Bandwidth Competition Research Articles

An Adaptive Bandwidth Management Algorithm for Next-Generation Vehicular Networks.

The popularity of video services such as video call or video on-demand has made it impossible for people to live without them in their daily lives. It can be anticipated that the explosive growth of vehicular communication owing to the widespread use of in-vehicle video infotainment applications in the future will result in increasing fragmentation and congestion of the wireless transmission spectrum. Accordingly, effective bandwidth management algorithms are demanded to achieve efficient communication and stable scalability in next-generation vehicular networks. To the best of our current knowledge, a noticeable gap remains in the existing literature regarding the application of the latest advancements in network communication technologies. Specifically, this gap is evident in the lack of exploration regarding how cutting-edge technologies can be effectively employed to optimize bandwidth allocation, especially in the realm of video service applications within the forthcoming vehicular networks. In light of this void, this paper presents a seamless integration of cutting-edge 6G communication technologies, such as terahertz (THz) and visible light communication (VLC), with the existing 5G millimeter-wave and sub-6 GHz base stations. This integration facilitates the creation of a network environment characterized by high transmission rates and extensive coverage. Our primary aim is to ensure the uninterrupted playback of real-time video applications for vehicle users. These video applications encompass video conferencing, live video, and on-demand video services. The outcomes of our simulations convincingly indicate that the proposed strategy adeptly addresses the challenge of bandwidth competition among vehicle users. Moreover, it notably boosts the efficient utilization of bandwidth from less crowded base stations, optimizes the fulfillment of bandwidth prerequisites for various video applications, and elevates the overall video quality experienced by users. Consequently, our findings serve as a successful validation of the practicality and effectiveness of the proposed methodology.

A blockchain-based interactive approach between digital twin-based manufacturing systems

Cloud manufacturing provides an interactive environment for collaboration between digital twin-based manufacturing systems. However, access to the enterprise cloud by a significant number of digital twin systems would lead to bandwidth competition and severe delays. Therefore, the data exchange process needs to be improved in a more reliable and effective way. To tackle this issue, a blockchain-based data interactive approach is proposed to form the peer-to-peer data exchange mechanism between digital twin-based manufacturing systems. First, the manufacturing edge pool is developed given specific manufacturing tasks. The blockchain enables the digital twin manufacturing systems to exchange data effectively and safely. Besides, the application of the blockchain reduces the dependencies of the digital twin systems on the enterprise cloud and improves the flexibility of the workshops. Finally, the effectiveness of this method is verified by a case study on the engine.

Scheduling Virtual Network Reconfigurations in Parallel in Hybrid Optical/Electrical Datacenter Networks

A hybrid optical/electrical datacenter network (HOE-DCN) integrates the benefits of electrical packet switching (EPS) and optical circuit switching (OCS) for better architectural scalability. Meanwhile, as each network service usually involves multiple virtual machines (VMs) that form a virtual network (VNT), how to reconfigure the VNTs in an HOE-DCN to adapt to the dynamic network environment becomes an interesting and important problem to tackle. In this work, we optimize the procedure of VNT reconfigurations that remap VNTs to given virtual network embedding (VNE) schemes, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , scheduling the required VM migrations and optical cross-connect (OXC) reconfiguration properly such that the overall VNT reconfiguration latency can be minimized. We first assume that all the VM migrations should be scheduled before the OXC reconfiguration to minimize service interruptions, and formulate a mixed integer linear programming (MILP) model to solve the scheduling problem exactly. Then, with the same assumption, we propose a time-efficient heuristic to schedule parallel VM migrations in batches such that the bandwidth competition can be significantly relieved. Finally, we divide the OXC reconfiguration into several progressive steps, and design a joint scheduling algorithm to arrange VM migrations together with the OXC reconfiguration steps.

Video streaming over HTTP/2: Design and evaluation of adaptive server-paced push

By using the server push of the hypertext transfer pro-tocol (HTTP) version 2.0, i.e., HTTP/2, along with the technique of server pacing, a novel scheme is proposed in this paper to deliver video segments. Furthermore, the load of bitrate adaptation is shifted to the server to avoid bandwidth competition and wastage caused by bitrate switching. It can be explicitly shown that no significant overhead is brought by our proposed scheme via complexity analysis. Moreover, our proposed scheme generates one HTTP request only. With extensive simulations, we successfully demonstrate that it is superior over the closely related schemes in terms of the average achievable bitrate, the number of buffer stalls, the ratio of unclaimed pushes, etc., in particular, when a harsh network condition arises.

The Yellow Active Queue Management Algorithm in ICN Routers Based on the Monitoring of Bandwidth Competition

Deploying the active queue management (AQM) algorithm on a router is an effective way to avoid packet loss caused by congestion. In an information-centric network (ICN), routers not only play a role of packets forwarding but are also content service providers. Congestion in ICN routers can be further summarized as the competition between the external forwarding traffic and the internal cache response traffic for limited bandwidth resources. This indicates that the traditional AQM needs to be redesigned to adapt to ICN. In this paper, we first demonstrated mathematically that allocating more bandwidth for the upstream forwarding flow could improve the quality of service (QoS) of the whole network. Secondly, we propose a novel AQM algorithm, YELLOW, which predicts the bandwidth competition event and adjusts the input rate of request and the marking probability adaptively. Afterwards, we model YELLOW through the totally asymmetric simple exclusion process (TASEP) and deduce the approximate solution of the existence condition for each stationary phase. Finally, we evaluated the performance of YELLOW by NS-3 simulator, and verified the accuracy of modeling results by Monte Carlo. The simulation results showed that the queue of YELLOW could converge to the expected value, and the significant gains of the router with low packet loss rate, robustness and high throughput.

6G technology based advanced virtual multi-purpose embedding algorithm to solve far-reaching network effects

At present, network virtualization is an essential technology that aims to resolve shortcomings including network ossification of the current design of the Internet. The Internet’s tremendous success have adopted digital business growth and increased competition for bandwidth in communication. Ultra-high-definition videos and vehicle systems require rapid bandwidth rates and increase network connection capacity, respectively. The rapid advancement of network virtualization and 6G technologies is driven by high bandwidth and high speed parallel communication. This innovation often introduces new link allocation problems to existing substrate networks in a network virtualization environment. In order to solve this far-reaching network effects, this paper proposes an Advanced Virtual Multi-Purpose Network Embedding Algorithm (AVMPNEA) through the fuzzy C-mean clustering, which is a learning algorithm. The clustering integrally evaluates the topological node structure, the latency and the related bandwidth between the nodes. The Nodes that enhance the longevity are used for selected mapping. The experimental result shows that the proposed AVMPNEA is outperformed in comparison with other traditional methods with high throughput, low latency and reduced energy consumption. Hence, it improves the throughput and the long-term application support.

Psched: A Priority-Based Service Scheduling Scheme for the Internet of Drones

With the continuous miniaturization of sensors and processors and ubiquitous wireless connectivity, unmanned aerial vehicles (UAVs), also referred to as drones, are finding many new uses in enhancing our life and paving the way to the realization of Internet of Drones (IoD). In the IoD, a myriad of multisized and heterogeneous drones seamlessly interact with Zone Service Providers (ZSPs) to achieve the goal of assisting drones in accessing controlled airspace and providing navigation services. However, due to the high mobility of drones and the limited communication bandwidth between drones and ZSP, service scheduling becomes a critical issue when a set of drones wants to upload/download data to/from ZSP. In this article, we propose a priority-based service scheduling scheme, also named Psched, to provide efficient data upload/download service at ZSP in the IoD. The basic idea is that the Psched objectively and equitably assigns a weight to multiple service scheduling parameters based on multiattribute decision making theory, calculates the serving priority of each service request group, and then serves the service request groups based on the calculated serving priority accordingly. In addition, the Psched takes into account of bandwidth competition between upload and download service requests, and provides a service request balancing to achieve the maximum benefits of service scheduling scheme. In the experimental study, we choose request deadline, data size, and data popularity as service scheduling parameters, and conduct extensive simulation experiments using OMNeT++ for performance evaluation and comparison. The simulation results show that the proposed priority-based service scheduling scheme can not only increase service ratio but also can improve fresh data service ratio and average request serving latency, indicating a viable and efficient approach for satisfying service requests at ZSP in the IoD.

Dynamic behavior analysis of an internet flow interaction model under cascading failures.

Cascading failures in the internet have attracted recent attention due to their unpredictability and destructive consequences. Exploring the failure behavior patterns is necessary because they can provide effective intervention approaches to prevent huge network disasters. To analyze internet flow behaviors during cascading failures (chain reactions in router and link failures), we characterize the internet as two coupled networks, the router network and the flow network. Here the flow network is an abstract representation of data correlations obtained from the router network. We use this coupled network to build a cascading failure model for studying flow transmission and competition, which is reflected in bandwidth competition given by limited link capacity. We first study the dependency between routers and flows to explore the flow transmission efficiency when a failure event occurs. Moreover, we find that rerouting enables flow competition area (the number of flows with which one flow has a competitive relationship) to initially remain stable during a failure episode, but that it then quickly drops due to poor physical network connectivity. Additionally, in the early stage after the failure event, the degree of flow competition sharply increases because of the growing number of the flows and congestion. Subsequently, the flow competition decreases due to the failure of flow transmission.

A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming

Video streaming has become a kind of main information carried by Unmanned Aerial Vehicles (UAVs). Unlike single transmission, when a cluster of UAVs execute the real-time video shooting and uploading mission, the insufficiency of wireless channel resources will lead to bandwidth competition among them and the competition will bring bad watching experience to the audience. Therefore, how to allocate uplink bandwidth reasonably in the cluster has become a crucial problem. In this paper, an intelligent and distributed allocation mechanism is designed for improving users’ video viewing satisfication. Each UAV in a cluster can independently adjust and select its video encoding rate so as to achieve flexible uplink allocation. This choice relies neither on the existence of the central node, nor on the large amount of information interaction between UAVs. Firstly, in order to distinguish video service from ordinary data, a utility function for the overall Quality of Experience (QoE) is proposed. Then, a potential game model is built around the problem. By a distributed self-learning algorithm with low complexity, all UAVs can iteratively update their own bandwidth strategy in a short time until equilibria, thus achieving the total quality optimization of all videos. Numeric simulation results indicate, after a few iterations, that the algorithm converges to a set of correlation equilibria. This mechanism not only solves the uplink allocation problem of video streaming in UAV cluster, but also guarantees the wireless resource providers in distinguishing and ensuring network service quality.

Priority-Based PCIe Scheduling for Multi-Tenant Multi-GPU Systems

Multi-GPU systems are widely used in data centers to provide significant speedups to compute-intensive workloads such as deep neural network training. However, limited PCIe bandwidth between the CPU and multiple GPUs becomes a major performance bottleneck. We observe that relying on a traditional Round-Robin-based PCIe scheduling policy can result in severe bandwidth competition and stall the execution of multiple GPUs. In this article, we propose a priority-based scheduling policy which aims to overlap the data transfers and GPU execution for different applications to alleviate this bandwidth contention. We also propose a dynamic priority policy for semi-QoS management that can help applications to meet QoS requirements and improve overall multi-GPU system throughput. Experimental results show that the system throughput is improved by 7.6 percent on average using our priority-based PCIe scheduling scheme as compared with a Round-Robin-based PCIe scheduler. Leveraging semi-QoS management can help to meet defined QoS goals, while preserving application throughput.

RDMA-driven MongoDB: An approach of RDMA enhanced NoSQL paradigm for large-Scale data processing

With the rapid development of big data and data center networks, NoSQL database has won great popularity for its excellent performance in accelerating the performance of many online and offline big data applications, such as HBase, Cassandra and MongoDB. However, due to massive and frequent Create/Update/Retrieval/Delete (CURD) operations, the traditional TCP/IP protocol stack has difficulty to provide the required request rates and response latency for the large-scale NoSQL system. For example, large-scale data migration or synchronization among multiple clusters in a data center results in competition for network bandwidth with high delay. To mitigate such transmission bottleneck, we propose an approach of RDMA-driven document NoSQL Paradigm’ RDMA_Mongo, based on MongoDB. The performance of CURD operations is enhanced by one-sided Remote Direct Memory Access (RDMA) primitives (such as RDMA Read/Write) without involving the TCP/IP stack or CPU. Evaluation under RDMA-enabled network demonstrates that RDMA_Mongo significantly improves the CURD performance, compared with plain MongoDB. The results show that the average insert throughput increases by approximately 30%, the average delete throughput by over 30%, the update by up to 17% and the query throughput by 15% when facing large-scale data requests.

Game of Streaming Players

The dramatic growth of HTTP adaptive streaming (HAS) traffic represents a practical challenge for service providers in satisfying the demand from their customers. Achieving this in a network where multiple players share the network capacity has so far proved hard because of the bandwidth competition among the HAS players. This competition is exacerbated by the bandwidth overestimation that is introduced due to the isolated and selfish behavior of the HAS players. Each player strives individually to select the maximum bitrate without considering the co-existing players or network resource dynamics. As a result, the HAS players suffer from video quality instability, quality unfairness, and network underutilization or oversubscription, and the players observe a poor quality of experience (QoE). To address this issue, we propose a fully distributed game theory and consensus-based collaborative adaptive bitrate solution for shared network environments, termed Game Theory and consensus-based Approach for Cooperative HAS delivery systems (GTAC). Our solution consists of two-stage games that run in parallel during a streaming session. We extensively evaluate GTAC on a broad set of trace-driven and real-world experiments. Results show that GTAC enhances the viewer QoE by up to 22%, presentation quality stability by up to 24%, fairness by at least 31%, and network utilization by 28% compared to the well-known schemes.

Minimizing the Impact of P2P-TV Applications in Access Links

Many studies are concentrated on provisioning certain levels of Quality of Service (QoS) employing different approaches related to different areas (control congestion algorithms, transport protocols, etc). This paper is focused on the improvement of the QoS of peer-to-peer TV (P2P-TV), and in particular, it proposes a methodology to minimise the impact of bursty traffic in the buffer of access devices using a traffic shaping technique. This paper shows how QoS can be improved by means of traffic shaping, specifically by using smoothing techniques. Some parameters such as packet loss, delay and throughput of a popular P2PTV application are analysed. The competition for the available bandwidth between the P2P-TV and some Transport Control Protocol (TCP) traffic flows is also studied. The results show a reduction in terms of loss percentage when the traffic of the P2P-TV application is smoothed, introducing small delays as a counterpart.

Blocking Evaluation and Wavelength Dimensioning of Dynamic WDM Networks Without Wavelength Conversion

In this paper, a new fast and accurate methodology to evaluate the blocking probability (burst loss probability) in dynamic WDM networks without wavelength conversion is presented. The proposed model considers different traffic loads at each network connection (heterogeneous traffic). To take into account the wavelength continuity constraint, the method sees the network as a sequence of networks where all the links have capacity 1. Every network in the sequence is evaluated separately using a heuristic-based analytical technique. Then, a procedure combines the results of these evaluations in a way that captures the dependencies that occur in real systems due to the competition for bandwidth between the different connections. The method efficiently achieves results very close to those obtained by simulation, but orders of magnitude faster, allowing the evaluation of the blocking probability of all users (connections) for mesh network topologies. We illustrate the use of this procedure in the dimensioning of a WDM network, that is, in calculating the number of wavelengths on every network link, by considering the traffic load of every user, the routing algorithm and the maximum connection blocking probability acceptable by every user (quality of service) are determined.

Data Acquisition Based on Stable Matching of Bipartite Graph in Cooperative Vehicle-Infrastructure Systems.

Existing studies on data acquisition in vehicular networks often take the mobile vehicular nodes as data carriers. However, their autonomous movements, limited resources and security risks impact the quality of services. In this article, we propose a data acquisition model using stable matching of bipartite graph in cooperative vehicle-infrastructure systems, namely, DAS. Contents are distributed to roadside units, while vehicular nodes support supplementary storage. The original distribution problem is formulated as a stable matching problem of bipartite graph, where the data and the storage cells compose two sides of vertices. Regarding the factors relevant with the access ratio and delay, the preference rankings for contents and roadside units are calculated, respectively. With a multi-replica preprocessing algorithm to handle the potential one-to-many mapping, the matching problem is addressed in polynomial time. In addition, vehicular nodes carry and forward assistant contents to deliver the failed packets because of bandwidth competition. Furthermore, an incentive strategy is put forward to boost the vehicle cooperation and to achieve a fair bandwidth allocation at roadside units. Experiments show that DAS achieves a high access ratio and a small storage cost with an acceptable delay.

An adaptive traffic distribution scheme for CMT based on Lotka-Volterra model in multihomed networks

CMT (concurrent multipath transfer) can increase throughput and transmission efficiency in multihomed networks. However, it is still an important challenge about distributing traffic adaptively into multiple access networks. Based on LV (Lotka-Volterra) model, we propose an adaptive traffic distribution scheme. In the scheme, two competition modes are concluded, multiple S-D (source-destination) streams competition for bandwidth of one path and multipath competition for traffic between each multihomed S-D host pair. Actually, each access network can establish a path for S-D pairs. So, in the first mode, each path is analogous to a predator, and overall traffic in a multihomed host is reducing file transmission time and increasing network throughput in FTP service analogous to prey. Then, each path has to compete for the traffic by path information, e.g., bandwidth and congestion level. In the other one, if several S-D pairs pass through a shared path simultaneously, they will compete for bandwidth of the path. Here, each S-D pair is analogous to a predator, and the bandwidth of the common path is analogous to the prey. At last, compared with other three schemes, uniform traffic distribution, greedy path selection, random path selection in OPNET simulator, the proposed scheme can perform better on

A Context-Aware Adaptive Streaming Media Distribution System in a Heterogeneous Network with Multiple Terminals

We consider the problem of streaming media transmission in a heterogeneous network from a multisource server to home multiple terminals. In wired network, the transmission performance is limited by network state (e.g., the bandwidth variation, jitter, and packet loss). In wireless network, the multiple user terminals can cause bandwidth competition. Thus, the streaming media distribution in a heterogeneous network becomes a severe challenge which is critical for QoS guarantee. In this paper, we propose a context-aware adaptive streaming media distribution system (CAASS), which implements the context-aware module to perceive the environment parameters and use the strategy analysis (SA) module to deduce the most suitable service level. This approach is able to improve the video quality for guarantying streaming QoS. We formulate the optimization problem of QoS relationship with the environment parameters based on the QoS testing algorithm for IPTV in ITU-T G.1070. We evaluate the performance of the proposed CAASS through 12 types of experimental environments using a prototype system. Experimental results show that CAASS can dynamically adjust the service level according to the environment variation (e.g., network state and terminal performances) and outperforms the existing streaming approaches in adaptive streaming media distribution according to peak signal-to-noise ratio (PSNR).

Efficient elastic bulky traffic transfer with a new pricing scheme based on number of flows

Emerging data center and cloud services carry large blocks of data, which leads to intensive bandwidth competition on today’s Internet, particularly during peak hours. Congestion pricing is considered to be an effective means to avoid peak-hour competition. Typical congestion pricing schemes proposed in the past require significant enhancements to existing network infrastructures. For instance, complex congestion information collection and price announcement mechanisms must be in place, hindering the deployments of many congestion pricing schemes. In this study, we present one simple yet effective congestion pricing scheme based on the number of flows in single-bottleneck networks carrying elastic traffic. During congested hours, data transmitters share bandwidth on the bottleneck and are also charged according to the number of flows they use. Under this pricing scheme, network capacity is used efficiently, and all users can achieve maximum utility with increasing and strictly concave utility functions. We investigated how a single congestion control parameter, the price index, affects the degree of network congestion. Through network simulation, we proved that under the proposed scheme, the big data transmitters sharply reduce their transfer during congested hours.

Scale-RS: An Efficient Scaling Scheme for RS-Coded Storage Clusters

It is indispensable to scale erasure-coded storage clusters to meet requirements of increased storage capacity and I/O performance. In this study, we propose an efficient scaling scheme for Reed-Solomon-coded storage clusters called Scale-RS, which has three salient features. First, Scale-RS achieves uniform data distribution by equally placing data blocks among old and new chunks using a transposed data layout. Second, Scale-RS minimizes data movement incurred in the procedures of data redistribution and parity update. Scale-RS not only reaches the lower bound of data migration traffic by transferring necessary data blocks from old data chunks to new chunks, but it also reduces update traffic via generating parity difference blocks from data blocks stored in an individual data chunk. Third, Scale-RS improves the I/O performance of scaled storage clusters in terms of read parallelism and write throughput. We implement Scale-RS along with two alternative scaling schemes in a Reed-Solomon-coded storage cluster, on which real-world I/O traces are replayed. Experimental results demonstrate that Scale-RS achieves the highest read performance among the three scaling schemes after data redistribution. When it comes to scaling from six data chunks to nine, Scale-RS can outperform the other two scaling schemes in terms of aggregate write throughput by a factor of 2.85 and 3.05 under online filling and offline filling, respectively. We also show that user response time is slightly enlarged during data redistribution due to bandwidth competition between migration and user I/Os.

QoE-driven in-network optimization for Adaptive Video Streaming based on packet sampling measurements

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for adaptive streaming solutions. In HAS, a video is temporally split into segments which are encoded at different quality rates. The client can then autonomously decide, based on the current buffer filling and network conditions, which quality representation it will download. Each of these players strives to optimize their individual quality, which leads to bandwidth competition, causing quality oscillations and buffer starvations. This article proposes a solution to alleviate these problems by deploying in-network quality optimization agents, which monitor the available throughput using sampling-based measurement techniques and optimize the quality of each client, based on a HAS Quality of Experience (QoE) metric. This in-network optimization is achieved by solving a linear optimization problem both using centralized as well as distributed algorithms. The proposed hybrid QoE-driven approach allows the client to take into account the in-network decisions during the rate adaptation process, while still keeping the ability to react to sudden bandwidth fluctuations in the local network. The proposed approach allows improving existing autonomous quality selection heuristics by at least 30%, while outperforming an in-network approach using purely bitrate-driven optimization by up to 19%.