arXiv subjects

José L. Risco-Martín

Publications and source records attributed to José L. Risco-Martín.

31 records · Page 2Linked to original sources

arXiv2023

The DEVStone Metric: Performance Analysis of DEVS Simulation Engines

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.

cs.SE↗

arXiv2023

Simulation-driven engineering for the management of harmful algal and cyanobacterial blooms

Harmful Algal and Cyanobacterial Blooms (HABs), occurring in inland and maritime waters, pose threats to natural environments by producing toxins that affect human and animal health. In the past, HABs have been assessed mainly by the manual collection and subsequent analysis of water samples and occasionally by automatic instruments that acquire information from fixed locations. These procedures do not provide data with the desirable spatial and temporal resolution to anticipate the formation of HABs. Hence, new tools and technologies are needed to efficiently detect, characterize and respond to HABs that threaten water quality. It is essential nowadays when the world's water supply is under tremendous pressure because of climate change, overexploitation, and pollution. This paper introduces DEVS-BLOOM, a novel framework for real-time monitoring and management of HABs. Its purpose is to support high-performance hazard detection with Model Based Systems Engineering (MBSE) and Cyber-Physical Systems (CPS) infrastructure for dynamic environments.

eess.SY↗

arXiv2023

Predictive and diagnosis models of stroke from hemodynamic signal monitoring

This work presents a novel and promising approach to the clinical management of acute stroke. Using machine learning techniques, our research has succeeded in developing accurate diagnosis and prediction real-time models from hemodynamic data. These models are able to diagnose stroke subtype with 30 minutes of monitoring, to predict the exitus during the first 3 hours of monitoring, and to predict the stroke recurrence in just 15 minutes of monitoring. Patients with difficult access to a \acrshort{CT} scan, and all patients that arrive at the stroke unit of a specialized hospital will benefit from these positive results. The results obtained from the real-time developed models are the following: stroke diagnosis around $98\%$ precision ($97.8\%$ Sensitivity, $99.5\%$ Specificity), exitus prediction with $99.8\%$ precision ($99.8\%$ Sens., $99.9\%$ Spec.) and $98\%$ precision predicting stroke recurrence ($98\%$ Sens., $99\%$ Spec.).

eess.SP↗

arXiv2023

Bringing AI to the edge: A formal M&S specification to deploy effective IoT architectures

The Internet of Things is transforming our society, providing new services that improve the quality of life and resource management. These applications are based on ubiquitous networks of multiple distributed devices, with limited computing resources and power, capable of collecting and storing data from heterogeneous sources in real-time. To avoid network saturation and high delays, new architectures such as fog computing are emerging to bring computing infrastructure closer to data sources. Additionally, new data centers are needed to provide real-time Big Data and data analytics capabilities at the edge of the network, where energy efficiency needs to be considered to ensure a sustainable and effective deployment in areas of human activity. In this research, we present an IoT model based on the principles of Model-Based Systems Engineering defined using the Discrete Event System Specification formalism. The provided mathematical formalism covers the description of the entire architecture, from IoT devices to the processing units in edge data centers. Our work includes the location-awareness of user equipment, network, and computing infrastructures to optimize federated resource management in terms of delay and power consumption. We present an effective framework to assist the dimensioning and the dynamic operation of IoT data stream analytics applications, demonstrating our contributions through a driving assistance use case based on real traces and data.

cs.NI↗

arXiv2023

Modeling glycemia in humans by means of Grammatical Evolution

Diabetes mellitus is a disease that affects to hundreds of millions of people worldwide. Maintaining a good control of the disease is critical to avoid severe long-term complications. In recent years, several artificial pancreas systems have been proposed and developed, which are increasingly advanced. However there is still a lot of research to do. One of the main problems that arises in the (semi) automatic control of diabetes, is to get a model explaining how glycemia (glucose levels in blood) varies with insulin, food intakes and other factors, fitting the characteristics of each individual or patient. This paper proposes the application of evolutionary computation techniques to obtain customized models of patients, unlike most of previous approaches which obtain averaged models. The proposal is based on a kind of genetic programming based on grammars known as Grammatical Evolution (GE). The proposal has been tested with in-silico patient data and results are clearly positive. We present also a study of four different grammars and five objective functions. In the test phase the models characterized the glucose with a mean percentage average error of 13.69\%, modeling well also both hyper and hypoglycemic situations.

cs.NE↗

arXiv2023

Sustainable Edge Computing: Challenges and Future Directions

An increasing amount of data is being injected into the network from IoT (Internet of Things) applications. Many of these applications, developed to improve society's quality of life, are latency-critical and inject large amounts of data into the network. These requirements of IoT applications trigger the emergence of Edge computing paradigm. Currently, data centers are responsible for a global energy use between 2% and 3%. However, this trend is difficult to maintain, as bringing computing infrastructures closer to the edge of the network comes with its own set of challenges for energy efficiency. In this paper, we propose our approach for the sustainability of future computing infrastructures to provide (i) an energy-efficient and economically viable deployment, (ii) a fault-tolerant automated operation, and (iii) a collaborative resource management to improve resource efficiency. We identify the main limitations of applying Cloud-based approaches close to the data sources and present the research challenges to Edge sustainability arising from these constraints. We propose two-phase immersion cooling, formal modeling, machine learning, and energy-centric federated management as Edge-enabling technologies. We present our early results towards the sustainability of an Edge infrastructure to demonstrate the benefits of our approach for future computing environments and deployments.

cs.DC↗

arXiv2023

Boosting the 3D thermal-aware floorplanning problem through a master-worker parallel MOEA

The increasing transistor scale integration poses, among others, the thermal-aware floorplanning problem; consisting of how to place the hardware components in order to reduce overheating by dissipation. Due to the huge amount of feasible floorplans, most of the solutions found in the literature include an evolutionary algorithm for, either partially or completely, carrying out the task of floorplanning. Evolutionary algorithms usually have a bottleneck in the fitness evaluation. In the problem of thermal-aware floorplanning, the layout evaluation by the thermal model takes 99.5\% of the computational time for the best floorplanning algorithm proposed so far.The contribution of this paper is to present a parallelization of this evaluation phase in a master$-$worker model to achieve a dramatic speed-up of the thermal-aware floorplanning process. Exhaustive experimentation was done over three dimensional integrated circuits, with 48 and 128 cores, outperforming previous published works.

cs.AR↗

arXiv2023

Evolutionary Design of the Memory Subsystem

The memory hierarchy has a high impact on the performance and power consumption in the system. Moreover, current embedded systems, included in mobile devices, are specifically designed to run multimedia applications, which are memory intensive. This increases the pressure on the memory subsystem and affects the performance and energy consumption. In this regard, the thermal problems, performance degradation and high energy consumption, can cause irreversible damage to the devices. We address the optimization of the whole memory subsystem with three approaches integrated as a single methodology. Firstly, the thermal impact of register file is analyzed and optimized. Secondly, the cache memory is addressed by optimizing cache configuration according to running applications and improving both performance and power consumption. Finally, we simplify the design and evaluation process of general-purpose and customized dynamic memory manager, in the main memory. To this aim, we apply different evolutionary algorithms in combination with memory simulators and profiling tools. This way, we are able to evaluate the quality of each candidate solution and take advantage of the exploration of solutions given by the optimization algorithm.We also provide an experimental experience where our proposal is assessed using well-known benchmark applications.

cs.AR↗

arXiv2023

Optimizing L1 cache for embedded systems through grammatical evolution

Nowadays, embedded systems are provided with cache memories that are large enough to influence in both performance and energy consumption as never occurred before in this kind of systems. In addition, the cache memory system has been identified as a component that improves those metrics by adapting its configuration according to the memory access patterns of the applications being run. However, given that cache memories have many parameters which may be set to a high number of different values, designers face to a wide and time-consuming exploration space. In this paper we propose an optimization framework based on Grammatical Evolution (GE) which is able to efficiently find the best cache configurations for a given set of benchmark applications. This metaheuristic allows an important reduction of the optimization runtime obtaining good results in a low number of generations. Besides, this reduction is also increased due to the efficient storage of evaluated caches. Moreover, we selected GE because the plasticity of the grammar eases the creation of phenotypes that form the call to the cache simulator required for the evaluation of the different configurations. Experimental results for the Mediabench suite show that our proposal is able to find cache configurations that obtain an average improvement of $62\%$ versus a real world baseline configuration.

cs.NE↗

arXiv2023

Multi-objective optimization of energy consumption and execution time in a single level cache memory for embedded systems

Current embedded systems are specifically designed to run multimedia applications. These applications have a big impact on both performance and energy consumption. Both metrics can be optimized selecting the best cache configuration for a target set of applications. Multi-objective optimization may help to minimize both conflicting metrics in an independent manner. In this work, we propose an optimization method that based on Multi-Objective Evolutionary Algorithms, is able to find the best cache configuration for a given set of applications. To evaluate the goodness of candidate solutions, the execution of the optimization algorithm is combined with a static profiling methodology using several well-known simulation tools. Results show that our optimization framework is able to obtain an optimized cache for Mediabench applications. Compared to a baseline cache memory, our design method reaches an average improvement of 64.43\% and 91.69\% in execution time and energy consumption, respectively.

cs.NE↗

arXiv2023

A Unified Cloud-Enabled Discrete Event Parallel and Distributed Simulation Architecture

Cloud simulation environments today are largely employed to model and simulate complex systems for remote accessibility and variable capacity requirements. In this regard, scalability issues in Modeling and Simulation (M\&S) computational requirements can be tackled through the elasticity of on-demand Cloud deployment. However, implementing a high performance cloud M\&S framework following these elastic principles is not a trivial task as parallelizing and distributing existing architectures is challenging. Indeed, both the parallel and distributed M\&S developments have evolved following separate ways. Parallel solutions has always been focused on ad-hoc solutions, while distributed approaches, on the other hand, have led to the definition of standard distributed frameworks like the High Level Architecture (HLA) or influenced the use of distributed technologies like the Message Passing Interface (MPI). Only a few developments have been able to evolve with the current resilience of computing hardware resources deployment, largely focused on the implementation of Simulation as a Service (SaaS), albeit independently of the parallel ad-hoc methods branch. In this paper, we present a unified parallel and distributed M\&S architecture with enough flexibility to deploy parallel and distributed simulations in the Cloud with a low effort, without modifying the underlying model source code, and reaching important speedups against the sequential simulation, especially in the parallel implementation. Our framework is based on the Discrete Event System Specification (DEVS) formalism. The performance of the parallel and distributed framework is tested using the xDEVS M\&S tool, Application Programming Interface (API) and the DEVStone benchmark with up to eight computing nodes, obtaining maximum speedups of $15.95\times$ and $1.84\times$, respectively.

cs.DC↗

arXiv2023

Efficient micro data centres deployment for mobile healthcare monitoring systems in IoT urban scenarios

In the last decade, the Internet of Things paradigm has caused an exponential increase in the number of connected devices. This trend brings the Internet closer to everyday activities and enables data collection that can be used to create and improve a great variety of services and applications. Despite its great benefits, this paradigm also comes with several challenges. More powerful storage and processing capabilities are required to service all these devices. Additionally, the need to deploy and manage the infrastructure to efficiently support these resources continues to pose a challenge. Modeling and simulation can help to design and analyze these scenarios, providing flexible and powerful mechanisms to study and compare different strategies and infrastructures. In this scenario, Micro Data Centers (MDCs) can be used as an effective way of reducing overwhelmed Cloud Data Center infrastructures. This paper explores an M\&S methodology to study the overall power consumption of a healthcare IoT scenario. The patients wear non-intrusive monitoring devices that periodically generate tasks to be executed in MDCs. We extract the layout of existing urban infrastructures, simulate the monitored population's behavior, and compare the power consumption of several data center configurations.

cs.AI↗

arXiv2022

Runtime data center temperature prediction using Grammatical Evolution techniques

Data Centers are huge power consumers, both because of the energy required for computation and the cooling needed to keep servers below thermal redlining. The most common technique to minimize cooling costs is increasing data room temperature. However, to avoid reliability issues, and to enhance energy efficiency, there is a need to predict the temperature attained by servers under variable cooling setups. Due to the complex thermal dynamics of data rooms, accurate runtime data center temperature prediction has remained as an important challenge. By using Gramatical Evolution techniques, this paper presents a methodology for the generation of temperature models for data centers and the runtime prediction of CPU and inlet temperature under variable cooling setups. As opposed to time costly Computational Fluid Dynamics techniques, our models do not need specific knowledge about the problem, can be used in arbitrary data centers, re-trained if conditions change and have negligible overhead during runtime prediction. Our models have been trained and tested by using traces from real Data Center scenarios. Our results show how we can fully predict the temperature of the servers in a data rooms, with prediction errors below 2 C and 0.5 C in CPU and server inlet temperature respectively.

cs.AI↗