Balanced Traffic Distribution Research Articles

A Link Status-Based Multipath Scheduling Scheme on Network Nodes

Traditional internet protocol (IP) networks, adhering to a “best-effort” service model, typically utilize shortest-path routing for data transmission. Nevertheless, this methodology encounters limitations, especially considering the increasing demands for both high reliability and high bandwidth. These demands reveal shortcomings in this routing strategy, notably its inefficient bandwidth utilization and fault recovery capabilities. The method of multipath transmission has been extensively researched as a solution to these challenges. With the emergence of innovative Internet architectures, notably information-centric networking (ICN), network nodes have gained enhanced capabilities, opening new avenues for multipath transmission design. This paper introduces a multipath scheduling approach for network nodes, capitalizing on the advanced features of these modern nodes. It reimagines the conventional next-hop node as a group of potential next-hop nodes based on both global and local routing strategies and assigns traffic shares to each node within this group for balanced traffic distribution. Network nodes are configured to periodically review and adjust traffic shares according to the link statuses. If scheduling cannot be completed within the set, feedback is sent to upstream nodes. Simulations demonstrate that this approach effectively leverages network path variety, improves bandwidth usage and throughput, and minimizes average data transmission time.

Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy.

With the expansion of user scale in LEO satellite networks, unbalanced regional load and bursty network traffic lead to the problem of load disequilibrium. A distributed hops-based back-pressure (DHBP) routing is proposed. DHBP theoretically derives a fast solution for the minimum end-to-end propagation hops between satellite nodes in inclined-orbit LEO satellite networks; hence, link weights are determined based on remaining hops between the next hop and destination satellites. In order to control the number of available retransmission paths, the permitted propagation region is restricted to a rectangular region consisting of source-destination nodes to reduce the propagation cost. Finally, DHBP is designed distributedly, to realize a dynamic selection of the shortest link with low congestion and balanced traffic distribution without obtaining the whole network topology. Network simulation results demonstrate that DHBP has higher throughput and lower delay under high load conditions compared with state-of-the-art routing protocols.

Load-Balanced Collaborative Offloading for LEO Satellite Networks

Low earth orbit (LEO) satellite networks have become one of the hot research areas as an essential part of satellite communication networks. The dynamic topology and unbalanced traffic demand may lead to inter-satellite link congestion, thus, improving network load balancing performance is one of the key issues to be addressed in LEO satellite networks. We propose a load-balanced collaborative offloading (LBCO) strategy to achieve a balanced traffic distribution in LEO satellite networks. LBCO strategy consists of two algorithms, namely, channel-aware gradient fair association (CAGFA) algorithm and inter-satellite links collaborative offloading (ISLCO) algorithm. The CAGFA algorithm aims to maximize the aggregate weighted utility, and the ISLCO algorithm aims to achieve the traffic offloading and download observation data from the LEO satellite network. Specifically, we first determine the actual downloading satellite set and neighboring satellite set by constructing an earth station (ES) time-share graph and a space-time topology graph. Then, the LBCO strategy uses the CAGFA algorithm to obtain the optimal satellite terminal association indicator and the load of downloading satellites. Finally, the ISLCO algorithm is proposed to achieve proportional offloading of traffic among the neighboring satellites and download massive observation data. Simulations show that the proposed CAGFA algorithm improves the weighted utility by 3.3% and the convergence by 47.6% compared with the benchmark stochastic gradient descent-based association (SGDA) algorithm. We also validate the performance of the LBCO strategy by data download throughput, which performs better than other benchmark algorithms under three different load scenarios.

Dynamic Load Balancing for Congestion Avoidance using Adaptive Neuro-Fuzzy Inference System in Mobile Communication Network

Network congestion is one of the key challenges to mobile communication services. This is because of rapid and constant growth in the mobile subscriber base that has led to an increase in network traffic. The more the network traffic the more economic opportunity from the business perspective for the operators and the challenges it poses on the network. If not followed by network capacity expansion it will defiantly pose a serious network issue like network congestion that leads to call drop and poor Quality of Service (QoS). Many research works adopted handover mechanism as a means of reducing congestion in a network. However, the decision on when to initiate a handover, which cell to receive the Mobile Station (MS) and how to ensure that the QoS requirements are maintained are the paramount research questions that must be resolved. Therefore, a handover process using soft computing in the Adaptive Neuro-Fuzzy Inference System (ANFIS) to ensure balanced traffic load distribution in-network and reduce the probability of congestion is proposed. The study allows simultaneous evaluation of three major network parameters Received Signal Strength (RSS), Received Signal Quality (RxQual) and network traffic using ANFIS to improve system performance. The results show that when the Hysteresis value approaches 6 the handover processes are triggered. The hysteresis value of the concerned MS with neighbouring cells is considered to determine the most suitable cell to handover. This work will be able to achieve dynamic load balancing, congestion avoidance and avoided the ‘ping pong’ effect that is often an issue with handover with less computation. At the end customer satisfaction will be achieve Quality of experience (QoE).

A Balanced Algorithm for In-City Parking Allocation: A Case Study of Al Madinah City

Parking in heavily populated areas has been considered one of the main challenges in the transportation systems for the past two decades given the limited parking resources, especially in city centres. Drivers often waste long periods of time hunting for an empty parking spot, which causes congestion and consumes energy during the process. Thus, finding an optimal parking spot depends on several factors such as street traffic congestion, trip distance/time, the availability of a parking spot, the waiting time on the lot gate, and the parking fees. Designing a parking spot allocation algorithm that takes those factors into account is crucial for an efficient and high-availability parking service. We propose a smart routing and parking algorithm to allocate an optimal parking space given the aforementioned limiting factors. This algorithm supports choosing the appropriate travel route and parking lot while considering the real-time street traffic and candidate parking lots. A multi-objective function is introduced, with varying weights of the five factors to produce the optimal parking spot with the least congested route while achieving a balanced utilization for candidate parking lots and a balanced traffic distribution. A queueing model is also developed to investigate the availability rate in candidate parking lots while considering the arrival rate, departure rate, and the lot capacity. To evaluate the performance of the proposed algorithm, simulation scenarios have been performed for different cases of high and low traffic intensity rates. We have tested the algorithm on in-city parking facility in the city of Al Madinah as a case study. The results show that the proposed algorithm is effective in achieving a balanced utilization of the parking lots, reducing traffic congestion rates on all routes to candidate parking lots, and minimizing the driving time to the assigned parking spot. Additionally, the proposed algorithm outperforms the MADM algorithm in terms of the selected three metrics for the five periods.

Dynamic Big-Data Broadcast in Fat-Tree Data Center Networks With Mobile IoT Devices

In this paper, we study the problem of throughput and delay-optimal dynamic big-data broadcast in fat-tree data center networks (DCNs) in the presence of mobile Internet-of-Things (IoT) devices, where one of the IoT devices acts as a source node. In existing literature, researchers studied that a balanced traffic distribution in DCNs is a NP-hard problem. With the integration of heterogeneous IoT devices in DCNs, the difficulty in achieving a balanced traffic distribution increases significantly. Hence, there is a need to design a throughput and delay-optimal big-data broadcast scheme in DCNs in the presence of IoT devices. In this paper, we propose a dynamic big-data broadcasting scheme, named D2B, using a single-leader-multiple-follower Stackelberg game for solving the aforementioned problem. Here, each switch acts as the leader, and the IoT devices act as the followers. We consider that the source node broadcasts the generated data in real time. We represent bandwidth distribution as a pseudo-Cournot competition, where each follower decides the optimal downloading bandwidth. The existence of the generalized Nash–Stackelberg equilibrium for D2B is evaluated theoretically. We observed that using D2B, the network throughput increases by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{55.32}\%$</tex-math></inline-formula> , while ensuring at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{33}\%$</tex-math></inline-formula> increase in the average bandwidth allocation per IoT device, and the overall delay in broadcasting is reduced.

Agent-Based Load Balancing and Qos Routing for Leo Satellite Networks

This paper proposes and evaluates the Agent-based load balancing and QoS routing (ALBQR) in the low earth orbit (LEO) satellite networks. Two kinds of agents are designed. Stationary agents located at the satellites calculate link cost using the sum of propagation and queuing delays, employ FARIMA model to predict the traffic of satellite node, modify the link cost based on the predicted traffic. Mobile agents migrate among satellites to gather link status information, notify stationary agents to update routing tables and reserve resource. Mobile agents carry user’s delay, bandwidth and packet loss rate, explore the routing path of satisfy the lowest path cost and QoS constraints. Through simulations on an Iridium satellite network, ALBQR is shown to achieve a balanced traffic distribution among satellites, provide better throughput, decrease packet loss and average end-to-end delay.

LOAD BALANCING METHOD IN THE CORE NETWORK OF MOBILE OPERATORS

The paper analyzes resource reservation algorithms and methods of traffic flows redistribution in the backbone mobile operator network. In these algorithms information is used about channels capacity and reliability that allows to increase the intensity of network segments, and thus the effective network capacity in general. Since requirements of data transfer for each service in network are different, so it should be used its network resource reservation algorithm for each service. Network traffic model was taken for consideration based on queuing system M/M/1-model. In the work, queuing system is considered as a prototype system for unloading the mobile operator core network. By using this system network load was investigated, and method of load balancing was developed in LTE transport network. The main differences between this method and most existing one is that it is dynamic, and allows to distribute load depending on the channel conditions between alternative routes with different value. As a result, the developed load balancing method in the mobile operator core network allows to achieve more balanced traffic distribution across the network and therefore higher performance.

Traffic scheduling mechanism based on interference avoidance for Meter Data Collection in wireless smart grid communication networks

Meter Data Collection Building Area Network (MDCBAN) deployed in high rises is playing an increasingly important role in wireless multi-hop smart grid meter data collection. Recently, increasingly numerous application layer data traffic makes MDCBAN be facing serious communication pressure. In addition, large density of meter data collection devices scattered in the limited geographical space of high rises results in obvious communication interference. To solve these problems, a traffic scheduling mechanism based on interference avoidance for meter data collection in MDCBAN is proposed. Firstly, the characteristics of network topology are analyzed and the corresponding traffic distribution model is proposed. Next, a wireless multi-channel selection scheme for different Floor Gateways and a single-channel time unit assignment scheme for data collection devices in the same Floor Network are proposed to avoid interference. At last, a data balanced traffic scheduling algorithm is proposed. Simulation results show that balanced traffic distribution and highly efficient and reliable data transmission can be achieved on the basis of effective interference avoidance between data collection devices.

Towards prolonged lifetime for deployed WSNs in outdoor environment monitoring

Recently, Wireless Sensor Networks (WSNs) emerged as a powerful and cost-efficient solution for unattended Outdoor Environment Monitoring (OEM) applications. These applications impose certain challenges on WSN deployment, including 3-Dimensional (3-D) settings, harsh operational conditions, and limited energy resources. To prolong lifetime of the deployed WSN, while mitigating the effects of these challenges, we propose the use of Relay Nodes (RNs) in addition to Sensor Nodes (SNs) in a distributed manner. While RNs facilitate reaching distant destinations, SNs can reserve their limited energy resources for sensing and data gathering. In addition, Mobile RNs (MRNs), which is a set of RNs capable of being reallocated (i.e. mobilized) at any point within the network lifetime, can be used to overcome possible link/node failure caused by the harsh conditions. It can also guarantee minimal energy consumption through imposing a balanced traffic distribution. This article proposes a 3-D grid-based deployment for heterogeneous WSNs (consisting of SNs, RNs, and MRNs). The problem is cast as a Mixed Integer Linear Program (MILP) optimization problem with the objective of maximizing the network lifetime while maintaining certain levels of fault-tolerance and cost-efficiency. Moreover, an Upper Bound (UB) on the deployed WSN lifetime, given that there are no unexpected node/link failures, has been driven. Based on practical/harsh experimental settings in OEM, intensive simulations show that the proposed grid-based deployment scheme can achieve an average of the expected UB.