Bottleneck Link Research Articles

QoS-Aware Power-Optimized Path Selection for Data Center Networks (Q-PoPS)

Data centers consume significant amounts of energy, contributing indirectly to environmental pollution through greenhouse gas emissions during electricity generation. According to the Natural Resources Defense Council, information and communication technologies and networks account for roughly 10% of global energy consumption. Reducing power consumption in Data Center Networks (DCNs) is crucial, especially given that many data center components operate at full capacity even under low traffic conditions, resulting in high costs for both service providers and consumers. Current solutions often prioritize power optimization without considering Quality of Service (QoS). Services such as video streaming and Voice over IP (VoIP) are particularly sensitive to loss or delay and require QoS to be maintained below certain thresholds. This paper introduces a novel framework called QoS-Aware Power-Optimized Path Selection (Q-PoPS) for software-defined DCNs. The objective of Q-PoPS is to minimize DCN power consumption while ensuring that an acceptable QoS is provided, meeting the requirements of DCN services. This paper describes the implementation of a prototype for the Q-PoPS framework that leverages the POX Software-Defined Networking (SDN) controller. The performance of the prototype is evaluated using the Mininet emulator. Our findings demonstrate the performance of the proposed Q-PoPS algorithm in three scenarios. Best-case: Enhancing real-time traffic protocol quality without increasing power consumption. midrange-case: Replacing bottleneck links while preserving real-time traffic quality. Worst-case: Identifying new paths that may increase power consumption but maintain real-time traffic quality. This paper underscores the need for a holistic approach to DCN management, optimizing both power consumption and QoS for critical real-time applications. We present the Q-PoPS framework as evidence that such an approach is achievable.

EXCLF: A LDoS attack detection & mitigation model based on programmable data plane

The SDN architecture decouples control plane from data plane, making it more susceptible to various abnormal traffic and network attacks, with LDoS attack being one of them. LDoS attackers periodically send short-duration pulses with high rate to bottleneck links to preempt legitimate TCP traffic bandwidth, severely disrupting the transmission of TCP traffic. Current researches on LDoS attacks are mostly implemented in SDN environments, making them exhibit poor portability during deployment and unavoidable time delays. This paper proposes EXCLF, a LDoS attack detection and mitigation model fully deployed on programmable data plane. To identify LDoS attacks, the model gathers features of the traffic going through the switch and feeds them into a decision tree. Once LDoS attack happens, the model collects data at flow level to pinpoint the attacker and initiates corresponding mitigation measures. Extensive experiments were conducted to evaluate the proposed model, and the results indicate that EXCLF achieves a correct rate of 96.39%, with false positive and false negative rates both below 3%. Additionally, the model demonstrates low detection latency and can quickly respond to attacks. The model proves to be an attack detection and mitigation method with good portability and efficiency.

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.

Randomized protocols for resilient peer-to-peer networks

This paper studies a few randomized algorithms (e.g., random walks, gossip) in peer-to-peer networks. We leverage the Docker virtual container technology to develop implementations of the peer-to-peer networks and of these distributed randomized algorithms executing on top of them. We can thus analyze their behavior and performance in realistic settings. We further consider the problem of identifying high-risk bottleneck links in the network with the objective of improving the resilience and reliability of peer-to-peer networks. We propose a randomized algorithm to solve this problem and evaluate its performance by simulations.

Swapping-Based Entanglement Routing Design for Congestion Mitigation in Quantum Networks

The quantum network is designed to connect numerous quantum nodes and support various ground-breaking quantum applications. Most of these applications require communicating parties to share entangled pairs. Therefore, entanglement routing, a technology distributing entangled pairs between distant quantum nodes, plays a vital role in realizing quantum networks’ capability. However, due to the limitation of quantum memory size and quantum decoherence, the entangled pairs shared by adjacent quantum nodes can hardly satisfy concurrent entanglement routing requests, thus leading to severe network congestion. In this paper, we propose a novel congestion mitigation (CM) scheme to tackle such bottleneck problems. The basic idea of CM is to “recycle” idle link-level entanglement resources from well-resourced links to bottleneck links utilizing a unique enabling technology of quantum networks, called entanglement swapping. CM can increase the capacity of each bottleneck link, thus overcoming resource limitations to improve resource utilization and network throughput. To complete our work, we also propose a swapping-based entanglement routing design, including path selection and resource allocation algorithms. Extensive simulations show that our design can significantly alleviate network congestion and improve the request service rate of quantum networks compared to the traditional entanglement routing designs.

SmartSBD: Smart shared bottleneck detection for efficient multipath congestion control over heterogeneous networks

Multipath TCP (MPTCP) has been widely adopted in today’s mobile devices. However, two types of congestion control algorithms, uncoupled congestion control (Uncoupled CC) and coupled congestion control (Coupled CC), cannot achieve both bottleneck friendliness and throughput maximization for both of the MPTCP subflow bottleneck sharing scenarios, shared bottleneck (SB) scenario and non-shared bottleneck (NSB) scenario, leading to performance degradation in practice. In this work, we seek to enable efficient MPTCP congestion control, by alternating between Uncoupled CC algorithms and Coupled CC algorithms via smartly detecting whether the two MPTCP subflows share the same bottleneck link. We propose SmartSBD, the first learning-based data-driven approach for shared bottleneck detection, which is accurate, adaptable, and easy-to-deploy. SmartSBD is based on the key insight that the properties of subflows that share the same bottleneck often have similar trends of variation or similar values. In the training phase, SmartSBD collects system logs when MPTCP is running in real-world heterogeneous networks, extracts features, and trains a binary classifier. In the runtime phase, SmartSBD makes periodic predictions on the bottleneck sharing condition of live MPTCP subflows, and uses the prediction results to alternate between Coupled CC and Uncoupled CC. Our evaluations demonstrate that SmartSBD outperforms existing approaches.

Collaborative passenger flow control for an urban rail transit network

AbstractAmid worsening passenger congestion, passenger flow control has become a major need in urban rail transit. However, existing passenger flow control strategies are fixed and do not consider the impacts of the spatial and temporal variations in passenger flow. To address the problems mentioned, a method for collaborative passenger flow control in urban rail transit is proposed. First, the temporal and spatial distribution of passengers in the network is obtained. Then, a model of collaborative passenger flow control for urban rail transit is built to reduce the number of controlled source points and controlled passengers and minimize the difference between the expected number of passengers on bottleneck links and the number of passengers with control. Next, a multilevel network that considers the time dimension is established based on the model. A forward–backward algorithm is introduced to make full use of the adaptive learning rate to solve the model. A case study based on a small‐scale network shows that the forward–backward algorithm has good convergence. To verify the effectiveness of the method, the passenger flow of Chengdu Metro during COVID‐19 is analyzed. The objective function of the forward–backward algorithm is 44% lower than that of the gradient descent method and 36% lower than that of adaptive moment estimate (ADAM). Its computational speed is also acceptable. The results show that compared with the fixed passenger flow control strategy of metro operators, the obtained strategy is more effective in reducing the number of controlled passengers and source points and alleviating congestion of bottleneck links.

Optimizing Data Transport Efficiency in Datacenters through Traffic Shaping of BBR Congestion Control

Compared to commonly used loss-based congestion control algorithms predominantly used in Transmission Control Protocol (TCP) implementations, congestion-based congestion control called BBR has shown much better performance in resource-abundant modern communication links. However, for a high influx of TCP sessions on the bottleneck switch, clusters in High-Performance Compute (HPC) nodes and data centers face resource constraints because of the immense workload during orchestration and relocation of workflows across the resource pool. This article discusses how to resolve this problem, commonly known as TCP incast, through efficient queue management of the bottleneck link and adding a shaper function in the standard BBR algorithm. We analyzed TCP incast issue for two efficient versions of congestion control i.e., BBR and CUBIC (named after the cubic function used instead of linear function), in a highly overloaded convergent switch of the cluster. It is noticeable that the queuing delay and buffer build-up are two essential parameters in causing TCP Incast. Hence, we used the M/G/1/B queuing model when multiple TCP sessions generate the network traffic and different buffer build-up scenarios are analyzed in the bottleneck node of HPC clusters. Based on the findings of our queuing analysis, we propose an incast recovery BBR algorithm that introduces additional controls like Incast shaping to deal with queue build-up during TCP incast. The effects of these modifications in BBR implementation are studied in terms of performance parameters like flow completion time, throughput, RTT variations, and fairness to other competing flows are significant compared with standard BBR and CUBIC implementations.

TCP-Puerto-Londero: A New Approach for End-to-End Queue Length Control

This paper presents a new TCP protocol called TCPPuerto-Londero, which makes dynamic tuning in the congestion window (cwnd) by means of adaptive control theory aiming to keep stable and small the queue length in the bottleneck link located on the path from source to destination. This adaptive control loop has the relative delay in the forward path measured as an input and the cwnd value as an output. Thus, unlike classic TCP protocols, TCP-Puerto-Londero does not use Round Trip Time (RTT) information. Also, unlike classic Active Queue Management (AQM) strategies and Explicit Congestion Notification (ECN) based protocols, TCP-Puerto-Londero employs end-to-end queue management without the need for ECN resources. Moreover, TCP-Puerto-Londero also aims to attend to the new challenges of the Industrial Internet of Things (IIoT). Its performance has been tested in a Dumbbell network topology shared by TCP and UDP-like Networked Control Systems (NCS) flows. Therefore, TCP-Puerto-Londero performance has been compared with ECN-based protocols like DCTCP, E-DCTCP, TCP-Jersey, and ENCN. Furthermore, this paper employs an approach for modeling, analysis, simulation, and verification of the communication network and NCS employing UPPAAL simulation software tool, where all network constituents (transmitters, channels, routers, receivers, controllers, and plants) were modeled employing timed automata, simplifying a formal verification of the complete studied system. Simulations and statistical verification indicate that even though utilizing fewer resources (as it does not require the AQM/ECN information) TCP-Puerto-Londero overcomes TCP-Jersey, DCTCP, and EDCTCP with regard to throughput and fairness for TCP flows. Also, TCP-Puerto-Londero flows are capable to keep the queue length more stable and smaller than other protocols that were compared, and consequently, it reduces the impact in NCS-UDPlike flows sharing the same network, whose performance was measured employing the Integral Time Absolute Error (ITAE), which is a desirable feature for Industrial IoT.

BBR-With Enhanced Fairness (BBR-EFRA): A new enhanced RTT fairness for BBR congestion control algorithm

Towards the end of 2016, the Google research team proposed and developed a new state-of-the-art TCP congestion control algorithm called Bottleneck Bandwidth and Round-trip propagation time (BBR). When deployed on various Google internal servers, BBR attained higher throughput and low latency performances on modern-day and sophisticated networks than traditional congestion control algorithms like Cubic, Reno, or Vegas. Unlike conventional congestion control algorithms, BBR controls data transmission by maximizing delivery rate and minimizing the round-trip time (RTT), therefore maximizing bandwidth utilization and improving throughput and latency delay performances. However, some experiments have reported persistent queue formation and massive packet retransmissions rate in the bottleneck link, which happens to be the main cause of unfairness between different RTT flows. BBR prefers and favors long RTT over short RTT flows; therefore, long RTT flows are allocated more bandwidth when competing with short RTT flows in the bottleneck link. It was also noted that even the minor difference between the two competing flows could be a source of throughput imbalance and unfairness. The dominance of long RTT flows is the central origin of the high queuing delay, packet loss and retransmission rates, and even severe BBR vulnerability that malicious users can exploit to obtain a larger share of bandwidth simply by increasing the RTT (delay). Therefore, we propose a BBR-With Enhanced Fairness (BBR-EFRA) to mitigate this major concern challenge. Our proposed algorithm adaptively controls the congestion window (CWND) by adjusting the bandwidth delay product (BDP) values of each BBR flow during data transmission based on buffer queue status computation. Our proposed approach guarantees different RTT flows to compete for the available bottleneck bandwidth more equally, hence improving the BBR fairness issue. We evaluated our algorithm on the NS3 simulating environment. BBR-EFRA shows improved RTT fairness by more than 16%, reduced retransmission rate by more than 20%, and queuing delay by more than 18%, and finally increased Jain’s fairness index by more than 1.3 times compared with other recently published BBR variants like an adaptive congestion window of BBR(BBR-ACW). Therefore, with BBR-EFRA, short RTT flows can fairly compete for the available bandwidth even with higher RTT differences under various network scenarios.

Location constrained virtual optical network embedding in space-division multiplexing elastic optical networks

Virtual optical network embedding (VONE) involves two processes, node mapping, and link mapping. Node mapping decides which data center to place the virtual nodes (VNs). Link mapping decides how to route virtual links between placed VNs. In location constrained VONE (LC-VONE), each VN can be mapped only to some substrate nodes. In this paper, we study the problem of LC-VONE in space-division multiplexing elastic optical networks (SDM-EONs). Aiming at load balancing, we formulate the first integer linear programming (ILP) to solve the static LC-VONE problem, where the set of VON requests is given. To achieve scalability, an integrated framework is devised to jointly conduct node and link mappings for each VON request. Three cost metrics are designed for finding the least-cost VON solution. Combining them with the integrated framework, three LC-VONE algorithms are proposed: minimum spectrum consumption (MSC), bottleneck link avoidance (BLA), and lowest traffic load (LTL). By applying them to solve both static and dynamic LC-VONE problems, we found that LTL always outperforms MSC and BLA.

Real-Time Detection and Mitigation of LDoS Attacks in the SDN Using the HGB-FP Algorithm

The software-defined network (SDN) has created the conditions for the optimization and development of network structures. However, its architecture is still not sufficient to resist or identify all denial of service (DoS) attacks, such as low-rate DoS (LDoS) attacks. Due to their low transporting rate and flash-crowd-like nature, LDoS attacks are well hidden in the background traffic and difficult to identify by anti-DoS mechanisms in the SDN. By implementing LDoS attacks in the SDN, we confirm that they can severely degrade the quality of service. We further propose a framework based on the histogram-based gradient boosting and finding peaks (HGB-FP) algorithm to detect LDoS attacks and mitigate their influence in the SDN in real-time. The histogram-based gradient boosting (HGB) algorithm, an ensemble learning with high quality and low complexity, can identify LDoS attacks quickly and accurately. The finding peaks (FP) algorithm locates the attacker via peak properties of the flow and installs flow rules on the switches to drop the attack flows. Experiments prove that our framework has higher accuracy and F-measure in identifying LDoS attacks than other machine learning approaches and mitigates the impact of LDoS attacks on bottleneck links in the SDN within seconds on average.

Coarse-Grained Load Balancing with Traffic-Aware Marking in Data Center Networks

In the data center environment, meeting the dissimilar requirements of transmission performance for long and short data flows is crucial for guaranteeing the data center service quality. It is well known that optimizing the network transmission performance is the key. Abundant research studies have reported that designing an effective load-balancing scheme can boost the network performance by enhancing the path utilization of multirooted data center networks. Unfortunately, the existing data center load-balancing schemes fail to provide satisfying flow-level transmission performance since they choose to ignore the constant changing path status and blindly assign paths to data flows regardless of their different performance requirements. In order to solve these issues, we propose a data center load-balancing scheme called TAM to meet the performance requirements of long and short flows simultaneously. In detail, TAM leverages the traffic-aware ECN marking mechanism to force long flows to proactively maintain the switch queue length at a low level and release bandwidth to short flows when they compete for the bottleneck link. On the contrary, TAM carries out the coarse-grained routing or rerouting for short and long flows, respectively, in real time by perceiving the path status. In this way, the short flows achieve low delay transmission while the long flows obtain high throughput. The simulation results based on NS2 tests show that TAM achieves up to 60% lower FCTs for short flows and a 40% improvement in goodput for long flows.

Age of Information in Multihop Connections With Tributary Traffic and No Preemption

Age of Information (AoI) has gained significant attention from the research community because of its applications to Internet of Things (IoT) monitoring and control. In this work, we treat multihop connections over queuing networks with tributary flows and non-preemptive service: packets cannot be discarded because they are utilized for other system objectives, such as data analytics. Without preemption, the key tool for optimizing AoI is then the scheduling policy between the different data flows at each intermediate node. This is the subject of our analysis, along with the impact of packet erasure on the age. We derive upper and lower bounds for the average AoI considering several queuing policies in arbitrary network topologies, and present the results in different scenarios. Network topology, tributary traffic load, and link characteristics such as packet erasure generate complex trade-offs, which affect the optimal operation point and the age performance. The scheduling strategy at each node can also affect performance and fairness among users, particularly at critical bottleneck links, which have a significant impact on the overall performance of the whole network.

NLPC: A nimble low-priority congestion control algorithm for high-speed and lossy networks

Thousands of bursty and concurrent flows persistently overwhelm the bottleneck bandwidth. The quality of experience of low-latency applications (e.g. Web, AR/VR, IoT, VoIP, etc.) is inevitably influenced by the background applications (e.g. file-sharing, system updates) which tend to overfill the buffer of bottleneck links. To solve the above issues, Less-than-Best-Effort (LBE) protocols were proposed to support the data transmission of background applications. Therein, Low Extra Delay Background Transport (LEDBAT) is one of the most widely deployed LBE protocols. To achieve the low-priority feature, LEDBAT senders saturate the residual bandwidth of bottleneck links while keeping the low queuing delay. However, through the simulation, we observed that LEDBAT performs a severe throughput degradation in high-speed and lossy networks. To resolve this issue, an enhanced LEDBAT, named NLPC, is presented in this paper. NLPC introduces a variable gain value, which can dynamically adapt to the network congestion degree, to update the sending rate. Extensive simulations in NS-2 and some small-scale real-world experiments indicate that in high-speed and lossy networks, NLPC increases throughput up to 40% in comparison with LEDBAT, and maintains the low-priority feature as the other LBE protocols.

Manufacturing diffusion trends from the perspective of trade network: the belt vs. the road

ABSTRACT With the deepening of the ‘Belt and Road (B&R)’ initiative (BRI), the trends of manufacturing relocation and diffusion along the B&R has accelerated. This paper first collects bilateral trade data of 16 categories of major commodities among 55 countries along the B&R for 2017. Second, according to current and future transportation accessibility, this paper constructs two types of topological networks among countries along the B&R from the perspectives of both competitive and complementary trade. Percolation theory is adopted to analyze the manufacturing diffusion trends and bottleneck links among the two types of topological trade networks, which provides a new perspective through which to study manufacturing relocation and diffusion patterns. The results show that (1) based on current transportation accessibility, main industries percolate and agglomerate among countries with rapid economic development along the 21st Century Maritime Silk Road and that (2) the improvement in the China-Europe Railway Express (CR Express) postpones diffusion trends and improves the relocation potential of Silk Road Economic Belt regions.

Distributed Bandwidth Allocation Strategy for QoE Fairness of Multiple Video Streams in Bottleneck Links

In QoE fairness optimization of multiple video streams, a distributed video stream fairness scheduling strategy based on federated deep reinforcement learning is designed to address the problem of low bandwidth utilization due to unfair bandwidth allocation and the problematic convergence of distributed algorithms in cooperative control of multiple video streams. The proposed strategy predicts a reasonable bandwidth allocation weight for the current video stream according to its player state and the global characteristics provided by the server. Then the congestion control protocol allocates the proportion of available bandwidth, matching its bandwidth allocation weight to each video stream in the bottleneck link. The strategy trains a local predictive model on each client and periodically performs federated aggregation to generate the optimal global scheme. In addition, the proposed strategy constructs global parameters containing information about the overall state of the video system to improve the performance of the distributed scheduling algorithm. The experimental results show that the introduction of global parameters can improve the algorithm’s QoE fairness and overall QoE efficiency by 10% and 8%, respectively. The QoE fairness and overall QoE efficiency are improved by 8% and 7%, respectively, compared with the latest scheme.

A Congestion Control Framework Based on In-Network Resource Pooling

Congestion control has traditionally relied on monitoring packet-level performance ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , latency, loss) through feedback signals propagating end-to-end together with various queue management practices ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , carefully setting various parameters, such as router buffer thresholds) in order to regulate traffic flow. Due to its end-to-end nature, this approach is known to transfer data according to the path’s slowest link, requiring several RTTs to transmit even a few tens of KB during slow start. In this paper, we take a radically different approach to control congestion, which obviates end-to-end performance monitoring and careful setting of network parameters. The resulting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In-Network Resource Pooling Protocol (INRPP)</i> extends the resource pooling principle to exploit in-network resources such as router storage and unused bandwidth along alternative sub-paths. In INRPP, content caches or large ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">possibly bloated</i> ) router buffers are used as a place of temporary custody for incoming data packets in a store and forward manner. Data senders push data in the network and when it hits the bottleneck link, in-network caches at every hop store data in excess of the link capacity; nodes progressively move/send data (from one cache to the next) towards the destination. At the same time <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">alternative sub-paths</i> are exploited to move data faster towards the destination. We demonstrate through extensive simulations that INRPP is TCP friendly, and improves flow completion time and fairness by as much as 50% compared to RCP, MPTCP and TCP, under realistic network conditions.

A fair dynamic content store-based congestion control strategy for named data networking

In this paper, the congestion control for named data networking (NDN) is studied. A novel dynamic content store-based congestion control strategy is proposed on account of the characteristic of in-network cache in NDN. A queuing network model is constructed to judge whether congestion occurs. If the network has the tendency of congestion or the congestion happened, the buffer of the output queue is dynamically expanded by borrowing NDN content store (CS), and the forwarding rates of data packets and corresponding interest packets are reduced so as to prevent or alleviate network congestion. In order to reflect fairness, the CS to be borrowed by the data output queue in the port is calculated in terms of the data output queue length and its weight. The simulation results based on ndnSIM show that the given scheme improves the bottleneck link utilization and maintains a low packet loss rate and average flow completion time.

Design and analysis of a fair, efficient and stable multi-server adaptive streaming protocol

Due to the surging bandwidth demand for applications like 360° video streaming, hypertext transfer protocol (HTTP) adaptive video streaming has been extended to fetch contents from a single server to multiple servers. However, when players share a bottleneck link, it fails to hold three critical properties: fairness, efficiency, and stability. Effective algorithms have been proposed to tackle the problem in the single-server case. We revisit the three properties and show that they degenerate in multi-server streaming. In this paper, we propose MultiFast, a protocol that offers the best user experience by achieving fair, efficient, and stable multi-server video streaming. MultiFast consists of (1) a bitrate selection algorithm that ensures fairness with concurrent connections; (2) a connection management algorithm that ensures efficiency and stability by managing connections based on network conditions. Our real-world experiments show that MultiFast achieves the best performance in fairness, efficiency, and stability metrics, outperforming its closest alternative.