Virtual Research Environments as-a-Service by gCube

Abstract

-Science is in continuous evolution and so are the methodologies and approaches scientists tend to apply by calling for appropriate supporting environments. This is in part due to the limitations of the existing practices and in part due to the new possibilities offered by technology advances. gCube is a software system promoting elastic and seamless access to research assets (data, services, computing) across the boundaries of institutions, disciplines and providers to favour collaborativeoriented research tasks. Its primary goal is to enable Hybrid Data Infrastructures facilitating the dynamic definition and operation of Virtual Research Environments. To this end, it offers a comprehensive set of data management commodities on various types of data and a rich array of “mediators” to interface well-established Infrastructures and Information Systems from various domains. Its effectiveness has been proved by operating the D4Science.org infrastructure and serving concrete, multidisciplinary, challenging, and large scale scenarios. This paper gives an overview of the gCube system.

I. INTRODUCTION

Science calls for ever innovative practices, tools and environments supporting the whole research lifecycle – from data collection and curation to analysis, visualisation and publishing [ 1 ], [ 2 ]. In particular, scientists are asking for integrated environments providing them with seamless access to data, software, services and computing resources they need in performing their research activities independently of organisational and technical barriers. In these settings, approaches based on ad-hoc and “from scratch” development are neither viable (e.g., high “time to market”) nor sustainable (e.g., technological obsolescence risk).

The as-a-Service model [ 3 ], [ 4 ], consisting in outsourcing functionality to professional and dedicated providers, is a very promising trend for both science and beyond. This model makes it possible to leverage economies of scale to keep costs low, to count on a known quality of service and to scale service delivery to peak demands.

Depending on the typologies of service(s) that are made available by a service provider, a gap between the offering and the scientist expectations might occur in terms of research environments (functional mismatch). The consumption of the available services and their exploitation to realise the scientific workflows should be an easy task that does not require extra skills nor distract effort from the pure scientific investigation (long learning curves, “entry barriers”). Science Gateways (SGs) [ 5 ] and Virtual Research Environments (VREs) [ 6 ] have been proposed to close the gap between service providers and scientific communities, both the functional mismatch and the entry barriers. Very often these are ad-hoc portals built to serve the needs of a specific community only.

The development of such environments expected to facilitate scientists tasks is challenging from the system engineering perspective, several technologies and skills are needed [ 7 ], [ 8 ]. Moreover, (a) people having the requested expertise (mainly the IT ones) are not always available in the scientific contexts calling for VREs, and (b) technology is in continuous evolution thus offering new opportunities for implementing existing facilities in innovative ways or integrating innovative facilities in existing VREs. This should discourage scientific communities from building their own solutions and should suggest to outsource this task to approaches that delivers them with the as-a-Service model.

This paper gives an overview of gCube1. gCube is a software system specifically conceived to enable the creation and operation of an innovative typology of e-Infrastructure (Hybrid Data Infrastructure) that by aggregating a wealth of resources from other infrastructures offers cutting-edge Virtual Research Environments as-a-Service. gCube complements the offerings of the aggregated infrastructures by implementing a comprehensive set of value-added services supporting the entire data management lifecycle in accordance with collaborative, user friendly, and Open Science compliant practices. gCube supports the D4Science.org infrastructure that hosts more than 50 VREs, supports more than 2500 scientists in 44 countries; integrates more than 50 data providers, executes more than 25,000 models & processes per month, provide access to over a billion quality records, 20,000 temporal datasets, 50,000 spatial datasets.

II. GCUBE SYSTEM OVERVIEW

In order to offer VREs as-a-Service, the gCube system has

been designed according to a number of guiding principles described below.

Component orientation: gCube is primarily organised in a number of physically distributed and networked services. These services offer functionality that can be combined together. In addition, it consists of (a) auxiliary components (software libraries) supporting services development, service-to-service integration, and service capabilities extension, and (b) components dedicated to realise the user interface (portlets).

Autonomic behaviour: Some components are dedicated

to manage the operation of a gCube-based infrastructure and its constituents, e.g., automatic (un-)deployment, relocation, replication. These components realise a middleware providing the resulting infrastructure with an autonomic behaviour that reduces its deployment and operation costs.

Openness: gCube supplies a set of generic frameworks

supporting data collection, storage, linking, transformation, curation, annotation, indexing and discovery, publishing and sharing. These frameworks are oriented to capture the needs of diverse application domains through their rich adaptation and customisation capabilities [ 9 ].

System of Systems: gCube includes components realis

ing a rich array of mediator services for interfacing with existing “systems” and their enabling technologies including middlewares for distributed computing (e.g., EMI [ 10 ]), cloud (e.g., Globus [ 11 ], OCCI [ 12 ]) and data repositories (e.g., OAI-PMH [ 13 ], SDMX [ 14 ]). Via these mediator services, the storage facilities, processing facilities and data resources of external infrastructures are conceptually unified to become gCube resources.

Policy-driven resources sharing: gCube manages a resource space where (a) resources include gCube-based services as well as third party ones, software libraries, portlets and data repositories, (b) resources exploitation and visibility is controlled by policies realising a number of overlay sets on the same resource space. This approach is key to have a flexible and dynamic mechanism for VRE creation, since they are realised as aggregations of resources.

As a Service: gCube offering is exposed by the “as a

Service” delivery model [ 3 ]. The advantage is that the actual management is in the hand of expert operators who manage the infrastructure (i) to provide reliable services, (ii) by leveraging economies of scale, and (iii) by using elastic approaches to scale. Via gCube nodes (servers enriched with microservices) the system offers storage and computing capacities as well as service instances management (dynamic (un)deployment, accounting, monitoring, alerting). Via gCube APIs the system gives a flexible and powerful platform to which developers can outsource data management tasks. Via gCube services the system offers a number of ready to use applications.

These guiding principles allow providing VREs as-a-Service, i.e., authorised users can aggregate – by using a wizard – existing resources (including data) to form innovative working environments and make them available via a plain web browser or even via a thin client.

gCube key components resulting from the above principles are organised in four main areas (cf. Fig. 1). These areas are described in the next sections.

gCube

Core Services

Collaborative Services Data Processing

Services

Data Space Management

Services

Resources Providers (Clouds, Services, Information Systems)

gCube core services offer basic facilities for resources management, security, and VRE management described below.

Resources Management: Facilities for the seamless management (discovery, deployment, monitoring, accounting) of resources (proprietary or third party ones) encompassing hosting nodes, services, software and datasets are offered. Included are (a) an Information System acting as a registry of the entire infrastructure and giving global and partial views of the resources and their operational state through query answering or notifications; (b) a Resource Management Service realising resource allocation and deployment strategies (e.g., dynamically assigning selected resources to a given context such as a VRE, assigning and activating both gCube software and external software on hosting nodes). (c) a Hosting Node, a software component that once installed on a (virtual) machine transforms it into a server managed by the infrastructure and makes it capable to host running instances of services and manage their lifecycle. This type of node can be configured to host a worker service thus making the machine capable to execute computing tasks (cf. Sec. II-C).

Security Framework: Facilities for authentication and authorisation are supported. They are based on standard protocols and technologies (e.g., SAML 2.0) providing: (a) an open and extensible AA architecture; (b) interoperability with external infrastructures and domains obtaining Identity Federation (e.g., OpenID). For authorisation, gCube implements a token based authorization system with an attribute-based access control paradigm. For authentication, users are requested to sign-in with their account (including third-party accounts like Google or LinkedIn). Once logged in, the user is provided with a user token that it transparently used to perform calls on behalf of the user. Whenever a user action implies a call to a service requiring authorization, gCube security framework (a) automatically collects the credentials to use from the credentials wallet, i.e., a service where the credentials to access each service are stored in encrypted form by using VREspecific symmetric keys, (b) decrypts them with the specific VRE key, and (c) performs the authorized call by passing the credentials and the user token. In this way the connection to the service is established in a secure way while the token is used to verify that the specific user is authorized to call the with associated properties. It is compliant with Java Conservice. tent Repository API and implemented by relying on Apache

VRE Management: Facilities for the specification Jackrabbit for the object structure and node properties, while (wizard-based) and automatic deployment of complete VREs the node content is outsourced to other services. Every data in terms of the data and services they should offer are sup- to be managed in a VRE has a manifestation in terms of ported [ 15 ]. These facilities are implemented by dynamically Information Objects. The unification of the entire data space acquiring and aggregating the needed resources, including makes it possible to realise services across the boundaries of user interface constituents, from the resource space. This is specific data typologies. Among these unifying services there a very straightforward activity consisting of: (i) a design is an innovative search engine [ 20 ] that makes it possible to phase where authorised users are provided with a wizard-based seamlessly discover objects in the data space. approach to specify the data and the services characterising Tabular data: The Tabular Data Manager offers a comthe envisaged environment by selecting among the available prehensive and flexible working environment for accessing, ones; (ii) a deployment phase where authorised users are curating, analysing and publishing tabular data. It enables a provided with a wizard-based approach to approve a VRE user to ingest data – from a file or a web location – that specification and monitor the automatic deployment of the are represented in formats including CSV, JSON and SDMX. real components needed to satisfy the specification; and (iii) In the case of “free” formats, namely CSV, the system offers an operation phase where authorised users are provided with facilities to transform data in a well-defined table format where facilities for managing the users of the VRE and altering the the types of the columns are basic data types including tempoVRE specification if needed. Details on this approach have ral and spatial dimensions as well as references to controlled been presented in previous works [ 15 ], [ 16 ] while a screenshot vocabularies, e.g., Code List. Tables formats can be defined of the wizard supporting the VRE specification is in Figure 2. in an interactive way as well as by relying on templates. Besides constraints on table column types, templates can B. Data Space Management Services contain additional validation rules as well as specification of data operations to be performed when an error occurs. Once

Data occupies a key role in science and scientific workflows, a tabular data resource is created, it can be manipulated and thus VREs are called to support their effective management. analysed by benefitting of well known tabular data operations, However, data to be managed are very heterogeneous (for- e.g., adding columns, filtering, grouping, as well as advanced mats, typologies, semantics), disaggregated and dispersed in data analytics tasks (cf. Sec. II-C). To guarantee a proper and multiple sites (including researchers drawers), possibly falling real time management of the data lineage, the service relies under the big data umbrella. In the context of a VRE, it on an underlying cluster of RDBMs where any operation on is likely that compound information units are produced by a tabular dataset leads to a new referable version. using constituents across the various solutions. To cope with Spatial data: gCube owns services realising the facilities this variety, gCube offers an array of solutions ranging from of a Spatial Data Infrastructure (SDI) by relying on statethose aiming at abstracting from the heterogeneity of data of-the-art technologies and standards [ 21 ]. It offers standard(file-oriented and information objects) to those focusing on based services for data discovery (a catalogue), storage and specific data typologies having different levels of semantic access (a federation of repositories), and visualisation (a map embodiment (tabular, spatial, biodiversity data). Independently container). The catalogue service enables the discovery of of the data typologies, all these solutions are characterised geospatial data residing in dedicated repositories by relying by (a) support for aggregation of data residing in existing on GeoNetwork and its indexing facilities. For data storage repositories; (b) scalability strategies enabling to dynamically and access, gCube offers a federation of repositories based add more capacity; (c) comprehensive metadata to capture key on GeoServer and THREDDS technologies. In essence, the aspects like context, attribution, usage licenses, lineage [ 17 ]; infrastructure hosts a number of repositories and a GIS Puband (d) policy-driven configurability to adapt the data space to lisher Service that enables a seamless publication of geospatial specific needs, e.g., by selecting the repositories and datasets. data while guaranteeing load balancing, failure management In particular, the following facilities are supported. and automatic metadata generation. It relies on an open set

File-oriented data: The Storage Manager is a Java based of back-end technologies for the actual storage and retrieval software library supporting a unique set of methods for ser- of the data. Because of this, the GIS Publisher Service is vices and applications to manage files efficiently. It relies on a designed with a plug-in-oriented approach where each plug-in network of distributed storage nodes managed by specialized interfaces with a given back-end technology. To enlarge the open-source software for document-oriented databases. In its array of supported technologies it is sufficient to develop a current implementation, three possible document store sys- dedicated plug-in. Metadata on available data are published by tems can be seamlessly used [ 18 ], MongoDB, Terrastore and the catalogue. For data visualisation, the infrastructure offers U.STORE [ 19 ], while new ones can be added by implementing Geo Explorer and GIS Viewer, two components dedicated to a specific mediator. support the browsing and visualisation of geospatial data. In

Information Objects: The Home Library is a Java based particular, the Geo Explorer is a web application that allows software library enabling objects consisting of a tree of nodes users to navigate, organize, search and discover layers from the catalogue via the CSW protocol. The GIS Viewer is a web application that allows users to interactively explore, manipulate and analyse geospatial data.

Biodiversity data: The Species Data Discovery and Ac

cess Service (SDDA) [ 22 ] provides users with facilities for the management of nomenclature data and species occurrences. As data are stored in authoritative yet heterogeneous information systems, SDDA is mainly conceived to dynamically “aggregate” data from them and unify their management. It is designed with a plug-in based architecture. Each plug-in interacts with an information system or database by relying on a standard protocol, e.g., TAPIR, or by interfacing with its proprietary protocol. Plug-ins conform queries and results from the query language and data model envisaged by SDDA to the features of a particular database. SDDA promotes a unifying discovery and access mechanism based on the names of the target species, whether the scientific or the common ones. To overcome the potential issues related to taxonomy heterogeneities across diverse data sources, the service supports an automatic query expansion mechanism, i.e., upon request the query is augmented with “similar” species names. Also, queries can be augmented with criteria aiming at explicitly selecting the databases to search in and the spatial and temporal coverage of the data. Discovered data are presented in a homogenised form, e.g., in a typical Darwin Core format.

C. Data Processing Services

In addition to the facilities for managing datasets, VREs offer services for their processing. It is almost impossible to figure out “all” the processing tasks needed by scientists, thus the solution is to have environments where scientists can easily plug and execute their tasks. gCube offers two typologies of engines: one oriented to enact tasks executions at system level, another oriented to enact task execution at user level. Both of them are conceived to rely on a distributed computing infrastructure to execute tasks. A description of these two engines is given below.

System oriented workflow engine: The Process Execution

Engine (PEng) is a system orchestrating flows of invocations (processes). It builds on principles of data flow processing appropriately expanded in the direction of interoperability [ 23 ]. According to this, PEng includes the plan (flow of execution), the operators (executable logic), the transport and control abstraction, the containers (areas of execution), the state holders (e.g., storage), the resource profiles (definitions of resources characteristics for exploitation in a plan). It allows to distribute jobs on several machines. Each job defines an atomic execution of a more complex process. Relations and hierarchies among the jobs are defined by means of Direct Acyclic Graphs (DAG). The DAGs are statically defined according to the Job Description Language (JDL) specifications [ 24 ].

Data analytics engine: The Statistical Manager (SM)

is conceived to provide end users with an environment to execute computational analysis of datasets through both service provided algorithms and user defined algorithms [ 25 ]. At VRE creation time, the SM is configured with respect to the algorithms to be offered in that context. The SM currently supplies more than 100 ready to use algorithm implementations which include real valued features clustering, functions and climate scenarios simulations, niche modelling, model performance evaluation, time series analysis, and analysis of marine species and geo-referenced data. New algorithms can be easily integrated, in fact the SM comes with a development framework dedicated to this. A scientist willing to integrate a new algorithm should develop it by implementing some basic Java interface defining algorithm’s inputs and outputs. In the case of non-Java software, e.g., R scripts, the framework provides functionalities to invoke it as external software. Integrated algorithms can be shared with coworkers by simply making them publicly available. The SM is designed to operate as a federation of SM instances, all sharing the same capabilities in terms of algorithms. Depending on the characteristics of the algorithm and the data, each SM instance executes the algorithm locally or outsources part of it to the underlying infrastructure including gCube workers (cf. Sec. II-A). A queue-based messaging system dispatches information about the computation, which includes (i) the location of the software containing the algorithm to be downloaded on the nodes and then executed, (ii) the subdivision of the input data space, which establishes the portion of the input to assign to each node, (iii) the location of the data to be processed, (iv) the algorithm parameters. Workers are data and software agnostic, which means that when ready to perform a task, they consume information from the queue and execute the software in a sandbox passing the experimental parameters as input. SM instances and workers share a data space for input and output consisting of a RDBMS, the Storage Manager, and the Home Library (cf. Sec. II-B).

D. Collaborative Services

III. RELATED WORK

Virtual Research Environments, Science Gateways, Virtual Laboratories and other similar terms [ 6 ] are used to indicate web-based systems emerged to provide researchers with integrated and user friendly access to data, computing and services of interest for a given investigation that are usually spread across many and diverse data and computing infrastructures. Moreover, they are conceived to enact and promote collaboration among their members for the sake of the investigation. There are many frameworks that can be used to build such systems. Shahand et al. [ 8 ] have recently identified eleven frameworks explicitly exploited to develop Science Gateway including Apache Airavata, Catania SG Gateway, Globus, HUBzero(+Pegasus), ICAT Job Portal, and WS-PGRADE/gUSE. Such frameworks are quite diverse, e.g., Apache Airavata offers its facilities via an API while the Catania SG Gateway offers its facilities via a GUI and a RESTful API. However, they share certain characteristics that make them operate at a lower level of abstraction with respect to the one of gCube. For data management, these frameworks mainly focus on files while gCube tries to capture an extensive domain offering specific services (cf. Sec. II-B).

Moreover, such specific services are conceived to make it easy to collect data from / interface with existing data providers thus to make their content available to VRE members. For data processing, the frameworks analysed by Shahand et al. focus on executing jobs while the gCube Data analytics engine (cf.

Sec. II-C) complements this key yet basic facility with mechanisms enabling scientists to easily plug their methods into an environment transparently relying on distributed computing solutions. Moreover, every single algorithm once successfully integrated is automatically exposed with a RESTful API (OGC Web Processing Service) thus making it possible to invoke it by workflows. Finally, the mechanism gCube offers for the creation of a VRE is unique (cf. Sec. II-A). In essence, authorised users can simply create a new VRE via a wizard driving them to produce a characterisation of the needed environment in terms of existing resources. The software (including GUI constituents) and the data needed to satisfy the VRE specification are automatically deployed, no sysadmin intervention is needed.

VREs are easy to use (e.g., requested skills do not exceed the average scientist’s ones), have limited adoption costs (e.g., no software to be installed), look like an integrated whole (e.g., the boundaries of the constituents are not perceived), and have an added value with respect to the single constituent’s capabilities (e.g., simplify data exchange).

gCube offers its VREs via thin clients, e.g., a plain web browser. All the facilities so far described are made consumable via specific components, i.e., portlets, that are webbased user interface constituents conceived to be aggregated, configured and made available by a portal at VREs creation.

To complete its offering and provide its users with added value services, gCube equips its VREs with a social networking area. IV. CONCLUSIONS

Social Networking: gCube offers facilities promoting in- gCube as a whole is a unique system since it covers novative practices that are compliant with Open Science [ 26 ]. the entire spectrum of facilities needed to deliver scientific Among the services there is a Home Social resembling a social applications as-a-Service. These gCube enabled applications network timeline where VRE users as well as applications are actually an integrated web-based environment resulting can post messages, information objects, processing results from the aggregation of an open set of constituents. Its data and files. Such posts can be discussed and favourited (or space management and data processing facilities are built by blamed) by VRE members. Every member is also provided cutting-edge technologies yet complementing them thus to with a Workspace, i.e., a folder-based virtual “file system” comply with the challenges arising in scientific domains. allowing complex information objects, including files, datasets, The experiences made while exploiting gCube to operate workflows, and maps. Objects residing in the workspace can the D4Science.org infrastructure somehow demonstrate that pre-exist the VRE or be created during the VRE lifetime, all the principles governing the VREs delivery and the system of them are managed in a simple way (e.g., drag & drop), can openness are key in the modern science settings [ 27 ]. The supbe downloaded as well as shared in few clicks. ported Virtual Research Environments serve diverse domains ranging from biodiversity [ 28 ] to environmental sciences [ 29 ], humanities research [ 30 ], and geosciences. The currently supported VREs are available via dedicated portals, some of these VREs are openly available for exploitation and test.2.

ACKNOWLEDGMENT

The authors would like to thank the colleagues of the

team contributing to the implementation of gCube. The work reported has been partially supported by the iMarine project (FP7 of the European Commission, FP7INFRASTRUCTURES-2011-2, Grant agreement No. 283644), the BlueBRIDGE project (European Union’s Horizon 2020 research and innovation programme, Grant agreement No. 675680), and the ENVRIplus project (European Union’s Horizon 2020 research and innovation programme, Grant agreement No. 654182).

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