Browsing by Author "Díaz-De-Arcaya, Josu"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item PADL: A Language for the Operationalization of Distributed Analytical Pipelines over Edge/Fog Computing Environments(Institute of Electrical and Electronics Engineers Inc., 2020-09-23) Díaz-De-Arcaya, Josu; Miñón, Raúl; Torre-Bastida, Ana I.; Del Ser, Javier; Almeida, Aitor; Solic, Petar; Nizetic, Sandro; Rodrigues, Joel J. P. C.; Rodrigues, Joel J. P.C.; Lopez-de-Ipina Gonzalez-de-Artaza, Diego; Perkovic, Toni; Catarinucci, Luca; Patrono, Luigi; HPA; IAIn this paper we introduce PADL, a language for modeling and deploying data-based analytical pipelines. The novelty of this language relies on its independence from both the infrastructure and the technologies used on it. Specifically, this descriptive language aims at embracing all the particularities and constraints of high-demanding deployment models, such as critical restrictions regarding latency, privacy and performance, by providing fully-compliant schemas for implementing data analytical workloads. The adoption of PADL provides means for the operationalization of these pipelines in a reproducible and resilient fashion. In addition, PADL is able to fully utilize the benefits of Edge and Fog computing layers. The feasibility of the language has been validated with an analytical pipeline deployed over an Edge computing environment to solve an Industry 4.0 use case. The promising results obtained therefrom pave the way towards the widespread adoption of our proposed language when deploying data analytical pipelines over real application scenarios.Item Towards an architecture for big data analytics leveraging edge/fog paradigms(Association for Computing Machinery, 2019-09-09) Díaz-De-Arcaya, Josu; Miñon, Raül; Torre-Bastida, Ana I.; Duchien, Laurence; Koziolek, Anne; Mirandola, Raffaela; Martinez, Elena Maria Navarro; Quinton, Clement; Scandariato, Ricardo; Scandurra, Patrizia; Trubiani, Catia; Weyns, Danny; HPAAn industry transformation is being boosted by Big Data and Cloud technologies. We present a Big Data architecture, which expands the life cycle of data processing through the Edge, Fog and Cloud computing layers. The proposed architecture takes advantage of the strengths of each: the Cloud layer executes heavy analytical processes, the Fog is responsible for the ingestion and performing aggregations, and the Edge manages devices and actuators. The proposed architecture tackles two main goals, 1) latencies and response times can be reduced by bringing the analytics closer to where the data is generated and 2) the use of computing resources is optimised. In order to conceptualise this architecture, an orchestration module is proposed with the goal of optimising the deployment of analytical workloads across the three layers, by evaluating their computing resources. In addition to this, another module is designed to monitor the performance of such workloads allowing the redistribution of tasks assigned to each node. These modules will be implemented in a real case scenario in the train domain.