On distributed mobile edge computing

Mobile Cloud Computing (MCC) has been proposed to offload the workloads of mobile applications from mobile devices to the cloud in order to not only reduce energy consumption of mobile devices but also accelerate the execution of mobile applications. Owing to the long End-to-End (E2E) delay between mobile devices and the cloud, offloading the workloads of many interactive mobile applications to the cloud may not be suitable. That is, these mobile applications require a huge amount of computing resources to process their workloads as well as a low E2E delay between mobile devices and computing resources, which cannot be satisfied by the current MCC technology. In order to reduce the E2E delay, a novel cloudlet network architecture is proposed to bring the computing and storage resources from the remote cloud to the mobile edge. In the cloudlet network, each mobile user is associated with a specific Avatar (i.e., a dedicated Virtual Machine (VM) providing computing and storage resources to its mobile user) in the nearby cloudlet via its associated Base Station (BS). Thus, mobile users can offload their workloads to their Avatars with low E2E delay (i.e., one wireless hop). However, mobile users may roam among BSs in the mobile network, and so the E2E delay between mobile users and their Avatars may become worse if the Avatars remain in their original cloudlets. Thus, Avatar handoff is proposed to migrate an Avatar from one cloudlet into another to reduce the E2E delay between the Avatar and its mobile user. The LatEncy aware Avatar handDoff (LEAD) algorithm is designed to determine the location of each mobile user\u27s Avatar in each time slot in order to minimize the average E2E delay among all the mobile users and their Avatars. The performance of LEAD is demonstrated via extensive simulations. The cloudlet network architecture not only facilitates mobile users in offloading their computational tasks but also empowers Internet of Things (IoT). Popular IoT resources are proposed to be cached in nearby brokers, which are considered as application layer middleware nodes hosted by cloudlets in the cloudlet network, to reduce the energy consumption of servers. In addition, an Energy Aware and latency guaranteed dynamic reSourcE caching (EASE) strategy is proposed to enable each broker to cache suitable popular resources such that the energy consumption from the servers is minimized and the average delay of delivering the contents of the resources to the corresponding clients is guaranteed. The performance of EASE is demonstrated via extensive simulations. The future work comprises two parts. First, caching popular IoT resources in nearby brokers may incur unbalanced traffic loads among brokers, thus increasing the average delay of delivering the contents of the resources. Thus, how to balance the traffic loads among brokers to speed up IoT content delivery process requires further investigation. Second, drone assisted mobile access network architecture will be briefly investigated to accelerate communications between mobile users and their Avatars

Open Platforms for Connected Vehicles

L'abstract è presente nell'allegato / the abstract is in the attachmen

Distributed deep learning inference in fog networks

Today's smart devices are equipped with powerful integrated chips and built-in heterogeneous sensors that can leverage their potential to execute heavy computation and produce a large amount of sensor data. For instance, modern smart cameras integrate artificial intelligence to capture images that detect any objects in the scene and change parameters, such as contrast and color based on environmental conditions. The accuracy of the object recognition and classification achieved by intelligent applications has improved due to recent advancements in artificial intelligence (AI) and machine learning (ML), particularly, deep neural networks (DNNs). Despite the capability to carry out some AI/ML computation, smart devices have limited battery power and computing resources. Therefore, DNN computation is generally offloaded to powerful computing nodes such as cloud servers. However, it is challenging to satisfy latency, reliability, and bandwidth constraints in cloud-based AI. Thus, in recent years, AI services and tasks have been pushed closer to the end-users by taking advantage of the fog computing paradigm to meet these requirements. Generally, the trained DNN models are offloaded to the fog devices for DNN inference. This is accomplished by partitioning the DNN and distributing the computation in fog networks. This thesis addresses offloading DNN inference by dividing and distributing a pre-trained network onto heterogeneous embedded devices. Specifically, it implements the adaptive partitioning and offloading algorithm based on matching theory proposed in an article, titled "Distributed inference acceleration with adaptive dnn partitioning and offloading". The implementation was evaluated in a fog testbed, including Nvidia Jetson nano devices. The obtained results show that the adaptive solution outperforms other schemes (Random and Greedy) with respect to computation time and communication latency

Universal Mobile Service Execution Framework for Device-To-Device Collaborations

There are high demands of effective and high-performance of collaborations between mobile devices in the places where traditional Internet connections are unavailable, unreliable, or significantly overburdened, such as on a battlefield, disaster zones, isolated rural areas, or crowded public venues. To enable collaboration among the devices in opportunistic networks, code offloading and Remote Method Invocation are the two major mechanisms to ensure code portions of applications are successfully transmitted to and executed on the remote platforms. Although these domains are highly enjoyed in research for a decade, the limitations of multi-device connectivity, system error handling or cross platform compatibility prohibit these technologies from being broadly applied in the mobile industry. To address the above problems, we designed and developed UMSEF - an Universal Mobile Service Execution Framework, which is an innovative and radical approach for mobile computing in opportunistic networks. Our solution is built as a component-based mobile middleware architecture that is flexible and adaptive with multiple network topologies, tolerant for network errors and compatible for multiple platforms. We provided an effective algorithm to estimate the resource availability of a device for higher performance and energy consumption and a novel platform for mobile remote method invocation based on declarative annotations over multi-group device networks. The experiments in reality exposes our approach not only achieve the better performance and energy consumption, but can be extended to large-scaled ubiquitous or IoT systems

IoT@run-time: a model-based approach to support deployment and self-adaptations in IoT systems

Today, most Internet of Things (IoT) systems leverage edge and fog computing to meet increasingly restrictive requirements and improve quality of service (QoS). Although these multi-layer architectures can improve system performance, their design is challenging because the dynamic and changing IoT environment can impact the QoS and system operation. In this thesis, we propose a modeling-based approach that addresses the limitations of existing studies to support the design, deployment, and management of self-adaptive IoT systems. We have designed a domain specific language (DSL) to specify the self-adaptive IoT system, a code generator that generates YAML manifests for the deployment of the IoT system, and a framework based on the MAPE-K loop to monitor and adapt the IoT system at runtime. Finally, we have conducted several experimental studies to validate the expressiveness and usability of the DSL and to evaluate the ability and performance of our framework to address the growth of concurrent adaptations on an IoT system.Hoy en día, la mayoría de los sistemas de internet de las cosas (IoT, por su sigla en inglés) aprovechan la computación en el borde (edge computing) y la computación en la niebla (fog computing) para cumplir requisitos cada vez más restrictivos y mejorar la calidad del servicio. Aunque estas arquitecturas multicapa pueden mejorar el rendimiento del sistema, diseñarlas supone un reto debido a que el entorno de IoT dinámico y cambiante puede afectar a la calidad del servicio y al funcionamiento del sistema. En esta tesis proponemos un enfoque basado en el modelado que aborda las limitaciones de los estudios existentes para dar soporte en el diseño, el despliegue y la gestión de sistemas de IoT autoadaptables. Hemos diseñado un lenguaje de dominio específico (DSL) para modelar el sistema de IoT autoadaptable, un generador de código que produce manifiestos YAML para el despliegue del sistema de IoT y un marco basado en el bucle MAPE-K para monitorizar y adaptar el sistema de IoT en tiempo de ejecución. Por último, hemos llevado a cabo varios estudios experimentales para validar la expresividad y usabilidad del DSL y evaluar la capacidad y el rendimiento de nuestro marco para abordar el crecimiento de las adaptaciones concurrentes en un sistema de IoT.Avui dia, la majoria dels sistemes d'internet de les coses (IoT, per la sigla en anglès) aprofiten la informàtica a la perifèria (edge computing) i la informàtica a la boira (fog computing) per complir requisits cada cop més restrictius i millorar la qualitat del servei. Tot i que aquestes arquitectures multicapa poden millorar el rendiment del sistema, dissenyar-les suposa un repte perquè l'entorn d'IoT dinàmic i canviant pot afectar la qualitat del servei i el funcionament del sistema. En aquesta tesi proposem un enfocament basat en el modelatge que aborda les limitacions dels estudis existents per donar suport al disseny, el desplegament i la gestió de sistemes d'IoT autoadaptatius. Hem dissenyat un llenguatge de domini específic (DSL) per modelar el sistema d'IoT autoadaptatiu, un generador de codi que produeix manifestos YAML per al desplegament del sistema d'IoT i un marc basat en el bucle MAPE-K per monitorar i adaptar el sistema d'IoT en temps d'execució. Finalment, hem dut a terme diversos estudis experimentals per validar l'expressivitat i la usabilitat del DSL i avaluar la capacitat i el rendiment del nostre marc per abordar el creixement de les adaptacions concurrents en un sistema d'IoT.Tecnologies de la informació i de xarxe