Relevant bibliographies by topics / Fog Computing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fog Computing'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2025

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Journal articles on the topic "Fog Computing"

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Bhatt, Chintan, and C. K. Bhensdadia. "Fog Computing." International Journal of Grid and High Performance Computing 9, no. 4 (October 2017): 105–13. http://dx.doi.org/10.4018/ijghpc.2017100107.

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The Internet of Things could be a recent computing paradigm, defined by networks of extremely connected things – sensors, actuators and good objects – communication across networks of homes, buildings, vehicles, and even individuals whereas cloud computing could be ready to keep up with current processing and machine demands. Fog computing provides architectural resolution to deal with some of these issues by providing a layer of intermediate nodes what's referred to as an edge network [26]. These edge nodes provide interoperability, real-time interaction, and if necessary, computational to the Cloud. This paper tries to analyse different fog computing functionalities, tools and technologies and research issues.
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Chen, Songqing, Tao Zhang, and Weisong Shi. "Fog Computing." IEEE Internet Computing 21, no. 2 (March 2017): 4–6. http://dx.doi.org/10.1109/mic.2017.39.

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Pagel, Peter, and Stefan Schulte. "Fog Computing." Informatik Spektrum 42, no. 4 (August 2019): 233–35. http://dx.doi.org/10.1007/s00287-019-01211-z.

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Matt, Christian. "Fog Computing." Business & Information Systems Engineering 60, no. 4 (April 19, 2018): 351–55. http://dx.doi.org/10.1007/s12599-018-0540-6.

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Mangla, Cherry, Shalli Rani, and Henry Kwame Atiglah. "Secure Data Transmission Using Quantum Cryptography in Fog Computing." Wireless Communications and Mobile Computing 2022 (January 22, 2022): 1–8. http://dx.doi.org/10.1155/2022/3426811.

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Fog computing’s idea is to bring virtual existence into objects used on a daily basis. The “objects” layer of fog architecture is also known as the smart object layer (SOL). SOL has provided the fog network with a strong platform to outperform. Although the fog architecture decentralizes data, uses more data centers, and collects and transmits it to adjacent servers for faster processing in fog networks, it faces several security challenges. The security problems of fog computing need to be alleviated for the exploitation of all benefits of fog computing in classical networks. This article has addressed the security challenges in fog computing, potential solutions via quantum cryptography, a use case portraying the importance of quantum cryptography in fog computing along future scope, and research directions.
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Sookhak, Mehdi, F. Richard Yu, Ying He, Hamid Talebian, Nader Sohrabi Safa, Nan Zhao, Muhammad Khurram Khan, and Neeraj Kumar. "Fog Vehicular Computing: Augmentation of Fog Computing Using Vehicular Cloud Computing." IEEE Vehicular Technology Magazine 12, no. 3 (September 2017): 55–64. http://dx.doi.org/10.1109/mvt.2017.2667499.

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R, Dhivya Sree. "Fog Computing in IoT." International Journal of Research Publication and Reviews 4, no. 4 (April 2023): 3214–15. http://dx.doi.org/10.55248/gengpi.4.423.36600.

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Ahuja, Sanjay P., and Niharika Deval. "From Cloud Computing to Fog Computing." International Journal of Fog Computing 1, no. 1 (January 2018): 1–14. http://dx.doi.org/10.4018/ijfc.2018010101.

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This article describes how in recent years, Cloud Computing has emerged as a fundamental computing paradigm that has significantly changed the approach of enterprises as well as end users towards implementation of Internet technology. The key characteristics such as on-demand resource provision, scalability, rapid elasticity, higher flexibility, and significant cost savings have influenced enterprises of all sizes in the wide and successful adoption of Cloud Computing. Despite numerous advantages, Cloud Computing has its fair share of downsides as well. One of those major concerns is latency issues which has relevance to the Internet of Things (IoT). A new computing paradigm has been proposed by Cisco in early 2014 and termed 'Fog Computing'. Fog Computing otherwise known as Edge Computing is the integration of Cloud Computing and IoT. Being located in close proximity to the IoT devices, the Fog assists with latency requirements of IoT related applications. It also meets the data processing needs of IoT devices which are resource constrained by bringing computation, communication, control and storage closer to the end users. Clouds continue to offer support for data analytics. One can think of the IoT-Fog-Cloud as being part of a continuum. This article surveys the current literature on Fog Computing and provides a discussion on the background, details and architecture of Fog Computing, as well as the application areas of Fog Computing. The article concludes with some recommendations in the areas of future research.
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Yacelga, Andres Leon, Nelson B. Arevalo, and Luis Albarracin Zambrano. "Fog Computing in the Industrial Internet of Things: Challenges, Trends, and Strategies." Fusion: Practice and Applications 13, no. 2 (2023): 91–105. http://dx.doi.org/10.54216/fpa.130208.

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The Industrial Internet of Things (IIoT) has ushered in a new era of connectivity and intelligence in industrial settings. At the heart of this transformative landscape lies Fog Computing, a distributed computing paradigm that brings processing power and intelligence closer to the edge of industrial networks. This paper provides a comprehensive survey of Fog Computing's pivotal role in IIoT, elucidating its significance, challenges, emerging trends, and strategies for successful implementation. We delve into the challenges that industrial environments present for Fog Computing, encompassing issues such as scalability, cybersecurity, data management, and interoperability. Strategies for mitigating these challenges are explored, ranging from efficient resource management to robust cybersecurity measures. Furthermore, we investigate recent developments and innovations in Fog Computing, including the integration of Edge AI, 5G networks, and hybrid cloud-fog architectures, shaping the landscape of IIoT. Promising research areas and opportunities are identified, with a focus on optimizing edge AI, secure data sharing, and sustainable Fog Computing practices.
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Menon, Varun G., and Joe Prathap. "Vehicular Fog Computing." International Journal of Vehicular Telematics and Infotainment Systems 1, no. 2 (July 2017): 15–23. http://dx.doi.org/10.4018/ijvtis.2017070102.

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In recent years Vehicular Ad Hoc Networks (VANETs) have received increased attention due to its numerous applications in cooperative collision warning and traffic alert broadcasting. VANETs have been depending on cloud computing for networking, computing and data storage services. Emergence of advanced vehicular applications has led to the increased demand for powerful communication and computation facilities with low latency. With cloud computing unable to satisfy these demands, the focus has shifted to bring computation and communication facilities nearer to the vehicles, leading to the emergence of Vehicular Fog Computing (VFC). VFC installs highly virtualized computing and storage facilities at the proximity of these vehicles. The integration of fog computing into VANETs comes with a number of challenges that range from improved quality of service, security and privacy of data to efficient resource management. This paper presents an overview of this promising technology and discusses the issues and challenges in its implementation with future research directions.
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Dissertations / Theses on the topic "Fog Computing"

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Bozios, Athanasios. "Fog Computing : Architecture and Security aspects." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-80178.

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As the number of Internet of Things (IoT) devices that are used daily is increasing, the inadequacy of cloud computing to provide neseccary IoT-related features, such as low latency, geographic distribution and location awareness, is becoming more evident. Fog computing is introduced as a new computing paradigm, in order to solve this problem by extending the cloud‟s storage and computing resources to the network edge. However, the introduction of this new paradigm is also confronted by various security threats and challenges since the security practices that are implemented in cloud computing cannot be applied directly to this new architecture paradigm. To this end, various papers have been published in the context of fog computing security, in an effort to establish the best security practices towards the standardization of fog computing. In this thesis, we perform a systematic literature review of current research in order to provide with a classification of the various security threats and challenges in fog computing. Furthermore, we present the solutions that have been proposed so far and which security challenge do they address. Finally, we attempt to distinguish common aspects between the various proposals, evaluate current research on the subject and suggest directions for future research.
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Rahafrouz, Amir. "Distributed Orchestration Framework for Fog Computing." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-77118.

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The rise of IoT-based system is making an impact on our daily lives and environment. Fog Computing is a paradigm to utilize IoT data and process them at the first hop of access network instead of distant clouds, and it is going to bring promising applications for us. A mature framework for fog computing still lacks until today. In this study, we propose an approach for monitoring fog nodes in a distributed system using the FogFlow framework. We extend the functionality of FogFlow by adding the monitoring capability of Docker containers using cAdvisor. We use Prometheus for collecting distributed data and aggregate them. The monitoring data of the entire distributed system of fog nodes is accessed via an API from Prometheus. Furthermore, the monitoring data is used to perform the ranking of fog nodes to choose the place to place the serverless functions (Fog Function). The ranking mechanism uses Analytical Hierarchy Processes (AHP) to place the fog function according to resource utilization and saturation of fog nodes’ hardware. Finally, an experiment test-bed is set up with an image-processing application to detect faces. The effect of our ranking approach on the Quality of Service is measured and compared to the current FogFlow.
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Grandi, Stefano. "Sviluppo di Servizi Android per applicazioni Fog Computing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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Il Cloud Computing è stata la risposta alla crescente domanda di capacità di calcolo, di memorizzazione di dati e di flessibilità nel loro approvvigionamento. Ma se da un lato il Cloud ha permesso di sopperire alle limitazioni hardware a cui i dispositivi utente sono sottoposti, questo nuovo paradigma soffre inevitabilmente di quelle che sono le latenze dovute allo scambio di informazioni attraverso Internet. Inoltre la rete Internet, già messa a dura prova dal continuo e crescente traffico dati, non ha beneficiato dell'utilizzo del paradigma Cloud e teme quello che si appresta a diventare di vastissimo ambito di utilizzo, il paradigma dell'Internet of Things (IoT), per il quale si intende una famiglia di tecnologie il cui scopo è rendere qualunque tipo di oggetto, un dispositivo collegato ad Internet. È proprio in questo scenario che nasce il paradigma del Fog Computing, cioè l'idea di costruire la stessa architettura del Cloud, fisicamente più vicina ai client che, a causa delle loro scarse capacità di elaborazione, memoria o autonomia energetica, necessitano di demandare al Cloud l'esecuzione di operazioni costose. Il Fog si pone quindi l'obiettivo di ridurre al minimo la latenza nelle comunicazioni tra i client e i nodi Fog (dispositivi fisici che si occupano dell'esecuzione delle richieste di servizi), permettendo ai client, di non sottostare più agli svantaggi dovuti alla comunicazione Internet che si hanno con il Cloud, e scaricando quindi la rete Internet di traffico in eccesso. Il progetto di tesi ha quindi visto lo sviluppo di una applicazione Android, chiamata Fog Network Application, in grado di creare e gestire una Fog Network, cioè una rete virtuale, interna alla rete locale LAN, alla quale tutti i dispositivi in possesso dell'applicazione siano in grado di connettersi, quindi fornire dei servizi generici e/o tramite la definizione di una interfaccia, e infine richiedere l'utilizzo di tali servizi.
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Valieri, Mario. "Dynamic Resource and Service Discovery in Fog Computing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22265/.

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The cloud computing is already a well-known paradigm, known and used in both business and consumers areas. It provides a lot of advantages, but today the necessity of data security and low latency is rapidly increasing. Nowadays, in next generation networks (NGN), the fog computing paradigm is able to satisfy strict latency and security requirements using distributed computational power. In a dynamic fog orchestration scenario, the discovery of available resources and services is a fundamental aspect to achieve a good quality system. This thesis is focused on the study and comparison of service and node discovery techniques and protocols, with the aim of finding an optimal solution to the problem related to a preexisting fog service orchestration system. Once the optimal solution is detected, a working implementation is proposed and evaluated with appropriate experimental measurements, particularly relevant for network administration.
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Machado, Miguel Chagas Bilhau. "Monitoring system based on fog computing." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/23462.

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Mestrado em Engenharia de Computadores e Telemática
This thesis is a contribution of an architectural solution, describing a system that represents an extra layer of computing power, placed between the cloud and sensor networks, acting both as a mediator whose central task is to manage, monitor and collect data from geographically-located groups of sensor nodes and as a communication hub to the cloud with which data is exchanged in a compact and minimalist fashion. The latter is accomplished by designing nodes as autonomous entities, able to organise themselves in smaller groups, within the system. Additionally, these entities possess inherent mechanisms which aim to accomplish fault tolerance within groups of nodes, maintaining the status quo of the overall system while performing in an ubiquitous environment, continuously embracing contextual changes. The overall solution was tested in a proof of concept where we conceived three test cases that helped us validate it.
Este documento apresenta uma arquitectura como solução para o desenvolvimento de uma camada extra de poder computacional entre os serviços na núvem e a Internet das Coisas, denominada de computação no nevoeiro. Esta camada é responsável pela gestão e recolha de dados provenientes de conjuntos de sensores, geograficamente distribuídos, em níveis inferiores. Assim, o nevoeiro permite servir como ponto de agregação comunicando directamente com a núvem, minimizando a quantidade de tráfego na rede. A solução descreve a camada de nevoeiro como um conjunto de grupos de nós que se agrupam e organizam como um todo, autonomamente. Existem ainda mecanismos auxiliares que permitem a existência de um certo grau de tolerância a falhas de forma a manter o status quo do sistema em ambientes ubíquos, lidando com as constantes alterações de contexto. A solução foi testada e validada através de uma prova de conceito onde foram realizados três casos de teste, concebidos de forma a abranger todos os componentes da mesma.
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Mebrek, Adila. "Fog Computing pour l’Internet des objets." Thesis, Troyes, 2020. http://www.theses.fr/2020TROY0028.

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Le Fog Computing constitue une approche prometteuse dans le contexte de l’Internet des Objets (IoT) car il fournit des fonctionnalités et des ressources à l’extrémité du réseau, plus près des utilisateurs finaux. Cette thèse étudie les performances du Fog Computing dans le cadre des applications IoT sensibles à la latence. La première problématique traitée concerne la modélisation mathématique d’un système IoT-fog-cloud, ainsi que les métriques de performances du système en termes d’énergie consommée et de latence. Cette modélisation nous permettra par la suite de proposer diverses stratégies efficaces de distribution de contenu et d’allocation des ressources dans le fog et le cloud. La deuxième problématique abordée dans cette thèse concerne la distribution de contenu et de données des objets dans des systèmes fog/cloud. Afin d’optimiser simultanément les décisions d’offloading et d’allocation des ressources du système, nous distinguons entre deux types d’applications IoT : (1) applications IoT à contenu statique ou avec des mises à jour peu fréquentes ; et (2) applications IoT à contenu dynamique. Pour chaque type d’application, nous étudions le problème d’offloading de requêtes IoT dans le fog. Nous nous concentrons sur les problèmes d'équilibrage de charge afin de minimiser la latence et l’énergie totale consommée par le système
Fog computing is a promising approach in the context of the Internet of Things (IoT) as it provides functionality and resources at the edge of the network, closer to end users. This thesis studies the performance of fog computing in the context of latency sensitive IoT applications. The first issue addressed is the mathematical modeling of an IoT-fogcloud system, and the performance metrics of the system in terms of energy consumed and latency. This modeling will then allow us to propose various effective strategies for content distribution and resource allocation in the fog and the cloud. The second issue addressed in this thesis concerns the distribution of content and object data in fog / cloud systems. In order to simultaneously optimize offloading and system resource allocation decisions, we distinguish between two types of IoT applications: (1) IoT applications with static content or with infrequent updates; and (2) IoT applications with dynamic content. For each type of application, we study the problem of offloading IoT requests in the fog. We focus on load balancing issues to minimize latency and the total power consumed by the system
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Huang, Chih-Kai. "Scalability of public geo-distributed fog computing federations." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS055.

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Construire une plateforme de fog computing publique, géo-distribuée, multi-tenant et à grande échelle, où n'importe quelle application peut être déployée, nécessite un grand nombre de ressources de calcul placées à différents endroits stratégiques couvrant un pays entier ou même un continent. L'un des défis pour réaliser cette plateforme publique de fog est la scalabilité. À cette fin, cette thèse se concentre sur la résolution de certains défis liés à l'évolutivité et propose une série de solutions. Tout d'abord, nous présentons le concept de méta-fédérations, où de nombreux fournisseurs de ressources locaux indépendants peuvent louer leurs ressources à plusieurs fournisseurs de fog afin de résoudre les problèmes de couverture de service et d'utilisation des ressources. Nous proposons UnBound, un système scalable de meta-federations qui aborde spécifiquement les défis difficiles du multi-tenancy introduits par les méta-fédérations. Ensuite, nous proposons deux systèmes de surveillance conçus pour les environnements de fédération de clusters géo-distribués, Acala et AdapPF, qui visent à réduire le trafic réseau inter-cluster de la surveillance tout en maintenant la précision des données de surveillance
Building a large-scale, multi-tenant, public, geo-distributed fog computing platform where any application can be deployed requires a large number of computing resources placed at different strategic locations spanning an entire country or even a continent. One of the challenges to realizing this public fog platform is scalability. To this end, this thesis focuses on addressing some scalability challenges and proposes a series of solutions. First, we present the meta-federations concept, where many independent local resource providers may lease their resources to multiple fog providers to solve the service coverage and resource utilization issues. We propose UnBound, a scalable meta-federations framework that specifically addresses the difficult multi-tenancy challenges introduced by meta-federations. Second, we propose two monitoring frameworks designed for geo-distributed cluster federation environments, Acala and AdapPF, which aim to reduce the cross-cluster network traffic of monitoring while maintaining the accuracy of the monitoring data
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Butterfield, Ellis H. "Fog Computing with Go: A Comparative Study." Scholarship @ Claremont, 2016. http://scholarship.claremont.edu/cmc_theses/1348.

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The Internet of Things is a recent computing paradigm, de- fined by networks of highly connected things – sensors, actuators and smart objects – communicating across networks of homes, buildings, vehicles, and even people. The Internet of Things brings with it a host of new problems, from managing security on constrained devices to processing never before seen amounts of data. While cloud computing might be able to keep up with current data processing and computational demands, it is unclear whether it can be extended to the requirements brought forth by Internet of Things. Fog computing provides an architectural solution to address some of these problems by providing a layer of intermediary nodes within what is called an edge network, separating the local object networks and the Cloud. These edge nodes provide interoperability, real-time interaction, routing, and, if necessary, computational delegation to the Cloud. This paper attempts to evaluate Go, a distributed systems language developed by Google, in the context of requirements set forth by Fog computing. Similar methodologies of previous literature are simulated and benchmarked against in order to assess the viability of Go in the edge nodes of Fog computing architecture.
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Struhar, Vaclav. "Improving Soft Real-time Performance of Fog Computing." Licentiate thesis, Mälardalens högskola, Inbyggda system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-55679.

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Fog computing is a distributed computing paradigm that brings data processing from remote cloud data centers into the vicinity of the edge of the network. The computation is performed closer to the source of the data, and thus it decreases the time unpredictability of cloud computing that stems from (i) the computation in shared multi-tenant remote data centers, and (ii) long distance data transfers between the source of the data and the data centers. The computation in fog computing provides fast response times and enables latency sensitive applications. However, industrial systems require time-bounded response times, also denoted as RT. The correctness of such systems depends not only on the logical results of the computations but also on the physical time instant at which these results are produced. Time-bounded responses in fog computing are attributed to two main aspects: computation and communication.    In this thesis, we explore both aspects targeting soft RT applications in fog computing in which the usefulness of the produced computational results degrades with real-time requirements violations. With regards to the computation, we provide a systematic literature survey on a novel lightweight RT container-based virtualization that ensures spatial and temporal isolation of co-located applications. Subsequently, we utilize a mechanism enabling RT container-based virtualization and propose a solution for orchestrating RT containers in a distributed environment. Concerning the communication aspect, we propose a solution for a dynamic bandwidth distribution in virtualized networks.
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Civolani, Lorenzo. "Fast Docker Container Deployment in Fog Computing infrastructures." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17701/.

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I contenitori software, meglio noti come container, realizzano ambienti virtuali in cui molteplici applicazioni possono eseguire senza il rischio di interferire fra di loro. L'efficienza e la semplicità dell'approccio hanno contribuito al forte incremento della popolarità dei contaier, e, tra le varie implementazioni disponibili, Docker è di gran lunga quella più diffusa. Sfortunatamente, a causa delle loro grandi dimensioni, il processo di deployment di un container da un registro remoto verso una macchina in locale tende a richiedere tempi lunghi. La lentezza di questa operazione è particolarmente svantaggiosa in un'architettura Fog computing, dove i servizi devono muoversi da un nodo all'altro in risposta alla mobilità degli utenti. Tra l'altro, l'impiego di server a basse prestazioni tipico di tale paradigma rischia di aggravare ulteriormente i ritardi. Questa tesi presenta FogDocker, un sistema che propone un approccio originale all'operazione di download delle immagini Docker con l'obiettivo di ridurre il tempo necessario per avviare un container. L'idea centrale del lavoro è di scaricare soltanto il contenuto essenziale per l'esecuzione del container e procedere immediatamente con l'avvio; poi, in un secondo momento, mentre l'applicazione è già al lavoro, il sistema può proseguire col recupero della restante parte dell'immagine. I risultati sperimentali confermano come FogDocker sia in grado di raggiungere una riduzione notevole del tempo necessario per avviare un container. Tale ottimizzazione si rivela essere particolarmente marcata quando applicata in un contesto a risorse computazionali limitate. I risultati ottenuti dal nostro sistema promettono di agevolare l'adozione dei software container nelle architetture di Fog computing, dove la rapidità di deployment è un fattore di vitale importanza.
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Books on the topic "Fog Computing"

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Tomar, Ravi, Avita Katal, Susheela Dahiya, Niharika Singh, and Tanupriya Choudhury. Fog Computing. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003188230.

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Mahmood, Zaigham, ed. Fog Computing. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94890-4.

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Buyya, Rajkumar, and Satish Narayana Srirama, eds. Fog and Edge Computing. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119525080.

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Tanwar, Sudeep, ed. Fog Computing for Healthcare 4.0 Environments. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-46197-3.

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Kumar, Mohit, Sukhpal Singh Gill, Jitendra Kumar Samriya, and Steve Uhlig, eds. 6G Enabled Fog Computing in IoT. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30101-8.

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Lin, Xiaodong, Jianbing Ni, and Xuemin Shen. Privacy-Enhancing Fog Computing and Its Applications. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02113-9.

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Rahmani, Amir M., Pasi Liljeberg, Jürgo-Sören Preden, and Axel Jantsch, eds. Fog Computing in the Internet of Things. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57639-8.

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Tiwari, Rajeev, Mamta Mittal, and Lalit Mohan Goyal, eds. Energy Conservation Solutions for Fog-Edge Computing Paradigms. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-3448-2.

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Chang, Wei, and Jie Wu, eds. Fog/Edge Computing For Security, Privacy, and Applications. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57328-7.

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Thakkar, Hiren Kumar, Chinmaya Kumar Dehury, Prasan Kumar Sahoo, and Bharadwaj Veeravalli, eds. Predictive Analytics in Cloud, Fog, and Edge Computing. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-18034-7.

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Book chapters on the topic "Fog Computing"

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Oppitz, Marcus, and Peter Tomsu. "Fog Computing." In Inventing the Cloud Century, 471–86. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61161-7_17.

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Rayes, Ammar, and Samer Salam. "Fog Computing." In Internet of Things From Hype to Reality, 155–80. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99516-8_6.

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Kumar, Sudhir. "Fog Computing." In Fundamentals of Internet of Things, 191–206. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003225584-7.

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Rayes, Ammar, and Samer Salam. "Fog Computing." In Internet of Things from Hype to Reality, 153–78. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90158-5_6.

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Chithaluru, Premkumar, Pallati Narsimhulu, N. Sudhakar Yadav, Priyanka Chawla, and Rajeev Tiwari. "Fog computing." In Cloud and Fog Optimization-based Solutions for Sustainable Developments, 280–98. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003494430-14.

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Misra, Sudip, Subhadeep Sarkar, and Subarna Chatterjee. "Fog Computing Applications." In Sensors, Cloud, and Fog: The Enabling Technologies for the Internet of Things, 167–86. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429293986-9.

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Thomas, Priya, and Deepa V. Jose. "Edge/Fog Computing." In Machine Intelligence, 47–64. Boca Raton: Auerbach Publications, 2023. http://dx.doi.org/10.1201/9781003424550-3.

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Lohani, Kaustubh, Prajwal Bhardwaj, and Ravi Tomar. "Fog Computing and Machine Learning." In Fog Computing, 133–51. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003188230-10.

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Jaiswal, Kabir, and Niharika Singh. "Fog Computing: Present and Future." In Fog Computing, 1–16. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003188230-1.

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Sunyaev, Ali. "Fog and Edge Computing." In Internet Computing, 237–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34957-8_8.

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Conference papers on the topic "Fog Computing"

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Šatkauskas, Nerijus, Algimantas Venčkauskas, and Nerijus Morkevičius. "Fog Computing Service Placement Orchestrator." In 2024 11th International Conference on Electrical and Electronics Engineering (ICEEE), 371–76. IEEE, 2024. https://doi.org/10.1109/iceee62185.2024.10779266.

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Rabay'a, Ahmad, Eduard Schleicher, and Kalman Graffi. "Fog Computing with P2P: Enhancing Fog Computing Bandwidth for IoT Scenarios." In 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE, 2019. http://dx.doi.org/10.1109/ithings/greencom/cpscom/smartdata.2019.00036.

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Al-Khafajiy, Mohammed, Thar Baker, Atif Waraich, Omar Alfandi, and Aseel Hussien. "Enabling High Performance Fog Computing through Fog-2-Fog Coordination Model." In 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA). IEEE, 2019. http://dx.doi.org/10.1109/aiccsa47632.2019.9035353.

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Yannuzzi, M., R. Milito, R. Serral-Gracia, D. Montero, and M. Nemirovsky. "Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing." In 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). IEEE, 2014. http://dx.doi.org/10.1109/camad.2014.7033259.

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Cao, Suzhi, Hao Han, Junyong Wei, Yi Zhao, Shuling Yang, and Lei Yan. "Space Cloud-Fog Computing." In the 3rd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3331453.3361637.

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Pisani, Flávia, and Edson Borin. "Fog vs. cloud computing." In INTESA: INTelligent Embedded Systems Architectures and Applications. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3285017.3285026.

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Thacker, Anjali, and Giri Gundu Hallur. "Fog computing in healthcare." In 11TH ANNUAL INTERNATIONAL CONFERENCE (AIC) 2021: On Sciences and Engineering. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0109837.

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Luo, Siqi, Zhi Zhou, Xiang Chen, and Weigang Wu. "Dewing in Fog: Incentive-Aware Micro Computing Cluster Formation for Fog Computing." In 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2018. http://dx.doi.org/10.1109/padsw.2018.8644977.

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Elmoghrapi, Asma N., Ahmed Bleblo, and Younis A. Younis. "Fog Computing or Cloud Computing: a Study." In 2022 International Conference on Engineering & MIS (ICEMIS). IEEE, 2022. http://dx.doi.org/10.1109/icemis56295.2022.9914131.

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Krishnan, Y. Navaneeth, Chandan N. Bhagwat, and Aparajit P. Utpat. "Fog computing — Network based cloud computing." In 2015 2nd International Conference on Electronics and Communication Systems (ICECS). IEEE, 2015. http://dx.doi.org/10.1109/ecs.2015.7124902.

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Reports on the topic "Fog Computing"

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Iorga, Michaela, Larry Feldman, Robert Barton, Michael J. Martin, Ned Goren, and Charif Mahmoudi. Fog computing conceptual model. Gaithersburg, MD: National Institute of Standards and Technology, March 2018. http://dx.doi.org/10.6028/nist.sp.500-325.

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Roig, Pedro Juan, Salvador Alcaraz, Katja Gilly, Cristina Bernad, and Carlos Juiz. Formal algebraic description of a fog computing environment. Peeref, April 2023. http://dx.doi.org/10.54985/peeref.2304p4360493.

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Percivall, George, ed. The Role of Geospatial in Edge-Fog-Cloud Computing - An OGC White Paper. Open Geospatial Consortium, Inc., July 2018. http://dx.doi.org/10.62973/18-004r1.

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Bui, Thai. Using Spammers' Computing Resources for Volunteer Computing. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1628.

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Wachen, John, and Steven McGee. Qubit by Qubit’s Middle School Quantum Camp Evaluation Report for Summer 2021. The Learning Partnership, August 2021. http://dx.doi.org/10.51420/report.2021.5.

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Abstract:
Qubit by Qubit’s Middle School Quantum Camp is one of the first opportunities for students as young as eleven to begin learning about the field of quantum computing. In this week-long summer camp, students learn about key concepts of quantum mechanics and quantum computing, including qubits, superposition, and entanglement, basic coding in Python, and quantum gates. By the end of the camp, students can code quantum circuits and run them on a real quantum computer. The Middle School Quantum Camp substantially increased participants’ knowledge about quantum computing, as exhibited by large gains on a technical assessment that was administered at the beginning and end of the program. On a survey of student motivation, students in the program showed a statistically significant increase in their expectancy of being successful in quantum computing and valuing quantum computing. Students experienced a significant increase in their sense of belonging in STEM and quantum computing following the camp. The camp substantially increased students’ interest in taking additional coursework in STEM and quantum, as well as pursuing careers in STEM and quantum computing.
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Killian, Edward. Advanced Computing Architectures for High Performance Computing Engineering Integration. Fort Belvoir, VA: Defense Technical Information Center, May 2010. http://dx.doi.org/10.21236/ada522412.

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Ross, Virginia W., and Scott E. Spetka. Grid Computing for High Performance Computing (HPC) Data Centers. Fort Belvoir, VA: Defense Technical Information Center, March 2007. http://dx.doi.org/10.21236/ada466685.

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Shires, Dale, Song Jun Park, Brian Henz, Jerry Clarke, Lam Nguyen, and Kelly Kirk. Asymmetric Core Computing for U.S. Army High-Performance Computing Applications. Fort Belvoir, VA: Defense Technical Information Center, April 2009. http://dx.doi.org/10.21236/ada499569.

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Debenedictis, Erik, Fredrick Rothganger, James Bradley Aimone, Matthew Marinella, Brian Robert Evans, Christina E. Warrender, and Patrick Mickel. Cognitive Computing for Security. Office of Scientific and Technical Information (OSTI), December 2015. http://dx.doi.org/10.2172/1234812.

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Mills, Kevin L. Networking for pervasive computing. Gaithersburg, MD: National Institute of Standards and Technology, 2005. http://dx.doi.org/10.6028/nist.sp.500-259.

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