Fog computing brings the cloud closer to a user with the help of a micro data center ($mdc$), leading to lower response times for delay sensitive applications.RT-SANE(Real-TimeSecurityAware scheduling on theNetworkEdge) supports batch and interactive applications, taking account of their deadline and security constraints. RT-SANE chooses between an$mdc$(in proximity to a user) and a cloud data center ($cdc$) by taking account of network delay and security tags. Jobs submitted by a user are tagged as: private, semi-private and public, and$mdcs$and$cdcs$are classified as: trusted, semi-trusted and untrusted. RT-SANE executes private jobs on a user’s local$mdcs$or pre-trusted$cdcs$, and semi-private and public jobs on remote$mdcs$and$cdcs$. A security and performance-aware distributed orchestration architecture and protocol is made use of in RT-SANE. For evaluation, workload traces from the CERIT-SC Cloud system are used. The effect of slow executing straggler jobs on the Fog framework are also considered, involving migration of such jobs. Experiments reveal thatRT-SANEoffers a higher “success ratio” (successfully completed jobs) to comparable algorithms, including consideration of security tags.

Recently, a promising trend has evolved from previous centralized computation to decentralized edge computing in the proximity of end-users to provide cloud applications. To ensure the Quality of Service (QoS) of such applications and Quality of Experience (QoE) for the end-users, it is necessary to employ a comprehensive monitoring approach. Requirement analysis is a key software engineering task in the whole lifecycle of applications; however, the requirements for monitoring systems within edge computing scenarios are not yet fully established. The goal of the present survey study is therefore threefold: to identify the main challenges in the field of monitoring edge computing applications that are as yet not fully solved; to present a new taxonomy of monitoring requirements for adaptive applications orchestrated upon edge computing frameworks; and to discuss and compare the use of widely-used cloud monitoring technologies to assure the performance of these applications. Our analysis shows that none of existing widely-used cloud monitoring tools yet provides an integrated monitoring solution within edge computing frameworks. Moreover, some monitoring requirements have not been thoroughly met by any of them.

Time critical applications have very high requirements on network and computing services, in particular on well-tuned software architecture with sophisticated optimisation on data communication. Their development is often customised to dedicated infrastructure, and system performance is difficult to maintain when infrastructure changes. This fatal weakness in existing architecture and software tools causes very high development costs, and makes it difficult to fully utilise the virtualised, programmable and quality-on-demand services provided by networked Clouds to improve the system productivity. The Software Workbench for Interactive, Time Critical and Highly self-adaptive Cloud applications (SWITCH) is a newly funded project by EU H2020 to address this urgent industrial need, it aims at improving the existing development and execution model of time critical applications by introducing a novel conceptual model called application-infrastructure co-programming and control model, in which application QoS/QoE together with the programmability and controllability of Cloud environments can be all included in the complete lifecycle of applications.

Summary EUBrazilOpenBio is a collaborative initiative addressing strategic barriers in biodiversity research by integrating open access data and user‐friendly tools widely available in Brazil and Europe. The project deploys the EU‐Brazil Hybrid Data Infrastructure that allows the sharing of hardware, software and data on‐demand. This infrastructure provides access to several integrated services and resources to seamlessly aggregate taxonomic, biodiversity and climate data, used by processing services implementing checklist cross‐mapping and ecological niche modelling. A Virtual Research Environment was created to provide users with a single entry point to processing and data resources. This article describes the architecture, demonstration use cases and some experimental results and validation. Copyright © 2014 John Wiley & Sons, Ltd.

OGSA-DAI is an open-source middleware designed to facilitate the access and integration of distributed heterogeneous data resources. It provides a ready-made framework that promotes locality and product transparency to connect data resources to the grid environment. This paper reports on the evaluation of OGSA-DAI for bioinformatics grid applications by the BioDA project. We based our study on dialogue with OGSA-DAI and grid project developers as well as through practical case studies involving current projects. We also reveal the challenges and strategies in developing an OGSA-DAI exemplar for one of these projects, BiodiversityWorld - a grid-based problem solving environment that specializes in the exploration and analysis of patterns in global biodiversity. Finally, we summarize the key issues for projects considering adopting OGSA-DAI.

In the Biodiversity World (BDW) project we have created a flexible and extensible Web services-based grid environment for biodiversity researchers to solve problems in biodiversity and analyse biodiversity patterns. In this environment, heterogeneous and globally distributed biodiversity-related resources such as data sets and analytical tools are made available to be accessed and assembled by users into workflows to perform complex scientific experiments. One such experiment is bioclimatic modelling of the geographical distribution of individual species using climate variables in order to predict past and future climate-related changes in species distribution. Data sources and analytical tools required for such analysis of species distribution are widely dispersed, available on heterogeneous platforms, present data in different formats and lack interoperability. The BDW system brings all these disparate units together so that the user can combine tools with little thought as to their availability, data formats and interoperability. We describe the architecture of the BDW problem solving environment (PSE) consisting of a number of components providing uniform access to heterogeneous resources and analytical tools. Architectural components of the BDW system include a workflow management tool, resource wrappers, a communications layer, BDW datatypes and a metadata repository.

In the GRAB (Grid and Biodiversity) project we are developing a prototype to illustrate some aspects of the GRID's potential for collaborative biodiversity research. A catalogue of life, two species information systems (SISs) and a climate database are made available in a problem solving environment that demonstrates how bioclimatic modelling can be performed by bringing together such resources. We use Globus to provide access to these resources, in a secure environment. We also discuss more generally the GRID's potential for biodiversity research, and identify the main areas of development within the GRID that are needed to support such research.

We demonstrate a prototype toolkit that uses constraints and constraint violation repair techniques to enable the automated detection and, where possible, the automated resolution of conflicts in taxonomic databases.

The LITCHI project is concerned with the integration and maintenance of databases of biological knowledge organised by species. We use constraints pertaining to good taxonomic practice in order to identify taxonomix conflicts in individual species databases and in databases formed by merging species databases from distinct sources. The LITCHI system can be used to resolve such conflicts incrementally. As the project has progressed, we have identified a number of distinctive features of the problem domain, and needs of the intended users, which have had a significant impact on the techniques and modes of operation that we found to be appropriate, especially in contrast with applications that handle rapidly-accumulating 'raw' data. It is upon these aspects of LITCHI that we concentrate in the present paper viewing LITCHI as an example of the more general problem of merging scientific data sets in which conflicts between the terminology used can occur.

Over recent years, international initiatives such as the 1993 UN Convention on Biological Diversity have highlighted the need for information about species diversity on a global scale. However, attempts to build global information systems by integrating smaller, independently created biodiversity databases have been hampered by differences in the sets of species names used. Some databases use different names to refer to the same species, while in other cases the same name can be applied to differing definitions of a species, or even entirely different species. The LITCHI project aims to assist biologists in the integration of databases by searching for conflicts within taxonomic checklists (i.e. lists of the species names used in a database and the relationships between them). In order to detect such conflicts, we have created a formal model of taxonomic practice, which describes (amongst other things) what it means for a checklist to be consistent and well-specified. This model has been used as the basis for a prototype tool that uses Prolog to search for naming conflicts within a relational database of checklists. We describe the background to our formal model and show how it has been used to implement the LITCHI system. Our prototype tool is already proving its worth by detecting conflicts and errors within real taxonomic checklists.

Summary form only given. The LITCHI project (Logic-based Integration of Taxonomic Conflicts in Heterogeneous Information Systems) aims to develop software to enable the automated detection and, where possible, resolution of conflicts in taxonomic checklists. A taxonomic checklist is a list of the names of species (and other taxa) used within a particular biological database. Since species names are typically used to gain access to data within biological databases, checklists provide a concise representation of the data values that can act as keys when querying such databases. More importantly, species names are also typically used as the join attribute when integrating several biological databases. However, naming of species is a subjective activity, and different scientific communities will have different ideas about the names that should be used for particular species. These conflicts of opinion arise as a result of the subjective nature of the classification process and geographical or historical differences in background knowledge. Some communities may use different names for the same species, while other groups of scientists may use the same name to refer to different species. Often, there is no one right naming scheme, but some consistent set of names must be used if biological databases are to be integrated. Therefore, there is a real need for a tool which will assist biologists in the integration of checklists, prior to the integration of species databases, so that these differences of opinion can be resolved.