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id	Vol-3194/paper15
wikidataid	Q117344934→Q117344934
title	Workflows for Bringing Data Science on the Cloud/Edge Computing Continuum
pdfUrl	https://ceur-ws.org/Vol-3194/paper15.pdf
dblpUrl	https://dblp.org/rec/conf/sebd/DazziGT22
volume	Vol-3194→Vol-3194
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Workflows for Bringing Data Science on the Cloud/Edge Computing Continuum

Workflows for Bringing Data Science on the
Cloud/Edge Computing Continuum
Patrizio Dazzi1 , Valerio Grossi2 and Roberto Trasarti2
1
    Department of Computer Science, University of Pisa, Italy
2
    Institute of Information Science and Technologies (ISTI), National Research Council (CNR), Pisa, Italy


                                         Abstract
                                         Research infrastructures play a crucial role in the development of data science. In fact, the conjunction
                                         of data, infrastructures and analytical methods enable multidisciplinary scientists and innovators to
                                         extract knowledge and to make the knowledge and experiments reusable by the scientific community,
                                         innovators providing an impact on science and society. Resources such as data and methods, help
                                         domain and data scientists to transform research in an innovation question into a responsible data-
                                         driven analytical process. On the other hand, Edge computing is a new computing paradigm that is
                                         spreading and developing at an incredible pace. Edge computing is based on the assumption that for
                                         certain applications is beneficial to bring the computation as closer as possible to data or end-users. This
                                         paper discusses about this topic by describing an approach for writing data science workflows targeting
                                         research infrastructures that encompass resources located at the edge of the network.

                                         Keywords
                                         Data-sharing, Data Science, Cloud Platforms, Federated Platforms




1. Introduction
The combined exploitation of data, infrastructures, and analytical methods enable multidis-
ciplinary scientists and innovators to extract knowledge and to make the knowledge and
experiments reusable by the scientific community, innovators providing an impact on science
and society. Data science can support policy-making, it offers novel ways to produce high-
quality and high-precision statistical information, can help to promote ethical uses of big data.
Research infrastructures (RIs) play a crucial role in the advent and development of data science.
Resources such as data and methods help data scientists to transform research or an innovation
question into a responsible data-driven analytical process. This process is executed onto the
platform, supporting experiments that yield scientific output, policy recommendations, or
innovative proofs-of-concept. An infrastructure offers means to define complex workflows, thus
bridging the gap between experts and analytical technology. As a collateral effect, experiments
generate new relevant data, methods, and workflows that can be integrated into the platform by
scientists, contributing to the expansion of the RI. As a drawback, the availability of data creates
opportunities but also new risks. The use of data science techniques could expose sensitive
traits of individual persons and invade their privacy.
SEBD 2022: The 30th Italian Symposium on Advanced Database Systems, June 19-22, 2022, Tirrenia (PI), Italy
$ patrizio.dazzi@unipi.it (P. Dazzi); valerio.grossi@isti.cnr.it (V. Grossi); roberto.trasarti@isti.cnr.it (R. Trasarti)
� 0000-0001-8504-1503 (P. Dazzi); 0000-0002-8735-5394 (V. Grossi); 0000-0001-5316-6475 (R. Trasarti)
                                       © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
    CEUR
    Workshop
    Proceedings
                  http://ceur-ws.org
                  ISSN 1613-0073
                                       CEUR Workshop Proceedings (CEUR-WS.org)
�   SoBigData RI is a platform for the design and execution of large-scale social mining ex-
periments, open to users with diverse backgrounds, accessible on cloud, and also exploiting
super-computing facilities. All the SoBigData components are introduced for implementing
data science: from raw data management to knowledge extraction, with particular attention
to legal and ethical aspects. SoBigData serves a cross-disciplinary community of scientists
studying all the elements of societal complexity from a data- and model-driven perspective.
Moreover, pushing the FAIR (Findable, Accessible, Interoperable, Reusable) and FACT (Fair,
Accountable, Confidential and Transparent) principles, furthermore SoBigData++ RI renders
social mining experiments more easily designed, adjusted and repeatable by experts that are
not data scientists. SoBigData++ RI moves forward from the simple awareness of ethical and
legal challenges in social mining to the development of concrete tools that operationalize ethics
with value-sensitive design, incorporating values and norms for privacy protection, fairness,
transparency and pluralism.
   From the perspective of the actual deployment on physical resources, a relevant challenge
for these tools consists in providing a way to exploit those resources that can not be directly
involved in the administrative domains of the research infrastructure; even if their exploitation
could be beneficial for supporting the execution of the application. A notable example of such
kind of resources are the Edge devices, i.e., those devices that can be exploited by using a
pay-per-use approach, typical of utility computing paradigm [1], but are not part of a large
centralized installation (like a Cloud) and are instead distributed on a large, dispersed, area.
The aim of this approach is in fact to provide a pervasive computing infrastructure with the
objective of bringing the computation as much close as possible to the data producers (e.g.,
sensors, cameras, etc.) and/or data consumers (e.g., users, applications, etc.). The resulting
research infrastructure extended to the edge, will encompass resources of different kinds; this
complex set of heterogeneous resources – having different capacities and means to access – has
the potentiality to enable quite more interesting scenario at a cost of an increased complexity
in its actual management. This complexity is not limited to the actual setup and operation of
the platform but also impacts on the approach to adopt for developing applications that should
run on top of this heterogeneous and distributed resource infrastructure.
   The aim of this paper is to briefly highlight how workflow-based solutions can be properly
instrumented an attempt to address the aforementioned challenges. The remaining of this
paper is structured as follows. Section 2 contextualize this work by placing it in the scientific
literature by presenting a few related works. Section 3 introduces the workflows as a solution for
developing solutions targeting traditional research infrastructures. Section 4 presents a receipt
for emending existing workflows approaches to match the peculiar needs of the aforementioned
extension of the infrastructure. Finally, Section 5 draws our conclusive remarks and introduces
some works that we plan to undertake in the near future.


2. Related Work
Liew et al. [2] have analyzed selected Workflow Management Systems (WMSs) that are widely
used by the scientific community; among those: Airavata [3], Pegasus [4], Taverna [5], and Swift
[6]. Such systems have been reviewed according to the following aspects: (i) processing elements,
�i.e., the building blocks of workflows envisaged to be either web services or executable programs;
(ii) coordination method, i.e., the mechanism controlling the execution of the workflow elements
envisaged to be either orchestration or choreography; (iii) workflow representation, i.e., the
specification of a workflow that can meet two goals human representation and/or computer
communication; (iv)data processing model, i.e., the mechanism through which the processing
elements process the data that can be bulk data or stream data; (v) optimization stage, i.e.,
when optimization of the workflow (if any) is expected to take place that can either be build
time or run-time (e.g., data workflow processing optimization [7, 8, 9]. The aforementioned
approaches are defined based on the assumption that workflows are composed of machine-
executable actions, i.e., performed by agents that can be programmatically invoked. They do not
address the needs, motivated by several scientific contexts, e.g., Big Data and Social Mining [10],
Biodiversity and Cheminformatics domains [11, 12], of defining workflows that include “manual
actions”, e.g., data manipulation and adaptation using editors or shell commands. Attempts
in this direction exist but embrace a fully manually-oriented approach, e.g., protocols.io [13],
enabling the digital representation, publishing, and sharing of digital fully manual workflows.
    The main contribution of this paper is an extension of workflow language and execution
platform, whose intuition was earlier presented in [14]. HyWare was designed to enable the
description of “hybrid” workflows, obtained as sequences of machine-executable and manually-
executable actions. As such, the language can serve the mission of Open Science by addressing
the reproducibility of digital science beyond traditional approaches in contexts where workflow
actions are not entirely performed by machines. In recent years there have been several efforts in
studying the most appropriate solution for structuring applications for Cloud/Edge environment.
TOSCA is one of the most successful standards. As Binz [15] states, the goals of TOSCA include
the automation of application deployment and the representation of the application in a cloud
agnostic way. This standard has been leveraged by several products and research initiatives,
e.g., BASMATI [16, 17], and Tosker [18]. Tosker works with an extended TOSCA YAML and
generates a deployment plan for Docker. TOSCA has also been used with Kubernetes [19]
to define application components along with their deployment and run-time adaptation on
Kubernetes clusters across different countries. All these solutions are general purpose and not
focus on a specific class of the application; that is instead the approach that we follow in this
paper.


3. Need for ad-hoc instruments
Workflows are tools for the representation of the scientific process and the steps the researchers
had to perform to execute an experiment using the e-infrastructure tools. Workflows can
inherently contribute to the implementation of two data principles which are at the base of
the modern data processing and analysis: (i) the FAIR data principles by accurately collecting,
processing, and managing data and metadata on behalf of the researchers, while tracking
provenance according to standards [20]; (ii) the FACT data principles stating that the data
processing should be fair, accurate, confidential and transparent. A workflow language [21].
Moreover, workflows are digital objects in their own right, they can be published, discovered,
shared, and cited for reproducibility and for scientific attribution of science like research articles,
�research data, and research software. Known approaches include: workflows as digital artefacts:
workflow files are published in a repository with bibliographic metadata (e.g. Zenodo1 , [22]) and
can be possibly related to their inputs and outputs [23, 24]; workflow-as-a-Service: workflows
are shared via a platform gateway that enables discovery and execution [25, 26, 27].
   SoBigData scientists can integrate their tools for VRE-integrated reuse but cannot represent
a sequence of actions in order to share it and reproduce it. We are working on equipping
SoBigData VREs with a workflow allowing scientists to attach to a specific result the entire
process used to obtain it. This makes the environment evolve into a living laboratory, which
contains not only the methods and the results but also the experience of the researcher using
the methods, and composing an analytical process with it. On the other side, our workflow has
to manage the computational component needed to execute an action both not only considering
federated ones [28]. In this paper, we consider only machine action, i.e., actions executable by a
computational node and characterized by a description, expressed by the respective properties,
but also by a standard way to invoke a third-party service and get back the results. To this aim,
for each machine action class will integrate a mediator capable of invoking the external service
with given action input parameters and collect the parameters to return them in accordance with
the action output type. Each machine action of our workflow language (Fig. 1) includes three
main aspects: (i) Configuration parameters: this information allows the system to instantiate a
generic action class of the method to a specific instance ready to be executed. (ii) Execution
annotation: represents a form of syntactic metadata that are directives to the workflow execution
environment and the Edge computing orchestrator (i.e. memory usage, multi-core-executable,
execution placement, latency constraints); for example, these annotations can be used to reduce
the latency of an execution of an action under a certain value, or constraints on data transmission.
[29, 30]. (i) Returning result: packaging the results in order for them to be available to the
subsequent action instance execution.
Driving Profile Example: computing driving profiles and monitoring driving behaviors of
users [31] will be done at different levels of the cloud/edge continuum: the single action class
will be instantiated as: (i) an instance of model computation at the device level to compute
the user profile using the personal data produced during its daily activities and an assessment
module to check if the model holds; (ii) a global modeling at the cloud level which combines
local models and updates it if something changes in the nodes.


4. A receipt for Workflows targeting edge computing
Workflows are effective approaches to structure applications describing scientific processes
enabling the development of many data science solutions. As such, the empowerment of
workflows will edge-enable a large amount of data-centric applications. To achieve this goal
our approach is based on the extension of our previously proposed workflow engine [14], with
an edge computing orchestrator able to properly manage the execution of workflow actions on
top of edge resources.
  Such an extension needs to revolve around the following aspects: (i) interoperability: allowing
the exploitation of Edge resources by enabling the actual deployment of workflow actions at
    1
        https://zenodo.org/
�Figure 1: Workflow engine architecture. The classes of actions can be instantiated according to the
underlying e-infrastructure and then combined into workflows.


the Edge. In spite of the many solutions proposed in the literature, an off-the-shelf solution
targeting extended research infrastructures is not existing. (ii) resource Indexing and Discovery
and representation: the workflow engine has to be provided with the ability of indexing
the resources available, on which to map the workflow actions; To this end we envision the
exploitation of solutions borrowed from the peer-to-peer field that we investigated in the
past [32, 33] demonstrated to be quite effective solutions to this end; (iii) application Monitoring
and Orchestration: a fundamental element for achieving an efficient exploitation of edge
resources is an efficient monitoring subsystem that feeds an orchestration subsystem aimed at
conducting a match-making process to provide applications with the best resources possible,
among the one available; In the literature have been presented several solutions to this end,
both from the domain of Clouds (e.g., Wen [34]) and Networks (e.g., Sahu [35]).


5. Conclusion
This paper discusses a solution for the extension of research infrastructure at the edge. The
approach we propose is based on the identification of a the key features represented by a set
of annotations that need to be integrated into a workflow engine. This work describes a first
step toward a definition of a workflow language enabling the use of extended computational
resources represented by Edge computing.


Acknowledgments
This work is supported by the European Community’s H2020 Program under the scheme
“INFRAIA-01-2018-2019”, Grant Agreement n.871042, “SoBigData++: European Integrated In-
frastructure for Social Mining and Big Data Analytics” (http://www.sobigdata.eu).
�References
 [1] C.-H. Youn, M. Chen, P. Dazzi, Cloud Broker and Cloudlet for Workflow Scheduling,
     Springer, 2017.
 [2] C. S. Liew, M. P. Atkinson, M. Galea, T. F. Ang, P. Martin, J. I. V. Hemert, Scientific workflows:
     Moving across paradigms, ACM Computing Surveys 49 (2016). doi:10.1145/3012429.
 [3] S. Marru, L. Gunathilake, C. Herath, P. Tangchaisin, M. Pierce, C. Mattmann, R. Singh,
     T. Gunarathne, E. Chinthaka, R. Gardler, A. Slominski, A. Douma, S. Perera, S. Weerawarana,
     Apache airavata: A framework for distributed applications and computational workflows,
     in: Proceedings of the 2011 ACM Workshop on Gateway Computing Environments, GCE
     ’11, ACM, New York, NY, USA, 2011, pp. 21–28. doi:10.1145/2110486.2110490.
 [4] E. Deelman, K. Vahi, G. Juve, M. Rynge, S. Callaghan, P. J. Maechling, R. Mayani, W. Chen,
     R. F. da Silva, M. Livny, K. Wenger, Pegasus, a workflow management system for science
     automation, Future Generation Computer Systems 46 (2015) 17 – 35. doi:10.1016/j.
     future.2014.10.008.
 [5] K. Wolstencroft, R. Haines, D. Fellows, A. Williams, D. Withers, S. Owen, S. Soiland-
     Reyes, I. Dunlop, A. Nenadic, P. Fisher, J. Bhagat, K. Belhajjame, F. Bacall, A. Hardisty,
     A. Nieva de la Hidalga, M. P. Balcazar Vargas, S. Sufi, C. Goble, The taverna workflow suite:
     designing and executing workflows of web services on the desktop, web or in the cloud,
     Nucleic Acids Research 41 (2013) W557–W561. URL: http://dx.doi.org/10.1093/nar/gkt328.
     doi:10.1093/nar/gkt328.
 [6] Y. Zhao, M. Hategan, B. Clifford, I. Foster, G. von Laszewski, V. Nefedova, I. Raicu, T. Stef-
     Praun, M. Wilde, Swift: Fast, reliable, loosely coupled parallel computation, in: IEEE
     Congress on Services (Services 2007), 2007, pp. 199–206. doi:10.1109/SERVICES.2007.
     63.
 [7] G. Kougka, A. Gounaris, A. Simitsis, The many faces of data-centric workflow optimization:
     a survey, International Journal of Data Science and Analytics 6 (2018) 81–107. doi:https:
     //doi.org/10.1007/s41060-018-0107-0.
 [8] A. Lulli, E. Carlini, P. Dazzi, C. Lucchese, L. Ricci, Fast connected components computation
     in large graphs by vertex pruning, IEEE Transactions on Parallel and Distributed systems
     28 (2016) 760–773.
 [9] M. M. Bersani, S. Distefano, L. Ferrucci, M. Mazzara, A timed semantics of workflows, in:
     A. Holzinger, J. Cardoso, J. Cordeiro, T. Libourel, L. A. Maciaszek, M. van Sinderen (Eds.),
     Software Technologies, Springer International Publishing, Cham, 2015, pp. 365–383.
[10] F. Giannotti, R. Trasarti, K. Bontcheva, V. Grossi, Sobigdata: Social mining & big data
     ecosystem, in: Companion of the The Web Conference 2018 on The Web Conference 2018,
     International World Wide Web Conferences Steering Committee, 2018, pp. 437–438.
[11] D. Garijo, P. Alper, K. Belhajjame, O. Corcho, Y. Gil, C. Goble, Common motifs in scientific
     workflows: An empirical analysis, Future Generation Computer Systems 36 (2014) 338 – 351.
     doi:10.1016/j.future.2013.09.018, special Section: Intelligent Big Data Processing
     Special Section: Behavior Data Security Issues in Network Information Propagation Special
     Section: Energy-efficiency in Large Distributed Computing Architectures Special Section:
     eScience Infrastructure and Applications.
[12] N. Schaduangrat, S. Lampa, S. Simeon, M. P. Gleeson, O. Spjuth, C. Nantasenamat, Towards
�     reproducible computational drug discovery, Journal of Cheminformatics 12 (2020) 9. URL:
     https://doi.org/10.1186/s13321-020-0408-x. doi:10.1186/s13321-020-0408-x.
[13] L. Teytelman, A. Stoliartchouk, L. Kindler, B. L. Hurwitz, Protocols.io: Virtual communities
     for protocol development and discussion, PLOS Biology 14 (2016) 1–6. URL: https://doi.
     org/10.1371/journal.pbio.1002538. doi:10.1371/journal.pbio.1002538.
[14] L. Candela, V. Grossi, P. Manghi, R. Trasarti, A workflow language for research
     e-infrastructures, International Journal of Data Science and Analytics (2021). URL:
     https://doi.org/10.1007/s41060-020-00237-x. doi:10.1007/s41060-020-00237-x.
[15] T. Binz, U. Breitenbücher, O. Kopp, F. Leymann, Tosca: portable automated deployment
     and management of cloud applications, in: Advanced Web Services, Springer, 2014, pp.
     527–549.
[16] J. Altmann, B. Al-Athwari, E. Carlini, M. Coppola, P. Dazzi, A. J. Ferrer, N. Haile, Y.-W.
     Jung, J. Marshall, E. Pages, E. Psomakelis, G. Z. Santoso, K. Tserpes, J. Violos, Basmati:
     An architecture for managing cloud and edge resources for mobile users, in: C. Pham,
     J. Altmann, J. Á. Bañares (Eds.), Economics of Grids, Clouds, Systems, and Services, Springer
     International Publishing, Cham, 2017, pp. 56–66.
[17] J. Violos, V. M. de Lira, P. Dazzi, J. Altmann, B. Al-Athwari, A. Schwichtenberg, Y.-W. Jung,
     T. Varvarigou, K. Tserpes, User behavior and application modeling in decentralized edge
     cloud infrastructures, in: International Conference on the Economics of Grids, Clouds,
     Systems, and Services, Springer, Cham, 2017, pp. 193–203.
[18] A. Brogi, L. Rinaldi, J. Soldani, Tosker: a synergy between tosca and docker for orchestrating
     multicomponent applications, Software: Practice and Experience 48 (2018) 2061–2079.
[19] D. Kim, H. Muhammad, E. Kim, S. Helal, C. Lee, Tosca-based and federation-aware cloud
     orchestration for kubernetes container platform, Applied Sciences 9 (2019) 191.
[20] C. Goble, S. Cohen-Boulakia, S. Soiland-Reyes, D. Garijo, Y. Gil, M. R. Crusoe,
     K. Peters, D. Schober, Fair computational workflows, Data Intelligence 2 (2020)
     108–121. URL: https://doi.org/10.1162/dint_a_00033. doi:10.1162/dint\_a\_00033.
     arXiv:https://doi.org/10.1162/dint_a_00033.
[21] B. Lepri, N. Oliver, E. Letouzé, A. Pentland, P. Vinck, Fair, transparent, and accountable
     algorithmic decision-making processes: The premise, the proposed solutions, and the open
     challenges, Philosophy & Technology 31 (2018). doi:10.1007/s13347-017-0279-x.
[22] D. D. Roure, C. Goble, R. Stevens, The design and realisation of the experimentmy virtual
     research environment for social sharing of workflows, Future Generation Computer
     Systems 25 (2009) 561 – 567. doi:10.1016/j.future.2008.06.010.
[23] D. Garijo, Y. Gil, O. Corcho, Abstract, link, publish, exploit: An end to end framework for
     workflow sharing, Future Generation Computer Systems 75 (2017) 271 – 283. doi:https:
     //doi.org/10.1016/j.future.2017.01.008.
[24] A. Shaon, S. Callaghan, B. Lawrence, B. Matthews, A. Woolf, T. Osborn, C. Harpham, A
     linked data approach to publishing complex scientific workflows, in: 2011 IEEE Seventh
     International Conference on eScience, 2011, pp. 303–310. doi:10.1109/eScience.2011.
     49.
[25] R. Filgueira, M. Atkinson, A. Bell, I. Main, S. Boon, C. Kilburn, P. Meredith, Escience
     gateway stimulating collaboration in rock physics and volcanology, volume 1, 2014, pp.
     187–195. doi:10.1109/eScience.2014.22.
�[26] M. Danelutto, P. Dazzi, et al., A java/jini framework supporting stream parallel computa-
     tions., in: PARCO, 2005, pp. 681–688.
[27] R. Baraglia, P. Dazzi, M. Mordacchini, L. Ricci, L. Alessi, Group: A gossip based building
     community protocol, in: Smart spaces and next generation wired/wireless networking,
     Springer, Berlin, Heidelberg, 2011, pp. 496–507.
[28] M. Coppola, P. Dazzi, A. Lazouski, F. Martinelli, P. Mori, J. Jensen, I. Johnson, P. Kershaw,
     The contrail approach to cloud federations, in: Proceedings of the International Symposium
     on Grids and Clouds (ISGC’12), volume 2, 2012, p. 1.
[29] M. Danelutto, P. Dazzi, Workflows on top of a macro data flow interpreter exploiting
     aspects, in: Making Grids Work, Springer, Boston, MA, 2008, pp. 213–224.
[30] M. Danelutto, P. Dazzi, D. Laforenza, M. Pasin, L. Presti, M. Vanneschi, Pal: High level
     parallel programming with java annotations, in: Proceedings of CoreGRID Integration
     Workshop (CIW 2006) Krakow, Poland, Academic Computer Centre CYFRONET AGH,
     2006, pp. 189–200.
[31] M. Nanni, R. Trasarti, A. Monreale, V. Grossi, D. Pedreschi, Driving profiles computation
     and monitoring for car insurance crm, ACM Trans. Intell. Syst. Technol. 8 (2016). URL:
     https://doi.org/10.1145/2912148. doi:10.1145/2912148.
[32] E. Carlini, M. Coppola, P. Dazzi, D. Laforenza, S. Martinelli, L. Ricci, Service and resource
     discovery supports over p2p overlays, in: 2009 International Conference on Ultra Modern
     Telecommunications & Workshops, IEEE, 2009, pp. 1–8.
[33] R. Baraglia, P. Dazzi, B. Guidi, L. Ricci, Godel: Delaunay overlays in p2p networks via
     gossip, in: IEEE 12th International Conference on Peer-to-Peer Computing (P2P), IEEE,
     2012, pp. 1–12.
[34] Z. Wen, R. Yang, P. Garraghan, T. Lin, J. Xu, M. Rovatsos, Fog orchestration for internet of
     things services, IEEE Internet Computing 21 (2017) 16–24.
[35] A. K. Sahu, T. Li, M. Sanjabi, M. Zaheer, A. Talwalkar, V. Smith, On the convergence of
     federated optimization in heterogeneous networks, arXiv preprint arXiv:1812.06127 3
     (2018).
�