New data structures, models, and algorithms for real-time resource management

Real-time resource management is the core and critical task in real-time systems. This dissertation explores new data structures, models, and algorithms for real-time resource management. At first, novel data structures, i.e., a class of Testing Interval Trees (TITs), are proposed to help build efficient scheduling modules in real-time systems. With a general data structure, i.e., the TIT* tree, the average costs of the schedulability tests in a wide variety of real-time systems can be reduced. With the Testing Interval Tree for Vacancy analysis (TIT-V), the complexities of the schedulability tests in a class of parallel/distributed real-time systems can be effectively reduced from 0(m²nlogn) to 0(mlogn+mlogm), where m is the number of processors and n is the number of tasks. Similarly, with the Testing Interval Tree for Release time and Laxity analysis (TIT-RL), the complexity of the online admission control in a uni-processor based real-time system can be reduced from 0(n²) to 0(nlogn), where n is the number of tasks. The TIT-RL tree can also be applied to a class of parallel/distributed real-time systems. Therefore, the TIT trees are effective approaches to efficient real-time scheduling modules. Secondly, a new utility accrual model, i.e., UAM+, is established for the resource management in real-time distributed systems. UAM+ is constructed based on the timeliness of computation and communication. Most importantly, the interplay between computation and communication is captured and characterized in the model. Under UAM+, resource managers are guided towards maximizing system-wide utility by exploring the interplay between computation and communication. This is in sharp contrast to traditional approaches that attempt to meet the timing constraints on computation and communication separately. To validate the effectiveness of UAM+, a resource allocation algorithm called IAUASA is developed. Simulation results reveal that IAUASA is far superior to two other resource allocation algorithms that are developed according to traditional utility accrual model and traditional idea. Furthermore, an online algorithm called IDRSA is also developed under UAM+, and a Dynamic Deadline Adjustment (DDA) technique is incorporated into IDRSA algorithm to explore the interplay between computation and communication. The simulation results show that the performance of IDRSA is very promising, especially when the interplay between computation and communication is tight. Therefore, the new utility accrual model provides a more effective approach to the resource allocation in distributed real-time systems. Thirdly, a general task model, which adapts the concept of calculus curve from the network calculus domain, is established for those embedded real-time systems with random event/task arrivals. Under this model, a prediction technique based on history window and calculus curves is established, and it provides the foundation for dynamic voltage-frequency scaling in those embedded real-time systems. Based on this prediction technique, novel energy-efficient algorithms that can dynamically adjust the operating voltage-frequency according to the predicted workload are developed. These algorithms aim to reduce energy consumption while meeting hard deadlines. They can accommodate and well adapt to the variation between the predicted and the actual arrivals of tasks as well as the variation between the predicted and the actual execution times of tasks. Simulation results validate the effectiveness of these algorithms in energy saving

Vehicular Fog Computing Enabled Real-time Collision Warning via Trajectory Calibration

Vehicular fog computing (VFC) has been envisioned as a promising paradigm for enabling a variety of emerging intelligent transportation systems (ITS). However, due to inevitable as well as non-negligible issues in wireless communication, including transmission latency and packet loss, it is still challenging in implementing safety-critical applications, such as real-time collision warning in vehicular networks. In this paper, we present a vehicular fog computing architecture, aiming at supporting effective and real-time collision warning by offloading computation and communication overheads to distributed fog nodes. With the system architecture, we further propose a trajectory calibration based collision warning (TCCW) algorithm along with tailored communication protocols. Specifically, an application-layer vehicular-to-infrastructure (V2I) communication delay is fitted by the Stable distribution with real-world field testing data. Then, a packet loss detection mechanism is designed. Finally, TCCW calibrates real-time vehicle trajectories based on received vehicle status including GPS coordinates, velocity, acceleration, heading direction, as well as the estimation of communication delay and the detection of packet loss. For performance evaluation, we build the simulation model and implement conventional solutions including cloud-based warning and fog-based warning without calibration for comparison. Real-vehicle trajectories are extracted as the input, and the simulation results demonstrate that the effectiveness of TCCW in terms of the highest precision and recall in a wide range of scenarios

A Rapid Testing Framework for a Mobile Cloud Infrastructure

Abstract—Mobile clouds such as network-connected vehicles and satellite clusters are an emerging class of systems that are extensions to traditional real-time embedded systems: they provide long-term mission platforms made up of dynamic clusters of heterogeneous hardware nodes communicating over ad hoc wireless networks. Besides the inherent complexities entailed by a distributed architecture, developing software and testing these systems is difficult due to a number of other reasons, including the mobile nature of such systems, which can require a model of the physical dynamics of the system for accurate simulation and testing. This paper describes a rapid development and testing framework for a distributed satellite system. Our solutions include a modeling language for configuring and specifying an application’s interaction with the middleware layer, a physics simulator integrated with hardware in the loop to provide the system’s physical dynamics and the integration of a network traffic tool to dynamically vary the network bandwidth based on the physical dynamics. I

Implementation Of A Novel Cooperative Protocol for Distributed Voltage Control in Active Distribution Networks

Microgrids are small localized grids that help to integrate many renewable-energy sources into the main electric grid. Microgrids can also operate separately from the main electric grid during faults to enhance the customers reliability. For a successful integration of microgrids we need to control the voltage at the distributed generation units in order to achieve the required sharing of reactive power. For this purpose a multiagent based distributed control scheme is implemented in this thesis. The objective of this thesis is to design and implement a multiagent system for the microgrid that has distributed battery energy storage systems (BESS) and renewable distributed generation (DG) units. The proposed multiagent system has been designed to coordinate among distributed generation (DG) units to control voltage. Multiagent system is composed of multiple agents that communicate to solve problems. The proposed multiagent system for the control of microgrid has been implemented on Texas Instruments Tiva-C controller boards. The real time simulator Opal-RT has been used to create a microgrid model. Hardware testing is done in real time

Timed test case execution for distributed real-time systems

A real-time system is a system that is required to react to stimuli from the environment (including the passage of physical time) within time intervals dictated by the environment. In real-time applications, the timing requirements are the main constraints and their mastering is the predominant factor for assessing the quality of service. We proposed two methods for timed test case execution for distributed real time systems modeled as TIOA. Ensuring the correctness of real time systems before the development and guaranteeing that it functions correctly within the specified time constraints are very critical to avoid catastrophic consequences. A number of design issues affect the testing strategies and the testability of the system. Existing methods of distributed test systems still have some drawbacks. Firstly, there is lack of coordination among local testers in a distributed system. Secondly, the centralized test method may result in performance problems, due to the fact that all TS-IUT interactions are achieved through the network. With an aim to alleviate the shortcomings, we developed a Synchronizer in centralized test system, which would control and coordinate the local testers in the test environment. We implemented these methods and tested them with a benchmark of real life automata model by using CORBA distributed architecture. As a result, the performance problem has been reduced in both methodologies. We distinguished between two methodologies and compare their performances in terms of functionalities, based on our implementation and illustrate their approaches using Train-Gate-Controller case stud

Distributed real-time hybrid simulation: Modeling, development and experimental validation

Real-time hybrid simulation (RTHS) has become a recognized methodology for isolating and evaluating performance of critical structural components under potentially catastrophic events such as earthquakes. Although RTHS is efficient in its utilization of equipment and space compared to traditional testing methods such as shake table testing, laboratory resources may not always be available in one location to conduct appropriate large-scale experiments. Consequently, distributed systems, capable of connecting multiple RTHS setups located at numerous geographically distributed facilities through information exchange, become essential. This dissertation focuses on the development, evaluation and validation of a new distributed RTHS (dRTHS) platform enabling integration of physical and numerical components of RTHS in geographically distributed locations over the Internet.^ One significant challenge for conducting successful dRTHS over the Internet is sustaining real-time communication between test sites. The network is not consistent and variations in the Quality of Service (QoS) are expected. Since dRTHS is delay-sensitive by nature, a fixed transmission rate with minimum jitter and latency in the network traffic should be maintained during an experiment. A Smith predictor can compensate network delays, but requires use of a known dead time for optimal operation. The platform proposed herein is developed to mitigate the aforementioned challenge. An easily programmable environment is provided based on MATLAB/xPC. In this method, (i) a buffer is added to the simulation loop to minimize network jitter and stabilize the transmission rate, and (ii) a routine is implemented to estimate the network time delay on-the-fly for the optimal operation of the Smith predictor.^ The performance of the proposed platform is investigated through a series of numerical and experimental studies. An illustrative demonstration is conducted using a three story structure equipped with an MR damper. The structure is tested on the shake table and its global responses are compared to RTHS and dRTHS configurations where the physical MR damper and numerical structural model are tested in local and geographically distributed laboratories.^ The main contributions of this research are twofold: (1) dRTHS is validated as a feasible testing methodology, alternative to traditional and modern testing techniques such as shake table testing and RTHS, and (ii) the proposed platform serves as a viable environment for researchers to develop, evaluate and validate their own tools, investigate new methods to conduct dRTHS and advance the research in this area to the limits

Software Reliability Prediction using Fuzzy Min-Max Algorithm and Recurrent Neural Network Approach

Fuzzy Logic (FL) together with Recurrent Neural Network (RNN) is used to predict the software reliability. Fuzzy Min-Max algorithm is used to optimize the number of the kgaussian nodes in the hidden layer and delayed input neurons. The optimized recurrentneural network is used to dynamically reconfigure in real-time as actual software failure. In this work, an enhanced fuzzy min-max algorithm together with recurrent neural network based machine learning technique is explored and a comparative analysis is performed for the modeling of reliability prediction in software systems. The model has been applied on data sets collected across several standard software projects during system testing phase with fault removal. The performance of our proposed approach has been tested using distributed system application failure data set

Management of Distributed Energy Storage Systems for Provisioning of Power Network Services

Because of environmentally friendly reasons and advanced technological development, a significant number of renewable energy sources (RESs) have been integrated into existing power networks. The increase in penetration and the uneven allocation of the RESs and load demands can lead to power quality issues and system instability in the power networks. Moreover, high penetration of the RESs can also cause low inertia due to a lack of rotational machines, leading to frequency instability. Consequently, the resilience, stability, and power quality of the power networks become exacerbated. This thesis proposes and develops new strategies for energy storage (ES) systems distributed in power networks for compensating for unbalanced active powers and supply-demand mismatches and improving power quality while taking the constraints of the ES into consideration. The thesis is mainly divided into two parts. In the first part, unbalanced active powers and supply-demand mismatch, caused by uneven allocation and distribution of rooftop PV units and load demands, are compensated by employing the distributed ES systems using novel frameworks based on distributed control systems and deep reinforcement learning approaches. There have been limited studies using distributed battery ES systems to mitigate the unbalanced active powers in three-phase four-wire and grounded power networks. Distributed control strategies are proposed to compensate for the unbalanced conditions. To group households in the same phase into the same cluster, algorithms based on feature states and labelled phase data are applied. Within each cluster, distributed dynamic active power balancing strategies are developed to control phase active powers to be close to the reference average phase power. Thus, phase active powers become balanced. To alleviate the supply-demand mismatch caused by high PV generation, a distributed active power control system is developed. The strategy consists of supply-demand mismatch and battery SoC balancing. Control parameters are designed by considering Hurwitz matrices and Lyapunov theory. The distributed ES systems can minimise the total mismatch of power generation and consumption so that reverse power flowing back to the main is decreased. Thus, voltage rise and voltage fluctuation are reduced. Furthermore, as a model-free approach, new frameworks based on Markov decision processes and Markov games are developed to compensate for unbalanced active powers. The frameworks require only proper design of states, action and reward functions, training, and testing with real data of PV generations and load demands. Dynamic models and control parameter designs are no longer required. The developed frameworks are then solved using the DDPG and MADDPG algorithms. In the second part, the distributed ES systems are employed to improve frequency, inertia, voltage, and active power allocation in both islanded AC and DC microgrids by novel decentralized control strategies. In an islanded DC datacentre microgrid, a novel decentralized control of heterogeneous ES systems is proposed. High- and low frequency components of datacentre loads are shared by ultracapacitors and batteries using virtual capacitive and virtual resistance droop controllers, respectively. A decentralized SoC balancing control is proposed to balance battery SoCs to a common value. The stability model ensures the ES devices operate within predefined limits. In an isolated AC microgrid, decentralized frequency control of distributed battery ES systems is proposed. The strategy includes adaptive frequency droop control based on current battery SoCs, virtual inertia control to improve frequency nadir and frequency restoration control to restore system frequency to its nominal value without being dependent on communication infrastructure. A small-signal model of the proposed strategy is developed for calculating control parameters. The proposed strategies in this thesis are verified using MATLAB/Simulink with Reinforcement Learning and Deep Learning Toolboxes and RTDS Technologies' real-time digital simulator with accurate power networks, switching levels of power electronic converters, and a nonlinear battery model

Transient stability assessment of hybrid distributed generation using computational intelligence approaches

Includes bibliographical references.Due to increasing integration of new technologies into the grid such as hybrid electric vehicles, distributed generations, power electronic interface circuits, advanced controllers etc., the present power system network is now more complex than in the past. Consequently, the recent rate of blackouts recorded in some parts of the world indicates that the power system is stressed. The real time/online monitoring and prediction of stability limit is needed to prevent future blackouts. In the last decade, Distributed Generators (DGs) among other technologies have received increasing attention. This is because DGs have the capability to meet peak demand, reduce losses, due to proximity to consumers and produce clean energy and thus reduce the production of CO₂. More benefits can be obtained when two or more DGs are combined together to form what is known as Hybrid Distributed Generation (HDG). The challenge with hybrid distributed generation (HDG) powered by intermittent renewable energy sources such as solar PV, wind turbine and small hydro power is that the system is more vulnerable to instabilities compared to single renewable energy source DG. This is because of the intermittent nature of the renewable energy sources and the complex interaction between the DGs and the distribution network. Due to the complexity and the stress level of the present power system network, real time/online monitoring and prediction of stability limits is becoming an essential and important part of present day control centres. Up to now, research on the impact of HDG on the transient stability is very limited. Generally, to perform transient stability assessment, an analytical approach is often used. The analytical approach requires a large volume of data, detailed mathematical equations and the understanding of the dynamics of the system. Due to the unavailability of accurate mathematical equations for most dynamic systems, and given the large volume of data required, the analytical method is inadequate and time consuming. Moreover, it requires long simulation time to assess the stability limits of the system. Therefore, the analytical approach is inadequate to handle real time operation of power system. In order to carry out real time transient stability assessment under an increasing nonlinear and time varying dynamics, fast scalable and dynamic algorithms are required. Transient Stability Assessment Of Hybrid Distributed Generation Using Computational Intelligence Approaches These algorithms must be able to perform advanced monitoring, decision making, forecasting, control and optimization. Computational Intelligence (CI) based algorithm such as neural networks coupled with Wide Area Monitoring System (WAMS) such as Phasor Measurement Unit (PMUs) have been shown to successfully model non-linear dynamics and predict stability limits in real time. To cope with the shortcoming of the analytical approach, a computational intelligence method based on Artificial Neural Networks (ANNs) was developed in this thesis to assess transient stability in real time. Appropriate data related to the hybrid generation (i.e., Solar PV, wind generator, small hydropower) were generated using the analytical approach for the training and testing of the ANN models. In addition, PMUs integrated in Real Time Digital Simulator (RTDS) were used to gather data for the real time training of the ANNs and the prediction of the Critical Clearing Time (CCT)

The CAT Vehicle Testbed: A Simulator with Hardware in the Loop for Autonomous Vehicle Applications

This paper presents the CAT Vehicle (Cognitive and Autonomous Test Vehicle) Testbed: a research testbed comprised of a distributed simulation-based autonomous vehicle, with straightforward transition to hardware in the loop testing and execution, to support research in autonomous driving technology. The evolution of autonomous driving technology from active safety features and advanced driving assistance systems to full sensor-guided autonomous driving requires testing of every possible scenario. However, researchers who want to demonstrate new results on a physical platform face difficult challenges, if they do not have access to a robotic platform in their own labs. Thus, there is a need for a research testbed where simulation-based results can be rapidly validated through hardware in the loop simulation, in order to test the software on board the physical platform. The CAT Vehicle Testbed offers such a testbed that can mimic dynamics of a real vehicle in simulation and then seamlessly transition to reproduction of use cases with hardware. The simulator utilizes the Robot Operating System (ROS) with a physics-based vehicle model, including simulated sensors and actuators with configurable parameters. The testbed allows multi-vehicle simulation to support vehicle to vehicle interaction. Our testbed also facilitates logging and capturing of the data in the real time that can be played back to examine particular scenarios or use cases, and for regression testing. As part of the demonstration of feasibility, we present a brief description of the CAT Vehicle Challenge, in which student researchers from all over the globe were able to reproduce their simulation results with fewer than 2 days of interfacing with the physical platform.Comment: In Proceedings SCAV 2018, arXiv:1804.0340