disadvantages of this approach: first, it cannot reach the heavy-traffic, the problem partly solved by model-based approach; second, the values of the congestion-control parameters cannot be reset, an issue eradicated by DRL as a result of its inherent adaptiveness. Consequently, new stability and increased performance can be achieved under various network conditions.

GTBTL-IoT: An Approach of Curtailing Task Offloading Time for Improved Responsiveness in IoT-MEC Model

INTRODUCTION: The Internet of Things (IoT) has transformed daily life by interconnecting digital devices via integrated sensors, software, and connectivity. Although IoT devices excel at real-time data collection and decision-making, their performance on complex tasks is hindered by limited power, resources, and time. To address this, IoT is often combined with cloud computing (CC) to meet time-sensitive demands. However, the distance between IoT devices and cloud servers can result in latency issues. OBJECTIVES: To mitigate latency challenges, Mobile Edge Computing (MEC) is integrated with IoT. MEC offers cloud-like services through servers located near network edges and IoT devices, enhancing device responsiveness by reducing transmission and processing latency. This study aims to develop a solution to optimize task offloading in IoT-MEC environments, addressing challenges like latency, uneven workloads, and network congestion. METHODS: This research introduces the Game Theory-Based Task Latency (GTBTL-IoT) algorithm, a two-way task offloading approach employing Game Matching Theory and Data Partitioning Theory. Initially, the algorithm matches IoT devices with the nearest MEC server using game-matching theory. Subsequently, it splits the entire task into two halves and allocates them to both local and MEC servers for parallel computation, optimizing resource usage and workload balance. RESULTS: GTBTL-IoT outperforms existing algorithms, such as the Delay-Aware Online Workload Allocation (DAOWA) Algorithm, Fuzzy Algorithm (FA), and Dynamic Task Scheduling (DTS), by an average of 143.75 ms with a 5.5 s system deadline. Additionally, it significantly reduces task transmission, computation latency, and overall job offloading time by 59%. Evaluated in an ENIGMA-based simulation environment, GTBTL-IoT demonstrates its ability to compute requests in real-time with optimal resource usage, ensuring efficient and balanced task execution in the IoT-MEC paradigm. CONCLUSION: The Game Theory-Based Task Latency (GTBTL-IoT) algorithm presents a novel approach to optimize task offloading in IoT-MEC environments. By leveraging Game Matching Theory and Data Partitioning Theory, GTBTL-IoT effectively reduces latency, balances workloads, and optimizes resource usage. The algorithm's superior performance compared to existing methods underscores its potential to enhance the responsiveness and efficiency of IoT devices in real-world applications, ensuring seamless task execution in IoT-MEC systems.

A novel analytical method for optimal management of network congestion caused by electric vehicle charging stations

Congestion management with the emergence of electric vehicle charging stations (EVCSs) can be considered as one of the challenges facing the network operator, whose incorrect management causes the price difference to increase and creates market power. In general, the effect of EVCSs on the congestion rent (CR) of grid lines has been studied in a few studies, which are considered a research gap. In this paper, the share of each EVCS to the variations in line CR and total congestion rent (TCR) is precisely determined. Also, the uncertainty regarding the capacity of the EVCSs is considered to make the problem more real. In addition, the share of other factors as well as marginal, expensive, and cheap units, grid loads, and congested lines are calculated on the CR of grid lines. The proposed method is divided into two parts, in which the share of the five factors to variations in the buses’ locational marginal price (LMP) and the flow of lines is determined in part 1. In part 2, the share of EVCSs to variations in the CR of lines and TCR is computed. The proposed method is implemented using the Kahn-Tucker condition method. This approach is implemented on an IEEE 24-bus system. The analysis of results indicate that the congestion of network lines in the best and worst cases of placing 3 EVCSs in the network increases by 0.0013 % (0.65 $/h) and 0.1 % (48.94 $/h), respectively. Therefore, determining the appropriate placement of the EVCS is of particular importance.

A blockchain based secure authentication technique for ensuring user privacy in edge based smart city networks

In the past decade, modernization of Information and Communication Technology (ICT), Edge Computing (EC), and Smart Cities has attracted significant academic interest due to its diverse applications in the fields of healthcare, transportation, agriculture, and defense. EC offers numerous advantages, including faster and more efficient services, lower latency, improved data processing, managed bandwidth consumption, scalable, real-time decision-making, security, reduced network congestion, and increased resilience. Despite these benefits, EC networks face persistent challenges, particularly related to security and privacy concerns. Addressing these security challenges requires strong authentication mechanisms, which demand extra resources like processing power and memory, often surpassing the limited capabilities of lightweight edge devices compared to cloud systems. This highlights the critical need for securing edge nodes and ensuring user privacy before real-world deployment and data transfer. User and edge device authentication is vital to prevent external and internal Impersonation and Reflection attacks that threaten system integrity and confidentiality. This paper presents a BlockChain based Authentication technique for Edge Networks (BCAuthEN) that utilizes a Consortium Blockchain (CB) with key agreements for biometric authentication, incorporating a Fuzzy Extractor (FE) to secure user biometrics and passwords. In addition, BCAuthEN offers multifactor and continuous authentication by monitoring user behavior and biometrics. BCAuthEN has been formally verified through Real-Or-Random (RoR) modeling and AVISPA tool, proving its effectiveness in enhancing privacy, and security. The proposed technique ensures robust security by preventing attackers at the potential entry points (edge nodes). In addition, BCAuthEN reduces computation cost, communication overhead and improves throughput. BCAuthEN provides strong resilience by achieving high detection accuracy and reduces false positives against impersonation and reflection attacks. Results have shown that BCAuthEN improves communication costs and reduces overhead by 10% and 7%, respectively, as compared to the recent biometric and key-based user authentication techniques.

Construction of a digital twin model for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints

In the multi type load information of hybrid microgrids, data loss or incompleteness may occur due to network congestion, signal interference, equipment failures, and other reasons. Especially with the continuous generation of new load data, gradually incorporating these new data into the existing aggregation process to achieve continuous updating and optimization of load information. Therefore, this article proposes a digital twin model construction method for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints. The Leida criterion and cubic exponential smoothing method are used to preprocess various load data of hybrid microgrids, remove abnormal data, reduce data fluctuations, and make the data more interpretable. Establish integrity constraints for multiple load data of hybrid microgrids and extract load characteristics of hybrid microgrids. Based on these, establish a digital twin model for the incremental aggregation of multiple load information in a hybrid microgrid, and solve the model using an improved K-means algorithm to achieve continuous updating and optimization of load information. The experimental results show that the data sharing delay of this method is 0.12 s, the load is basically consistent with the actual value, and the relative error of the load data is 4%.

Exploring the Impact of Resource Management Strategies on Simulated Edge Cloud Performance: An Experimental Study

Edge computing has emerged as a critical technology for meeting the needs of latency-sensitive applications and reducing network congestion. This goal is achieved mainly by distributing computational resources closer to end users and away from traditional data centers. Optimizing the utilization of limited edge cloud resources and improving the performance of edge computing systems requires efficient resource-management techniques. In this paper, we primarily discuss the use of simulation tools—EdgeSimPy in particular—to assess edge cloud resource management methods. We give a summary of the main difficulties in managing a limited pool of resources in edge cloud computing, and we go over how simulation programs like EdgeSimPy work and evaluate resource management algorithms. The scenarios we consider for this evaluation involve edge computing while taking into account variables like user location, resource availability, and network structure. We evaluate four resource management algorithms in a fixed, simulated edge computing environment to determine their performance regarding their CPU usage, memory usage, disk usage, power consumption, and latency performance metrics to determine which method performs better in a fixed scenario. This allows us to determine the optimal algorithm for tasks that prioritize minimal resource use, low latency, or a combination of the two. Furthermore, we outline areas of unfilled research needs and potential paths forward for improving the reliability and realism of edge cloud simulation tools.