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Multi-faceted modeling in the analysis and optimization of iot complex systems

Multi-faceted modeling in the analysis and optimization of i...

2020 Summer computer simulation Conference, SCSC 2020, Held at the 2020 Summer simulation Multi-Conference, SummerSim 2020

作者： Cárdenas, Román Arroba, Patricia Moya, José M. Risco-Martín, José L. CCS-Center for Computational Simulation Universidad Politécnica de Madrid ETSI Telecomunicación Avenida Complutense 30 Madrid28040 Spain Dpt. of Computer Architecture and Automation CCS-Center for Computational Simulation Universidad Complutense de Madrid C/ Prof. José García Santesmases 9 Madrid28040 Spain

ISBN: (纸本)9781713819462

Modeling, simulation, and Optimization tools are used in the development process of complex systems for improving their overall performance. However, enforcing full coverage in the validation process of these models may add unbearable simulation time overheads. In this research, we propose the implementation of multi-faceted sets of models with different degrees of complexity for optimizing IoT complex systems. With this approach, elements of the system are optimized using simpler and faster models that reduce simulation times, enabling the exploration of more scenarios in less time. Optimized components can be then validated extensively using more complex models, ensuring full coverage, and reducing potential failures. ©2020 Society for Modeling & simulation International (SCS)

关键词： System of systems

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Advanced migraine prediction hardware system 50

Advanced migraine prediction hardware system

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50th Summer computer simulation Conference, SCSC 2018, Part of the 2018 Summer simulation Multi-Conference, SummerSim 2018

作者： Henares, Kevin Pagán, Josué Ayala, José L. Risco-Martín, José L. Dept. of Computer Architecture and Automation Complutense University of Madrid Madrid Spain Electronic Engineering Department Universidad Politécnica de Madrid Spain CCS-Center for Computational Simulation Madrid Spain Dept. of Computer Architecture and Automation Complutense University of Madrid CCS-Center for Computational Simulation Madrid Spain

ISBN: (纸本)9781510860209

Migraine is one of the most disabling neurological diseases. Its prevalence reaches 15% of the population in developed countries and lead to high economic costs for private and national health systems. There is no cure for the migraine yet. Objective and accurate migraine prediction turns necessary in order to increase the effectiveness of the current treatments. To tackle this problem authors demonstrated in previous works the feasibility of the migraine modeling and presented a DEVS-based model for migraine prediction in a real time framework. This work presents the first step to bring migraine predictive models and simulations to real devices for the first time in the literature. To do this, the DEVS-based model has been ported to a robust hardware description language (VHDL) to be implemented into a FPGA. This paper leads to the final stage: the physical implementation of a predictive migraine monitoring device. © 2018 Society for Modeling & simulation International (SCS).

关键词： Field programmable gate arrays (FPGA)

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Advanced migraine prediction simulation system 49

Advanced migraine prediction simulation system

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49th Summer computer simulation Conference, SCSC 2017, Part of the 2017 Summer simulation Multi-Conference, SummerSim 2017

作者： Pagán, Josué Moya, José M. Mittal, Saurabh Ayala, José L. Department of Computer Architecture and Automation CCS-Center for Computational Simulation Complutense University of Madrid Madrid Spain LSI - Electronic Engineering Department CCS-Center for Computational Simulation Technical University of Madrid Madrid Spain Complex Systems Research Lab Dunip Technologies LLC HerndonVA United States Department of Computer Architecture and Automation Computer Science Faculty Complutense University of Madrid Spain

ISBN: (纸本)9781510838284

In the Internet of Things (IoT) era, there is growing interest in wireless monitoring sensors for detection, classification and prediction of health symptoms. The prediction of symptoms in chronic diseases such as migraines brings new hope to improve patients' lives. The prediction of a migraine symptomatic event through monitoring hemodynamic variables has been previously demonstrated in our earlier work. In this paper, a simulation-based approach for a real-Time migraine prediction system is described. The simulation has been implemented using the specifications of the formal description language Discrete EVent Systems (DEVS). The simulation system is a proof of concept that is ready for testing in a real-world ambulatory monitoring environment. The results obtained encourage developing a hardware/software (HW/SW) cosimulation system that incorporates Hardware-in-The-Loop (HIL) components as prior step to the expensive and slow hardware implementation of a complete migraine prediction device. When such a system is used in a real-Time setting, it can simulate failures in sensors and trigger alarms for active patient response. © 2017 Society for Modeling & simulation International (SCS).

关键词： Forecasting

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Building accurate models to determine the current CPU utilization of a host within a virtual machine allocated on it 49

Building accurate models to determine the current CPU utiliz...

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49th Summer computer simulation Conference, SCSC 2017, Part of the 2017 Summer simulation Multi-Conference, SummerSim 2017

作者： Briongos, Samira Malagón, Pedro Risco, José L. Moya, José M. Department of Computer Architecture and Automation Computer Science Faculty Complutense University of Madrid Spain LSI - Electronic Engineering Department CCS-Center for Computational Simulation Universidad Poliécnica de Madrid Madrid Spain

ISBN: (纸本)9781510838284

In cloud computing environments there are several virtual machines running in the same host. This fact opens the door for possible side channel-Attacks. Prior to perform an attack it is mandatory to determine coresidency with the victim. An synchronized variation in the CPU activity in the host is a possible indicator of the presence of neighboring processes. However, cloud providers do not give information about the hosts CPU load, so we have to figure out a way of estimating it. We estimate the host CPU load considering its impact on the performance of a virtual machine (VM) running on it. In this work, we show that it is possible to calculate the CPU load of the host by executing a reference process, measuring the time it takes to execute, and using this information as an input to generate the CPU load models. We explore regression methods and regression methods tuned with genetic algorithms for the model generation. As a result, considering a CPU load value between 0 (no load) and 100 (maximum load), we obtain models which compute the host load with a mean squared error of around 5%, 10% and 30% (depending on the host architecture) when estimating the load every second. © 2017 Society for Modeling & simulation International (SCS).

关键词： Genetic algorithms

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Fast Energy Estimation Through Partial Execution of HPC Applications 29

Fast Energy Estimation Through Partial Execution of HPC Appl...

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29th IEEE International Conference on Application-Specific Systems, architectures and Processors, ASAP 2018

作者： Salinas-Hilburg, Juan Carlos Zapater, Marina Moya, Jose M. Ayala, Jose L. Dept. of Computer Architecture and Automation Complutense University of Madrid Madrid Spain Lausanne Switzerland Integrated Systems Laboratory Technical University of Madrid Madrid Spain CCS-Center for Computational Simulation Madrid Spain

ISBN: (纸本)9781538674796

In order to optimize the energy use of servers in Data centers, techniques such as power capping or power budgeting are usually deployed. These techniques rely on the prediction of the power and execution time of applications. These data are obtained via dynamic profiling which requires a full execution of the application. This is not feasible in High Performance Computing (HPC) applications with long execution times. In this paper, we present a methodology to estimate the dynamic CPU and memory energy consumption of an application without executing it completely. Our methodology merges static code analysis information and dynamic profiling via the partial execution of the application. We do so by leveraging the concept of application signature, defined as a reduced version of the application in terms of execution time and power profile. We validate our methodology with a set of CPU -intensive, memory-intensive benchmarks and multi-threaded applications in a presently shipping enterprise server. Our energy estimation methodology shows an overall error below 8.0% when compared to the dynamic energy of the whole execution of the application. Also, our energy estimation methodology allows to estimate the energy of multi-threaded applications with an RMSE equal to 12.7% when compared to the dynamic energy from the complete parallel execution. © 2018 IEEE.

关键词： Budget control

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Decision Support Framework for Automating the Optimization of Edge Computing Federations

Decision Support Framework for Automating the Optimization o...

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Annual Modeling and simulation Conference (ANNSIM)

作者： Antonio F. Rodríguez-Liria Román Cárdenas Patricia Arroba José M. Moya José L. Risco-Martín Gabriel Wainer Laboratorio de Sistemas Integrados (LSI) CCS–Center for Computational Simulation Universidad Politécnica de Madrid Madrid Spain Dpt. of Computer Architecture and Automation Universidad Complutense de Madrid Madrid Spain Dpt. of Systems and Computer Engineering Carleton University Ottawa ON Canada

Modern IoT applications present demanding Quality of Service (QoS) requirements that can no longer rely on a centralized solution to offload compute-intensive tasks. Edge Computing tackles these limitations by extending the Cloud to the edge of the network. However, the distributed and complex nature of Edge infrastructures introduces new technical challenges that must be addressed to develop a successful solution. Modeling, simulation, and Optimization (M&S&O) tools can aid in the automatic design of modern Edge deployments. Here we present a decision support module that allows practitioners to automatically improve aspects of their Edge infrastructure by applying different optimization algorithms, such as simulated annealing, tabu search, and stochastic hill climbing. Furthermore, the structure of this module allows us to extend the available optimization methods if needed. We illustrate how this tool can help us to improve energy consumption policies of Edge Computing facilities and reduce their operational costs.

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A New Family of XDEVS Simulators for Enhanced Performance

A New Family of XDEVS Simulators for Enhanced Performance

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Annual Modeling and simulation Conference (ANNSIM)

作者： Román Cárdenas Patricia Arroba José L. Risco-Martín Laboratorio de Sistemas Integrados (LSI) CCS–Center for Computational Simulation Universidad Politécnica de Madrid ETSI Telecomunicación Madrid Spain Dpt. of Computer Architecture and Automation Universidad Complutense de Madrid Madrid Spain

Modeling and simulation (M&S) techniques are extensively used for analyzing complex systems. Among different approaches, the Discrete EVent System specification (DEVS) mathematical formalism presents a robust methodology for defining complex models. The xDEVS framework is a cross-platform tool that provides a uniform Application Programming Interface (API) to simulate DEVS-based models using different programming languages like C++, Java, or Python. However, executing DEVS models on embedded devices equipped with small processors usually requires lower-level programming languages. This paper introduces xDEVS/Rust and xDEVS/C, two new xDEVS interfaces written in Rust and C, respectively. These new implementations bring xDEVS closer to supporting the execution of DEVS models in embedded systems. Also, they feature remarkably high performance, making them a valuable option for simulating complex systems in traditional platforms. We illustrate how competitive these new implementations are by comparing them with some of the fastest state-of-the-art simulators.

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Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases

arXiv

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arXiv 2024年

作者： Pagán, Josue Risco-Martín, José L. Moya, José M. Ayala, José L. Dpt. of Computer Architecture and Automation Complutense University of Madrid Madrid28040 Spain CCS-Center for Computational Simulation Campus de Montegancedo UPM Boadilla del Monte28660 Spain LSI-Integrated Systems Laboratory Technical University of Madrid Madrid28040 Spain

Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40 minutes, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises. © 2024, CC BY-SA.

关键词： Forecasting

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Mercury: a Modeling, simulation, and Optimization Framework for Data Stream-Oriented IoT Applications

arXiv

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arXiv 2023年

作者： Cárdenas, Román Arroba, Patricia Blanco, Roberto Malagón, Pedro Risco-Martín, José L. Moya, José M. Laboratorio de Sistemas Integrados Universidad Politécnica de Madrid 28040 Spain Dpt. of Computer Architecture and Automation Universidad Complutense de Madrid Madrid28040 Spain Center for Computational Simulation Universidad Politécnica de Madrid 28660 Spain

The Internet of Things is transforming our society by monitoring users and infrastructures' behavior to enable new services that will improve life quality and resource management. These applications require a vast amount of localized information to be processed in real-time so, the deployment of new fog computing infrastructures that bring computing closer to the data sources is a major concern. In this context, we present Mercury, a Modeling, simulation, and Optimization (M&S&O) framework to analyze the dimensioning and the dynamic operation of real-time fog computing scenarios. Our research proposes a location-aware solution that supports data stream analytics applications including FaaS-based computation offloading. Mercury implements a detailed structural and behavioral simulation model, providing fine-grained simulation outputs, and is described using the Discrete Event System Specification (DEVS) mathematical formalism, helping to validate the model's implementation. Finally, we present a case study using real traces from a driver assistance scenario, offering a detailed comparison with other state-of-the-art simulators. © 2023, CC BY-SA.

关键词： Fog computing

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The DEVStone Metric: Performance Analysis of DEVS simulation Engines

arXiv

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arXiv 2023年

作者： Cárdenas, Román Henares, Kevin Arroba, Patricia Risco-Martín, José L. Wainer, Gabriel A. Integrated Systems Laboratory Universidad Politécnica de Madrid Madrid Spain Dept. of Systems and Computer Engineering Carleton University OttawaON Canada Dpt. of Computer Architecture and Automation Universidad Complutense de Madrid Madrid Spain Center for Computational Simulation Universidad Politécnica de Madrid Madrid Spain

The DEVStone benchmark allows us to evaluate the performance of discrete-event simulators based on the DEVS formalism. It provides model sets with different characteristics, enabling the analysis of specific issues of simulation engines. However, this heterogeneity hinders the comparison of the results among studies, as the results obtained on each research work depend on the chosen subset of DEVStone models. We define the DEVStone metric based on the DEVStone synthetic benchmark and provide a mechanism for specifying objective ratings for DEVS-based simulators. This metric corresponds to the average number of times that a simulator can execute a selection of 12 DEVStone models in one minute. The variety of the chosen models ensures we measure different particularities provided by DEVStone. The proposed metric allows us to compare various simulators and to assess the impact of new features on their performance. We use the DEVStone metric to compare some popular DEVS-based simulators. © 2023, CC BY-SA.

关键词： Discrete event simulation

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