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- Distributed and decentralized orchestration of containers on edge cloudsPublication . Pires, André; Simão, José; Veiga, LuísCloud Computing has been successful in providing substantial amounts of resources to deploy scalable and highly available applications. However, there is a growing necessity of lower latency services and cheap bandwidth access to accommodate the expansion of IoT and other applications that reside at the internet's edge. The development of community networks and volunteer computing, together with the today's low cost of compute and storage devices, is making the internet's edge filled with a large amount of still underutilized resources. Due to this, new computing paradigms like Edge Computing and Fog Computing are emerging. This work presents Caravela a Docker's container orchestrator that utilizes volunteer edge resources from users to build an Edge Cloud where it is possible to deploy applications using standard Docker containers. Current cloud solutions are mostly tied to a centralized cluster environment deployment. Caravela employs a completely decentralized architecture, resource discovery and scheduling algorithms to cope with (1) the large amount of volunteer devices, volatile environment, (2) wide area networks that connects the devices and (3) nonexistent natural central administration.
- Programming languages for data-Intensive HPC applications: A systematic mapping studyPublication . Amaral, Vasco; Norberto, Beatriz; Goulão, Miguel; Aldinucci, Marco; Benkner, Siegfried; Bracciali, Andrea; Carreira, Paulo; Celms, Edgars; Correia, Luís; Grelck, Clemens; Karatza, Helen; Kessler, Christoph; Kilpatrick, Peter; Martiniano, Hugo; Mavridis, Ilias; PLLANA, Sabri; Respicio, Ana; Simão, José; Veiga, Luís; Visa, Ari Juha EljasA major challenge in modelling and simulation is the need to combine expertise in both software technologies and a given scientific domain. When High-Performance Computing (HPC) is required to solve a scientific problem, software development becomes a problematic issue. Considering the complexity of the software for HPC, it is useful to identify programming languages that can be used to alleviate this issue. Because the existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles. We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and keywords to 152 relevant articles published in the period 2006-2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. We compared the outcome of the mapping study with results of our questionnaire-based survey that involved 57 HPC experts. The mapping study and the survey revealed that the desired features of programming languages for data-intensive HPC applications are portability, performance and usability. Furthermore, we observed that the majority of the programming languages used in the context of data-intensive HPC applications are text-based general-purpose programming languages. Typically these have a steep learning curve, which makes them difficult to adopt. We believe that the outcome of this study will inspire future research and development in programming languages for data-intensive HPC applications.