A distributed analysis and monitoring framework for the compact Muon solenoid experiment and a pedestrian simulation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The design of a parallel and distributed computing system is a very complicated task. It requires a detailed understanding of the design issues and of the theoretical and practical aspects of their solutions. Firstly, this thesis discusses in detail the major concepts and components required to make parallel and distributed computing a reality. A multithreaded and distributed framework capable of analysing the simulation data produced by a pedestrian simulation software was developed. Secondly, this thesis discusses the origins and fundamentals of Grid computing and the motivations for its use in High Energy Physics. Access to the data produced by the Large Hadron Collider (LHC) has to be provided for more than five thousand scientists all over the world. Users who run analysis jobs on the Grid do not necessarily have expertise in Grid computing. Simple, userfriendly and reliable monitoring of the analysis jobs is one of the key components of the operations of the distributed analysis; reliable monitoring is one of the crucial components of the Worldwide LHC Computing Grid for providing the functionality and performance that is required by the LHC experiments. The CMS Dashboard Task Monitoring and the CMS Dashboard Job Summary monitoring applications were developed to serve the needs of the CMS community

A new MDA-SOA based framework for intercloud interoperability

Cloud computing has been one of the most important topics in Information Technology which aims to assure scalable and reliable on-demand services over the Internet. The expansion of the application scope of cloud services would require cooperation between clouds from different providers that have heterogeneous functionalities. This collaboration between different cloud vendors can provide better Quality of Services (QoS) at the lower price. However, current cloud systems have been developed without concerns of seamless cloud interconnection, and actually they do not support intercloud interoperability to enable collaboration between cloud service providers. Hence, the PhD work is motivated to address interoperability issue between cloud providers as a challenging research objective. This thesis proposes a new framework which supports inter-cloud interoperability in a heterogeneous computing resource cloud environment with the goal of dispatching the workload to the most effective clouds available at runtime. Analysing different methodologies that have been applied to resolve various problem scenarios related to interoperability lead us to exploit Model Driven Architecture (MDA) and Service Oriented Architecture (SOA) methods as appropriate approaches for our inter-cloud framework. Moreover, since distributing the operations in a cloud-based environment is a nondeterministic polynomial time (NP-complete) problem, a Genetic Algorithm (GA) based job scheduler proposed as a part of interoperability framework, offering workload migration with the best performance at the least cost. A new Agent Based Simulation (ABS) approach is proposed to model the inter-cloud environment with three types of agents: Cloud Subscriber agent, Cloud Provider agent, and Job agent. The ABS model is proposed to evaluate the proposed framework.Fundação para a Ciência e a Tecnologia (FCT) - (Referencia da bolsa: SFRH SFRH / BD / 33965 / 2009) and EC 7th Framework Programme under grant agreement n° FITMAN 604674 (http://www.fitman-fi.eu

Internet of Things Applications - From Research and Innovation to Market Deployment

The book aims to provide a broad overview of various topics of Internet of Things from the research, innovation and development priorities to enabling technologies, nanoelectronics, cyber physical systems, architecture, interoperability and industrial applications. It is intended to be a standalone book in a series that covers the Internet of Things activities of the IERC – Internet of Things European Research Cluster from technology to international cooperation and the global "state of play".The book builds on the ideas put forward by the European research Cluster on the Internet of Things Strategic Research Agenda and presents global views and state of the art results on the challenges facing the research, development and deployment of IoT at the global level. Internet of Things is creating a revolutionary new paradigm, with opportunities in every industry from Health Care, Pharmaceuticals, Food and Beverage, Agriculture, Computer, Electronics Telecommunications, Automotive, Aeronautics, Transportation Energy and Retail to apply the massive potential of the IoT to achieving real-world solutions. The beneficiaries will include as well semiconductor companies, device and product companies, infrastructure software companies, application software companies, consulting companies, telecommunication and cloud service providers. IoT will create new revenues annually for these stakeholders, and potentially create substantial market share shakeups due to increased technology competition. The IoT will fuel technology innovation by creating the means for machines to communicate many different types of information with one another while contributing in the increased value of information created by the number of interconnections among things and the transformation of the processed information into knowledge shared into the Internet of Everything. The success of IoT depends strongly on enabling technology development, market acceptance and standardization, which provides interoperability, compatibility, reliability, and effective operations on a global scale. The connected devices are part of ecosystems connecting people, processes, data, and things which are communicating in the cloud using the increased storage and computing power and pushing for standardization of communication and metadata. In this context security, privacy, safety, trust have to be address by the product manufacturers through the life cycle of their products from design to the support processes. The IoT developments address the whole IoT spectrum - from devices at the edge to cloud and datacentres on the backend and everything in between, through ecosystems are created by industry, research and application stakeholders that enable real-world use cases to accelerate the Internet of Things and establish open interoperability standards and common architectures for IoT solutions. Enabling technologies such as nanoelectronics, sensors/actuators, cyber-physical systems, intelligent device management, smart gateways, telematics, smart network infrastructure, cloud computing and software technologies will create new products, new services, new interfaces by creating smart environments and smart spaces with applications ranging from Smart Cities, smart transport, buildings, energy, grid, to smart health and life. Technical topics discussed in the book include: • Introduction• Internet of Things Strategic Research and Innovation Agenda• Internet of Things in the industrial context: Time for deployment.• Integration of heterogeneous smart objects, applications and services• Evolution from device to semantic and business interoperability• Software define and virtualization of network resources• Innovation through interoperability and standardisation when everything is connected anytime at anyplace• Dynamic context-aware scalable and trust-based IoT Security, Privacy framework• Federated Cloud service management and the Internet of Things• Internet of Things Application

Resource discovery for distributed computing systems: A comprehensive survey

Large-scale distributed computing environments provide a vast amount of heterogeneous computing resources from different sources for resource sharing and distributed computing. Discovering appropriate resources in such environments is a challenge which involves several different subjects. In this paper, we provide an investigation on the current state of resource discovery protocols, mechanisms, and platforms for large-scale distributed environments, focusing on the design aspects. We classify all related aspects, general steps, and requirements to construct a novel resource discovery solution in three categories consisting of structures, methods, and issues. Accordingly, we review the literature, analyzing various aspects for each category

Programming models for mobile environments

Premi extraordinari doctorat UPC curs 2017-2018. Àmbit d’Enginyeria de les TICFor the last decade, mobile devices have grown in popularity and became the best-selling computing devices. Despite their high capabilities for user interactions and network connectivity, the computing power of mobile devices is low and the lifetime of the application running on them limited by the battery. Mobile Cloud Computing (MCC) is a technology that tackles the limitations of mobile devices by bringing together their mobility with the vast computing power of the Cloud. Programming applications for Mobile Cloud Computing (MCC) environments is not as straightforward as coding monolithic applications. Developers have to deal with the issues related to parallel programming for distributed infrastructures while considering the battery lifetime and the variability of the network produced by the high mobility of this kind of devices. As with any other distributed environment, developers turn to programming models to improve their productivity by avoiding the complexity of manually dealing with these issues and delegate on the corresponding model all the management of these concerns. This thesis contributes to the current state of the art with an adaptation of the COMPSs programming model for MCC environments. COMPSs allows application programmers to code their applications in a sequential, infrastructure-agnostic fashion without calls to any COMPSs-specific API using the native language for the target platform as if they were to run on the mobile device. At execution time, a runtime system automatically partitions the application into tasks and orchestrates their execution on top of the available resources. This thesis contributes with an extension to the programming model to allow task polymorphism and let the runtime exploit computational resources other than the CPU of the resources. Besides, the runtime architecture has been redesigned with the characteristics of MCC in mind, and it runs as a common service which all the applications running simultaneously on the mobile device contact for submitting the execution of their tasks. For collaboratively exploiting both, local and remote resources, the runtime clusters the computational devices into Computing Platforms according to the mechanisms required to provide the processing elements with the necessary input values, launch the task execution avoiding resource oversubscription and fetching the results back from them. The CPU Platform run tasks on the cores of the CPU. The GPU Platform leverages on OpenCL to run tasks as kernels on GPUs or other accelerators embedded in the mobile device. Finally, the Cloud Platform offloads the execution of tasks onto remote resources. To holistically decide whether is worth running a task on embedded or on remote resources, the runtime considers the the costs -- time, energy and money -- of running the computation on each of the platforms and picks the best. Each platform manages internally its resources and orchestrates the execution of tasks on them using different scheduling policies. Using local and remote computing devices forces the runtime to share data values among the nodes of the infrastructure. This data is potentially privacy-sensitive, and the runtime exposes it to possible attackers when transferring it through the network. To protect the application user from data leaks, the runtime has to provide communications with secrecy, integrity and authenticity. In the extreme case of a network breakdown that isolates the mobile device from the remote nodes, the runtime has to ensure that the execution continues to provide the application user with the expected result even if the connection never re-establishes. The mobile device has to respond using only the resources embedded in it, what could incur in the re-execution of computations already ran on the remote resources. Remote workers have to continue with the execution so that, in case of reconnection, both parts synchronize its progress to reduce the impact of the disruption.Els últims anys, els dispositius mòbils han guanyat en popularitat i s'han convertit en els dispositius més venuts. Tot i la connectivitat i la bona interacció amb l'usuari que ofereixen, la seva capacitat de càlcul is baixa i limitada per la vida de la bateria. El Mobile Cloud Computing (MCC) és una tecnologia que soluciona les limitacions d'aquests dispositius ajuntant la seva mobilitat amb la gran capacitat de còmput del Cloud. Programar aplicacions per entorns MCC no és tan directe com fer aplicacions monolítiques. Els desenvolupadors han de tractar amb els problemes relacionats amb la programació paral·lela mentre tenen en compte la duració de la bateria i la variabilitat de la xarxa degut a la mobilitat inherent a aquest tipus de dispositius. Com per qualsevol altre entorn distribuït, els desenvolupadors recorren a models de programació que millorin la seva productivitat i els evitin tractar manualment amb aquests problemes delegant la seva gestió en el model. Aquesta tesis contribueix a l'estat de l'art actual amb una adaptació del model de programació COMPSs als entorns MCC. COMPSs permet als desenvolupadors programar les aplicacions de forma agnòstica a la infraestructura i seqüencial sense necessitat d'invocar cap API específica utilitzant el llenguatge natiu de la platforma com si l'aplicació s'executés directament en el mòbil. En temps d'execució, una eina (runtime) automàticament divideix l'aplicació en tasques i n'orquestra la seva execució sobre els recursos disponibles. Aquesta tesis estèn el model de programació per tal de permetre polimorfisme a nivell de tasca i deixar al runtime explotar els recursos computacionals dels que disposa el mòbil a part de la CPU. A més a més, l'arquitectura del runtime s'ha redissenyat tenint en compte les característiques pròpies del MCC, i aquest s'executa com un servei comú al que totes les aplicacions del mòbil contacten per tal d'executar les seves tasques. Per explotar col·laborativament tots els recursos, locals i remots, el runtime agrupa els recursos en Computing Platforms en funció dels mecanismes necessaris per proveir el recurs amb les dades d'entrada necessàries, llançar l'execució i recuperar-ne els resultats. La CPU Platform executa tasques en els nuclis de la CPU. La GPU Platform utilitza OpenCL per executar tasques en forma de kernels a la GPU o altres acceleradors integrats en el mòbil. Finalment, la Cloud Platform descàrrega l'execució de tasques en recursos remots. Per decidir holisticament si és millor executar una tasca en un recurs local o en un remot, el runtime considera els costs (temporal, energètic econòmic) d'executar la tasca en cada una de les plataformes i n'escull la millor. Cada plataforma gestiona internament els seus recursos i orquestra l'execució de les tasques en ells seguint diferents polítiques de planificació. L'ús de recursos locals i remots força la compartició de dades entre els nodes de la infraestructura. Aquestes dades són potencialment sensibles i de caràcter privat i el runtime les exposa a possibles atacs que les transfereix per la xarxa. Per tal de protegir l'usuari de possibles fuites de dades, el runtime ha de dotar les comunicacions amb confidencialitat, integritat i autenticitat. En el cas extrem en que un error de xarxa aïlli el dispositiu mòbil dels nodes remots, el runtime ha d'assegurar que l'execució continua i que eventualment l'usuari rebrà el resultat esperat fins i tot en cas de que la connexió no és restableixi mai. El mòbil ha de ser capaç d'executar l'aplicació utilitzant únicament les dades i recursos disponibles en aquell moment, la qual cosa pot forçar la re-execució d'algunes tasques ja calculades en els recursos remots. Els recursos remots han de continuar l'execució per tal que en cas de reconnexió, ambdues parts sincronitzin el seu progrés i es minimitzi l'impacte de la desconnexió.Award-winningPostprint (published version

Programming models for mobile environments

For the last decade, mobile devices have grown in popularity and became the best-selling computing devices. Despite their high capabilities for user interactions and network connectivity, the computing power of mobile devices is low and the lifetime of the application running on them limited by the battery. Mobile Cloud Computing (MCC) is a technology that tackles the limitations of mobile devices by bringing together their mobility with the vast computing power of the Cloud. Programming applications for Mobile Cloud Computing (MCC) environments is not as straightforward as coding monolithic applications. Developers have to deal with the issues related to parallel programming for distributed infrastructures while considering the battery lifetime and the variability of the network produced by the high mobility of this kind of devices. As with any other distributed environment, developers turn to programming models to improve their productivity by avoiding the complexity of manually dealing with these issues and delegate on the corresponding model all the management of these concerns. This thesis contributes to the current state of the art with an adaptation of the COMPSs programming model for MCC environments. COMPSs allows application programmers to code their applications in a sequential, infrastructure-agnostic fashion without calls to any COMPSs-specific API using the native language for the target platform as if they were to run on the mobile device. At execution time, a runtime system automatically partitions the application into tasks and orchestrates their execution on top of the available resources. This thesis contributes with an extension to the programming model to allow task polymorphism and let the runtime exploit computational resources other than the CPU of the resources. Besides, the runtime architecture has been redesigned with the characteristics of MCC in mind, and it runs as a common service which all the applications running simultaneously on the mobile device contact for submitting the execution of their tasks. For collaboratively exploiting both, local and remote resources, the runtime clusters the computational devices into Computing Platforms according to the mechanisms required to provide the processing elements with the necessary input values, launch the task execution avoiding resource oversubscription and fetching the results back from them. The CPU Platform run tasks on the cores of the CPU. The GPU Platform leverages on OpenCL to run tasks as kernels on GPUs or other accelerators embedded in the mobile device. Finally, the Cloud Platform offloads the execution of tasks onto remote resources. To holistically decide whether is worth running a task on embedded or on remote resources, the runtime considers the the costs -- time, energy and money -- of running the computation on each of the platforms and picks the best. Each platform manages internally its resources and orchestrates the execution of tasks on them using different scheduling policies. Using local and remote computing devices forces the runtime to share data values among the nodes of the infrastructure. This data is potentially privacy-sensitive, and the runtime exposes it to possible attackers when transferring it through the network. To protect the application user from data leaks, the runtime has to provide communications with secrecy, integrity and authenticity. In the extreme case of a network breakdown that isolates the mobile device from the remote nodes, the runtime has to ensure that the execution continues to provide the application user with the expected result even if the connection never re-establishes. The mobile device has to respond using only the resources embedded in it, what could incur in the re-execution of computations already ran on the remote resources. Remote workers have to continue with the execution so that, in case of reconnection, both parts synchronize its progress to reduce the impact of the disruption.Els últims anys, els dispositius mòbils han guanyat en popularitat i s'han convertit en els dispositius més venuts. Tot i la connectivitat i la bona interacció amb l'usuari que ofereixen, la seva capacitat de càlcul is baixa i limitada per la vida de la bateria. El Mobile Cloud Computing (MCC) és una tecnologia que soluciona les limitacions d'aquests dispositius ajuntant la seva mobilitat amb la gran capacitat de còmput del Cloud. Programar aplicacions per entorns MCC no és tan directe com fer aplicacions monolítiques. Els desenvolupadors han de tractar amb els problemes relacionats amb la programació paral·lela mentre tenen en compte la duració de la bateria i la variabilitat de la xarxa degut a la mobilitat inherent a aquest tipus de dispositius. Com per qualsevol altre entorn distribuït, els desenvolupadors recorren a models de programació que millorin la seva productivitat i els evitin tractar manualment amb aquests problemes delegant la seva gestió en el model. Aquesta tesis contribueix a l'estat de l'art actual amb una adaptació del model de programació COMPSs als entorns MCC. COMPSs permet als desenvolupadors programar les aplicacions de forma agnòstica a la infraestructura i seqüencial sense necessitat d'invocar cap API específica utilitzant el llenguatge natiu de la platforma com si l'aplicació s'executés directament en el mòbil. En temps d'execució, una eina (runtime) automàticament divideix l'aplicació en tasques i n'orquestra la seva execució sobre els recursos disponibles. Aquesta tesis estèn el model de programació per tal de permetre polimorfisme a nivell de tasca i deixar al runtime explotar els recursos computacionals dels que disposa el mòbil a part de la CPU. A més a més, l'arquitectura del runtime s'ha redissenyat tenint en compte les característiques pròpies del MCC, i aquest s'executa com un servei comú al que totes les aplicacions del mòbil contacten per tal d'executar les seves tasques. Per explotar col·laborativament tots els recursos, locals i remots, el runtime agrupa els recursos en Computing Platforms en funció dels mecanismes necessaris per proveir el recurs amb les dades d'entrada necessàries, llançar l'execució i recuperar-ne els resultats. La CPU Platform executa tasques en els nuclis de la CPU. La GPU Platform utilitza OpenCL per executar tasques en forma de kernels a la GPU o altres acceleradors integrats en el mòbil. Finalment, la Cloud Platform descàrrega l'execució de tasques en recursos remots. Per decidir holisticament si és millor executar una tasca en un recurs local o en un remot, el runtime considera els costs (temporal, energètic econòmic) d'executar la tasca en cada una de les plataformes i n'escull la millor. Cada plataforma gestiona internament els seus recursos i orquestra l'execució de les tasques en ells seguint diferents polítiques de planificació. L'ús de recursos locals i remots força la compartició de dades entre els nodes de la infraestructura. Aquestes dades són potencialment sensibles i de caràcter privat i el runtime les exposa a possibles atacs que les transfereix per la xarxa. Per tal de protegir l'usuari de possibles fuites de dades, el runtime ha de dotar les comunicacions amb confidencialitat, integritat i autenticitat. En el cas extrem en que un error de xarxa aïlli el dispositiu mòbil dels nodes remots, el runtime ha d'assegurar que l'execució continua i que eventualment l'usuari rebrà el resultat esperat fins i tot en cas de que la connexió no és restableixi mai. El mòbil ha de ser capaç d'executar l'aplicació utilitzant únicament les dades i recursos disponibles en aquell moment, la qual cosa pot forçar la re-execució d'algunes tasques ja calculades en els recursos remots. Els recursos remots han de continuar l'execució per tal que en cas de reconnexió, ambdues parts sincronitzin el seu progrés i es minimitzi l'impacte de la desconnexió

Elastic phone : towards detecting and mitigating computation and energy inefficiencies in mobile apps

Mobile devices have become ubiquitous and their ever evolving capabilities are bringing them closer to personal computers. Nonetheless, due to their mobility and small size factor constraints, they still present many hardware and software challenges. Their limited battery life time has led to the design of mobile networks that are inherently different from previous networks (e.g., wifi) and more restrictive task scheduling. Additionally, mobile device ecosystems are more susceptible to the heterogeneity of hardware and from conflicting interests of distributors, internet service providers, manufacturers, developers, etc. The high number of stakeholders ultimately responsible for the performance of a device, results in an inconsistent behavior and makes it very challenging to build a solution that improves resource usage in most cases. The focus of this thesis is on the study and development of techniques to detect and mitigate computation and energy inefficiencies in mobile apps. It follows a bottom-up approach, starting from the challenges behind detecting inefficient execution scheduling by looking only at apps’ implementations. It shows that scheduling APIs are largely misused and have a great impact on devices wake up frequency and on the efficiency of existing energy saving techniques (e.g., batching scheduled executions). Then it addresses many challenges of app testing in the dynamic analysis field. More specifically, how to scale mobile app testing with realistic user input and how to analyze closed source apps’ code at runtime, showing that introducing humans in the app testing loop improves the coverage of app’s code and generated network volume. Finally, using the combined knowledge of static and dynamic analysis, it focuses on the challenges of identifying the resource hungry sections of apps and how to improve their execution via offloading. There is a special focus on performing non-intrusive offloading transparent to existing apps and on in-network computation offloading and distribution. It shows that, even without a custom OS or app modifications, in-network offloading is still possible, greatly improving execution times, energy consumption and reducing both end-user experienced latency and request drop rates. It concludes with a real app measurement study, showing that a good portion of the most popular apps’ code can indeed be offloaded and proposes future directions for the app testing and computation offloading fields.Los dispositivos móviles se han tornado omnipresentes y sus capacidades están en constante evolución acercándolos a los computadoras personales. Sin embargo, debido a su movilidad y tamaño reducido, todavía presentan muchos desafíos de hardware y software. Su duración limitada de batería ha llevado al diseño de redes móviles que son inherentemente diferentes de las redes anteriores y una programación de tareas más restrictiva. Además, los ecosistemas de dispositivos móviles son más susceptibles a la heterogeneidad de hardware y los intereses conflictivos de las entidades responsables por el rendimiento final de un dispositivo. El objetivo de esta tesis es el estudio y desarrollo de técnicas para detectar y mitigar las ineficiencias de computación y energéticas en las aplicaciones móviles. Empieza con los desafíos detrás de la detección de planificación de ejecución ineficientes, mirando sólo la implementación de las aplicaciones. Se muestra que las API de planificación son en gran medida mal utilizadas y tienen un gran impacto en la frecuencia con que los dispositivos despiertan y en la eficiencia de las técnicas de ahorro de energía existentes. A continuación, aborda muchos desafíos de las pruebas de aplicaciones en el campo de análisis dinámica. Más específicamente, cómo escalar las pruebas de aplicaciones móviles con una interacción realista y cómo analizar código de aplicaciones de código cerrado durante la ejecución, mostrando que la introducción de humanos en el bucle de prueba de aplicaciones mejora la cobertura del código y el volumen de comunicación de red generado. Por último, combinando la análisis estática y dinámica, se centra en los desafíos de identificar las secciones de aplicaciones con uso intensivo de recursos y cómo mejorar su ejecución a través de la ejecución remota (i.e.,"offload"). Hay un enfoque especial en el "offload" no intrusivo y transparente a las aplicaciones existentes y en el "offload"y distribución de computación dentro de la red. Demuestra que, incluso sin un sistema operativo personalizado o modificaciones en la aplicación, el "offload" en red sigue siendo posible, mejorando los tiempos de ejecución, el consumo de energía y reduciendo la latencia del usuario final y las tasas de caída de solicitudes de "offload". Concluye con un estudio real de las aplicaciones más populares, mostrando que una buena parte de su código puede de hecho ser ejecutado remotamente y propone direcciones futuras para los campos de "offload" de aplicaciones.Postprint (published version

Elastic phone : towards detecting and mitigating computation and energy inefficiencies in mobile apps

Mobile devices have become ubiquitous and their ever evolving capabilities are bringing them closer to personal computers. Nonetheless, due to their mobility and small size factor constraints, they still present many hardware and software challenges. Their limited battery life time has led to the design of mobile networks that are inherently different from previous networks (e.g., wifi) and more restrictive task scheduling. Additionally, mobile device ecosystems are more susceptible to the heterogeneity of hardware and from conflicting interests of distributors, internet service providers, manufacturers, developers, etc. The high number of stakeholders ultimately responsible for the performance of a device, results in an inconsistent behavior and makes it very challenging to build a solution that improves resource usage in most cases. The focus of this thesis is on the study and development of techniques to detect and mitigate computation and energy inefficiencies in mobile apps. It follows a bottom-up approach, starting from the challenges behind detecting inefficient execution scheduling by looking only at apps’ implementations. It shows that scheduling APIs are largely misused and have a great impact on devices wake up frequency and on the efficiency of existing energy saving techniques (e.g., batching scheduled executions). Then it addresses many challenges of app testing in the dynamic analysis field. More specifically, how to scale mobile app testing with realistic user input and how to analyze closed source apps’ code at runtime, showing that introducing humans in the app testing loop improves the coverage of app’s code and generated network volume. Finally, using the combined knowledge of static and dynamic analysis, it focuses on the challenges of identifying the resource hungry sections of apps and how to improve their execution via offloading. There is a special focus on performing non-intrusive offloading transparent to existing apps and on in-network computation offloading and distribution. It shows that, even without a custom OS or app modifications, in-network offloading is still possible, greatly improving execution times, energy consumption and reducing both end-user experienced latency and request drop rates. It concludes with a real app measurement study, showing that a good portion of the most popular apps’ code can indeed be offloaded and proposes future directions for the app testing and computation offloading fields.Los dispositivos móviles se han tornado omnipresentes y sus capacidades están en constante evolución acercándolos a los computadoras personales. Sin embargo, debido a su movilidad y tamaño reducido, todavía presentan muchos desafíos de hardware y software. Su duración limitada de batería ha llevado al diseño de redes móviles que son inherentemente diferentes de las redes anteriores y una programación de tareas más restrictiva. Además, los ecosistemas de dispositivos móviles son más susceptibles a la heterogeneidad de hardware y los intereses conflictivos de las entidades responsables por el rendimiento final de un dispositivo. El objetivo de esta tesis es el estudio y desarrollo de técnicas para detectar y mitigar las ineficiencias de computación y energéticas en las aplicaciones móviles. Empieza con los desafíos detrás de la detección de planificación de ejecución ineficientes, mirando sólo la implementación de las aplicaciones. Se muestra que las API de planificación son en gran medida mal utilizadas y tienen un gran impacto en la frecuencia con que los dispositivos despiertan y en la eficiencia de las técnicas de ahorro de energía existentes. A continuación, aborda muchos desafíos de las pruebas de aplicaciones en el campo de análisis dinámica. Más específicamente, cómo escalar las pruebas de aplicaciones móviles con una interacción realista y cómo analizar código de aplicaciones de código cerrado durante la ejecución, mostrando que la introducción de humanos en el bucle de prueba de aplicaciones mejora la cobertura del código y el volumen de comunicación de red generado. Por último, combinando la análisis estática y dinámica, se centra en los desafíos de identificar las secciones de aplicaciones con uso intensivo de recursos y cómo mejorar su ejecución a través de la ejecución remota (i.e.,"offload"). Hay un enfoque especial en el "offload" no intrusivo y transparente a las aplicaciones existentes y en el "offload"y distribución de computación dentro de la red. Demuestra que, incluso sin un sistema operativo personalizado o modificaciones en la aplicación, el "offload" en red sigue siendo posible, mejorando los tiempos de ejecución, el consumo de energía y reduciendo la latencia del usuario final y las tasas de caída de solicitudes de "offload". Concluye con un estudio real de las aplicaciones más populares, mostrando que una buena parte de su código puede de hecho ser ejecutado remotamente y propone direcciones futuras para los campos de "offload" de aplicaciones

Memorias del Congreso Argentino en Ciencias de la Computación - CACIC 2021

Trabajos presentados en el XXVII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de Salta los días 4 al 8 de octubre de 2021, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Universidad Nacional de Salta (UNSA).Red de Universidades con Carreras en Informátic