Search Results (31)

Network flow task management involves the efficient allocation and scheduling of data flow tasks within dynamic satellite networks, aiming to effectively address frequent changes in network topology and dynamic traffic fluctuations. Existing research primarily emphasizes traffic prediction and scheduling using spatiotemporal models and machine learning. However, these approaches often depend on extensive historical data for training, making real-time adaptation to rapidly changing network topologies and traffic patterns challenging in dynamic satellite environments. Additionally, their high computational complexity and slow convergence rates hinder their efficiency in large-scale networks. To address these issues, this paper proposes a collaborative optimization framework based on Coding Multi-Path Theory (CMPT). The framework utilizes a Nash bargaining game model to simulate resource competition among the different participants, ensuring fair resource distribution and load balancing. It also integrates real-time network state monitoring with optimization algorithms, within a multi-path scheduling strategy, enabling the dynamic selection of optimal transmission paths to accommodate frequent network topology changes and traffic variations. Experimental results indicate that the proposed method reduced resource allocation task execution time by at least 18.03% compared to traditional methods and enhanced task scheduling efficiency by at least 14.01%. Although CMPT exhibited a slightly higher task latency on certain small-scale datasets compared to some baseline algorithms, its performance remains exceptional in large-scale and high-dimensional scenarios. Full article

With the rapid development of mobile networks and devices, real-time video transmission has become increasingly important worldwide. Constrained by the bandwidth limitations of single networks, extensive research has shifted towards video transmission in multi-network environments. However, differences in bandwidth and latency in heterogeneous networks (such as LTE and Wi-Fi) lead to high latency and packet loss issues, severely affecting video quality and user experience. This paper proposes a Forward Error Correction (FEC)-based Low-Delay Multipath Scheduling algorithm (LDMP-FEC). This algorithm combines the Gilbert model with a continuous Markov chain to adaptively adjust FEC redundancy, thereby enhancing data integrity. Through the FEC Recovery Priority Scheduling (FEC-RPS) algorithm, it dynamically optimizes the transmission order of data packets, reducing the number of out-of-order packets (OFO-packets) and end-to-end latency. Experimental results show that LDMP-FEC significantly reduces the number of out-of-order packets in heterogeneous network environments, improving performance by 50% compared to the round-robin and MinRtt algorithms, while maintaining end-to-end latency within 150 ms. Under various packet loss conditions, LDMP-FEC sustains a playable frame rate (PFR) above 90% and a Peak Signal-to-Noise Ratio (PSNR) exceeding 35 dB, providing an efficient and reliable solution for real-time video and other low-latency applications. Full article

The growing demand for large-scale data distribution and sharing presents significant challenges to content transmission within the current TCP/IP network architecture. To address these challenges, Information-Centric Networking (ICN) has emerged as a promising alternative, offering inherent support for multipath forwarding and in-network caching to improve data transmission performance. However, most existing ICN caching strategies primarily focus on utilizing resources along the default transmission path and its neighboring nodes, without fully exploiting the additional resources provided by multipath forwarding. To address this gap, we propose an efficient multipath-based caching strategy that optimizes cache placement by decomposing the problem into two steps, multipath selection and cache node selection along the paths. First, multipath selection considers both transmission and caching resources across multiple paths, prioritizing the caching of popular content while efficiently transmitting less popular content. Next, along the selected paths, cache node selection evaluates cache load based on cache utilization and available capacity, prioritizing nodes with the lowest cache load. Extensive simulations across diverse topologies demonstrate that the proposed strategy reduces data transmission latency by at least 12.22%, improves cache hit rate by at least 16.44%, and enhances cache node load balancing by at least 18.77%, compared to the neighborhood collaborative caching strategies. Full article

Named data networking (NDN) is a novel networking paradigm characterized by in-network caching, receiver-driven communication, and multi-source, multi-path data retrieval, which poses new challenges for congestion control. Existing work has largely focused on receiver-driven mechanisms. Due to delays in obtaining network control information (timeouts, NACKs) within NDN, consumers are unable to access the network congestion status from this information in a timely manner. To address the issues above, this paper combines the Q-learning algorithm with the NDN architecture, proposing Q-NDN. In Q-NDN, consumers can dynamically adjust the congestion window (cwnd) through the real-time monitoring of network status, leveraging the Q-learning algorithm, achieving automatic congestion control for the NDN architecture. Additionally, this paper introduces content popularity-based traffic scheduling for multi-user scenarioswhich adjusts the transmission rates of content with different popularity levels to maintain a dynamic balance in the network. The experimental results show that Q-NDN can converge quickly, make full use of bandwidth resources, and keep the packet loss rate to 0 in the basic network topology. In competing network topologies, Q-NDN can rapidly address conflict issues, efficiently utilize bandwidth resources, and maintain a relatively low packet loss rate. Full article

Fifth-generation mobile networks (5G) are designed to support enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and massive Machine-Type Communications. To meet these diverse needs, 5G uses technologies like network softwarization, network slicing, and artificial intelligence. Multi-connectivity is crucial for boosting mobile device performance by using different Wireless Access Technologies (WATs) simultaneously, enhancing throughput, reducing latency, and improving reliability. This paper presents a multi-connectivity testbed from the 5G-CLARITY project for performance evaluation. MultiPath TCP (MPTCP) was employed to enable mobile devices to send data through various WATs simultaneously. A new MPTCP scheduler was developed, allowing operators to better control traffic distribution across different technologies and maximize aggregated throughput. Our proposal mitigates the impact of limitations on one path affecting others, avoiding the Head-of-Line blocking problem. Performance was tested with real equipment using 5GNR, Wi-Fi, and LiFi —complementary WATs in the 5G-CLARITY project—in both static and dynamic scenarios. The results demonstrate that the proposed scheduler can manage the traffic distribution across different WATs and achieve the combined capacities of these technologies, approximately 1.4 Gbps in our tests, outperforming the other MPTCP schedulers. Recovery times after interruptions, such as coverage loss in one technology, were also measured, with values ranging from 400 to 500 ms. Full article

The expansion of megaconstellation networks (MCNs) represents a promising solution for achieving global Internet coverage. To meet the growing demand for satellite services, multipath routing allows the simultaneous establishment of multiple transmission paths, enabling the transmission of flows in parallel. Nevertheless, the mobility of satellites and time-varying link states presents a challenge for the discovery of optimal paths and traffic scheduling in multipath routing. Given the inflexibility of traditional static deep reinforcement learning (DRL)-based routing algorithms in dealing with time-varying constellation topologies, DRL-based multipath routing (DMR) enabled by a graph neural network (GNN) is proposed as a means of enhancing the transmission performance of MCNs. DMR decouples the stochastic optimization problem of multipath routing under traffic and bandwidth constraints into two subproblems: multipath routing discovery and multipath traffic scheduling. Firstly, the minimum hop count-based multipath route discovery algorithm (MHMRD) is proposed for the computation of multiple available paths between all source and destination nodes. Secondly, the GNN-based multipath traffic scheduling scheme (GMTS) is proposed as a means of dynamically scheduling the traffic on each available path for each data stream, based on the state information of ISLs and traffic demand. Simulation results demonstrate that the proposed scheme can be scaled to constellations with different configurations without the necessity for repeated training and enhance the throughput, completion ratio, and delay by 42.64%, 17.39%, and 3.66% in comparison with the shortest path first algorithm (SPF), respectively. Full article

Multipath wireless access aims to seamlessly aggregate multiple access networks to increase data rates and decrease latency. It is currently being standardized through the ATSSS architectural framework as part of the fifth-generation (5G) cellular networks. However, facilitating efficient multi-access communication in next-generation wireless networks poses several challenges due to the complex interplay between congestion control (CC) and packet scheduling. Given that enhanced ATSSS steering functions for traffic splitting advocate the utilization of multi-access tunnels using congestion-controlled multipath network protocols between user equipment and a proxy, addressing the issue of nested CC becomes imperative. In this paper, we evaluate the impact of such nested congestion control loops on throughput over multi-access tunnels using the recently introduced Multipath DCCP (MP-DCCP) tunneling framework. We evaluate different combinations of endpoint and tunnel CC algorithms, including BBR, BBRv2, CUBIC, and NewReno. Using the Cheapest Path First scheduler, we quantify and analyze the impact of the following on the performance of tunnel-based multipath: (1) the location of the multi-access proxy relative to the user; (2) the bottleneck buffer size, and (3) the choice of the congestion control algorithms. Furthermore, our findings demonstrate the superior performance of BBRv2 as a tunnel CC algorithm. Full article

How to select the transmitting path in MPTCP scheduling is an important but open problem. This paper proposes an intelligent data scheduling algorithm using spatiotemporal synchronous graph attention neural networks to improve MPTCP scheduling. By exploiting the spatiotemporal correlations in the data transmission process and incorporating graph self-attention mechanisms, the algorithm can quickly select the optimal transmission path and ensure fairness among similar links. Through simulations in NS3, the algorithm achieves a throughput gain of 7.9% compared to the PDAA3C algorithm and demonstrates improved packet transmission performance. Full article

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. Full article

Although NAND (Not And) flash-based Solid-State Drive (SSD) has recently demonstrated a significant performance advantage against hard disk, it still suffers from non-negligible performance under-utilization issues as the access conflict often occurs during servicing IO requests due to the share mechanism (e.g., several chips share one channel bus, several planes share one data register inside the die). Many research works have been devoted to minimizing access conflict by redesigning IO scheduling, cache replacement, and so on. These works have achieved reasonable results; however, the potential data similarity characterization is not utilized fully in prior works to alleviate access conflict. The basic idea is that, as data duplication is common in many workloads where data with the same content from different requests could be distributed to the address with minimized access conflict (i.e., the address does not share the same channel or chip), the logic address is mapped to more than one physical address. Therefore, the data can be read out from candidate pages when the channel or chip of its original address is busy. Motivated by this idea, we propose Data Similarity aware Flash Translation Layer (DS-FTL), which mainly includes a content-aware page allocation scheme and a multi-path read scheme. The DS-FTL enables maximization of the channel-level and chip-level parallelism and avoids the read stall induced by bus-shared mechanisms. We also conducted a series of experiments on SSDsim, with the subsequent results depicting the effectiveness of our scheme. Compared with the state-of-art, our scheme reduces read latency by 35.3% on average in our workloads. Full article

This paper proposes an energy-efficient scheduling scheme for multi-path TCP (MPTCP) in heterogeneous wireless networks, aiming to minimize energy consumption while ensuring low latency and high throughput. Each MPTCP sub-flow is controlled by an agent that cooperates with other agents using the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. This approach enables the agents to learn decentralized policies through centralized training and decentralized execution. The scheduling problem is modeled as a multi-agent decision-making task. The proposed energy-efficient scheduling scheme, referred to as EE-MADDPG, demonstrates significant energy savings while maintaining lower latency and higher throughput compared to other state-of-the-art scheduling techniques. By adopting a multi-agent deep reinforcement learning approach, the agents can learn efficient scheduling policies that optimize various performance metrics in heterogeneous wireless networks. Full article

In recent years, low-Earth-orbit (LEO) constellations have been proposed for Positioning, Navigation and Timing (PNT) applications. Moreover, a couple of test satellites have already been launched and many more are scheduled in the near future. LEO constellations are characterised by their rapid change in geometry in comparison to the current medium-Earth-orbit (MEO) Global Navigation Satellite Systems (GNSSs). In this study, we analyse the impact of this high geometry change rate on the code-range error induced by multipaths. We develop a simulation environment with a static receiver and a nearby large building. We track the multipath signal using classical delay- and phase-locked loops (DLL and PLL). Multiple scenarios are simulated and analysed, comparing different orbit heights, MEO and LEO, and carrier frequencies (L-, S- and C-band). The LEO scenarios show up to 96% less code-range error for fast-changing multipath components. We show that this phenomenon is linked to the large phase delay rate between the direct signal and the multipath components, which is up to 75 times higher for LEO satellites when compared to MEO satellites. The phase delay rate reaches values higher than the DLL bandwidth. As a result, the DLL filters out the errors induced by fast-changing reflected signals, partially eliminating the multipath-induced code-range errors. The presented effect is coupled to the wavelength of the used carrier frequency. Our simulations show a reduction in multipath-induced code-range error for S- and C-band LEO-PNT signals in comparison to L-band signals. Full article

Reducing transmission traffic delay is one of the most important issues that need to be considered for routing protocols, especially in the case of multimedia applications over vehicular ad hoc networks (VANET). To this end, we propose an extension of the FzGR (fuzzy geographical routing protocol), named MNH-FGR (multi-next-hops fuzzy geographical routing protocol). MNH-FGR is a multipath protocol that gains great extensibility by employing different link metrics and weight functions. To schedule multimedia traffic among multiple heterogeneous links, MNH-FGR integrates the weighted round-robin (WRR) scheduling algorithm, where the link weights, needed for scheduling, are computed using the multi-constrained QoS metric provided by the FzGR. The main goal is to ensure the stability of the network and the continuity of data flow during transmission. Simulation experiments with NS-2 are presented in order to validate our proposal. Additionally, we present a neural network algorithm to analyze and optimize the performance of routing protocols. The results show that MNH-FGR could satisfy critical multimedia applications with high on-time constraints. Also, the DNN model used can provide insights about which features had an impact on protocol performance. Full article

This paper explores the advancements of drones in the context of sixth-generation mobile communication technology (6G) green Internet of Things (IoT) through the utilization of digital twin (DT) technology within unmanned aerial vehicle (UAV) networks. We propose a framework for DT-based UAV applications in the realm of green IoT, where distinct tasks within the digital twin interact with physical-world UAVs through task manager scheduling. We characterize the radio frequency (RF) attributes of the DT using three-dimensional (3D) millimeter-wave (mmWave) radar imaging on UAVs. The wireless channel modeling, based on ray tracing, underscores the alignment of RF domains between the DT and the physical UAV in a bid to take advantage of multipath reflections and save communication energy. Our numerical findings have justified the efficacy of the drone-enabled DT platform in achieving accurate RF representation of UAVs for the intelligent operation and management of IoT-based green UAV networks. Full article

Designing reasonable MAC scheduling strategies is an important means to ensure transmission quality in wireless sensor networks (WSNs). When there exist multiple available routes from the source to the destination, it is necessary to combine a data traffic allocation mechanism and design a multi-path MAC scheduling scheme in order to ensure QoS. This paper develops a multi-path resource allocation method for multi-channel wireless sensor networks, which uses random-access technology to complete MAC scheduling and selects the transmission path for each packet according to the probability. Through theoretical analysis and simulation experiments, it can be found that the proposed strategy can provide a reliable throughput capacity region. Meanwhile, due to the use of random-access technology, the computational complexity of the proposed algorithm can be independent of the number of links and channels. Full article