Cloud computing provides a great opportunity for scientists, as it enables large-scale experiments that cannot are too long to run on local desktop machines. Cloud-based computations can be highly parallel, long running and data-intensive, which is desirable for many kinds of scientific experiments. However, to unlock this power, we need a user-friendly interface and an easy-to-use methodology for conducting these experiments. For this reason, we introduce here a formal model of a cloud-based platform and the corresponding open-source implementation. The proposed solution allows to conduct experiments without having a deep technical understanding of cloud-computing, HPC, fault tolerance, or data management in order to leverage the benefits of cloud computing. In the current version, we have focused on biophysics and structural chemistry experiments, based on the analysis of big data from synchrotrons and atomic force microscopy. The domain experts noted the time savings for computing and data management, as well as user-friendly interface.
While developing the model, we focused on its understandability and readability aspects. There are several approaches on model readability, cf. @cite_27 @cite_18 @cite_21 @cite_9 @cite_30 . The development of formal models and architectures for system involved in cloud computing is a more recent area of system engineering, cf. @cite_26 , @cite_20 , @cite_32 , @cite_22 , @cite_0 , @cite_1 . Several approaches have proposed the data stream processing systems for clouds, e.g., @cite_8 introduces an approach towards component-based stream processing in clouds, @cite_10 presents a work on data stream sharing. Yusuf and Schmidt have shown that the fault-tolerance is best achieved by reflecting the computational flow in such complex scientific system architectures, cf. @cite_28 .
@cite_30 @cite_18 @cite_26 @cite_22 @cite_8 @cite_28 @cite_9 @cite_21 @cite_1 @cite_32 @cite_0 @cite_27 @cite_10 @cite_20
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The ability to capture different levels of abstraction in a system model is especially important for remote integration, testing verification, and manufacturing of cyber-physical systems (CPSs). However, the complexity of modelling and testing of CPSs makes these processes extremely prone to human error. In this paper we present our ongoing work on introducing human-centred considerations into modelling and testing of CPSs, which allow for agile iterative refinement processes of different levels of abstraction when errors are discovered or missing information is completed. Despite that formal and informal quality aspects are of significantimportance to business process modeling, there is only little empiricalwork reported on process model quality and its impact factors. Inthis paper we investigate understandability as a proxy for quality of processmodels and focus on its relations with personal and model characteristics.We used a questionnaire in classes at three European universitiesand generated several novel hypotheses from an exploratory data analysis.Furthermore, we interviewed practitioners to validate our findings.The results reveal that participants tend to exaggerate the differences inmodel understandability, that self-assessment of modeling competenceappears to be invalid, and that the number of arcs in models has animportant influence on understandability. Cloud computing has recently emerged as a new paradigm for hosting and delivering services over the Internet. Cloud computing is attractive to business owners as it eliminates the requirement for users to plan ahead for provisioning, and allows enterprises to start from the small and increase resources only when there is a rise in service demand. However, despite the fact that cloud computing offers huge opportunities to the IT industry, the development of cloud computing technology is currently at its infancy, with many issues still to be addressed. In this paper, we present a survey of cloud computing, highlighting its key concepts, architectural principles, state-of-the-art implementation as well as research challenges. The aim of this paper is to provide a better understanding of the design challenges of cloud computing and identify important research directions in this increasingly important area. In earlier work, we reported on modeling of stream processing in terms of distributed components (as exemplified in the EU CoreGrid project ProActive), showing how a stream processing system can be built from components in composition, with dynamic reconfiguration and distributed management of the streams. In this paper, we introduce the Web Service Stream Deployer (WSSD), for the remote establishment and deployment of streams across widely distributed resources, allowing a user to set-up, control and reconfigure a stream remotely and dynamically. We demonstrate this concept with streams on a cloud testbed, using Nimbus cloud infrastructure at the University of Chicago. In particular, we show that our web-services based WSSD exhibits minimal adverse latency effect when used over an intercontinental network to manipulate, from our client in Australia, a cloud-based stream in the USA; we believe that this represents a useful mode of remote interaction with cloud-based applications. We outline some ideas in scheduling of cloud-based streams; we present a model of interaction with Gridbus, a widely used framework for exploring different scheduling algorithms (such as economic scheduling), whereby scheduling criteria expressed in Gridbus can be used to automatically deploy stream components. Cloud computing presents a unique opportunity for science and engineering with benefits compared to traditional high-performance computing, especially for smaller compute jobs and entry-level users to parallel computing. However, doubts remain for production high-performance computing in the cloud, the so-called science cloud, as predictable performance, reliability and therefore costs remain elusive for many applications. This paper uses parameterised architectural patterns to assist with fault tolerance and cost predictions for science clouds, in which a single job typically holds many virtual machines for a long time, communication can involve massive data movements, and buffered streams allow parallel processing to proceed while data transfers are still incomplete. We utilise predictive models, simulation and actual runs to estimate run times with acceptable accuracy for two of the most common architectural patterns for data-intensive scientific computing: MapReduce and Combinational Logic. Run times are fundamental to understand fee-for-service costs of clouds. These are typically charged by the hour and the number of compute nodes or cores used. We evaluate our models using realistic cloud experiments from collaborative physics research projects and show that proactive and reactive fault tolerance is manageable, predictable and composable, in principle, especially at the architectural level. Modularity is a widely advocated strategy for handling complexity in conceptual models. Nevertheless, a systematic literature review revealed that it is not yet entirely clear under which circumstances modularity is most beneficial. Quite the contrary, empirical findings are contradictory, some authors even show that modularity can lead to decreased model understandability. In this work, we draw on insights from cognitive psychology to develop a framework for assessing the impact of hierarchy on model understandability. In particular, we identify abstraction and the split-attention effect as two opposing forces that presumably mediate the influence of modularity. Based on our framework, we describe an approach to estimate the impact of modularization on understandability and discuss implications for experiments investigating the impact of modularization on conceptual models. Research into grid computing has been driven by the need to solve large-scale, increasingly complex problems for scientific applications. Yet the applications of grid computing for business and casual users did not begin to emerge until the development of the concept of cloud computing, fueled by advances in virtualization techniques, coupled with the increased availability of ever-greater Internet bandwidth. The appeal of this new paradigm is mainly based on its simplicity, and the affordable price for seamless access to both computational and storage resources. This timely text reference introduces the fundamental principles and techniques underlying grids, clouds and virtualization technologies, as well as reviewing the latest research and expected future developments in the field. Readers are guided through the key topics by internationally recognized experts, enabling them to develop their understanding of an area likely to play an ever more significant role in coming years. Topics and features: presents contributions from an international selection of experts in the field; provides a thorough introduction and overview of existing technologies in grids, clouds and virtualization, including a brief history of the field; examines the basic requirements for performance isolation of virtual machines on multi-core servers, analyzing a selection of system virtualization technologies; examines both business and scientific applications of grids and clouds, including their use in the life sciences and for high-performance computing; explores cloud building technologies, architectures for enhancing grid infrastructures with cloud computing, and cloud performance; discusses energy aware grids and clouds, workflows on grids and clouds, and cloud and grid programming models. This useful text will enable interested readers to familiarize themselves with the key topics of grids, clouds and virtualization, and to contribute to new advances in the field. Researchers, undergraduate and graduate students, system designers and programmers, and IT policy makers will all benefit from the material covered. Even though the technology faces several significant challenges, many vendors and industry observers predict a bright future for cloud computing. In Software Engineering many text-based languages and supporting tools are used, forming the grammarware technical space. Currently model driven engineering is the new emerging paradigm for software engineering, which uses modelling languages and tools, forming the modelware technical space. Transition to the new technical space and interoperability between these two technical spaces is needed in many development scenarios. Building a bridge between these two technical spaces is a tedious task, that has to be repeated for each language to be transformed. Therefore, we propose a generic bridge between grammarware and modelware technical spaces, that can generate a specific bridge based on the EBNF of a given language semi-automatically. The generation comprises of two steps, (1) automatic generation of metamodel corresponding to the EBNF and (2) annotations to provide the additional semantics not captured by the EBNF. The generated bridge is capable of bi-directional transformations between sentences (programs) and corresponding models and can be used in re-engineering applications and for integration of text-based and model-based tools. Recent research efforts in the fields of data stream processing and data stream management systems (DSMSs) show the increasing importance of processing data streams, e. g., in the e-science domain. Together with the advent of peer-to-peer (P2P) networks and grid computing, this leads to the necessity of developing new techniques for distributing and processing continuous queries over data streams in such networks. In this paper, we present a novel approach for optimizing the integration, distribution, and execution of newly registered continuous queries over data streams in grid-based P2P networks. We introduce Windowed XQuery (WXQuery), our XQuery-based subscription language for continuous queries over XML data streams supporting window-based operators. Concentrating on filtering and window-based aggregation, we present our stream sharing algorithms as well as experimental evaluation results from the astrophysics application domain to assess our approach. Summary form only given. Grids and peer-to-peer (P2P) networks have emerged as popular platforms for the next generation parallel and distributed computing. In these environments, resources are geographically distributed, managed and owned by various organizations with different policies, and interconnected by wide-area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, and dynamic resource conditions. In these dynamic distributed computing environments, it is hard and challenging to carry out resource management design studies in a repeatable and controlled manner as resources and users are autonomous and distributed across multiple organizations with their own policies. Therefore, simulations have emerged as the most feasible technique for analyzing policies for resource allocation. This paper presents emerging trends in distributed computing and their promises for revolutionizing the computing field, and identifies distinct characteristics and challenges in building them. We motivate opportunities for modeling and simulation communities and present our discrete-event grid simulation toolkit, called GridSim, used by researchers world-wide for investigating the design of utility-oriented computing systems such as data centers and grids. We present various case studies on the use of GridSim in modeling and simulation of business grids, parallel applications scheduling, workflow scheduling, and service pricing and revenue management.