A Network Congestion control Protocol (NCP)

The transmission control protocol (TCP) which is the dominant congestion control protocol at the transport layer is proved to have many performance problems with the growth of the Internet. TCP for instance results in throughput degradation for high bandwidth delay product networks and is unfair for flows with high round trip delays. There have been many patches and modifications to TCP all of which inherit the problems of TCP in spite of some performance improve- ments. On the other hand there are clean-slate design approaches of the Internet. The eXplicit Congestion control Protocol (XCP) and the Rate Control Protocol (RCP) are the prominent clean slate congestion control protocols. Nonetheless, the XCP protocol is also proved to have its own performance problems some of which are its unfairness to long flows (flows with high round trip delay), and many per-packet computations at the router. As shown in this paper RCP also makes gross approximation to its important component that it may only give the performance reports shown in the literature for specific choices of its parameter values and traffic patterns. In this paper we present a new congestion control protocol called Network congestion Control Protocol (NCP). We show that NCP can outperform both TCP, XCP and RCP in terms of among other things fairness and file download times.unpublishe

Dynamics analysis and integrated design of real-time control systems

Real-time control systems are widely deployed in many applications. Theory and practice for the design and deployment of real-time control systems have evolved significantly. From the design perspective, control strategy development has been the focus of the research in the control community. In order to develop good control strategies, process modelling and analysis have been investigated for decades, and stability analysis and model-based control have been heavily studied in the literature. From the implementation perspective, real-time control systems require timeliness and predictable timing behaviour in addition to logical correctness, and a real-time control system may behave very differently with different software implementations of the control strategies on a digital controller, which typically has limited computing resources. Most current research activities on software implementations concentrate on various scheduling methodologies to ensure the schedulability of multiple control tasks in constrained environments. Recently, more and more real-time control systems are implemented over data networks, leading to increasing interest worldwide in the design and implementation of networked control systems (NCS). Major research activities in NCS include control-oriented and scheduling-oriented investigations. In spite of significant progress in the research and development of real-time control systems, major difficulties exist in the state of the art. A key issue is the lack of integrated design for control development and its software implementation. For control design, the model-based control technique, the current focus of control research, does not work when a good process model is not available or is too complicated for control design. For control implementation on digital controllers running multiple tasks, the system schedulability is essential but is not enough; the ultimate objective of satisfactory quality-of-control (QoC) performance has not been addressed directly. For networked control, the majority of the control-oriented investigations are based on two unrealistic assumptions about the network induced delay. The scheduling-oriented research focuses on schedulability and does not directly link to the overall QoC of the system. General solutions with direct QoC consideration from the network perspective to the challenging problems of network delay and packet dropout in NCS have not been found in the literature. This thesis addresses the design and implementation of real-time control systems with regard to dynamics analysis and integrated design. Three related areas have been investigated, namely control development for controllers, control implementation and scheduling on controllers, and real-time control in networked environments. Seven research problems are identified from these areas for investigation in this thesis, and accordingly seven major contributions have been claimed. Timing behaviour, quality of control, and integrated design for real-time control systems are highlighted throughout this thesis. In control design, a model-free control technique, pattern predictive control, is developed for complex reactive distillation processes. Alleviating the requirement of accurate process models, the developed control technique integrates pattern recognition, fuzzy logic, non-linear transformation, and predictive control into a unified framework to solve complex problems. Characterising the QoC indirectly with control latency and jitter, scheduling strategies for multiple control tasks are proposed to minimise the latency and/or jitter. Also, a hierarchical, QoC driven, and event-triggering feedback scheduling architecture is developed with plug-ins of either the earliest-deadline-first or fixed priority scheduling. Linking to the QoC directly, the architecture minimises the use of computing resources without sacrifice of the system QoC. It considers the control requirements, but does not rely on the control design. For real-time NCS, the dynamics of the network delay are analysed first, and the nonuniform distribution and multi-fractal nature of the delay are revealed. These results do not support two fundamental assumptions used in existing NCS literature. Then, considering the control requirements, solutions are provided to the challenging NCS problems from the network perspective. To compensate for the network delay, a real-time queuing protocol is developed to smooth out the time-varying delay and thus to achieve more predictable behaviour of packet transmissions. For control packet dropout, simple yet effective compensators are proposed. Finally, combining the queuing protocol, the packet loss compensation, the configuration of the worst-case communication delay, and the control design, an integrated design framework is developed for real-time NCS. With this framework, the network delay is limited to within a single control period, leading to simplified system analysis and improved QoC

Overlay networks monitoring

The phenomenal growth of the Internet and its entry into many aspects of daily life has led to a great dependency on its services. Multimedia and content distribution applications (e.g., video streaming, online gaming, VoIP) require Quality of Service (QoS) guarantees in terms of bandwidth, delay, loss, and jitter to maintain a certain level of performance. Moreover, E-commerce applications and retail websites are faced with increasing demand for better throughput and response time performance. The most practical way to realize such applications is through the use of overlay networks, which are logical networks that implement service and resource management functionalities at the application layer. Overlays offer better deployability, scalability, security, and resiliency properties than network layer based implementation of services. Network monitoring and routing are among the most important issues in the design and operation of overlay networks. Accurate monitoring of QoS parameters is a challenging problem due to: (i) unbounded link stress in the underlying IP network, and (ii) the conflict in measurements caused by spatial and temporal overlap among measurement tasks. In this context, the focus of this dissertation is on the design and evaluation of efficient QoS monitoring and fault location algorithms using overlay networks. First, the issue of monitoring accuracy provided by multiple concurrent active measurements is studied on a large-scale overlay test-bed (PlanetLab), the factors affecting the accuracy are identified, and the measurement conflict problem is introduced. Then, the problem of conducting conflict-free measurements is formulated as a scheduling problem of real-time tasks, its complexity is proven to be NP-hard, and efficient heuristic algorithms for the problem are proposed. Second, an algorithm for minimizing monitoring overhead while controlling the IP link stress is proposed. Finally, the use of overlay monitoring to locate IP links\u27 faults is investigated. Specifically, the problem of designing an overlay network for verifying the location of IP links\u27 faults, under cost and link stress constraints, is formulated as an integer generalized flow problem, and its complexity is proven to be NP-hard. An optimal polynomial time algorithm for the relaxed problem (relaxed link stress constraints) is proposed. A combination of simulation and experimental studies using real-life measurement tools and Internet topologies of major ISP networks is conducted to evaluate the proposed algorithms. The studies show that the proposed algorithms significantly improve the accuracy and link stress of overlay monitoring, while incurring low overheads. The evaluation of fault location algorithms show that fast and highly accurate verification of faults can be achieved using overlay monitoring. In conclusion, the holistic view taken and the solutions developed for network monitoring provide a comprehensive framework for the design, operation, and evolution of overlay networks

Application of learning algorithms to traffic management in integrated services networks.

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN027131 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach

Electroencephalogram (EEG) is a common base signal used to monitor brain activity and diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep experts and is limited by inter-rater reliability. In this paper, we propose an automatic sleep stage annotation method called SleepEEGNet using a single-channel EEG signal. The SleepEEGNet is composed of deep convolutional neural networks (CNNs) to extract time-invariant features, frequency information, and a sequence to sequence model to capture the complex and long short-term context dependencies between sleep epochs and scores. In addition, to reduce the effect of the class imbalance problem presented in the available sleep datasets, we applied novel loss functions to have an equal misclassified error for each sleep stage while training the network. We evaluated the proposed method on different single-EEG channels (i.e., Fpz-Cz and Pz-Oz EEG channels) from the Physionet Sleep-EDF datasets published in 2013 and 2018. The evaluation results demonstrate that the proposed method achieved the best annotation performance compared to current literature, with an overall accuracy of 84.26%, a macro F1-score of 79.66% and Cohen's Kappa coefficient = 0.79. Our developed model is ready to test with more sleep EEG signals and aid the sleep specialists to arrive at an accurate diagnosis. The source code is available at https://github.com/SajadMo/SleepEEGNet

Analyses and optimizations of timing-constrained embedded systems considering resource synchronization and machine learning approaches

Nowadays, embedded systems have become ubiquitous, powering a vast array of applications from consumer electronics to industrial automation. Concurrently, statistical and machine learning algorithms are being increasingly adopted across various application domains, such as medical diagnosis, autonomous driving, and environmental analysis, offering sophisticated data analysis and decision-making capabilities. As the demand for intelligent and time-sensitive applications continues to surge, accompanied by growing concerns regarding data privacy, the deployment of machine learning models on embedded devices has emerged as an indispensable requirement. However, this integration introduces both significant opportunities for performance enhancement and complex challenges in deployment optimization. On the one hand, deploying machine learning models on embedded systems with limited computational capacity, power budgets, and stringent timing requirements necessitates additional adjustments to ensure optimal performance and meet the imposed timing constraints. On the other hand, the inherent capabilities of machine learning, such as self-adaptation during runtime, prove invaluable in addressing challenges encountered in embedded systems, aiding in optimization and decision-making processes. This dissertation introduces two primary modifications for the analyses and optimizations of timing-constrained embedded systems. For one thing, it addresses the relatively long access times required for shared resources of machine learning tasks. For another, it considers the limited communication resources and data privacy concerns in distributed embedded systems when deploying machine learning models. Additionally, this work provides a use case that employs a machine learning method to tackle challenges specific to embedded systems. By addressing these key aspects, this dissertation contributes to the analysis and optimization of timing-constrained embedded systems, considering resource synchronization and machine learning models to enable improved performance and efficiency in real-time applications with stringent constraints

Systematic mapping of power system models: Expert survey

The power system is one of the main subsystems of larger energy systems. It is a complex system in itself, consisting of an ever-changing infrastructure used by a large number of actors of very different sizes. The boundaries of the power system are characterised by ever-evolving interfaces with equally complex subsystems such as gas transport and distribution, heating and cooling, and, increasingly, transport. The situation is further complicated by the fact that electricity is only a carrier, able to fulfil demand for such things as lighting, heat or mobility. One specific and fundamental feature of the electricity system is that demand and generation must match at any time, while satisfying technical and economic constraints. In most of the world’s power systems, only relatively small quantities of electricity can be stored, and only for limited periods of time. A detailed analysis of supply and demand is thus needed for short time intervals. Mathematical models facilitate power system planning, operation, transmission and distribution, demonstrating problems that need to be solved over different timescales and horizons. The use of modelling to understand these processes is not only vital for the system’s direct actors, i.e. the companies involved in the generation, trade, transmission, distribution and use of electricity, but also for policy-makers and regulators. Power system models can provide evidence to support policy-making at European Union, Member State and Regional level. As a consequence of the growth in computing power, mathematical models for power systems have become more accessible. The number of models available worldwide, and the degree of detail they provide, is growing fast. A proper mapping of power system models is therefore essential in order to: - provide an overview of power system models and their applications available in, or used by, European organisations; - analyse their modelling features; - identify modelling gaps. Few reviews have been conducted to date of the power system modelling landscape. The mission of the Knowledge for the Energy Union Unit of the Joint Research Centre (JRC) is to support policies related to the Energy Union by anticipating, mapping, collating, analysing, quality checking and communicating all relevant data/knowledge, including knowledge gaps, in a systematic and digestible way. This report therefore constitutes: - From the energy modelling perspective, a useful mapping exercise that could help promote knowledge-sharing and thus increase efficiency and transparency in the modelling community. It could trigger new, unexplored avenues of research. It also represents an ideal starting point for systematic review activities in the context of the power system. - From the knowledge management perspective, a useful blueprint to be adopted for similar mapping exercises in other thematic areas. Finally, this report is aligned with the objectives of the European Commission's Competence Centre on Modelling, (1) launched on 26 October 2017 and hosted by the JRC, which aims to promote a responsible, coherent and transparent use of modelling to support the evidence base for European Union policies. In order to meet the objectives of this report, an online survey was used to collect detailed and relevant information about power system models. The participants’ answers were processed to categorise and describe the modelling tools identified. The survey, conducted by the Knowledge for the Energy Union Unit of the JRC, comprised a set of questions for each model to ascertain its basic information, its users, software characteristics, modelling properties, mathematical description, policy-making applications, selected references, and more. The survey campaign was organised in two rounds between April and July 2017. 228 surveys were sent to power system experts and organisations, and 82 questionnaires were completed. The answers were processed to map the knowledge objectively. (2) The main results of the survey can be summarised as follows: - Software-related features: about two thirds of the models require third-party software such as commercial optimisation solvers or off-the-shelf software. Only 14% of the models are open source, while 11% are free to download. - Modelling-related features: models are mostly defined as optimisation problems (78%) rather than simulation (33%) or equilibrium problems (13%). 71% of the models solve a deterministic problem while 41% solve probabilistic or stochastic problems. - Modelled power system problems: the economic dispatch problem is the most commonly modelled problem with a share of approximately 70%, followed by generation expansion planning, unit commitment, and transmission expansion planning, with around 40‒43% each. Most of the models (57%) have non-public input data while 31% of models use open input data. - Modelled technologies: hydro, wind, thermal, storage and nuclear technologies are widely taken into account, featuring in around 83‒94% of models. However, HVDC, wave tidal, PSTs, and FACTS (3) are not often found unless the analysis is specifically performed for those technologies. - Applicability in the context of European energy policy: more than half of the mapped models (56%) were used to answer a specific policy question. Of the five Energy Union strategic dimensions, integration of the European Union internal energy market was addressed the most often (27%), followed by climate action (23%), research, innovation and competitiveness (21%), and energy efficiency (15%). This report includes JRC recommendations based on the results of the survey, on future research avenues for power system modelling and its applicability within the Energy Union strategic dimensions. More attention should be paid, for example, to model uncertainty features, and collaboration among researchers and practitioners should be promoted to intensify research into specific power system problems such as AC (4) optimal power flow. The report includes factsheets for each model analysed, summarising relevant characteristics based on the participants’ answers. While this report represents a scientific result per se, one of the expected (and welcomed) outcomes of this mapping exercise is to raise awareness of power system modelling activities among European policy makers.JRC.C.7-Knowledge for the Energy Unio