Prevent Network Congestion Research Articles

Filtering algorithm for systems with transmission random failures and packet loss prediction compensation

Abstract The problem of least-squares linear filtering is explored for a specific type of networked systems, where the measurements are affected by random parameters and experience random delays and packet dropouts. To prevent network congestion, only one packet is transmitted per sampling interval. However, due to these phenomena, either a single packet, two packets, or no data at all may be received at each sampling time. In the latter case, the estimator compensates by utilizing a predicted value. An innovation-based method is used to derive a recursive filter that does not require the signal dynamics, but only some statistical properties (first-order and second-order moments) of the stochastic processes in the observation model.

A Multi-Agent RL Algorithm for Dynamic Task Offloading in D2D-MEC Network with Energy Harvesting.

Delay-sensitive task offloading in a device-to-device assisted mobile edge computing (D2D-MEC) system with energy harvesting devices is a critical challenge due to the dynamic load level at edge nodes and the variability in harvested energy. In this paper, we propose a joint dynamic task offloading and CPU frequency control scheme for delay-sensitive tasks in a D2D-MEC system, taking into account the intricacies of multi-slot tasks, characterized by diverse processing speeds and data transmission rates. Our methodology involves meticulous modeling of task arrival and service processes using queuing systems, coupled with the strategic utilization of D2D communication to alleviate edge server load and prevent network congestion effectively. Central to our solution is the formulation of average task delay optimization as a challenging nonlinear integer programming problem, requiring intelligent decision making regarding task offloading for each generated task at active mobile devices and CPU frequency adjustments at discrete time slots. To navigate the intricate landscape of the extensive discrete action space, we design an efficient multi-agent DRL learning algorithm named MAOC, which is based on MAPPO, to minimize the average task delay by dynamically determining task-offloading decisions and CPU frequencies. MAOC operates within a centralized training with decentralized execution (CTDE) framework, empowering individual mobile devices to make decisions autonomously based on their unique system states. Experimental results demonstrate its swift convergence and operational efficiency, and it outperforms other baseline algorithms.

Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network Management.

The ISP (Internet Service Provider) industry relies heavily on internet traffic forecasting (ITF) for long-term business strategy planning and proactive network management. Effective ITF frameworks are necessary to manage these networks and prevent network congestion and over-provisioning. This study introduces an ITF model designed for proactive network management. It innovatively combines outlier detection and mitigation techniques with advanced gradient descent and boosting algorithms, including Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), CatBoost Regressor (CBR), and Stochastic Gradient Descent (SGD). In contrast to traditional methods that rely on synthetic datasets, our model addresses the problems caused by real aberrant ISP traffic data. We evaluated our model across varying forecast horizons-six, nine, and twelve steps-demonstrating its adaptability and superior predictive accuracy compared to traditional forecasting models. The integration of the outlier detection and mitigation module significantly enhances the model's performance, ensuring robust and accurate predictions even in the presence of data volatility and anomalies. To guarantee that our suggested model works in real-world situations, our research is based on an extensive experimental setup that uses real internet traffic monitoring from high-speed ISP networks.

Secure memory adaptive event‐triggered filter design of nonlinear network system under hybrid network attacks with application to tunnel diode circuit

AbstractThis article investigates the problem of secure memory adaptive event‐triggered filter design for nonlinear network systems under hybrid network attacks. First, the introduction of memory adaptive event‐triggered mechanism and numerical quantization is employed to enhance system performance, optimize network channel utilization, and prevent network congestion. Second, we consider the impact of deceptive attacks and DoS attacks occurring in the network channel on data transmission, which form the hybrid network attacks studied in this article. Subsequently, based on the established error model, we derive sufficient conditions for the mean‐square exponential stability of the system under a given performance index using Lyapunov–Krasovskii function and linear matrix inequality (LMI) techniques. Furthermore, the filter parameters are determined using the LMI method. Finally, we illustrate the feasibility and generality of the conclusions through numerical simulation examples and a tunnel diode circuit system.

A New WRR Algorithm for an Efficient Load Balancing System in IoT Networks under SDN

The Internet of Things (IoT) connects various smart objects and manages a vast network using diverse technologies, which present numerous challenges. Software-defined networking (SDN) is a system that addresses the challenges of traditional networks and ensures the centralized configuration of network entities to manage network integrity. Furthermore, the uneven distribution of IoT network load results in the depletion of IoT device resources. To address this issue, traffic must be distributed equally, requiring efficient load balancing to be ensured. This requires the development of an efficient architecture for IoT networks. The main goal of this paper is to propose a novel architecture that leverages the potential of SDN, the clustering technique, and a new weighted round-robin (N-WRR) protocol. The objective of this architecture is to achieve load balancing, which is a crucial aspect in the development of IoT networks as it ensures the network’s efficiency. Furthermore, to prevent network congestion and ensure efficient data flow by redistributing traffic from overloaded paths to less burdened ones. The simulation results demonstrate that our N-WRR algorithm achieves highly efficient load balancing compared to the simple weighted round-robin (WRR), and without the application of any load balancing method. Furthermore, our proposed approach enhances throughput, data transfer, and bandwidth availability. This results in an increase in processed requests.

Model Predictive Control Strategies for Load-Balancing in Content-based Publish-Subscribe Networks*

Publish-subscribe messaging is a popular asynchronous information-dissemination pattern well-suited for use in distributed systems. Several publish-subscribe implementations rely on intermediate nodes, termed brokers, to route information from publishers to subscribers. Brokers serve as intermediaries that decouple publishers and subscribers. Managing the traffic load handled by the brokers is critical for preventing network congestion and long message delivery delays. This paper proposes global and local strategies for managing the allocation of topics to brokers based on energy consumption and load balancing considerations. Our topic-to-broker assignment strategies are developed within a model predictive control framework that seeks to minimize energy over-consumption, balance the message load handled by each broker, and minimize the number of topic reassignments to reduce control messaging overhead. Numerical experiments for a simulated environment are used to illustrate the performance of the proposed strategies.

P4SQA: A P4 Switch-Based QoS Assurance Mechanism for SDN

Data center networks include a wide variety of service categories, which leads to a complex network topology. As a result, the QoS of different service flows cannot be guaranteed. In this paper, we propose a QoS assurance mechanism based on P4 switches (P4SQA) to make full use of network resources. P4SQA includes two functions: in-network traffic classification and in-network queue management. To reduce the communication overhead and computational pressure on the Software Defined Network (SDN) controller, we offload part of the neural computation from the control plane to the data plane. A fully connected neural network-based traffic classification method is deployed in the programmable data plane to classify different traffic types. In addition, the optimized active queue management algorithm CoDeL is integrated into the P4 switch (PCoDeL) to prevent network congestion while guaranteeing QoS more effectively. The evaluation results show that traffic classification accuracy has improved, especially in terms of the F1_score, which reached 0.97, significantly better than other state-of-the-art methods. Moreover, compared to traditional queue management algorithms, PCoDeL can improve network throughput and reduce transmission delay, which verifies the effectiveness and superiority of P4SQA in ensuring QoS.

Traffic prediction in SDN for explainable QoS using deep learning approach

The radical increase of multimedia applications such as voice over Internet protocol (VOIP), image processing, and video-based applications require better quality of service (QoS). Therefore, traffic Predicting and explaining the prediction models is essential. However, elephant flows from those applications still needs to be improved to satisfy Internet users. Elephant flows lead to network congestion, resulting in packet loss, delay and inadequate QoS delivery. Recently, deep learning models become a good alternative for real-time traffic management. This research aims to design a traffic predicting model that can identify elephant flows to prevent network congestion in advance. Thus, we are motivated to develop elephant flow prediction models and explain those models explicitly for network administrators’ use in the SDN network. H2O, Deep Autoencoder, and autoML predicting algorithms, including XGBoost, GBM and GDF, were employed to develop the proposed model. The performance of Elephant flow prediction models scored 99.97%, 99.99%, and 100% in validation accuracy of under construction error of 0.0003952, 0.001697, and 0.00000408 using XGBoost, GBM, and GDF algorithms respectively. The models were also explicitly explained using Explainable Artificial Intelligence. Accordingly, packet size and byte size attributes need much attention to detect elephant flows.

Collaborative computation offloading for scheduling emergency tasks in SDN-based mobile edge computing networks

Due to the growing demand for time-intensive tasks, several tasks coexist, some of which last for a long time or only occur once in a while, such as emergencies. Emergencies such as natural disasters, accidents or medical emergencies require immediate attention and often demand real-time execution. Such tasks are the most difficult to plan for and foresee. Planning, resource allocation, and clear communication between all involved parties and network layers are crucial for scheduling emergency tasks. In the extensive literature, when an emergency task triggers, it will be handled like any other regular task. If the deadline is missed, a catastrophe will ensue. They ignored the impact of insufficient mobile edge computing (MEC) capacity and network congestion, which might result in execution delays. Collaborative offloading, which involves distributing computation tasks across multiple servers, shows promise in enhancing MEC network performance, particularly in emergency scenarios. In this context, efficient task scheduling plays a vital role in minimizing the total execution time of regular tasks while meeting the deadlines of emergency tasks. Our proposed scheme utilizes a collaborative offloading approach to schedule emergency tasks in MEC networks, leveraging the computing capacity of edge-deployed MEC servers. By utilizing the software-defined networking (SDN)’s global view of the network, task requests are collected and allocated to suitable MEC servers capable of meeting the demands. To address key challenges, our scheme propose four scheduling algorithms to address the following issues: (i) ensuring tasks are assigned to the nearest MEC server with sufficient computational resources, (ii) controlling a threshold to prevent network congestion, (iii) selecting an optimal collaborative MEC server for executing overloaded tasks based on collaborative offloading decisions, and (iv) allocating resources for emergency tasks when triggered to meet urgent deadlines by stealing resources from regular tasks without compromising their deadlines. Extensive simulations were conducted to assess the effectiveness of the proposed scheme. The results clearly illustrate its enhanced performance in terms of the total execution time of regular tasks and the ability to meet deadlines for emergency tasks.

Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory, and Autoencoder

Currently, the widespread of real-time applications such as VoIP and videos-based applications require more data rates and reduced latency to ensure better quality of service (QoS). A well-designed traffic classification mechanism plays a major role for good QoS provision and network security verification. Port-based approaches and deep packet inspection (DPI) techniques have been used to classify and analyze network traffic flows. However, none of these methods can cope with the rapid growth of network traffic due to the increasing number of Internet users and the growth of real-time applications. As a result, these methods lead to network congestion, resulting in packet loss, delay, and inadequate QoS delivery. Recently, a deep learning approach has been explored to address the time-consumption and impracticality gaps of the abovementioned methods and maintain existing and future traffics of real-time applications. The aim of this research is then to design a dynamic traffic classifier that can detect elephant flows to prevent network congestion. Thus, we are motivated to provide efficient bandwidth and fast transmission requirements to many Internet users using SDN capability and the potential of deep learning. Specifically, DNN, CNN, LSTM, and Deep autoencoder are used to build elephant detection models that achieve an average accuracy of 99.12%, 98.17%, and 98.78%, respectively. Deep autoencoder is also one of the promising algorithms that do not require human class labeler. It achieves an accuracy of 97.95% with a loss of 0.13. Since the loss value is closer to zero, the performance of the model is good. Therefore, the study has a great importance to Internet service providers, Internet subscribers, as well as for future researchers in this area.

Flexible Resource Scheduling for Software-Defined Cloud Manufacturing with Edge Computing

This research focuses on the realization of rapid reconfiguration in a cloud manufacturing environment to enable flexible resource scheduling, fulfill the resource potential and respond to various changes. Therefore, this paper first proposes a new cloud and software-defined networking (SDN)-based manufacturing model named software-defined cloud manufacturing (SDCM), which transfers the control logic from automation hard resources to the software. This shift is of significance because the software can function as the “brain” of the manufacturing system and can be easily changed or updated to support fast system reconfiguration, operation, and evolution. Subsequently, edge computing is introduced to complement the cloud with computation and storage capabilities near the end things. Another key issue is to manage the critical network congestion caused by the transmission of a large amount of Internet of Things (IoT) data with different quality of service (QoS) values such as latency. Based on the virtualization and flexible networking ability of the SDCM, we formalize the time-sensitive data traffic control problem of a set of complex manufacturing tasks, considering subtask allocation and data routing path selection. To solve this optimization problem, an approach integrating the genetic algorithm (GA), Dijkstra’s shortest path algorithm, and a queuing algorithm is proposed. Results of experiments show that the proposed method can efficiently prevent network congestion and reduce the total communication latency in the SDCM.

Design and implementation of geographic information system with mobile terminal and 5G network

AbstractRemote insurance mainly completes quotation, insurance application and payment through the PC terminal and mobile terminal, and the premium directly enters the insurance account. This article takes remote insurance as an example. First, it analyses the requirements of the remote insurance and insurance system, and then collects data from the mobile terminal. The system geographic information system (GIS) server subsystem and GIS information management subsystem are designed in three aspects. GPS data and video image data are collected and processed in the data acquisition subsystem, forwarded by the GIS server, and sent to the GIS information management subsystem. Geographic information system information management realises online loss determination through basic information query, geographic location identification, scheduling, path planning, real‐time tracking, image information etc. on the mobile terminal, combined with insurance and insurance business. In terms of implementation, the GIS information management platform and Google Maps are used to realise the monitoring, scheduling and path planning of the mobile terminal GIS online platform. And conduct experimental research on the content‐centric 5G structure. The results show that the transmission interference time of the content‐centric network‐based 5G structure is 390 ms, which is much lower than the 800 ms demanded for GSM‐R system. It proves that the content‐centric network‐based 5G structure has good mobile performance; the test of the content‐centric network verifies that the content‐centric network has the advantages of balancing network load and preventing network congestion; meanwhile, the test of the proposed The functions and performance of the GIS system proposed in this paper are tested. The results meet the user requirements.

Integration of Edge Computing and Blockchain for Provision of Data Fusion and Secure Big Data Analysis for Internet of Things

Intelligent computing provides efficient, real-time, and secure data analysis services for the Internet of Things (IoT). As the number of IoT devices increases, IoT generates massive, diverse, and multisourcing datasets that can be used to improve IoT services further. Models trained by intelligent computing from a single system or sensor are often not global, and sending all data directly to the computing platform wastes network bandwidth and may cause network congestion and even privacy leakage. To ensure IoT applications’ quality of service and privacy, we propose a framework that integrates edge computing and blockchain to provide lightweight data fusion and secure data analysis for IoT. We propose a lightweight data fusion method that can reduce the amount of data at the node level and prevent network congestion and bandwidth waste. Furthermore, we propose a hierarchical fuzzy hashing method to check and locate anomalies of IoT machine learning models to ensure the validity of IoT intelligent computing and the security of sensitive data. Finally, we demonstrate the effectiveness of the method proposed in this paper through experiments.

A Hybrid Deep Learning Framework for Network Flow Forecasting of Power Grid Enterprise

With the expansion of the digital business line, the network flow behind the digital power grid is also exploding. To prevent network congestion, this article proposes a novel network flow forecasting model, which is composed of variational mode decomposition (VMD), GRU‐xgboost block, and a forecasting adjustment block, to grasp the changing patterns and trends of network flow in advance, and to formulate reasonable and effective flow management strategies and meet the requirements of users for network service quality. The network flow series in power grid enterprise always contain complex patterns and outliers, and VMD is applied to adaptively process complex net flow time series into several subseries with simpler patterns. A GRU‐xgboost block is designed to reconstruct the features of historical series. Then, xgboost model is applied to generate predictions for all decomposed subsignals. For the final predictions, we design a forecasting adjustment block to further remove the influence of random noise. Finally, the empirical results show the superior performance of the proposed model on network flow forecasting task.

Improving Cached Data Offloading Optimization Based on Enhanced Hybrid Ant Colony Genetic Algorithm

The data offloading mechanism is one of the critical strategies needed on edge networks to help cloud computing network performance in serving user data requests. This strategy should be optimized to prevent network congestion. The main problem of this strategy is how to assess the priority of cached data so that the cache memory buffer capacity can be optimized. In this paper, we modeled the cached data offloading strategy using the Knapsack Problem 0/1 (KP01) approach. Several researchers proposed a meta-heuristic algorithm to solve cached data offloading using the KP01 approach. Meta-heuristic algorithms require a reliable solution selection method to find the global optimal solution. However, some studies still use the roulette wheel selection method to provide a set of solutions. The RWS method has a weakness of imbalance the particle fitness with its cumulative probability. Therefore, it is difficult to find the global optimal solution. This study proposed a nested-Roulette Wheel Selection (nRWS) method on hybrid Ant Colony Optimization (ACO) and Genetic Algorithm (GA) to address the cached data offloading optimization using the KP01 approach. The simulation results show that the proposed nRWS method is able to find the global optimal solution in terms of the value of the objective function and hit ratio which is superior to previous studies.

False Event Message Detection Robust to Burst Attacks in Wireless Sensor Networks

A sensor device can be used to detect target events at low cost. Moreover, there is a significant risk of sensor nodes being compromised or captured within large wireless sensor networks (WSNs). The transmission of valid event messages to users by a WSN can be hindered by network congestion due to false event messages in a compromised node. Several methods are available for detecting false event messages. However, they rely on long bits of authentication codes and hence do not provide a fundamental solution to prevent network congestion. In the proposed method, various hashing vectors, which are space-efficient data structures that can determine whether the given data are an element of a set, are created based on the correct combination of authentication codes and placed in each node in advance. An event message contains an XOR of the authentication codes, and each node verifies it based on its hashing vector. If a node is acquired illegally, the information of the hashing vector and the XOR information of the authentication codes assigned to the correct event message is compromised, so we propose an algorithm to update the information securely. Compared to existing research, the number of hops required to detect a false event message increases by only about one hop, but the amount of traffic that a malicious node can generate can be reduced by about 60% or more. In other words, the proposed method effectively reduces the amount of traffic an attacker can generate with false event messages, which also reduces the overall network congestion.

Investigation of algorithms for prevention of congestion in computer networks at level of TCP protocol using Mininet network emulator

This article examines the performance and values of network parameters when applying different algorithms to control network congestion. The Mininet network emulator is used, which creates software-defined networks that give us the opportunity to conduct various experiments with the network and manipulate its parameters. The conclusion is made for the best algorithm to prevent network congestion. An estimate of the relative value of the error was made, which shows that in Mininet deviations in the values can be observed with a larger number of hosts in the network.

HOPASS: A two-layer control framework for bandwidth and delay guarantee in datacenters

In data center networks (DCNs), flows with different objectives coexist and compete for limited network resources (such as bandwidth and buffer space). Without harmonious resource planning, chaotic competition among these flows would lead to severe performance degradation. Furthermore, low latency is critical for many emerging applications such as augmented reality(AR), virtual reality(VR) and telepresence, making the network control problem even more challenging. To address the above issues, this paper novelly proposes a receiver-driven two-layer control framework called HOPASS, which incorporates a slow control layer and a fast control layer to strike a balance among multiple network sharing objectives and achieve low latency. The slow control layer ensures bandwidth guarantee in an aggregated flow level by solving a multi-objective network utility maximization (NUM) problem using an online learning approach. Then the results will be dispatched to the data plane by configuring weights in the switches with weighted fair queue functionality. Under the configuration dictated by the slow control layer, the fast control layer leverages the token packets sent by receivers to dynamically probe and reserve network capacity, so that it can proactively prevent network congestion and guarantee low latency data delivery. To evaluate the proposed framework, we have implemented HOPASS in ns-3 and conduct extensive experiments under various network scenarios. The simulation results show that HOPASS achieves near-optimal performance in terms of bandwidth allocation in multi-objective scenarios and also guarantees low end-to-end delay. Moreover, it outperforms DCTCP and NewReno in terms of average bandwidth utilization and global total network utility at the aggregated flow level. Therefore, we conclude that HOPASS provides an effective framework for DCNs when considering both multi-objective optimization and a low latency network.

Intersection-Based V2X Routing via Reinforcement Learning in Vehicular Ad Hoc Networks

With the rapid development of the Internet of vehicles (IoV), routing in vehicular ad hoc networks (VANETs) has become a popular research topic. Due to the features of the dynamic network structure, constraints of road topology and variable states of vehicle nodes, VANET routing protocols face many challenges, including intermittent connectivity, large delay and high communication overhead. Location-based geographic routing is the most suitable method for VANETs, and such routing performs well on paths with an appropriate vehicle density and network load. We propose an intersection-based V2X routing protocol that includes a learning routing strategy based on historical traffic flows via Q-learning and monitoring real-time network status. The hierarchical routing protocol consists of two parts: a multidimensional Q-table, which is established to select the optimal road segments for packet forwarding at intersections; and an improved greedy strategy, which is implemented to select the optimal relays on paths. The monitoring models can detect network load and adjust routing decisions in a timely manner to prevent network congestion. This method minimizes the communication overhead and latency and ensures reliable transmission of packets. We compare our algorithm with three benchmark algorithms in an extensive simulation. The results show that our algorithm outperforms the existing methods in terms of network performance, including packet delivery ratio, end-to-end delay, and communication overhead.

Research on QoS routing method based on NSGAII in SDN

Software Defined Network (SDN) makes routing decisions according to the shortest path. This routing scheme does not consider link information such as link residual bandwidth, forwarding delay, packet loss rate and so on. In the case of large data flow, it is easy to cause network congestion, thus reducing the network quality of service assurance. For this reason, using the ability of SDN network controller to monitor the underlying network information and control the route forwarding, we try to set multiple objective functions according to different QoS parameters, and use the non-dominated sorting multi-objective optimization genetic algorithm (NSGA II) based on the elite strategy to make routing decisions. Compared with other routing algorithms, the proposed multi-objective optimization genetic algorithm reduces forwarding delay and packet loss rate, so can effectively prevent network congestion.