Foggy clouds and cloudy fogs: a real need for coordinated management of fog-to-cloud computing systems

The recent advances in cloud services technology are fueling a plethora of information technology innovation, including networking, storage, and computing. Today, various flavors have evolved of IoT, cloud computing, and so-called fog computing, a concept referring to capabilities of edge devices and users' clients to compute, store, and exchange data among each other and with the cloud. Although the rapid pace of this evolution was not easily foreseeable, today each piece of it facilitates and enables the deployment of what we commonly refer to as a smart scenario, including smart cities, smart transportation, and smart homes. As most current cloud, fog, and network services run simultaneously in each scenario, we observe that we are at the dawn of what may be the next big step in the cloud computing and networking evolution, whereby services might be executed at the network edge, both in parallel and in a coordinated fashion, as well as supported by the unstoppable technology evolution. As edge devices become richer in functionality and smarter, embedding capacities such as storage or processing, as well as new functionalities, such as decision making, data collection, forwarding, and sharing, a real need is emerging for coordinated management of fog-to-cloud (F2C) computing systems. This article introduces a layered F2C architecture, its benefits and strengths, as well as the arising open and research challenges, making the case for the real need for their coordinated management. Our architecture, the illustrative use case presented, and a comparative performance analysis, albeit conceptual, all clearly show the way forward toward a new IoT scenario with a set of existing and unforeseen services provided on highly distributed and dynamic compute, storage, and networking resources, bringing together heterogeneous and commodity edge devices, emerging fogs, as well as conventional clouds.Peer ReviewedPostprint (author's final draft

High Performance Computing for Tumor Propagation Agent-based Model

Agent based modeling (ABM) and High Performance Computing (HPC) techniques are very popular in investigation and understanding cellular and molecular systems. The complex nature of these systems and the demand for emulation and comprehension at different levels in these models creates the expectation for new effective simulation strategies and tools. The present paper peruses the foresaid demands and the approaches for developing simulation in tumor model and its interactions using ABM and HPC. ABM allows the analysis of the actions and interactions of autonomous agents (cells in this case) to evaluate their effects on the system as a whole in order to re-create and predict the appearance of a complex phenomenon. This is a parametric model and it is necessary to explore the data model space to determine which combinations of adjustments cause the behaviors which are of interest. In this case, HPC is a useful tool to perform experiments in acceptable time.XVIII Workshop de Procesamiento Distribuido y Paralelo (WPDP).Red de Universidades con Carreras en Informática (RedUNCI

Performance Analysis of ABM Distributed Simulation for Real Crowd Evacuation Scenarios

Managing crowds is a key problem in a world with a growing population. Being able to predict and manage possible disasters directly affects the safety of crowd events. This kind of problem can be modeled using Agent-Based Model techniques and consequently simulated in order to study evacuation strategies. Our aim from this paper is to prove that this model albeit simple can be expanded and adapted for experts to test various scenarios and validate the outcome of their design. Preliminary experiments are carried out using different initial locations for the agents inside Fira of Barcelona building, whose results are presented, validated and discussed. We shown that crowd evacuation problem has bottlenecks in reality, and the initial location for all agents can increase or decrease the bottlenecks. Finally, we draw some conclusions and point out ways in which this work can be further extended.XVIII Workshop de Procesamiento Distribuido y Paralelo (WPDP).Red de Universidades con Carreras en Informática (RedUNCI

Scalable agent-based model simulation using distributed computing on system biology

El modelat basat en agents és una eina informàtica molt útil que permet simular un comportament complex utilitzant regles tant a escales micro com macro. La complexitat d’aquest tipus de modelat està en la definició de les regles que tendran els agents per definir elements estructurals o els patrons de comportament estàtics i/o dinàmics. La present tesis aborda la definició de models complexos de xarxes biològiques que representen cèl·lules canceroses per obtenir comportaments sobre diferents escenaris mitjançant simulació i conèixer l’evolució del procés de metàstasi per a usuaris no-experts en sistemes de còmput. A més es desenvolupa una prova de concepte de com incorporar tècniques d’anàlisi de xarxes dinàmiques i d’aprenentatge automàtic en els models basats en agents a partir del desenvolupament d’un sistema de simulació federat per millorar el procés de presa de decisions. Per al desenvolupament d’aquesta tesi s’ha tingut que abordar, des del punt de vista de la simulació, la representació de xarxes biològiques complexes basades en grafs i investigar com integrar la topologia i funcions d’aquest tipus de xarxes interactuant amb un model basat en agents. En aquest objectiu, s’ha utilitzat el model ABM com a base per a la construcció, agrupament i classificació dels elements de la xarxa i que representen l’estructura d’una xarxa biològica complexa i escalable. La simulació d’un model complex de múltiples escales i múltiples agents, proporciona una eina útil per a que un científic, no-expert en computació, pugui executar un model complex i paramètric i utilitzar-ho com a eina d’anàlisi d’escenaris o predicció de variacions segons els diferents perfils de pacients considerats. El desenvolupament s’ha centrat en un model de tumor basat en agents que ha evolucionat des d’un model ABM simple i bé conegut, al qual se li han incorporat les variables i dinàmiques referenciades per l’Hallmarks of Cancer, fins a un models basat en grafs. Aquest model, basat en grafs, permet representar a diferents nivells d’interacció i dinàmiques dins de les cèl·lules en l’evolució d’un tumor que permet diferents graus de representacions (a nivell molecular/cel·lular). Tot això s’ha posat en funcionament en un entorn de simulació i ha creat un flux de treball (workflow) per construir una xarxa escalable complexa basada en un escenari de creixement tumoral i on s’apliquen tècniques dinàmiques per conèixer el creixement de la xarxa tumoral sobre diferents patrons. L’experimentació s’ha realitzat utilitzant l’entorn de simulació desenvolupat considerat l’execució de models per a diferents perfils de pacients, com a mostra de la seva funcionalitat, per a paràmetres d’interès per a l’expert no-informàtic com per exemple l’evolució del volum del tumor. L’entorn ha estat dissenyat per descobrir i classificar subgrafs del model de tumor basat en agents, que permetran distribuir els models en un sistema de còmput d’altes prestacions per poder analitzar escenaris complexos i/o diferents perfils de pacients amb patrons tumorals amb un alt nombre de cèl·lules canceroses en un temps reduït.El modelado basado en agentes es una herramienta computacional muy útil que permite simular un comportamiento complejo utilizando reglas tanto en escalas micro como macro. La complejidad de este tipo de modelado radica en la definición de las reglas que tendrán los agentes para definir los elementos estructurales o los patrones de comportamiento estáticos y/o dinámicos. La presente tesis aborda la definición de modelos complejos de redes biológicas que representan células cancerosas para obtener comportamientos sobre diferentes escenarios mediante simulación y conocer la evolución del proceso de metástasis para usuarios no expertos en sistemas de cómputo. Además se desarrolla una prueba de concepto de cómo incorporar técnicas de análisis de redes dinámicas y de aprendizaje automático en los modelos basados en agentes a partir del desarrollo de un sistema de simulación federado para mejorar el proceso de toma de decisiones. Para el desarrollo de esta tesis se han tenido que abordar, desde el punto de vista de la simulación, la representación de redes biológicas complejas basadas en grafos e investigar como integrar la topología y funciones de este tipo de redes interactuando un modelo basado en agentes. En este objetivo, se ha utilizado el modelo ABM como base para la construcción, agrupamiento y clasificación de los elementos de la red y que representan la estructura de una red biológica compleja y escalable. La simulación de un modelo complejo de múltiples escalas y múltiples agentes, proporciona una herramienta útil para que un científico, no-experto en computación, pueda ejecutar un modelo complejo paramétrico y utilizarlo como herramienta de análisis de escenarios o predicción de variaciones según los diferentes perfiles de pacientes considerados. El desarrollo se ha centrado en un modelo de tumor basado en agentes que ha evolucionado desde un modelo ABM simple y bien conocido, al cual se le han incorporado las variables y dinámicas referenciadas por el Hallmarks of Cancer, a un modelo complejo basado en grafos. Este modelo, basado en grafos, se utiliza para representar a diferentes niveles de interacción y dinámicas dentro de las células en la evolución de un tumor que permite diferentes grado de representaciones (a nivel molecular/celular). Todo ello se ha puesto en funcionamiento en un entorno de simulación y se ha creado un flujo de trabajo (workflow) para construir una red escalable compleja basada en un escenario de crecimiento tumoral y donde se aplican técnicas dinámicas para conocer el crecimiento de la red tumoral sobre diferentes patrones. La experimentación se ha realizado utilizando el entorno de simulación desarrollado considerado la ejecución de modelos para diferentes perfiles de pacientes, como muestra de su funcionalidad, para calcular parámetros de interés para el experto no-informático como por ejemplo la evolución del volumen del tumor. El entorno ha sido diseñado para descubrir y clasificar subgrafos del modelo de tumor basado en agentes, que permitirá distribuir los modelos en un sistema de cómputo de altas prestaciones y así poder analizar escenarios complejos y/o diferentes perfiles de pacientes con patrones tumorales con un alto número de células cancerosas en un tiempo reducido.Agent-based modeling is a very useful computational tool to simulate complex behavior using rules at micro and macro scales. This type of modeling’s complexity is in defining the rules that the agents will have to define the structural elements or the static and dynamic behavior patterns. This thesis considers the definition of complex models of biological networks that represent cancer cells obtain behaviors on different scenarios by means of simulation and to know the evolution of the metastatic process for non-expert users of computer systems. Besides, a proof of concept has been developed to incorporate dynamic network analysis techniques and machine learning in agent-based models based on developing a federated simulation system to improve the decision-making process. For this thesis’s development, the representation of complex biological networks based on graphs has been analyzed, from the simulation point of view, to investigate how to integrate the topology and functions of this type of networks interacting with an agent-based model. For this purpose, the ABM model has been used as a basis for the construction, grouping, and classification of the network elements representing the structure of a complex and scalable biological network. The simulation of complex models with multiple scales and multiple agents provides a useful tool for a scientist, non-computer expert to execute a complex parametric model and use it to analyze scenarios or predict variations according to the different patient’s profiles. The development has focused on an agent-based tumor model that has evolved from a simple and well-known ABM model. The variables and dynamics referenced by the Hallmarks of Cancer have been incorporated into a complex model based on graphs. Based on graphs, this model is used to represent different levels of interaction and dynamics within cells in the evolution of a tumor with different degrees of representations (at the molecular/cellular level). A simulation environment and workflow have been created to build a complex, scalable network based on a tumor growth scenario. In this environment, dynamic techniques are applied to know the tumor network’s growth using different patterns. The experimentation has been carried out using the simulation environment developed considering the execution of models for different patient profiles, as a sample of its functionality, to calculate parameters of interest for the non-computer expert, such as the evolution of the tumor volume. The environment has been designed to discover and classify subgraphs of the agent-based tumor model to execute these models in a high-performance computer system. These executions will allow us to analyze complex scenarios and different profiles of patients with tumor patterns with a high number of cancer cells in a short time.Universitat Autònoma de Barcelona. Programa de Doctorat en Informàtic

Delivering Business Intelligence Performance by Data Warehouse and ETL Tuning

Abstract The aim of this thesis is to show how numerous organizations such as CGI Consultant attempt to introduce BI-Solutions through IT and other operational methods in order to deal with large companies, which want to make their competitive market position stronger. This aim is achieved by Gap Analyzing in the BI roadmap and available Data Warehouses based on one of the company projects which were handed over to CGI from Lithuania. The fundamentals in achieving the BI-Solutions through IT, which has built the thesis methodology by research are, data warehousing, content analytics and performance management, data movement (Extract, Transform and Load) and CGI BI methodology, business process management, TeliaSonera Maintenance Management Model (TSM3) and AM model of CGI in the high level. The part of the thesis basically requires some research and practical work on Informatica PowerCenter, Microsoft SQL Server Management Studio and low level details such as database tuning, DBMS tuning implementation and ETL workflows optimization.   Keywords: BI, ETL, DW, DBMS, TSM3, AM, Gap Analysin

HPC for ABM using Netlogo

The modeling of large-scale stochastic systems of heterogeneous individuals and their interactions, where multiple behaviors exist, requires a large number of scenarios and repetitions of simulation experiments. In these areas, the agent-based simulation (ABM) is the common tool and the High-Performance Computing can provide an adequate infrastructure for this type of simulations. The present work shows the methodology and the tools developed to allow the execution of multiple simulation scenarios based on ABM Netlogo model simulation in an HPC environment. The goal is to provide a user-friendly environment for ABM simulation to non-technological users

Foggy clouds and cloudy fogs: a real need for coordinated management of fog-to-cloud computing systems

The recent advances in cloud services technology are fueling a plethora of information technology innovation, including networking, storage, and computing. Today, various flavors have evolved of IoT, cloud computing, and so-called fog computing, a concept referring to capabilities of edge devices and users' clients to compute, store, and exchange data among each other and with the cloud. Although the rapid pace of this evolution was not easily foreseeable, today each piece of it facilitates and enables the deployment of what we commonly refer to as a smart scenario, including smart cities, smart transportation, and smart homes. As most current cloud, fog, and network services run simultaneously in each scenario, we observe that we are at the dawn of what may be the next big step in the cloud computing and networking evolution, whereby services might be executed at the network edge, both in parallel and in a coordinated fashion, as well as supported by the unstoppable technology evolution. As edge devices become richer in functionality and smarter, embedding capacities such as storage or processing, as well as new functionalities, such as decision making, data collection, forwarding, and sharing, a real need is emerging for coordinated management of fog-to-cloud (F2C) computing systems. This article introduces a layered F2C architecture, its benefits and strengths, as well as the arising open and research challenges, making the case for the real need for their coordinated management. Our architecture, the illustrative use case presented, and a comparative performance analysis, albeit conceptual, all clearly show the way forward toward a new IoT scenario with a set of existing and unforeseen services provided on highly distributed and dynamic compute, storage, and networking resources, bringing together heterogeneous and commodity edge devices, emerging fogs, as well as conventional clouds.Peer Reviewe