Scientific data analysis experiments and applications require software capable of handling domain-specific and data-intensive workflows. The increasing volume of scientific data is further exacerbating these data management and analytics challenges, pushing the community towards the definition of novel programming environments for dealing efficiently with complex experiments, while abstracting from the underlying computing infrastructure.ECASLab provides a user-friendly data analytics environment to support scientists in their daily research activities, in particular in the climate change domain, by integrating analysis tools with scientific datasets (e.g., from the ESGF data archive) and computing resources (i.e., Cloud and HPC-based). It combines the features of the ENES Climate Analytics Service (ECAS) and the JupyterHub service, with a wide set of scientific libraries from the Python landscape for data manipulation, analysis and visualization. ECASLab is being set up in the frame of the European Open Science Cloud (EOSC) platform - in the EU H2020 EOSC-Hub project - by CMCC (https://ecaslab.cmcc.it/) and DKRZ (https://ecaslab.dkrz.de/), which host two major instances of the environment.ECAS, which lies at the heart of ECASLab, enables scientists to perform data analysis experiments on large volumes of multi-dimensional data by providing a workflow-oriented, PID-supported, server-side and distributed computing approach. ECAS consists of multiple components, centered around the Ophidia High Performance Data Analytics framework, which has been integrated with data access and sharing services (e.g., EUDAT B2DROP/B2SHARE, Onedata), along with the EGI federated cloud infrastructure. The integration with JupyterHub provides a convenient interface for scientists to access the ECAS features for the development and execution of experiments, as well as for sharing results (and the experiment/workflow definition itself). ECAS parallel data analytics capabilities can be easily exploited in Jupyter Notebooks (by means of PyOphidia, the Ophidia Python bindings) together with well-known Python modules for processing and for plotting the results on charts and maps (e.g., Dask, Xarray, NumPy, Matplotlib, etc.). ECAS is also one of the compute services made available to climate scientists by the EU H2020 IS-ENES3 project.Hence, this integrated environment represents a complete software stack for the design and run of interactive experiments as well as complex and data-intensive workflows. One class of such large-scale workflows, efficiently implemented through the environment resources, refers to multi-model data analysis in the context of both CMIP5 and CMIP6 (i.e., precipitation trend analysis orchestrated in parallel over multiple CMIP-based datasets).

Abstract. The need for open science has been recognized by the communities of meteorology and climate science. However, while these domains are mature in terms of applying digital technologies, these are lagging behind where the implementation of open science methodologies is concerned. In a session on Weather and Climate Science in the Digital Era at the 14th IEEE International eScience conference domain specialists and data and computer scientists discussed the road towards open weather and climate science. The studies presented in the conference session showed the added value of shared data, software and platforms through, for instance, combining data sets from disparate sources, increased accuracy and skill of simulations and forecasts at local scales, and improved consistency of data products. We observed that sharing data and code is important, but not sufficient to achieve open weather and climate science and that here are important issues to address. At the level of technology, the implementation of the FAIR principles to many datasets used in weather and climate science remains a challenge due to their origin, scalability, or legal barriers. Furthermore, the complexity of current software platforms limits collaboration between researchers and optimal use of open science tools and methods. The main challenges we observed, however, were non-technical and impact the system of science as a whole. There is a need for new roles and responsibilities at the interface of science and digital technology, e.g., data stewards and research software engineers. This requires the personnel portfolio of academic institutions to be more diverse, and in addition, a broader consideration of the impact of academic work, beyond publishing and teaching. Besides, new policies regarding open weather and climate science should be developed in an inclusive way to engage all stakeholders, including non-academic parties such as meteorological institutions. We acknowledge that open weather and climate science requires effort to change, but the benefits are large. As can already be observed from the studies presented in the conference it leads to much faster progress in understanding the world.

Research data currently face a huge increase of data objects with an increasing variety of types（data types, formats） and variety of workflows by which objects need to be managed across their lifecycle by data infrastructures. Researchers desire to shorten the workflows from data generation to analysis and publication, and the full workflow needs to become transparent to multiple stakeholders, including research administrators and funders. This poses challenges for research infrastructures and user-oriented data services in terms of not only making data and workflows findable, accessible, interoperable and reusable, but also doing so in a way that leverages machine support for better efficiency. One primary need to be addressed is that of findability, and achieving better findability has benefits for other aspects of data and workflow management. In this article, we describe how machine capabilities can be extended to make workflows more findable, in particular by leveraging the Digital Object Architecture, common object operations and machine learning techniques.

The European Network for Earth System Modelling (ENES) Climate Analytics Service (ECAS) is a new service from the EOSC-hub project. It offers a Virtual Research Environment (VRE) to scientific users, combining a Python (Jupyter) work environment with support services for data access, computing and data sharing. ECAS is motivated by providing users with remote access to extensive computing and storage resources beyond what they may have access to locally, reducing the need to conduct costly data transfer, and helping to realize the vision of FAIR data management. ECAS aims at providing a paradigm shift for the ENES community and beyond with a strong focus on data intensive analysis, provenance management, and server-side approaches as opposed to the current ones mostly client-based, sequential and with limited or missing end-to-end analytics workflow and provenance capabilities. Furthermore, the integrated data analytics service enables basic data provenance tracking by establishing a graph of persistent identifiers (PIDs) through the whole chain, and thereby improving reusability, traceability, and reproducibility. ECAS targets multiple user groups, including researchers in lack of local computing and storage resources, researchers with interest in the high-volume climate data pools, and use within education and training scenarios.

Within the scope of the EOSC-hub project, we are developing the ENES Climate Analytics Service (ECAS), which aims at enabling scientific end-users mainly from the climate domain to perform data analysis experiments on large volumes of climate data, by exploiting a PID-enabled, server-side, and parallel approach. ECAS is part of the service catalogue that will constitute the hub of the future European Open Science Cloud (EOSC).

In this paper, we present an approach for the specification and the execution of complex scientific workflows in cloud-like environments. The approach strives to support scientists during the modeling, deployment and the monitoring of their workflows. This work takes advantages from Petri nets and more pointedly the so called reference nets formalism, which provide robust modeling/implementation techniques. Meanwhile, we present the implementation of a new tool named RenewGrass. It allows the modeling as well as the execution of image processing workflows from the remote sensing domain. In terms of usability, we provide an easy way to support unskilled researchers during the specification of their workflows. Then, we use the Enhanced Vegetation Index (EVI) workflow as a showcase of the implementation. At last, we introduce our methodology to move the actual implementation to the Cloud.

In this paper, we present the implementation and the integration of an image processing tool named RenewGrass. It is integrated in Renew, which is a powerful modeling editor and simulator of Petri nets, especially reference nets. The objective of this work is to allow Petri net-based modeling and execution of image processing workflows with more ease and transparency. Additionally, RenewGrass has been employed to specify, implement and enact workflows from the remote sensing domain. We specifically focus on the computation of vegetation indices corresponding to satellite imagery. The first evaluations show that our contribution permits to reduce the gap between modeling and implementation of image processing workflows. Moreover, the deployment of RenewGrass into a Cloud environment is also discussed at the end of the paper.

The design of a Cloud ecosystem is usually tackled in technical way and it is vendor-dependent. There is a lack of conceptual foundation to specify the processes (workflows) running in the Cloud and their life-cycle. Furthermore, enabling workflows in such an environment still encounter many obstacles regarding workflow definition, deployment and execution. In fact, each Cloud provider has its own specification and APIs, which affects negatively their accessibility for both Cloud developers and Cloud consumers. The objective of this paper is to address these issues differently by introducing the Inter-Cloud Nets (ICNETS), which are a set of Petri net-based modeling and implementation techniques to specify Cloud-based workflows and their management. Our work aims at reducing the complexity of designing and implementing components that target Inter-Cloud environments. Moreover, we provide practical examples clarifying the concepts discussed through the paper. The examples are related to the remote sensing domain and implemented over a private Cloud based on the OpenStack framework. Another feature of this work is to avoid interoperability issues when developing Inter-Cloud based applications. For this purpose, we provide a supplementary layer to retrieve services from different Cloud providers.

In this paper we propose and discuss mechanisms and implementation issues for moving the execution of computationand timeconsuming workflows into the Cloud. These complex workflows are specified by Petri nets, more precisely reference nets using the Renew tool. We believe that Cloud technology is a suitable solution to (i) overcome the lack of resources on-premises and to (ii) improve the performance of the whole system based on quality of service (QoS) constraints. As execution target for simulations, tests have been performed on an OpenStack Cloud. Furthermore, the integration and interfaces between workflows, Cloud computing and agent concepts are also addressed.

Despite the several attractive features that offers the cloud technology, managing, controlling processes and resources are among the serious obstacles that cloud service providers need to overcome. These issues increase when cloud providers intend to exploit services from several distributed platforms to satisfy client's requests and requirements. At this moment, they need to deal with some critical problems like heterogeneity, collaboration, coordination and communication between different types of participants.In another side, the most known properties of an agent are: autonomy, pro-activity, cooperation and mobility. These features are attractive and have a great importance to design and implement software systems that operate in distributed and open environments such like cloud and grid. Our main goal through this thesis is to propose an approach and architectures to permit the integration of cloud/grid and multi-agent systems concepts and technologies for managing workflows in distributed service-oriented environments. Explicitly, in an Inter-Cloud environment.

Cooperation always contains some control flow. This control flow can be treated by workflow models in order to coordinate actions of a group of distributed participants. While centralized intra-organizational workflow management systems are nowadays very well understood and implemented via tools, the idea of inter-organizational workflow management systems (IOWfMS) still needs conceptual and technical support. This paper discusses the use of agents and multi-agents as underlying concepts of such systems on the basis of agent- and organization-oriented Petri nets. Powerful modeling concepts need appropriate powerful constructs in modeling languages and their tools. As an overall solution we provide Inter-Cloud Workflow Petri Nets (IC-WPN) and sketch their technical embedding. They combine central conceptual ideas of systems-within-systems models, WfMS, as well as agent and organization theories with technical solutions from agent and Cloud technology.

In this paper we present an architecture for enabling complex workflow execution in Cloud-like environments. We focus mainly on modeling concepts and techniques to enhance accessibility to Cloud services by different kind of users. Complex workflow tasks need in some cases to be mapped to distributed resources and involves the cooperation between several partners. Workflow management is critical to a successful long-term Cloud computing strategy. The notion of inter-organizational workflow still needs conceptual and technical support especially in complex and dynamic environments like Clouds. New ways to tackle this problem have to be found. Therefore, existing workflow architectures need to be adapted for the Cloud and workflow management systems (WfMS) should be integrated with Cloud infrastructure and resources [3]. In this paper we use Inter-Cloud Workflow Petri Nets (ICWPN), an approach for enabling workflows in an (Inter)-Cloud environment. A specialized Cloud Task Transition (CTT) is introduced to facilitate the connection to the Cloud and to support Quality of Service (QoS) management [1]. The CTT (see Fig. 1 (a)) is based on the Workflow Task Transition [2], which is the core of the workflow net formalism in Renew1 (Reference Net Workshop). Workflow modelers specify their requirements as parameters to the CTT in form of tuples (S, Q, I), which correspond respectively to the Cloud service (S) that they want to use (it can be a storage or a compute service), the QoS constraints (Q) consisting of deadlines or costs and input data (I) consisting either of required files in case of a storage or scripts if they want to execute their codes on the Cloud. Synchronous channels are used to make the connection with the WfMS, which controls the completion of the task. It either initiates the firing or cancels it and all input parameters are put back onto the input places. To see how the CTT is used in practice, we introduce a Cloud-based workflow architecture, it is depicted in Fig. 1 (b). It includes three basic layers from top to bottom: user applications layer (UL), middle-ware layer (ML) and the resource layer (RL), which consits mainly of Cloud services. In our approach we view the process of executing an application in an Inter-Cloud environment as a 6-phase process: (1) Users use the offered modeling tools consisting mostly of Renew and the introduced CTT to specify the requirements (Cloud services, QoS constraints, specific input data) for their applications using Petri nets models. (2) 1 Renew is available at http://www.renew.de (a) The Cloud Transition (b) General Cloud Workflow Architecture Fig. 1. Cloud-based Workflow Management A list of requirements is created consisting of required services as well as their related QoS constraints. (3) Make a request to the Cloud Service Repository (CSR) which is accessible by the WfMS to achieve workflow tasks (4) Based on the above steps (2-3) a decision is made by the Decision Maker who determines whether the workflow tasks will be executed locally or using Cloud resources. (5) After that the workflow tasks are mapped to the adequate resources. (6) When the workflow is deployed, information about Cloud providers and the state of their services are constantly updated. Here we focused primarily on Cloud technologies. Nevertheless, the introduced model (see Fig. 1(b)) can be also applicable to other dynamic domains where distributed resources are shared and dynamically allocated and usually priced.

Extended Abstract In this paper, we present the Cloud Workflow Transition. An extension of Petri nets formalisms to adopt Cloud interactions. This allows workflows to request compute or storage services from the cloud. Such refinements permit to codify operational procedures into Petri net models and reduce user implication during the specification of their workflows. The main purpose behind our proposed refinements is to allow users to automatically execute workflows on distributed infrastructures (Cloud, SOA, grid, cluster). Through the Cloud transition users can specify their requests formulated as tasks and parameters (see Figure 1). These requests will be treated in a transparent way i.e. that technical information is hidden from the user. The WFMS will then either accept the request and make the connection to the specified Cloud services according to user inputs or will reject it. The input places of the Cloud transition model the preconditions of an event, the input data for the computational task. The output places of the transition model the post-conditions associated with an event, the results of the computational task. Our approach uses workflow Petri nets [1]. More specifically we use the reference net formalism [2] extended with a specialized workflow task transition [3]. Renew, the Reference Net Workshop, is our chosen tool for modeling with reference nets. A very interesting and useful property of reference nets in Renew is their use of the so-called shadow layer. It hides the technical details from the user, who can concentrate on simply the nets. The technical integration of the Cloud transition into our workflow nets and workflow managment system is carried out in three main steps: The integration into the existing workflow net formalism for Renew [3], the integration into the current WFMS in Renew [4] and finally the integration into the user interface. Due to the dynamic aspect of the cloud computing, further integration issues are investigated such as including Quality-of-Service (QoS) requirements (time and expenditure limit). The WFMS should be able to identify and handle failures and support reliable execution in the presence of concurrency to guarantee a high level of performance and availability of services.