Biblio

The excess buffering of packets in network elements, also referred to as bufferbloat, results in high latency. Considering the requirements of traffic generated by video conferencing systems like Zoom, cloud rendered gaming platforms like Google Stadia, or even video streaming services such as Netflix, Amazon Prime and YouTube, timeliness of such traffic is important. Ensuring low latency to IP flows with a high throughput calls for the application of Active Queue Management (AQM) schemes. This introduces yet another problem as the co-existence of scalable and classic congestion controls leads to the starvation of classic TCP flows. Technologies such as Low Latency Low Loss Scalable Throughput (L4S) and the corresponding dual queue coupled AQM, DualPI2, provide a robust solution to these problems. However, their deployment on hardware targets such as programmable switches is quite challenging due to the complexity of algorithms and architectural constraints of switching ASICs. In this study, we provide proof of concept implementations of two AQMs that enable the co-existence of scalable and traditional TCP traffic, namely DualPI2 and the preceding single-queue PI2 AQM, on an Intel Tofino switching ASIC. Given the fixed operation of the switch’s traffic manager, we investigate to what extent it is possible to implement a fully RFC-compliant version of the two AQMs on the Tofino ASIC. The study shows that an appropriate split between control and data plane operations is required while we also exploit fixed functionality of the traffic manager to support such solutions.

This paper is devoted to boundary control design for urban traffic described on a macroscopic scale. The state corresponds to vehicle density that evolves on a continuum two-dimensional domain that represents a continuous approximation of a urban network. Its parameters are interpolated as a function of distance to physical roads. The dynamics are governed by a new macroscopic multi-directional traffic model that encompasses a system of four coupled partial differential equations (PDE) each describing density evolution in one direction layer: North, East, West and South (NEWS). We analyse the class of desired states that the density governed by NEWS model can achieve. Then a boundary control is designed to drive congested traffic to an equilibrium with the minimal congestion level. The result is validated numerically using the real structure of Grenoble downtown (a city in France).

Since its inception, the Internet has experienced tremendous speed and functionality improvements. Among these developments are innovative approaches such as the design and deployment of Internet Protocol version six (IPv6) and the continuous modification of TCP. New transport protocols like Stream Communication Transport Protocol (SCTP) and Multipath TCP (MPTCP), which can use multiple data paths, have been developed to overcome the IP-coupled challenge in TCP. However, given the difficulties of packet modifiers over the Internet that prevent the deployment of newly proposed protocols, e.g., SCTP, a UDP innovative approach with QUIC (Quick UDP Internet Connection) has been put forward as an alternative. QUIC reduces the connection establishment complexity in TCP and its variants, high security, stream multiplexing, and pluggable congestion control. Motivated by the gains and acceptability of MPTCP, Multipath QUIC has been developed to enable multipath transmission in QUIC. While several researchers have reviewed the progress of improvement and application of MPTCP, the review on MPQUIC improvement is limited. To breach the gap, this paper provides a brief survey on the practical application and progress of MPQUIC in data communication. We first review the fundamentals of multipath transport protocols. We then provide details on the design of QUIC and MPQUIC. Based on the articles reviewed, we looked at the various applications of MPQUIC, identifying the application domain, tools used, and evaluation parameters. Finally, we highlighted the open research issues and directions for further investigations.

With the development of IoT and 5G networks, the demand for the next-generation intelligent transportation system has been growing at a rapid pace. Dynamic mapping has been considered one of the key technologies to reduce traffic accidents and congestion in the intelligent transportation system. However, as the number of vehicles keeps growing, a huge volume of mapping traffic may overload the central cloud, leading to serious performance degradation. In this paper, we propose and prototype a CUPS (control and user plane separation)-based edge computing architecture for the dynamic mapping and quantify its benefits by prototyping. There are a couple of merits of our proposal: (i) we can mitigate the overhead of the networks and central cloud because we only need to abstract and send global dynamic mapping information from the edge servers to the central cloud; (ii) we can reduce the response latency since the dynamic mapping traffic can be isolated from other data traffic by being generated and distributed from a local edge server that is deployed closer to the vehicles than the central server in cloud. The capabilities of our system have been quantified. The experimental results have shown our system achieves throughput improvement by more than four times, and response latency reduction by 67.8% compared to the conventional central cloud-based approach. Although these results are still obtained from the preliminary evaluations using our prototype system, we believe that our proposed architecture gives insight into how we utilize CUPS and edge computing to enable efficient dynamic mapping applications.

Software Defined Networking (SDN) provides opportunities for flexible and dynamic traffic engineering. However, in current SDN systems, routing strategies are based on traditional mechanisms which lack in real-time modification and less efficient resource utilization. To overcome these limitations, deep learning is used in this paper to improve the routing computation in SDN. This paper proposes Convolutional Deep Reinforcement Learning (CoDRL) model which is based on deep reinforcement learning agent for routing optimization in SDN to minimize the mean network delay and packet loss rate. The CoDRL model consists of Deep Deterministic Policy Gradients (DDPG) deep agent coupled with Convolution layer. The proposed model tends to automatically adapts the dynamic packet routing using network data obtained through the SDN controller, and provides the routing configuration that attempts to reduce network congestion and minimize the mean network delay. Hence, the proposed deep agent exhibits good convergence towards providing routing configurations that improves the network performance.

In recent years, integration of Passive Optical Net-work(PON) and WiMAX (Worldwide Interoperability Microwave Access Network) network is attracting huge interest among many researchers. The continuous demand for large bandwidths with wider coverage area are the key drivers to this technology. This integration has led to high speed and cost efficient solution for internet accessibility. This paper investigates the issues related to traffic grooming, routing and resource allocation in the hybrid networks. The Elastic Optical Network forms Backbone and is integrated with WiMAX. In this novel approach, traffic grooming is carried out using light trail technique to minimize the bandwidth blocking ratio and also reduce the network resource consumption. The simulation is performed on different network topologies, where in the traffic is routed through three modes namely the pure Wireless Network, the Wireless-Optical/Optical-Wireless Network, the pure Optical Network keeping the network congestion in mind. The results confirm reduction in bandwidth blocking ratio in all the given networks coupled with minimum network resource utilization.

The progressive expansion of renewable energies, especially wind power plants being promoted in Germany as part of the energy transition, places new demands on the transmission grid. As an alternative to grid expansion, sector coupling of the gas and electricity sector through Power-to-Gas (PtG) technology is seen as a great opportunity to make the energy transmission more flexible and reliable in the future as well as make use of already existing gas infrastructure. In this paper, PtG plants are dimensioned and placed in a model of the German transmission grid. Time series based load flow calculations are performed allowing conclusions about the line loading for the exemplary year 2016.

Over the last couple of decades, mobile ad-hoc networks (MANETs) have been at the forefront of research, yet still are afflicted by high network fragmentation, due to their continuous node mobility and geographical dispersion. To address these concerns, a new paradigm was proposed, Information-Centric Networks (ICN), whose focus is the delivery of Content based on names. This article aims to use ICN concepts towards the delivery of both urgent and non-urgent information in urban mobile environments. In order to do so, a context-based forwarding strategy was proposed, with a very clear goal: to take advantage of both packet Names and Data, and node's neighborhood analysis in order to successfully deliver content into the network in the shortest period of time, and without worsening network congestion. The design, implementation and validation of the proposed strategy was performed using the ndnSIM platform along with real mobility traces from communication infrastructure of the Porto city. The results show that the proposed context-based forwarding strategy presents a clear improvement regarding the Data resolution, while maintaining network overhead at a constant.

With the development of modern High-Speed Railway (HSR) and mobile communication systems, network operators have a strong demand to provide high-quality on-board Internet services for HSR passengers. Multi-path TCP (MPTCP) provides a potential solution to aggregate available network bandwidth, greatly overcoming throughout degradation and severe jitter using single transmission path during the high-speed train moving. However, the choose of MPTCP algorithms, i.e., Coupled or Uncoupled, has a great impact on the performance. In this paper, we investigate this interesting issue in the practical datasets along multiple HSR lines. Particularly, we collect the first-hand network datasets and analyze the characteristics and category of traffic flows. Based on this statistics, we measure and analyze the transmission performance for both mice flows and elephant ones with different MPTCP congestion control algorithms in HSR scenarios. The simulation results show that, by comparing with the coupled MPTCP algorithms, i.e., Fully Coupled and LIA, the uncoupled EWTCP algorithm provides more stable throughput and balances congestion window distribution, more suitable for the HSR scenario for elephant flows. This work provides significant reference for the development of on-board devices in HSR network systems.

We present ctrlTCP, a method to combine the congestion controls of multiple TCP connections. In contrast to the previous methods such as the Congestion Manager, ctrlTCP can couple all TCP flows that leave one sender, traverse a common bottleneck (e.g., a home user's thin uplink) and arrive at different destinations. Using ns-2 simulations and an implementation in the FreeBSD kernel, we show that our mechanism reduces queuing delay, packet loss, and short flow completion times while enabling precise allocation of the share of the available bandwidth between the connections according to the needs of the applications.

Traffic congestion is a critical problem in urban area. In this study, our objective is the control of traffic lights in an urban environment, in order to avoid traffic jams and optimize vehicle traffic; we aim to minimize the total waiting time. Our system is based on a new paradigm, which is deep reinforcement learning; it can automatically learn all the useful characteristics of traffic data and develop a strategy optimizing adaptive traffic light control. Our system is coupled to a microscopic simulator based on agents (Simulation of Urban MObility - SUMO) providing a synthetic but realistic environment in which the exploration of the results of potential regulatory actions can be carried out.

Traffic Signal Control using Reinforcement Learning has been proved to have potential in alleviating traffic congestion in urban areas. Although research has been conducted in this field, it is still an open challenge to find an effective but low-cost solution to this problem. This paper presents multiple deep reinforcement learning-based traffic signal control systems that can help regulate the flow of traffic at intersections and then compares the results. The proposed systems are coupled with SUMO (Simulation of Urban MObility), an agent-based simulator that provides a realistic environment to explore the outcomes of the models.

The demand for increasing flexibility use in power systems is stressed by the changing grid utilization. Making use of largely untapped flexibility potential is possible through novel flexibility markets. Different approaches for these markets are being developed and vary considering their handling of transaction schemes and relation of participating entities. This paper delivers the conceptual development of a holistic system architecture for the realization of an interregional flexibility market, which targets a market based congestion management in the transmission and distribution system through trading between system operators and flexibility providers. The framework combines a market mechanism with the required supplements like appropriate control algorithms for emergency situations, cyber-physical system monitoring and cyber-security assessment. The resulting methods are being implemented and verified in a remote-power-hardware-in-the-loop setup coupling a real world low voltage grid with a geographically distant real time simulation using state of the art control system applications with an integration of the aforementioned architecture components.

With the increasing expansion of wind and solar power plants, these technologies will also have to contribute control reserve to guarantee frequency stability within the next couple of years. In order to maintain the security of supply at the same level in the future, it must be ensured that wind and solar power plants are able to feed in electricity into the distribution grid without bottlenecks when activated. The present work presents a grid state assessment, which takes into account the special features of the control reserve supply. The identification of a future grid state, which is necessary for an ex ante evaluation, poses the challenge of forecasting loads. The Boundary Load Flow method takes load uncertainties into account and is used to estimate a possible interval for all grid parameters. Grid congestions can thus be detected preventively and suppliers of control reserve can be approved or excluded. A validation in combination with an exemplary application shows the feasibility of the overall methodology.

In wireless sensor networks (WSNs), congestion control is a very essential region of concern. When the packets that are coming get increased than the actual capacity of network or nodes results into congestion in the network. Congestion in network can cause reduction in throughput, increase in network delay, and increase in packet loss and sensor energy waste. For that reason, new complex methods are mandatory to tackle with congestion. So it is necessary to become aware of congestion and manage the congested resources in wireless sensor networks for enhancing the network performance. Diverse methodologies for congestion recognition and prevention have been presented in the previous couple of years. To handle some of the problems, this paper exhibits a new technique for controlling the congestion. An efficient and reliable routing protocol (ERRP) based on bio inspired algorithms is introduced in this paper for solving congestion problem. In the proposed work, a way is calculated to send the packets on the new pathway. The proposed work has used three approaches for finding the path which results into a congestion free path. Our analysis and simulation results shows that our approach provides better performance as compared to previous approaches in terms of throughput, packet loss, delay etc.

With the world population becoming increasingly urban and the multiplication of mega cities, urban leaders have responded with plans calling for so called smart cities relying on instantaneous access to information using mobile devices for an intelligent management of resources. Coupled with the advent of the smartphone as the main platform for accessing the Internet, this has created the conditions for the looming wireless bandwidth crunch. This paper presents a content delivery infrastructure relying on off-the-shelf technology and the public transportation network (PTN) aimed at relieving the wireless bandwidth crunch in urban centers. Our solution proposes installing WiFi access points on selected public bus stations and buses and using the latter as data mules, creating a delay tolerant network capable of carrying content users can access while using the public transportation. Building such an infrastructure poses several challenges, including congestion points in major hubs and the cost of additional hardware necessary for secure communications. To address these challenges we propose a 3-Tier architecture that guarantees end-to-end delivery and minimizes hardware cost. Trace-based simulations from three major European cities of Paris, Helsinki and Toulouse demonstrate the viability of our design choices. In particular, the 3-Tier architecture is shown to guarantee end-to-end connectivity and reduce the deployment cost by several times while delivering at least as many packets as a baseline architecture.

The major challenge of Real Time Protocol is to balance efficiency and fairness over limited bandwidth. MPTCP has proved to be effective for multimedia and real time networks. Ideally, an MPTCP sender should couple the subflows sharing the bottleneck link to provide TCP friendliness. However, existing shared bottleneck detection scheme either utilize end-to-end delay without consideration of multiple bottleneck scenario, or identify subflows on switch at the expense of operation overhead. In this paper, we propose a lightweight yet accurate approach, EMPTCP, to detect shared bottleneck. EMPTCP uses the widely deployed ECN scheme to capture the real congestion state of shared bottleneck, while at the same time can be transparently utilized by various enhanced MPTCP protocols. Through theory analysis, simulation test and real network experiment, we show that EMPTCP achieves higher than 90% accuracy in shared bottleneck detection, thus improving the network efficiency and fairness.

Reconfigurability is very popular in advanced highly integrated wireless communication circuits and systems, which is valuable for mitigating spectrum congestion and reducing signal interference. To reduce interference and meet the different wireless standards in different countries, frequency reconfigurable filters are promising. Concurrently, due to the conductor and semiconductor properties of VO2 at different temperatures or pressures, the phase transition characteristics of new material VO2 are applied to reconfigurable filters. This paper mainly discusses the application of phase transition characteristics of VO2 materials in filter design and proposes a frequency reconfigurable microstrip bandpass filter based on VO2 materials, in which the microstrip filter adopts the design form of end coupling. Through theoretical calculation, data analysis, and the establishment of the equivalent model of VO2 phase transition, a related design is proposed. An end-coupled microband bandpass filter centered at a reconfigurable frequency (6 GHz to 6.5 GHz) with fractional bandwidth of 2.8% has been designed, which shows consistent match with the expected ones and verify the validity of the proposed method.

Coupling regular topologies with optimized routing algorithms is key in pushing the performance of interconnection networks of HPC systems. In this paper we present Dmodc, a fast deterministic routing algorithm for Parallel Generalized Fat-Trees (PGFTs) which minimizes congestion risk even under massive topology degradation caused by equipment failure. It applies a modulo-based computation of forwarding tables among switches closer to the destination, using only knowledge of subtrees for pre-modulo division. Dmodc allows complete re-routing of topologies with tens of thousands of nodes in less than a second, which greatly helps centralized fabric management react to faults with high-quality routing tables and no impact to running applications in current and future very large-scale HPC clusters. We compare Dmodc against routing algorithms available in the InfiniBand control software (OpenSM) first for routing execution time to show feasibility at scale, and then for congestion risk under degradation to demonstrate robustness. The latter comparison is done using static analysis of routing tables under random permutation (RP), shift permutation (SP) and all-to-all (A2A) traffic patterns. Results for Dmodc show A2A and RP congestion risks similar under heavy degradation as the most stable algorithms compared, and near-optimal SP congestion risk up to 1% of random degradation.

In multi-tenant datacenters, the hardware may be homogeneous but the traffic often is not. For instance, customers who pay an equal amount of money can get an unequal share of the bottleneck capacity when they do not open the same number of TCP connections. To address this problem, several recent proposals try to manipulate the traffic that TCP sends from the VMs. VCC and AC/DC are two new mechanisms that let the hypervisor control traffic by influencing the TCP receiver window (rwnd). This avoids changing the guest OS, but has limitations (it is not possible to make TCP increase its rate faster than it normally would). Seawall, on the other hand, completely rewrites TCP's congestion control, achieving fairness but requiring significant changes to both the hypervisor and the guest OS. There seems to be a need for a middle ground: a method to control TCP's sending rate without requiring a complete redesign of its congestion control. We introduce a minimally-invasive solution that is flexible enough to cater for needs ranging from weighted fairness in multi-tenant datacenters to potentially offering Internet-wide benefits from reduced interflow competition.

The ever-increasing growth of urban populations coupled with recent mobile data usage trends has led to an unprecedented increase in wireless devices, services and applications, with varying quality of service needs in terms of latency, data rate, and connectivity. To cope with these rising demands and challenges, next-generation wireless networks have resorted to cloud radio access network (Cloud-RAN) technology as a way of reducing latency and network traffic. A concrete example of this is New York City's LinkNYC network infrastructure, which replaces the city's payphones with kiosk-like structures, called Links, to provide fast and free public Wi-Fi access to city users. When enabled with data storage capability, these Links can, for example, play the role of edge cloud devices to allow in-network content caching so that access latency and network traffic are reduced. In this paper, we propose HybridCache, a hybrid proactive and reactive in-network caching scheme that reduces content access latency and network traffic congestion substantially. It does so by first grouping edge cloud devices in clusters to minimize intra-cluster content access latency and then enabling cooperative-proactively and reactively-caching using LSTM-based prediction to minimize in-network content redundancy. Using the LinkNYC network as the backbone infrastructure for evaluation, we show that HybridCache reduces the number of hops that content needs to traverse and increases cache hit rates, thereby reducing both network traffic and content access latency.

Renewable energy resource plays an important role due to increasing energy claim. Power generation by PV technology is one of the fastest growing renewable energy sources due to its clean, economical and sustainable property. Grid integrated PV systems plays an important role in power generation sector. As the energy demand is increasing day by day, the power transfer capability of transmission line is increasing which leads various problems like stability, increase in fault current, congestion etc. To overcome the problem, we can use either FACTS device or battery storage or construct additional lines which is cost effective. This paper deals with grid connected PV system, which functions as PV-STATCOM. Voltage and damping control are used to elevate the power transfer capacity and to achieve regulated voltage within the limits at the point of common coupling (PCC). The studies are performed on SMIB and the simulation is carried out in MATLAB/SIMULINK environment.

In this paper, according to the electricity business process including collecting and transmitting power information and sending control instructions, a coupling model of control-communication flow is built which is composed of three main matrices: control-communication, communication-communication, communication-control incidence matrices. Furthermore, the effective path change between two communication nodes is analyzed and a calculation method of connectivity probability for information network is proposed when considering a breakdown in communication links. Then, based on Bayesian conditional probability theory, the effect of the communication interruption on the energy Internet is analyzed and the metric matrix of controllability is given under communication congestion. Several cases are given in the final of paper to verify the effectiveness of the proposed method for calculating controllability matrix by considering different link interruption scenarios. This probability index can be regarded as a quantitative measure of the controllability of the power service based on the communication transmission instructions, which can be used in the power business decision-making in order to improve the control reliability of the energy Internet.

Incast traffic is a many-to-one communication pattern used in many applications, including distributed storage, web-search with partition/aggregation design pattern, and MapReduce, commonly in data centers. It is generally composed of short-lived flows that may be queued behind large flows' packets in congested switches where performance degradation is observed. Smart buffering at the switch level is sensed to mitigate this issue by automatically and dynamically adapting to traffic conditions changes in the highly dynamic data center environment. But for this dynamic and smart buffer management to become effectively beneficial for all the traffic, and especially for incast the most critical one, incast performance models that provide insights on how various factors affect it are needed. The literature lacks these types of models. The existing ones are analytical models, which are either tightly coupled with a particular protocol version or specific to certain empirical data. Motivated by this observation, we propose a machine-learning-based incast performance inference. With this prediction capability, smart buffering scheme or other QoS optimization algorithms could anticipate and efficiently optimize system parameters adjustment to achieve optimal performance. Since applying machine learning to networks managed in a distributed fashion is hard, the prediction mechanism will be deployed on an SDN control plane. We could then take advantage of SDN's centralized global view, its telemetry capabilities, and its management flexibility.

Communication between two Internet hosts using parallel connections may result in unwanted interference between the connections. In this dissertation, we propose a sender-side solution to address this problem by letting the congestion controllers of the different connections collaborate, correctly taking congestion control logic into account. Real-life experiments and simulations show that our solution works for a wide variety of congestion control mechanisms, provides great flexibility when allocating application traffic to the connections, and results in lower queuing delay and less packet loss.