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FedApp: A research sandbox for application orchestration in federated clouds using OpenStack

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arXiv 2021年

作者： Ruuskanen, Johan Peng, Haorui Åkesson, Alfred Larsson, Lars Kihl, Maria Department of Automatic Control Lund University Lund Sweden Department of Electrical and Information Technology Lund University Lund Sweden Department of Computer Science Lund University Lund Sweden Department of Computing Science Umeå University Umeå Sweden

Multi-cluster federation is envisioned to be the next-generation cloud infrastructure, where it will play a vital part in the realization of concepts such as edge and fog computing. Orchestrating applications in federated environments poses new challenges to well-known research problems in various fields, such as load-balancing, auto-scaling, resource allocation and service migration. However, as access to real multi-cluster infrastructure is limited, a test-bed that provides similar characteristics to a real system is in demand. To enable researchers in associated fields to quickly setup experiments in a federated cloud environment, we have created the open-source sandbox FedApp that simplifies the process of deploying multiple virtual clusters in an OpenStack environment with the possibility of adding realistic network characteristics between sites. Each cluster comes deployed with the open-source and production-grade container orchestrator Kubernetes, complete with federation-wide monitoring using Prometheus/Grafana and simplified inter-cluster microservice communication using Istio. © 2021, CC BY.

关键词： Platform as a Service (PaaS)

Customizable Inverse Sensor Model for Bayesian and Dempster-Shafer Occupancy Grid Frameworks 20th

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Customizable Inverse Sensor Model for Bayesian and Dempster-...

20th Polish control Conference, PCC 2020

作者： Porębski, Jakub Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland Aptiv Services Poland S.A. Kraków Poland

ISBN: (纸本)9783030509354

Occupancy grid mapping is an important component in a road scene understanding for autonomous driving. It can encapsulate data from heterogeneous sensor sources like radars, LiDARs, cameras and ultrasonics. At the core of occupancy grid (OG) generation, there is usually an inverse sensor model (ISM), which infers the occupancy representation from the sensor readings. Traditional ISMs are characterized by a very rigid structure, suited only for one sensor type, and specific occupancy grid representation. This paper proposes a novel ISM framework, which offers a separation between free and occupied space, supporting both Bayesian and Dempster-Shafer OG representations. The framework is especially useful when dealing with multiple different sensors where custom or preselected probability distribution can be applied. The presented ISM architecture is modular and flexible, which is described in an illustrative example of application customized for different detection sources. © 2020, Springer Nature Switzerland AG.

关键词： Probability distributions

Optimal intervention in transportation networks

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arXiv 2021年

作者： Cianfanelli, Leonardo Como, Giacomo Ozdaglar, Asuman Parise, Francesca Dipartimento di Scienze Matematiche Politecnico di Torino Italy Department of Automatic Control Lund University BOX 118 LundSE-22100 Sweden Department of Electrical Engineering and Computer Science MIT United States Department of Electrical and Computer Engineering Cornell University United States

We study a network design problem (NDP) where the planner aims at selecting the optimal single-link intervention on a transportation network to minimize the travel time under Wardrop equilibrium flows. Our first result is that, if the delay functions are affine and the support of the equilibrium is not modified with interventions, the NDP may be formulated in terms of electrical quantities computed on a related resistor network. In particular, we show that the travel time variation corresponding to an intervention on a given link depends on the effective resistance between the endpoints of the link. We suggest an approach to approximate such an effective resistance by performing only local computation, and exploit it to design an efficient algorithm to solve the NDP. We discuss the optimality of this procedure in the limit of infinitely large networks, and provide a sufficient condition for its optimality. We then provide numerical simulations, showing that our algorithm achieves good performance even if the equilibrium support varies and the delay functions are *** Codes 91A14, 91A43, 91A16, 90B20 Copyright © 2021, The Authors. All rights reserved.

关键词： Travel time

Two-layer nonlinear control of DC-DC buck converters with meshed network topology

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arXiv 2021年

作者： Baldivieso-Monasterios, Pablo R. Sadabadi, Mahdieh Konstantopoulos, George C. Department of Automatic Control & Systems Engineering University of Sheffield Mappin Street SheffieldS1 3JD United Kingdom Department of Electrical and Computer Engineering University of Patras Patras26500 Greece

In this paper, we analyse the behaviour of a buck converter network that contains arbitrary, up to mild regularity assumptions, loads. Our analysis of the network begins with the study of the current dynamics;we propose a novel Lyapunov function for the current in closed-loop with a bounded integrator. We leverage on these results to analyse the interaction properties between voltages and bounded currents as well as between node voltages and to propose a two-layer optimal controller that keeps network voltages within a compact neighbourhood of the nominal operational voltage. We analyse the stability of the closed loop system in two ways: one considering the interconnection properties which yields a weaker ISS type property and a second that contemplates the network in closed loop with a distributed optimal controller. For the latter, we propose a novel distributed way of controlling a Laplacian network using neighbouring information which results in asymptotic stability. We demonstrate our results in a meshed topology network containing 6 power converters, each converter feeding an individual constant power load with values chaning arbitrarily within a pre-specified range. © 2021, CC BY-SA.

关键词： Asymptotic stability

Maneuvering and robustness issues in undirected displacement-consensus-based formation control

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arXiv 2020年

作者： de Marina, Hector Garcia Department of Computer Architecture and Automatic Control at the Faculty of Physics Universidad Complutense de Madrid Spain

n this paper, we first propose a novel maneuvering technique compatible with displacement-consensus-based formation controllers. We show that the formation can be translated with an arbitrary velocity by modifying the weights in the consensus Laplacian matrix. In fact, we demonstrate that the displacement-consensus-based formation control is a particular case of our more general method. We then uncover robustness issues with undesired steady-state motions and resultant distorted shapes in undirected displacement-consensus-based formation control. In particular, these issues are triggered when neighboring agents mismeasure their relative positions, e.g., their onboard sensors are misaligned and have different scale factors. We will show that if all the sensing is close to perfect but different among the agents, then the stability of the system is compromised. Explicit expressions for the eventual non-desired velocity and shape’s distortion are given as functions of the scale factors and misalignments for formations based on tree graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： System stability

Bounding the difference between model predictive control and neural networks

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arXiv 2022年

作者： Drummond, Ross Duncan, Stephen R. Turner, Matthew C. Pauli, Patricia Allgöwer, Frank Department of Engineering Science University of Oxford 17 Parks Road OxfordOX1 3PJ United Kingdom Department of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart70569 Germany

There is a growing debate on whether the future of feedback control systems will be dominated by data-driven or model-driven approaches. Each of these two approaches has their own complimentary set of advantages and disadvantages, however, only limited attempts have, so far, been developed to bridge the gap between them. To address this issue, this paper introduces a method to bound the worst-case error between feedback control policies based upon model predictive control (MPC) and neural networks (NNs). This result is leveraged into an approach to automatically synthesize MPC policies minimising the worst-case error with respect to a NN. Numerical examples highlight the application of the bounds, with the goal of the paper being to encourage a more quantitative understanding of the relationship between data-driven and model-driven control. © 2022, CC BY.

关键词： Model predictive control

Scale Fragilities in Localized Consensus Dynamics

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arXiv 2022年

作者： Tegling, Emma Bamieh, Bassam Sandberg, Henrik Department of Automatic Control Lund University P.O. Box 118 LundSE-221 00 Sweden Department of Mechanical Engineering The University of California at Santa Barbara Santa BarbaraCA93106 United States School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden

We consider distributed consensus in networks where the agents have integrator dynamics of order two or higher (n ≥ 2). We assume all feedback to be localized in the sense that each agent has a bounded number of neighbors and consider a scaling of the network through the addition of agents in a modular manner, i.e., without re-tuning controller gains upon addition. We show that standard consensus algorithms, which rely on relative state feedback, are subject to what we term scale fragilities, meaning that stability is lost as the network scales. For high-order agents (n ≥ 3), we prove that no consensus algorithm with fixed gains can achieve consensus in networks of any size. That is, while a given algorithm may allow a small network to converge, it causes instability if the network grows beyond a certain finite size. This holds in families of network graphs whose algebraic connectivity, that is, the smallest non-zero Laplacian eigenvalue, is decreasing towards zero in network size (e.g. all planar graphs). For second-order consensus (n = 2) we prove that the same scale fragility applies to directed graphs that have a complex Laplacian eigenvalue approaching the origin (e.g. directed ring graphs). The proofs for both results rely on Routh-Hurwitz criteria for complex-valued polynomials and hold true for general directed network graphs. We survey classes of graphs subject to these scale fragilities, discuss their scaling constants, and finally prove that a sub-linear scaling of nodal neighborhoods can suffice to overcome the issue. Copyright © 2022, The Authors. All rights reserved.

关键词： State feedback

Attack prevention in IoT through hybrid optimization mechanism and deep learning framework

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Measurement: Sensors 2022年 24卷

作者： Nagaraju, Regonda Pentang, Jupeth Toriano Abdufattokhov, Shokhjakhon CosioBorda, Ricardo Fernando Mageswari, N. Uganya, G. Department of Information Technology St.Martin's Engineering College Dhulapally Secunderabad500100 India Western Philippines University Philippines Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Universidad Autónoma del Perú Lima Peru Department of ECE Ashoka Women's Engineering College Andhra Pradesh Kurnool India Department of ECE Saveetha School of Engineering SIMATS Assistant Professor Chennai India

The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge data set, the cybersecurity warning systems index system is first constructed, then the index factors are picked and measured, and finally, the situation evaluation is *** bio-inspired techniques were used to enhance the productivity of an IDS by lowering the data dimensionality and deleting unnecessary and noisy input. The Grey Wolf Optimization algorithm (GWO) is a developed bio-inspired algorithm that improves the efficacy of the IDS in detecting both regular and abnormal congestion in the network. The smart initialization step integrates the different pre-processing strategies to make sure that informative features are incorporated in the early development stages, has been improved. Researchers pick multi-source material in a big data environment for the identification and verification of index components and present a parallel reduction approach based on the classification significance matrix to decrease data underlying data characteristics. For the simulation of this situation, grey wolf optimization and whale optimization were combined to detect the attack prevention and the deep learning approach was presented. Utilizing system software plagiarism, the TensorFlow deep neural network is intended to classify stolen software. To reduce the noise from the signal and to zoom the significance of each word in the perspective of open-source plagiarism, the tokenization and weighting feature approaches are utilized. Malware spec

关键词： Internet of things

Performance Analysis of a Cloud Computing System using Queuing Model with Correlated Task Reneging 5

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Performance Analysis of a Cloud Computing System using Queui...

5th International Scientific Conference on Information, control, and Communication Technologies, ICCT 2021

作者： Kumar, Rakesh Soodan, Bhavneet Singh Kuaban, Godlove Suila Czekalski, Piotr Sharma, Sapana School of Mathematics Shri Mata Vaishno Devi University Jammu and Kashmir Katra182320 India Department of Mathematics and Statistics Namibia University of Science and Technology Private Bag 13388 Windhoek Namibia 13 Jackson Kaujeua Street Windhoek Namibia Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44 - 100 Poland Department of Computer Graphics. Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Poland Department of Mathematics Cluster University of Jammu Jammu and Kashmir India

Queuing theory has been extensively used in the modelling and performance analysis of cloud computing systems. The phenomenon of the task (or request) reneging, that is, the dropping of requests from the request queue often occur in cloud computing systems, and it is important to consider it when developing performance evaluations models for cloud computing infrastructures. Majority of studies in the performance evaluation of cloud computing data centres with the use of queuing theory do not consider the fact that the tasks could be removed from queue without being serviced. The removal of tasks from the queue could be due to the user impatience, execution deadline expiration, security reasons, or as an active queue management strategy. The reneging could be correlated in nature, that is, if a request is dropped (or reneged) at any time epoch, and then there is a probability that a request may or may not be dropped at the next time epoch. This kind of dropping (or reneging) of requests is referred to as correlated request reneging. In this paper we have modelled a cloud computing infrastructure with correlated request reneging using queuing theory. An M/M/1/N queuing model with correlated reneging has been used to study the performance analysis of the load balancing server of a cloud computing system. The steady-state as well as the transient performance analyses have been carried out. Important measures of performance like a vera ge queue size, average delay, probability of task blocking, and the probability of no waiting in the queue are studied. Finally, some comparisons are performed which describe the effect of correlated task reneging over simple exponential reneging. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Cloud computing