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Although smart devices markets are increasing their sales figures, their computing capabilities are not sufficient to provide good-enough-quality services. This paper proposes a solution to organize the devices within the Cloud-Edge Continuum in such a way that each one, as an autonomous individual –Agent–, processes events/data on its embedded compute resources while offering its computing capacity to the rest of the infrastructure in a Function-as-a-Service manner. Unlike other FaaS solutions, the described approach proposes to transparently convert the logic of such functions into task-based workflows backing on task-based programming models; thus, agents hosting the execution of the method generate the corresponding workflow and offloading part of the workload onto other agents to improve the overall service performance. On our prototype, the function-to-workflow transformation is performed by COMPSs; thus, developers can efficiently code applications of any of the three envisaged computing scenarios – sense-process-actuate, streaming and batch processing – throughout the whole Cloud-Edge Continuum without struggling with different frameworks specifically designed for each of them. This work has been supported by the Spanish Government (PID2019-107255GB), by Generalitat de Catalunya (contract 2014-SGR-1051), and by the European Commission through the Horizon 2020 Research and Innovation program under Grant Agreement No. 101016577 (AI-SPRINT project).

The Map-Join-Reduce programming model effectively supports the join operation among different heterogeneous data sets by adding the Join module and processes the multiway joining directly. In this paper, we propose a rigorous description of the Map-Join-Reduce that performs as an executable specification. First, this paper briefly introduces the differences between the Map-Join-Reduce and the MapReduce. Then, we use Haskell to specify each module of the Map-Join-Reduce programming model and analyze the structure and function of each module. Finally, we test the specification by analyzing an example of the mall sales records. The executable specification contributes to helping the developers to unscramble the relationship between the MapReduce and the Map-Join-Reduce, which may serve as a basis for further development of the theory of related programming model design. Furthermore, the most important function of an executable specification is guaranteeing the target informal or semi-formal model with interesting properties. This paper is a forward step to prepare for verifying related properties and, even, providing verified prototypes.

System design where cyber-physical applications are securely coordinated from the cloud may simplify the development process. However, all private data are then pushed to these remote “swamps,” and human users lose actual control as compared to when the applications are executed directly on their devices. At the same time, computing at the network edge is still lacking support for such straightforward multidevice development, which is essential for a wide range of dynamic cyber-physical services. This article proposes a novel programming model as well as contributes the associated secure-connectivity framework for leveraging safe coordinated device proximity as an additional degree of freedom between the remote cloud and the safety-critical network edge, especially under uncertain environment constraints. This article is part of a special issue on Software Safety and Security Risk Mitigation in Cyber-physical Systems. acceptedVersion

Today's dominant design for the Internet of Things (IoT) is a Cloud-based system, where devices transfer their data to a back-end and in return receive instructions on how to act. This view is challenged when delays caused by communication with the back-end become an obstacle for IoT applications with, for example, stringent timing constraints. In contrast, Fog Computing approaches, where devices communicate and orchestrate their operations collectively and closer to the origin of data, lack adequate tools for programming secure interactions between humans and their proximate devices at the network edge. This paper fills the gap by applying Action-Oriented Programming (AcOP) model for this task. While originally the AcOP model was proposed for Cloud-based infrastructures, presently it is re-designed around the notion of coalescence and disintegration, which enable the devices to collectively and autonomously execute their operations in the Fog by serving humans in a peer-to-peer fashion. The Cloud's role has been minimized—it is being leveraged as a development and deployment platform. publishedVersion

Resources related to remote-sensing data, computing, and models are scattered globally. The use of remote-sensing images for disaster-monitoring applications is data-intensive and involves complex algorithms. These characteristics make the timely and rapid processing of disaster-monitoring applications challenging and inefficient. Cloud computing provides a dynamically scalable resource over the Internet. The rapid development of cloud computing has led to an increase in the computational performance of data-intensive computing, providing powerful throughput by distributing computation across many distributed computers. However, the use of current cloud computing models in scientific applications using remote-sensing image data has been limited to a single image-processing algorithm rather than a well-established model and method. This poses problems for the development of complex disaster-monitoring applications on cloud platform architectures. For example, distributed computing strategies and remote-sensing image-processing algorithms are highly coupled and not reusable. The aims of this paper are to identify computational characteristics of various disaster-monitoring algorithms and classify them according to different computational characteristics, explore a reusable processing model based on the MapReduce programming model for disaster-monitoring applications, and then establish a programming model for each type of algorithm. This approach provides a simpler programming method for programmers to implement disaster-monitoring applications. Finally, some examples are given to explain the proposed method and test its performance.

Metaheuristics are search procedures used to solve complex, often intractable problems for which other approaches are unsuitable or unable to provide solutions in reasonable times. Although computing power has grown exponentially with the onset of Cloud Computing and Big Data platforms, the domain of metaheuristics has not yet taken full advantage of this new potential. In this paper, we address this gap by proposing HyperSpark, an optimization framework for the scalable execution of user-defined, computationally-intensive heuristics. We designed HyperSpark as a flexible tool meant to harness the benefits (e.g., scalability by design) and features (e.g., a simple programming model or ad-hoc infrastructure tuning) of state-of-the-art big data technology for the benefit of optimization methods. We elaborate on HyperSpark and assess its validity and generality on a library implementing several metaheuristics for the Permutation Flow-Shop Problem (PFSP). We observe that HyperSpark results are comparable with the best tools and solutions from the literature. We conclude that our proof-of-concept shows great potential for further research and practical use.

Using the GPUs embedded in mobile devices allows for increasing the performance of the applications running on them while reducing the energy consumption of their execution. This article presents a task-based solution for adaptative, collaborative heterogeneous computing on mobile cloud environments. To implement our proposal, we extend the COMPSs-Mobile framework – an implementation of the COMPSs programming model for building mobile applications that offload part of the computation to the Cloud – to support offloading computation to GPUs through OpenCL. To evaluate our solution, we subject the prototype to three benchmark applications representing different application patterns. This work is partially supported by the Joint-Laboratory on Extreme Scale Computing (JLESC), by the European Union through the Horizon 2020 research and innovation programme under contract 687584 (TANGO Project), by the Spanish Goverment (TIN2015-65316-P, BES-2013-067167, EEBB-2016-11272, SEV-2011-00067) and the Generalitat de Catalunya (2014-SGR-1051).

This paper presents a framework to develop and execute applications in distributed and highly dynamic computing systems composed of cloud resources and fog devices such as mobile phones, cloudlets, and micro-clouds. The work builds on the COMPSs programming framework, which includes a programming model and a runtime already validated in HPC and cloud environments for the transparent execution of parallel applications. As part of the proposed contribution, COMPSs has been enhanced to support the execution of applications on mobile platforms that offer GPUs and CPUs. The scheduling component of COMPSs is under design to be able to offload the computation to other fog devices in the same level of the hierarchy and to cloud resources when more computational power is required. The framework has been tested executing a sample application on a mobile phone offloading task to a laptop and a private cloud. This work is partly supported by the Spanish Ministry of Science and Technology through project TIN2015-65316-P and grant BES-2013-067167, by the Generalitat de Catalunya under contracts 2014-SGR-1051 and 2014-SGR-1272, and by the European Union through the Horizon 2020 research and innovation programme under grant 730929 (mF2C Project).

The AXIOM hardware/software platform aims at bringing easy programmability on top of a cluster of processors by using a fast interconnect and FPGA as a basis for building a scalable embedded system. The Smart Home is one of the key scenarios in which AXIOM could be useful for the Internet-of-Things (IoT). In Smart Homes, everything is linked to the flow of information that from the “on the field” devices needs to arrive to the cloud servers. The information sensed in the environment will not be transmitted as is to the higher layers, but is somehow interpreted to provide a synthetic light-weight representation of the environment. In such a scenario, it is then clear that there is a need for peripheral nodes as well as intermediate gateways which needs to be able to perform high-performance computational loads. AXIOM provides the possibility of designing a cluster of low-power/low-budget boards, which could be packed inside a “high-performance embedded low-cost product.” The AXIOM boards are heterogeneous, thus allowing for even greater diversity which is needed in those kind of IoT scenarios. The cluster itself can then be integrated inside the IoT architectures as “computational-power node,” which could be the center of a distributed intelligence near the edges of the IoT network.

Die stetige Digitalisierung und Vernetzung von allt��glichen Gegenst��nden f��hrte in den letzten Jahren zu einem enormen Wachstum an datenproduzierenden Ger��ten. Das resultierende Internet der Dinge (engl. Internet of Things, IoT) und dessen Verbreitung erfordern eine Ver��nderung in der Verwendung eben solcher Ger��te, um eine erfolgreiche Daten��bermittlung und Datenverarbeitung zu erm��glichen. Aufgrund der Tatsache, dass diese Ger��te nicht nur Daten generieren, sondern auch Rechen- und Speicherkapazit��ten aufweisen, entstand ein neues IT-Paradigma. Dieser dezentralisierte Ansatz verlagert den Fokus von der zentralen Cloud-Computing-Umgebung and den Rand des Netzwerks und f��hrt zur Einf��hrung einer neuen Zwischenebene - dem Fog Computing. Die zentrale Idee von Fog Computing besteht darin, bestehende Ressourcen am Rande des Netzwerks zu nutzen, indem IoT-Dienste auf verf��gbaren Ger��ten ausgef��hrt werden. Die Entwicklung einer dynamischen Fog-Computing-Softwareumgebung birgt folgende Forschungsl��cken, die es zu l��sen gilt: dezentrale Datenverarbeitung, Ressourcenvirtualisierung, IoT-Ger��teverwaltung und die Bereitstellung von Diensten und Ressourcen sowohl in der Cloud- Umgebung als auch in der Fog-Landschaft. Das Schlie��en besagter Forschungsl��cken resultiert in einer Softwareumgebung, f��hig die dynamischen Ressourcen am Rande des Netzwerks zu nutzen, den Datentransfer zu verringern und somit die Cloud zu entlasten und das IoT zu unterst��tzen. Eine derartige dynamische Softwareumgebung ist ein Fog-Computing-Framework. Der aktuelle Stand der Technik konzentriert sich haupts��chlich auf Cloud Computing, Fog- Computing-Konzeptarchitekturen und Ans��tze zur cloud-basierten Ressourcenbereitstellung. Da sich nur wenige wissenschaftliche Beitr��ge mit der Entwicklung eines konkreten Fog- Computing-Frameworks befassen, werden in dieser Arbeit IoT-Anwendungsf��lle, verwandte IoT-Frameworks und Best-Practices im Bereich verteilter Systeme analysiert. Basierend auf dieser Analyse wird ein dynamisches und erweiterbares Fog-Computing-Framework entwickelt. Das entwickelte Fog-Computing-Framework schafft die Vorraussetzungen f��r die Verwaltung zugeh��riger IoT-Dienste in einer realen Fog-Landschaft. Dar��ber hinaus erm��glicht das Framework die Verwaltung der Ger��te, Kommunikation zwischen den Ger��ten und realisiert die Bereitstellung von Ressourcen und IoT-Diensten in der Fog-Landschaft. Zus��tzlich ist das Framework in der Lage, auf verschiedene Systemereignisse, z. B. Ger��tebeitritt, Ger��teversagen und Ger��te��berlastung, zu reagieren. Die Evaluierung des Frameworks befasst sich neben der Handhabung besagter Ereignisse mit der Analyse weiterer Metriken, z. B. Kosten, Ausf��hrungszeiten und variierenden Dienstanfragen. So bietet das Framework die M��glichkeit, die Dynamik der Fog-Landschaft zu bew��ltigen und f��hrt zu geringeren Bereitstellungszeiten von IoT-Diensten und erheblichen Kostenvorteilen. Schlie��lich erlaubt die Gestaltung des Frameworks zuk��nftigen Forschern, die Komponenten des Frameworks so zu konfigurieren, zu erweitern und zu verbessern, dass es den individuellen Anforderungen und Forschungsproblemen gerecht wird., The prevalence of ubiquitous computing devices encouraged by the expanding technology trend of the Internet of Things (IoT) demands a change in how these devices are used and where the generated data is processed. Since these computing devices not only generate data but feature computational and storage capabilities, a new paradigm evolved. The decentralized computing paradigm that shifts the focus of interest away from a centralized cloud computing environment towards the edge of the network is called fog computing. The main idea of fog computing is to utilize the processing and storage resources at the edge of network by deploying IoT services on available edge devices to reduce latency and processing cost. The research challenges to be tackled in order to develop a dynamic software environment realizing the vision of fog computing are decentralized data processing, resource virtualization, service deployment, IoT device orchestration, and resource provisioning in both the cloud environment and the fog landscape. Such a dynamic decentralized software environment is a fog computing framework. State of the art approaches are mostly focusing on cloud computing, conceptual fog computing architectures, and cloud-based resource provisioning approaches. Only few contributions deal with the development of a concrete fog computing framework. Therefore, in this work, IoT use cases, related IoT frameworks, and best practices in the area of distributed systems are analyzed in order to design and implement a dynamic, extensible, and flexible fog computing framework. The fog computing framework provides the tools to manage IoT services in the fog landscape by means of a real-world test-bed. Furthermore, the framework facilitates the communication between the devices, fog device orchestration, IoT service deployment, and dynamic resource provisioning in the fog landscape. In addition to these main functionalities, the framework is able to react on various system events, e.g., device accedence, device failure, and device overload. The consequent event handling and assessment of other important metrics, e.g., costs, deployment times, and service arrival patterns, is performed in the evaluation of the framework. As a result, the framework provides the utilities to deal with the dynamism of the fog landscape and yields lower deployment times of IoT services and considerable cost benefits. Finally, the design of the framework allows future researchers to configure, extend, and enhance the components of the framework to fit the individual requirements and research challenges.

COMP Superscalar és un model de programació que busca explotar el paral·lelisme a nivell de tasca inherent a les aplicacions i simplificar el desenvolupament d'aplicacions pel Grid i altres plataformes de computació distribuïda. L'usuari pot programar les seves aplicacions sense tenir en compte la plataforma en la que s'executarà; només ha de seleccionar quines tasques han d'executar-s'hi sense haver de realitzar cap tipus de crida que hi estigui relacionada Per obtenir un bon rendiment i fer un bon ús dels recursos, les decisions es prenen en temps d'execució. Això comporta la necessitat d'un runtime que sigui capaç de gestionar els recursos disponibles i decidir en cada moment quina és la millor decisió a l'hora d'assignar les tasques. El resultat final del projecte ha de ser una modificació d'aquest runtime per tal d'oferir un model de programació orientat als serveis que ofereixen les infraestructures Cloud respectant sempre el model de programació en sí, per tal de simplificar a l'usuari el pas de la versió pel Grid a la nova versió i així mantenir la simplicitat en la programació de les aplicacions.