Big Data Analytics for Smart Cities: The H2020 CLASS Project
Abstract
Applying big-data technologies to field applications has resulted in several new needs. First, processing data across a compute continuum spanning from cloud to edge to devices, with varying capacity, architecture etc. Second, some computations need to be made predictable (real-time response), thus supporting both data-in-motion processing and larger-scale data-at-rest processing. Last, employing an event-driven programming model that supports mixing different APIs and models, such as Map/Reduce, CEP, sequential code, etc.
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Big Data Analytics for Smart Cities: The H2020 CLASS Project Eduardo Quiñones Barcelona Supercomputing Center Barcelona, Spain [email protected] Marko Bertogna University of Modena Modena, Italy [email protected] Erez Hadad IBM Research - Haifa Haifa, Israel [email protected] Ana Juan Ferrer Atos Research Barcelona, Spain [email protected] Luca Chiantore City of Modena Modena, Italy [email protected]. it Alfredo Reboa Maserati Modena, Italy alfredo.r[email protected] CCS CONCEPTS •Computer systems organization →Cloud computing ; Embedded systems ; •Software and its engineering →Real-time schedulability ; •Theory of computation →MapReduce algorithms; 1 INTRODUCTION Applying big-data technologies to field applications has resulted in several new needs. First, processing data across a compute continuum spanning from cloud to edge to devices, with varying capacity, architecture etc. Second, some computations need to be made predictable (real-time response), thus supporting both data-in-motion processing and larger-scale data-at-rest processing. Last, employing an event-driven programming model that supports mixing different APIs and models, such as Map/Reduce, CEP, sequential code, etc. 2 THE H2020 CLASS PROJECT CLASS aims to create a platform that allows users to develop and execute their applications efficiently. At the resource level, fog[ 3 ] principles are applied, of distributing computation across the continuum using COMPSs [ 1 ], and taking decisions closer to the data origin to reduce communication. On the edge, new embedded architectures (e.g., GPUs, manycore) are used. On the cloud side CLASS aims to maximize throughput while maintaining latency requirements. Last, CLASS aims to explore a serverless polyglot event-driven platform of Apache OpenWhisk [ 2 ] and extend it with realtime requirements, both for analytics foundation and for the programming model. 3 SMART CITY USE-CASE CLASS software will be evaluated in the Modena Automotive Smart Area (MASA), a real urban laboratory in Modena, Italy, equipped with sensors, cellular and optic connectivity. Maserati provides prototypes of highly-connected cars with sensors, such as radars. The use-case consists of the following set of CLASS applications: •Intelligent traffic management , dynamically controlling traffic lights and smart road signals based on traffic conditions, e.g., to reduce fuel consumption and providing “green routes” for emergency vehicles. •Advanced driving assistance providing obstacle avoidance, dynamic path/route planning and parking assistance. 4 ACKNOWLEDGEMENTS The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the CLASS Project (www.class-project.eu), grant agreement No. 780622. REFERENCES [1] Barcelona Supercomputing Center. 2018. COMP Superscalar. (2018). https://www.bsc.es/research-and-development/software-and-apps/ software-list/compsuperscalar [2] IBM / Apache community. 2018. Apache OpenWhisk is a serverless, open source cloud platform. (2018). http://openwhisk.apache.org [3] OpenFog Consortium. 2017. OpenFog Reference Architecture for Fog Computing. (February 2017). https: //www.openfogconsortium.org/wp-content/uploads/OpenFog_ Reference_Architecture_2_09_17-FINAL.pdf © {Owner/Author | ACM} {2019}. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in SYSTOR '18 Proceedings of the 11th ACM International Systems and Storage Conference, https://dl.acm.org/citation.cfm?id=3211914. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permited. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifyc permission and/or a fee. Request permissions from [email protected]