Bandwidth Allocation In Networks Research Articles

Proactive Bandwidth Allocation for V2X Networks With Multi-Attentional Deep Graph Learning

Proactive Bandwidth Allocation for V2X Networks With Multi-Attentional Deep Graph Learning

A cloud-edge collaborative task scheduling method based on model segmentation

With the continuous development and combined application of cloud computing and artificial intelligence, some new methods have emerged to reduce task execution time for training neural network models in a cloud-edge collaborative environment. The most attractive method is neural network model segmentation. However, many factors affect the segmentation point, such as resource allocation, system energy consumption, load balancing, and network Bandwidth allocation. Some segmentation methods consider the shortest task execution time, which ignores the utilization of resources at the edge and can result in resource waste. Additionally, these factors are difficult to measure, which presents a challenge in calculating the best segmentation point to achieve the goal of maximum resource utilization and minimum task execution time. To solve this problem, this paper proposes a cloud-edge collaborative task scheduling method based on model segmentation (CECMS). This method first analyzes the factors affecting the segmentation point of the model and then obtains accurate factors that affect the segmentation point calculation through the pre-execution method. Furthermore, a multi-objective solution algorithm is improved to calculate the optimal model segmentation point. And tasks are separately offloaded to the edge and cloud based on the optimal model segmentation point. Finally, the experiments are conducted to verify the effectiveness of this method. Finally, the effectiveness of the CECMS method was verified through simulation experiments. Compared with the Dynamic Adaptive DNN Surgery (DADS) method and an adaptive DNN inference acceleration framework algorithm with end–edge–cloud collaborative computing algorithm (ADC), CECMS achieves the same effectiveness as DADS and ADC in optimizing task execution time by comprehensively considering the utilization of edge resources and minimizing task execution time, while also effectively ensuring resource utilization.

Solution of the Simultaneous Routing and Bandwidth Allocation Problem in Energy-Aware Networks Using Augmented Lagrangian-Based Algorithms and Decomposition

We discuss several algorithms for solving a network optimization problem of simultaneous routing and bandwidth allocation in green networks in a decomposed way, based on the augmented Lagrangian. The problem is difficult due to the nonconvexity caused by binary routing variables. The chosen algorithms, which are several versions of the Multiplier Method, including the Alternating Direction Method of Multipliers (ADMM), have been implemented in Python and tested on several networks’ data. We derive theoretical formulations for the inequality constraints of the Bertsekas, Tatjewski and SALA methods, formulated originally for problems with equality constraints. We also introduce some modifications to the Bertsekas and Tatjewski methods, without which they do not work for an MINLP problem. The final comparison of the performance of these algorithms shows a significant advantage of the augmented Lagrangian algorithms, using decomposition for big problems. In our particular case of the simultaneous routing and bandwidth allocation problem, these algorithms seem to be the best choice.

Efficient Approximation Methods for Lexicographic Max-Min Optimization

Lexicographic max-min (LMM) optimization is of considerable importance in many fairness-oriented applications. LMM problems can be reformulated in a way that allows to solve them by applying the standard lexicographic maximization algorithm. However, the reformulation introduces a large number of auxiliary variables and linear constraints, making the process computationally complex. In this paper, two approximation schemes for such a reformulation are presented, resulting in problem size reduction and significant performance gains. Their influence on the quality of the solution is shown in a series of computational experiments concerned with the fair network dimensioning and bandwidth allocation problem.

Joint Optimization of QoE and Fairness for Adaptive Video Streaming in Heterogeneous Mobile Environments

The rapid growth of mobile video traffic and user demand poses a more stringent requirement for efficient bandwidth allocation in mobile networks where multiple users may share a bottleneck link. This provides content providers an opportunity to jointly optimize multiple users’ experiences but users often suffer short connection durations and frequent handoffs because of their high mobility. In this paper, we propose an end-to-end scheme, VSiM, for supporting mobile video streaming applications in heterogeneous wireless networks. The key idea is allocating bottleneck bandwidth among multiple users based on their mobility profiles and Quality of Experience (QoE)-related knowledge to achieve max-min QoE fairness. Besides, the QoE of buffer-sensitive clients is further improved by the novel server push strategy based on HTTP/3 protocol without affecting the existing bandwidth allocation approach or sacrificing other clients’ view quality. VSiM is lightweight and easy to deploy in the real world without touching the underlying network infrastructure. We evaluated VSiM experimentally in both simulations and a lab testbed on top of the HTTP/3 protocol. We find that the clients’ QoE fairness of VSiM achieves more than 40% improvement compared with state-of-the-art solutions, i.e., the viewing quality of clients in VSiM can be improved from 720p to 1080p in resolution. Meanwhile, VSiM provides about 20% improvement of average QoE.

Joint User Association and Bandwidth Allocation in Semantic Communication Networks

Joint User Association and Bandwidth Allocation in Semantic Communication Networks

Securing Pervasive Computing Networks: Enhancing Network Security via Network Virtualization in Wireless Communications Infrastructure

The seamless integration of technology for computing into everyday items and environments is known as pervasive computing. To protect against cyber threats and vulnerabilities, robust security mechanisms are necessary. Conventional security measures, including gateways and the use of encryption, may not be sufficient to address the unique challenges encountered in ubiquitous computing systems. But these techniques are still vital. In addition to the variety of devices, resource limitations, mobility needs, and the possibility of large-scale distributed attacks, these obstacles also include the potential for attack. Network virtualization, that abstracts and separates network facilities and functions, is a promising way to increasing security in pervasive computing deployments: it abstracts and isolates network resources and processes. Wireless communication play a significant part in the development of a digital infrastructure that is both resilient and trustworthy. The processes of dynamic resource allocation, isolation, and management of network bandwidth are made possible through the utilization of virtualization, leads to the proposal of Secure Wireless Virtual Resource Allocation and Authentication Algorithm(SWVRA3) to make the abstraction of the network's physical resources into virtualized entities By using network virtualization, pervasive computing applications and services can be secured with logically segregated virtual networks. The cross-contamination and security breaches can be reduced by this separation. Furthermore, flexible configuration, dynamic allocation of resources, and centralized virtual control are allowed by network visualization that improves threat incidence response, enforcement of policies, and security surveillance.

Connectivity Status of Intuitionistic Fuzzy Graphs and Its Applications

The connectivity is one of the crucial parameters of network used to transport network flow, routing problems and bandwidth allocation problems. The increased connectivity makes a network more stable. In this paper, a new parameter called connectivity status of a vertex is introduced in the intuitionistic fuzzy graph. The related definitions and propositions of connectivity status of a vertex are proposed and investigated in an intuitionistic fuzzy graph. Specifically, connectivity status and status sequence are defined and analysed with various examples. After deleting a vertex, we classify the vertices of an intuitionistic fuzzy graph as connectivity status enhancing vertices, connectivity status neutral vertices and connectivity status reducing vertices because of the change of connectivity status. Finally, we establish two algorithms for these concepts and give an application to illustrate feasibility of algorithms.

Fuzzy logic based dynamic wavelength and bandwidth allocation for 5G front haul networks

The Quality of Service (QoS) requirements for 5G are very strict in terms of latency of Fifth Generation (5G) traffic. 5G fronthaul networks between the baseband unit and remote radio heads are planned to use Next Generation - Passive Optical Networks 2 (NG-PON2) to carry their traffic. However, these Time Wavelength Division Multiplexing (TWDM) PON based networks also provide service to other traffic such as residential and corporate users. Therefore, it is crucial to provide QoS guarantees to 5G traffic. The main issue includes allocating transmission over multiple wavelengths while maintaining QoS. Although using all the available wavelengths all the time may seem to be the simple solution, each active wavelength increases the cost of operation. To solve these issues, this paper proposes a technique using fuzzy logic to dynamically allocate wavelength and bandwidth for 5G fronthaul networks. Two types of traffic, i.e., 5G traffic and normal traffic are considered while the problem of Dynamic Wavelength and Bandwidth Allocation (DWBA) is formulated as a queueing theory problem using penalties and a cost function. Penalties are assigned for waiting packets as well as each active wavelength. Then, modeling the number of active wavelengths, the number of packets in the system, the total cost, and the traffic intensity as input fuzzy variables while modeling the change in the number of active wavelengths as the fuzzy output variable. The Mamdani implication is used for the fuzzy inference engine and the height method is used for defuzzification of the output variable. Simulations in MATLAB show that the proposed technique can maintain the latency of 5G traffic lower than the defined threshold while also significantly reducing the number of active wavelengths. A comparison with the existing state-of-the-art techniques shows that the proposed technique results in an improvement of at least 46% and 29% in terms of the average number of active wavelengths and the average latency for 5G traffic, respectively.

Manajemen Penjadwalan Waktu Blokir Akses Internet pada Mikrotik RouterOS

Restricting internet access at certain hours allows for more efficient allocation of network resources and bandwidth. This limitation ensures that internet usage is not excessive, preventing the network from slowing down or being burdened by unproductive activities during critical hours. The research methodology employed involves the analysis of hardware and software requirements, network topology design, and network configuration. The research findings demonstrate the effectiveness of limiting internet access at specific times, supporting remote learning, and enhancing internet usage efficiency. System testing was conducted on Windows 10 and Debian 10, demonstrating the successful implementation of the scheduled blocking system. Although successful in simulation, this study has the potential to provide further benefits in real-world network environments.

Increased Task Execution with a Bandwidth-Aware Hadoop Scheduling Approach

This research presents a novel bandwidth-aware Hadoop scheduling method that addresses the challenge of task scheduling in Hadoop clusters while considering the real-time network conditions. The proposed method involves the establishment of a job time completion model and a mathematical model for a Hadoop scheduling system. Furthermore, it transforms the Hadoop task scheduling problem into an optimization problem to find the task scheduling method that minimizes job completion time. By leveraging Software-Defined Networking (SDN) capabilities, a time slot-based network bandwidth allocation mechanism is introduced to allocate bandwidth fairly across network links. The proposed method also takes into account task locality and network bandwidth availability when allocating computational nodes for individual tasks. Through this approach, the limitations of existing methods, which fail to simultaneously consider global task scheduling and actual network bandwidth availability, are overcome. Experimental evaluations demonstrate the effectiveness of the proposed method in enhancing the performance of Hadoop task scheduling.

Joint offloading design and bandwidth allocation for RIS-aided multiuser MEC networks

This article examines a multiuser intelligent reflecting surface (RIS) aided mobile edge computing (MEC) system, where multiple edge nodes (ENs) with powerful calculating resources at the network can help compute the calculating tasks from the users through wireless channels. We evaluate the system performance by using the performance metric of communication and computing delay. To enhance the system performance by reducing the network delay, we jointly optimize the unpacking design and wireless bandwidth allocation, whereas the task unpacking optimization is solved by using the deep deterministic policy gradient (DDPG) algorithm. As to the bandwidth allocation, we propose three analytical solutions, where criterion I performs an equal bandwidth allocation, criterion II performs the allocation based on the transmission data rate, while criterion III performs the allocation based on the transmission delay. We finally provide simulation results to show that the proposed optimization on the task unpacking and bandwidth allocation is effective in decreasing the network delay.

Collaborative Long-Short Term Bandwidth Allocation for Satellite-Terrestrial Networks

To improve users’ communication experience when they access satellite-terrestrial networks, we propose a long-short term bandwidth allocation strategy for beam-hopping low-earth-orbit satellites. Our strategy consists of two collaboratively functioned parts, the prediction-based long-term (PLT) bandwidth allocation and the trend-based short-term (TST) bandwidth allocation. At the beginning of each long term, the PLT method allocates bandwidth resources according to the maximum traffic volume predicted by Hull moving average (HMA), and the Q-learning assisted TST method adjusts the bandwidth allocation of the last short term with consideration of the traffic trend when necessary. Simulation results demonstrate that the proposed strategy is lightweight and can effectively reduce the blocking ratio and improve the supply-demand ratio of satellite-terrestrial communications.

Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet

The next-generation cellular systems, including fifth-generation cellular systems (5G), are empowered with the recent advances in artificial intelligence (AI) and other recent paradigms. The internet of things (IoT) and the tactile internet are paradigms that can be empowered with AI solutions and integrated with 5G systems to deliver novel services that impact the future. Machine learning technologies (ML) can understand examples of nonlinearity from the environment and are suitable for network traffic prediction. Network traffic prediction is one of the most active research areas that integrates AI with information networks. Traffic prediction is an integral approach to ensure security, reliability, and quality of service (QoS) requirements. Nowadays, it can be used in various applications, such as network monitoring, resource management, congestion control, network bandwidth allocation, network intrusion detection, etc. This paper performs time series prediction for IoT and tactile internet delays, using the k-step-ahead prediction approach with nonlinear autoregressive with external input (NARX)-enabled recurrent neural network (RNN). The ML was trained with four different training functions: Bayesian regularization backpropagation (Trainbr), Levenberg–Marquardt backpropagation (Trainlm), conjugate gradient backpropagation with Fletcher–Reeves updates (Traincgf), and the resilient backpropagation algorithm (Trainrp). The accuracy of the predicted delay was measured using three functions based on ML: mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE).

Energy-Efficient Bandwidth and Power Allocation in Relay-Assisted Multi-Layer Heterogeneous Networks with Energy Harvesting

Energy-Efficient Bandwidth and Power Allocation in Relay-Assisted Multi-Layer Heterogeneous Networks with Energy Harvesting

Polarization-transparent silicon photonic add-drop multiplexer with wideband hitless tuneability

Flexible optical networks require reconfigurable devices with operation on a wavelength range of several tens of nanometers, hitless tuneability (i.e. transparency to other channels during reconfiguration), and polarization independence. All these requirements have not been achieved yet in a single photonic integrated device and this is the reason why the potential of integrated photonics is still largely unexploited in the nodes of optical communication networks. Here we report on a fully-reconfigurable add-drop silicon photonic filter, which can be tuned well beyond the extended C-band (almost 100 nm) in a complete hitless (>35 dB channel isolation) and polarization transparent (1.2 dB polarization dependent loss) way. This achievement is the result of blended strategies applied to the design, calibration, tuning and control of the device. Transmission quality assessment on dual polarization 100 Gbit/s (QPSK) and 200 Gbit/s (16-QAM) signals demonstrates the suitability for dynamic bandwidth allocation in core networks, backhaul networks, intra- and inter-datacenter interconnects.

Probabilistic neural network based efficient bandwidth allocation in wireless sensor networks

In Wireless Sensor Networks the efficient use of resources is the basic challenge. One such significant challenge is the utilization of bandwidth. The bandwidth is the fundamental resource for transmitting data in a network. The bandwidth must be maximum while gathering and forwarding from the sensor nodes to the Base Station. In this paper, Particle Swarm Optimization algorithm is employed for the formation of successful clustering in wireless sensor network and in choosing the cluster head. Clusters are formed by considering the fitness value of every particle. To reduce the workload of every cluster head, a corresponding cluster assistant node, with maximum fitness value, is selected. Also Probabilistic Neural Network approach is used here for allocating maximum bandwidth for the nodes and for dynamic channel assignment of the nodes. The cluster head which has the global best fitness value will transmit the data to the base station. Experimental results show that the proposed method is better than the conventional methods.

Research on Internet Surfing Behavior of College Students Based on Big Data

With the rapid development of computer technology, the network behavior in the era of big data has become an important activity in students' campus life, which is quietly changing students' study and life. However, the behavior of students' network users directly reflects the purpose and demand of users and the state and performance of the network. The author analyzes the data of student users' online logs, and uses Apriori algorithm to analyze its association rules. Summarize the characteristics of student users' online behavior, including online time analysis, user's visit to websites and other user behavior analysis, which is of great significance for network optimization, personalizedj and differentiated design of services, standardized management, rational allocation of network bandwidth, enhancing information security, improving the efficiency of daily management of college counselors and network administrators, and ensuring the stability and efficiency of campus network environment.

Pricing, Bandwidth Allocation, and Service Competition in Heterogeneous Wireless Networks

Small-cells deployed in licensed spectrum can expand wireless service to low mobility users, which potentially reduces the demand for macro-cellular networks with wide-area coverage. Introducing such heterogeneity also makes network resource allocation more complicated. To understand these challenges and tradeoffs we present a two-tier heterogeneous wireless network model with two types of users: mobile users that can only connect to macro-cells; and fixed users that can associate with either macro-cells or small-cells. We study pricing strategies and bandwidth allocation across macro- and small-cells, assuming both monopoly and competitive Service Providers (SPs). For a monopoly SP, we characterize the revenue-maximizing prices and bandwidth allocations. We then consider a competitive scenario, and we show the existence of a unique Nash equilibrium. The possible Nash equilibria for different system parameters are sorted into four categories corresponding to whether or not different SPs assign bandwidth to the macro- and/or small-cells. We also study the allocations that maximize social welfare. For the competitive scenario, we characterize the conditions under which the optimal social welfare is obtained in equilibria as the number of SPs tends to infinity. Case study examples and numerical results illustrate the corresponding pricing and bandwidth allocations.

Joint D2D Assignment, Bandwidth and Power Allocation in Cognitive UAV-Enabled Networks

This paper considers a cognitive communication network, which consists of a flying base station deployed by an unmanned aerial vehicle (UAV) to serve its multiple downlink ground terminals (GTs), and multiple underlaid device-to-device (D2D) users. To support the GTs’ throughput while guaranteeing the quality-of-service for the D2D users, the paper proposes the joint design of D2D assignment, bandwidth, and power allocation. This design task poses a computationally challenging mixed-binary optimization problem, for which a new computational method for its solution is developed. Multiple binary (discrete) constraints for the D2D assignment are equivalently expressed by continuous constraints to leverage systematic processes of continuous optimization. As a result, this problem of mixed-binary optimization is reformulated by an exactly penalized continuous optimization problem, for which an alternating descent algorithm is proposed. Each round of the algorithm invokes two simple convex optimization problems of low computational complexity. The theoretical convergence of the algorithm can be easily proved and the provided numerical results demonstrate its rapid convergence to an optimal solution. Such a cognitive network is even more desirable as it outperforms a non-cognitive network, which uses a partial bandwidth for D2D users only.