TY - GEN
T1 - Towards an architecture for big data analytics leveraging edge/fog paradigms
AU - Díaz-De-Arcaya, Josu
AU - Miñon, Raül
AU - Torre-Bastida, Ana I.
N1 - Publisher Copyright:
© 2019 ACM.
PY - 2019/9/9
Y1 - 2019/9/9
N2 - An 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.
AB - An 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.
KW - Big data
KW - Cloud computing
KW - Data analytics
KW - Fog computing
KW - IoT
KW - Predictive maintenance
UR - http://www.scopus.com/inward/record.url?scp=85081946473&partnerID=8YFLogxK
U2 - 10.1145/3344948.3344987
DO - 10.1145/3344948.3344987
M3 - Conference contribution
AN - SCOPUS:85081946473
T3 - ACM International Conference Proceeding Series
SP - 173
EP - 176
BT - 13th European Conference on Software Architecture, ECSA 2019 - Companion Proceedings
A2 - Duchien, Laurence
A2 - Koziolek, Anne
A2 - Mirandola, Raffaela
A2 - Martinez, Elena Maria Navarro
A2 - Quinton, Clement
A2 - Scandariato, Ricardo
A2 - Scandurra, Patrizia
A2 - Trubiani, Catia
A2 - Weyns, Danny
PB - Association for Computing Machinery
T2 - 13th European Conference on Software Architecture, ECSA 2019
Y2 - 9 September 2019 through 13 September 2019
ER -