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Deep learning (DL) algorithms have already proved their effectiveness on a wide variety of application domains, including speech recognition, natural language processing, and image classification. To foster their pervasive adoption in applications where low latency, privacy issues and data bandwidth are paramount, the current trend is to perform inference tasks at the edge. This requires deployment of DL algorithms on low-energy and resource-constrained computing nodes, often heterogenous and parallel, that are usually more complex to program and to manage without adequate support and experience. In this paper, we present ALOHA, an integrated tool flow that tries to facilitate the design of DL applications and their porting on embedded heterogenous architectures. The proposed tool flow aims at automating different design steps and reducing development costs. ALOHA considers hardware-related variables and security, power efficiency, and adaptivity aspects during the whole development process, from pre-training hyperparameter optimization and algorithm configuration to deployment.

In process algebras such as ACP (Algebra of Communicating Processes), parallel processes are considered to be interleaved in an arbitrary way. In the case of multi-threading as found in contemporary programming languages, parallel processes are actually interleaved according to some interleaving strategy. An interleaving strategy is what is called a process-scheduling policy in the field of operating systems. In many systems, for instance hardware/software systems, we have to do with both parallel processes that may best be considered to be interleaved in an arbitrary way and parallel processes that may best be considered to be interleaved according to some interleaving strategy. Therefore, we extend ACP in this paper with the latter form of interleaving. The established properties of the extension concerned include an elimination property, a conservative extension property, and a unique expansion property., Comment: 19 pages, this version is a revision of the published version

We address the problem of detecting highly raised crowd density in situations such as indoor dance events.We propose a new method for estimating crowd density by anonymous, non-participatory, indoor Wi-Fi localization of smart phones. Using a probabilistic model inspired by statistical mechanics, and relying only on big data analytics, we tackle three challenges: (1) the ambiguity of Wi-Fi based indoor positioning, which appears regardless of whether the latter is performed with machine learning or with optimization, (2) the MAC address randomization when a device is not connected, and (3) the volatility of packet interarrival times. The main result is that our estimation becomes more -- rather than less -- accurate when the crowd size increases. This property is crucial for detection of dangerous crowd density.

In the embedded computer system domain, MPSoC systems have become increasingly popular due to the ever-increasing performance demands of modern embedded applications. The number of processing elements in these MPSoCs also steadily increases. Whereas current MPSoCs still contain a limited number of processing elements, future MPSoCs will feature tens up to hundreds of (heterogeneous) processing elements that are all integrated on a single chip. On these future large-scale MPSoC systems, the mapping of applications onto the hardware resources plays an important role to fully explore the parallelism of applications. In this article, a hierarchical run-time adaptive resource allocation framework which uses an intelligent task remapping approach is proposed to improve the system performance for large-scale MPSoCs.

In this paper we present a methodology for gaining a better understanding of the contribution behavior, interests and expertise of communities of Wikipedia users. Starting from a list of core articles and their main editors, we identify which other articles (outside of the initial list) they contributed to ‘significantly’. The ordering is based on (empirical) Bayesian estimates of the contribution probabilities for each of the articles. By constructing a co-contribution network, we can identify the general themes the community expresses exceptional interest (or disinterest) in. In order to show what type of insights one might gain from employing the proposed method, we use the editors that contributed to the articles on designer drugs as a case study. We find that the users in this community contribute significantly to articles on pharmaceuticals, popular party drugs, chemistry, mental illnesses, diseases, medicine and cell biology.

The abundance and heterogeneity of IT resources available, together with the ability to dynamically scale applications poses significant usability issues to users. Without understanding the performance profile of available resources users are unable to efficiently scale their applications in order to meet performance objectives. High quality media collaborations, like CineGrid, are one example of such diverse environments where users can leverage dynamic infrastructures to move and process large amounts of data. This paper describes our user-centric approach to executing high quality media processing workloads over dynamic infrastructures. Our main contribution is the CGtoolkit ?environment, an integrated system which aids users cope with the infrastructure complexity and large data sets specific to the digital cinema domain. We describe the general challenges user face in heterogeneous it infrastructure environments and identify their instance in the case of CineGrid.We characterize the most frequent workloads in the CineGrid collaboration including their infrastructure resource requirements.We present the design and implementation of a system which simplifies workload resource definition by leveraging semantic web description languages for infrastructure.We provide Pareto-optimal resource configurations for workload execution.

Increases in graph size and analytics complexity have brought graph processing at the forefront of HPC. However, the HPC shift towards manycore accelerators (e.g., GPUs) is not favourable: traditional graph processing is hardly suitable for regular parallelism. Previous work has focused on parallel algorithms for specific graph operations, often using assumptions about the structure of the input graph. However, there has been very little systematic investigation of how strongly a graph’s structure impacts the efficiency of graph operations.With this article we make propose a step to quantify this impact, focusing on a typical operation: neighbour iteration. We design and implement four strategies for neighbour iteration and introduce a simple model to reason about the expected impact of a graph’s structure on the performance of each strategy. We then use the PageRank algorithm to validate our model. We show that performance is significantly affected by the ability to effectively load-balance the work performed by these strategies across the GPU’s cores.

This is a report on ICTIR'17, which continued its tradition of being the premier forum for presentation of research on theoretical aspects of Information Retrieval (IR). This includes theory in a broad sense, including conceptual papers that explore key concepts, theoretical papers that model concepts and/or relations between concepts, and papers that study theory in experimental or industrial settings. The importance of information access and information processing in industry and academia is growing in a revolutionary way, making the discussion on fundamental and long term aspects, and their relation to short term success and failure, more urgent than ever before. To highlight the increasingly strong connections between Information Retrieval and its neighboring disciplines, ICTIR'17 explicitly welcomed papers in IR areas that overlap with Human Information Access, Machine Learning, Natural Language Processing and Perception. ICTIR'17 in Amsterdam was a memorable conference. For those who like numbers: we had 142 attendees (biggest ICTIR ever), and 97 submissions with 52 accepted (largest ICTIR ever), 450/250 euro registration for everything (cheapest ICTIR ever), and for the first time in history the organization and keynotes of a leading IR conference where majority female (but selected for being outstanding researchers, of course).

The multiclass queueing network (McQN) arises as a natural multiclass extension of the traditional (single-class) Jackson network. In a single-class network, subcriticality (i.e., subunitary nominal workload at every station) entails stability, but this is no longer sufficient when jobs/customers of different classes (i.e., with different service requirements and/or routing scheme) visit the same server; therefore, analytical conditions for stability of McQNs are lacking, in general. In this note, we design a numerical (simulation-based) method for determining the stability region of a McQN, in terms of arrival rate(s). Our method exploits certain (stochastic) monotonicity properties enjoyed by the associated Markovian queue-configuration process. Stochastic monotonicity is a quite common feature of queueing models and can be easily established in the single-class framework (Jackson networks); recently, also for a wide class of McQNs, including first-come-first-serve networks, monotonicity properties have been established. Here, we provide a minimal set of conditions, under which the method performs correctly. Eventually, we illustrate the use of our numerical method by presenting a set of numerical experiments, covering both single- and multiclass networks.

Part 12: Service Specification and Composition; International audience; The upward integration endeavor is making informatics systems (I-systems) increasingly complex. The modeling techniques, methodologies, development strategies, deployment and execution environment, maintenance and evolution, and governance, to mention just a few aspects are making the resulted (un)integrated informatics technology system a vendor lock-in landscape. The relation between informatics science and engineering and the organization’s business or control processes automation, or services provisioning and adaptation, has demonstrated to be difficult to converge to a common understanding of clear computational responsibility borders. Existing approaches and standards fail to be complete with respect to establishing a landscape of informatics technology under vendor agnostic model (lock-in free). In this context, this paper extends previous research by proposing an organization´s level modularity framework aiming at formally identifying an agnostic, and open informatics system of systems (ISoS). A definition of its components is provided, and a validation case study is discussed.

While it is well-known and acknowledged that the performance of graph algorithms is heavily dependent on the input data, there has been surprisingly little research to quantify and predict the impact the graph structure has on performance. Parallel graph algorithms, running on many-core systems such as GPUs, are no exception: most research has focused on how to efficiently implement and tune different graph operations on a specific GPU. However, the performance impact of the input graph has only been taken into account indirectly as a result of the graphs used to benchmark the system. In this work, we present a case study investigating how to use the properties of the input graph to improve the performance of the breadth-first search (BFS) graph traversal. To do so, we first study the performance variation of 15 different BFS implementations across 248 graphs. Using this performance data, we show that significant speed-up can be achieved by combining the best implementation for each level of the traversal. To make use of this data-dependent optimization, we must correctly predict the relative performance of algorithms per graph level, and enable dynamic switching to the optimal algorithm for each level at runtime. We use the collected performance data to train a binary decision tree, to enable high-accuracy predictions and fast switching. We demonstrate empirically that our decision tree is both fast enough to allow dynamic switching between implementations, without noticeable overhead, and accurate enough in its prediction to enable significant BFS speedup. We conclude that our model-driven approach (1) enables BFS to outperform state of the art GPU algorithms, and (2) can be adapted for other BFS variants, other algorithms, or more specific datasets.

Secure Autonomous Response NETworks (SARNET) is a framework for auto­mated response against attacks on computer network infrastructures. The framework addresses several cyber-security problems at three crucial levels: strategic, tactical and operational.

Supporting exploratory search tasks with the help of structured data is an effective way to go beyond keyword search, as it provides an overview of the data, enables users to zoom in on their intent, and provides assistance during their navigation trails. However, finding a good starting point for a search episode in the given structure can still pose a considerable challenge, as users tend to be unfamiliar with exact, complex hierarchical structure. Thus, providing lookahead clues can be of great help and allow users to make better decisions on their search trajectory.In this paper, we investigate the behaviour of users when a recommendation engine is employed along with the browsing tool in an exploratory search system. We make use of an exploratory search system that facilitates browsing by mapping the data on a hierarchical structure. We designed and developed a path recommendation engine as a feature for this system, which given a text query, ranks different browsing paths in the hierarchy based on their likelihood of covering relevant documents. We conduct a user study comparing the baseline system with the featured system.Our main findings are as follows: We observe that, using the baseline system the users tend to explore the data in a breadth-first-like approach by visiting different data points at the same level of abstraction to choose one of them to expand and go deeper. Conversely, with browsing path recommendation (BPR) as a feature, the users tend to drive their search in a more depth-first-like approach by quickly going deep into the data hierarchy. While the users still incline to explore different parts of the search space by using BPR, they are able to restrain or augment their search focus more quickly and access smaller but more promising regions of the data. Therefore, they can complete their tasks with less time and effort

High performance graph processing poses significant challenges for both algorithm and platform designers due to the large performance variability it exhibits: performance depends on the algorithm, the dataset, and the (hardware/software) platform. Traditionally, performance variability is tackled by extensive benchmarking, modeling, and, eventually, better or smarter algorithms. Our own research into the impact of datasets on the performance of graph algorithms has convinced us that such extensive benchmarking is very difficult for graph processing simply because we lack input data: the public datasets and graph generation tools currently available are insufficient for a systematic investigation of the impact of different graph properties. In this work we propose to alleviate this problem by using evolutionary computing as a method to generate graphs. Our goal is allow users to request graphs with a given set of properties (e.g., number of vertices, number of edges, degree distribution), and enable the generator to evolve graphs until they satisfy the request. Such a fine-grain, flexible exploration of graphs and their properties will finally enable a statistical take on performance analysis and modeling for graph processing. To this end, our work-in-progress paper presents the design of our generator, discusses the challenges and trade-offs to be encountered, and reveals our preliminary results.

This work addresses the problem of choosing an appropriate plan for achieving a goal in any realistic complex situation where an agent has to respond and act upon uncertain and/or an unknown information. We use the belief-desire-intention (BDI) model, a popular model for developing agents. The flexibility of choosing among different plans to achieve a desired goal is one of the benefits of this model. This paper describes a particular algorithm for selecting the most appropriate plan. Since the agent may have to reason with incomplete or uncertain information, we explore how to integrate probabilities in the agent model for taking an appropriate action and keeping the system behavior within acceptable boundaries and compliance to acceptable norms. Considering the uncertainty of the current state of the environment, this process relies on probability and utility theory. The plan selection algorithm has been implemented with Jadex

Software abstractions are crucial to effectively program heterogeneous Multi-Processor Systems on Chip (MPSoCs). Prime examples of such abstractions are Kahn Process Networks (KPNs) and execution traces. When modeling computation as a KPN, one of the key challenges is to obtain a good mapping, i.e., an assignment of logical computation and communication to physical resources. In this paper we compare two system-level frameworks for solving the mapping problem: Sesame and MAPS. These frameworks, while superficially similar, embody different approaches. Sesame, motivated by modeling and design-space exploration, uses evolutionary algorithms for mapping. MAPS, being a compiler framework, uses simple and fast heuristics instead. In this work we highlight the value of common abstractions, such as KPNs and traces, as a vehicle to enable comparisons between large independent frameworks. These types of comparisons are fundamental for advancing research in the area. At the same time, we illustrate how the lack of formalized models at the hardware level are an obstacle to achieving fair comparisons. Additionally, using a set of applications from the embedded systems domain, we observe that genetic algorithms tend to outperform heuristics by a factor between 1× and 5×, with notable exceptions. This performance comes at the cost of a longer computation time, between 0 and 2 orders of magnitude in our experiments.

This paper presents results of the ongoing development of the Cloud Services Delivery Infrastructure (CSDI) that provides a basis for infrastructure centric cloud services provisioning, operation and management in multi-cloud multi-provider environment defined as a Zero Touch Provisioning, Operation and Management (ZTP/ZTPOM) model. The presented work refers to use cases from data intensive research that require high performance computation resources and large storage volumes that are typically distributed between datacenters often involving multiple cloud providers. Automation for large scale scientific (and industrial) applications should include provisioning of both inter-cloud network infrastructure and intra-cloud application resources. It should provide support for the complete application operation workflow together with the possible application infrastructure and resources changes that can occur during the application lifecycle. The authors investigate existing technologies for automation of the service provisioning and management processes aiming to cross-pollinate best practices from currently disconnected domains such as cloud based applications provisioning and multi-domain high-performance network provisioning. The paper refers to the previous and legacy research by authors, the Open Cloud eXchange (OCX), that has been proposed to address the last mile problem in cloud services delivery to campuses over trans-national backbone networks such as GEANT. OCX will serve as an integral component of the prospective ZTP infrastructure over the GEANT network. Another important component, the Marketplace, is defined for providing cloud services and applications discovery (in generally intercloud environment) and may also support additional services such as services composition and trust brokering for establishing customer-provider federations.

Data centers are a cost-effective infrastructure for hosting Cloud and Grid applications, but they do incur tremendous energy cost and CO2 emissions. Today's data center network architectures such as Fat-tree and BCube are over-provisioned to guarantee large network capacity and meet peak performance requirement. Networks suffer from inefficient power usage when data center traffic is not high. A solution to this problem is the adoption of network management platform such as OpenNaaS, which can be augmented with energy-aware capabilities. We developed a component for energy monitoring and routing in OpenNaaS. Energy-aware OpenNaaS can support different types of OpenFlow controller; it inherits and enhances network management capabilities, e.g. dynamically obtaining power and topology. In this paper we also discuss the evaluation and selection of energy-aware routing strategies based on an initial prototype of energy-aware OpenNaaS. The target strategies are fine-grained as they combine flow routing algorithms that make routing decisions for the flows and flow scheduling algorithms that schedule the flows on the same link. Differently from previous routing work which focuses on power-minimization problem in data center networks, we aim to optimize energy consumption. Our simulation shows that the combination of priority-based shortest routing and exclusive flow scheduling achieves about 5%-35% higher energy efficiency without performance degradation.

Text-based and semantics-based methods are both studied intensively as methods for document retrieval. In order to gain insight in the respective merits of these two approaches, we have performed a controlled experiment where we executed a real-life task using both textbased and semantics-based techniques. To maximise the lessons that we could draw about the two approaches, we have performed an experiment where we used the same task (searching papers from the scientific literature needed for updating a medical guideline), the same test-case (updating the 2004 Dutch national breast-cancer guideline), the same gold standard (the updated 2012 Dutch national breast-cancer guideline) and the same corpus (PubMed). We then performed this task using two different methods: retrieving papers based on keywords (text-based approach) and retrieving papers based on semantic annotations (semantics-based approach). Based on this experiment, we discuss the insights that we gained from this dual set of experiments.

Smart grids aim at evolving the traditional electrical grid system by making increasing use of sophisticated control and communication network technology, to properly deal with the high penetration of controllable assets, such as distributed generators and flexible loads, and their associated challenges. Since electrical grids are critical infrastructures, control strategies regulating their operation need to face both efficiency and cost aspects, as well as resilience related ones in order to assure reliable service. Keeping the focus on the medium voltage control functionality, in this paper we present a study of the event-triggered voltage control algorithm to satisfy voltage control requirements. In particular, the opportunity to introduce soft bounds to improve preserving voltage values in bounds notwithstanding the effect of control delay is explored in a variety of scenarios, including fault presence due to attacks to the communication network. Through a developed stochastic model-based framework, quantitative analyses are performed on a realistic MV testbed grid, to demonstrate the feasibility and utility of the proposed contribution.

Malleable applications are programs that may run with varying numbers of threads and thus on varying numbers of cores because the exact number of threads/cores used is irrelevant for the program logic and typically determined at program startup. We argue that any fixed choice of kernel threads is suboptimal for both performance and energy consumption. Firstly, an application may temporarily expose less concurrency than the underlying hardware offers, leading to waste of energy. Secondly, the number of hardware cores effectively available to an application may dynamically change in multi-application and/or multi-user environments. This leads to an over-subscription of the available hardware by individual applications, costly time scheduling by the operating system and, as a consequence, to both waste of energy and loss of performance. We propose an active resource management service that continuously mediates betwen dynamically changing intra-application requirements as well as on dynamically changing system load characteristics in a near-optimal way.

New generations of many-core hardware become available frequently and are typically attractive extensions for data-centers because of power-consumption and performance benefits. As a result, supercomputers and clusters are becoming heterogeneous and start to contain a variety of many-core devices. Obtaining performance from a homogeneous cluster-computer is already challenging, but achieving it from a heterogeneous cluster is even more demanding. Related work primarily focuses on homogeneous many-core clusters. In this paper we present Cashmere, a programming system for heterogeneous many-core clusters. Cashmere is a tight integration of two existing systems: Satin is a programming system that provides a divide-and-conquer programming model with automatic load-balancing and latency-hiding, while Many-Core Levels is a programming system that provides a powerful methodology to optimize computational kernels for varying types of many-core hardware. We evaluate our system with several classes of applications and show that Cashmere achieves high performance and good scalability. The efficiency of heterogeneous executions is comparable to the homogeneous runs and is >90% in three out of four applications.

In the contemporary cloud computing environment, the various cloud providers adopt different architectures for their systems and usually these are not compatible. The scalable exchange of information about these heterogeneous resources is often not addressed; still this represents an interesting challenge, since different Cloud systems and vendors have different ways to describe and invoke their services, to specify and communicate requirements. Hence a way to provide a common access to Cloud services and to discover and use required services in Cloud federations is highly desirable. The IEEE P2302 Intercloud approach is to define a scalable architecture and to adopt the OMN (Open-Multinet) ontology for supporting its operation. Our work specifically describes the inner workings of the Intercloud system in terms of the Intercloud Federation Application Programmers Interface (API), which are in turn based on the semantic definition of resources, Service Level Agreements (SLAs), and Bearer Network Provisioning Metadata.

Both within the Business and e-Science world, the use of virtualized resources is growing rapidly. These resources are increasingly delivered by multiple converged infrastructures, e.g. clouds that combine server, storage, and network resources from different providers. Such development requires careful re-thinking of the trust framework used between providers. As the scale and complexity of virtualization grows, so does the complexity of authorizing resource chains that are arranged across multiple providers. This type of authorization requires pre-establishment of trust relationships between providers and arranging some level of power. This paper studies the roles of trust and power when considering the requirements of authorization protocol exchanges between providers. Establishing power in the form of impersonal rules is a key element to conduce the necessary trust between providers. The Service Provider Group (SPG) is a way to arrange such power. The SPG framework provides a way to organize thinking about multi-provider services and can be used to describe emerging collaborations such as those found within the realm of optical network service provisioning.

CloudComputing has several provisionmodels, e.g. Infrastructure as a service (IaaS). However, cloud users (tenants) have limited or no control over the underlying network resources. Network as a Service (NaaS) is emerging as a novelmodel to fill this gap.However,NaaS requires an approach capable of modeling the underlying network resources capabilities in abstracted and vendor-independent form. In this paper we elaborate on SemNaaS, a Semantic Web based approach for developing and supporting operations of NaaS systems. SemNaaS can workwith any NaaS provider. We integrated it with the existing OpenNaaS framework. We propose Network Markup Language (NML) as the ontology for describing networking infrastructures.Based on that ontology, we develop a network modeling system and integrate with OpenNaaS. Furthermore, we demonstrate the capabilities that Semantic Web can add to the NaaS paradigm by applying SemNaaS operations to a specific NaaS use case.

The Internet remains an unfinished work. There are several approaches to enhancing it that have been experimentally validated within federated testbed environments. To best gain scientific knowledge from these studies, reproducibility and automation are needed in all areas of the experiment life cycle. Within the GENI and FIRE context, several architectures and protocols have been developed for this purpose. However, a major open research issue remains, namely the description and discovery of the heterogeneous resources involved. To remedy this, we propose a semantic information model that can be used to allow declarative interoperability, build dependency graphs, validate requests, infer knowledge and conduct complex queries. The requirements for such an information model have been extracted from current international Future Internet research projects and the practicality of the model is being evaluated through initial implementations. The main outcome of this work is the definition of the Open-Multinet Upper Ontology and related sub-ontologies, which can be used to describe and manage federated infrastructures and their resources.

Applying suitable application models is essential to achieve efficient execution of the applications, effective development process and assure application portability and reusability. We observe that execution environments and the cloud environment in particular, lack of tools that would make it more available from a programmer perspective. Complexity and variety of libraries and authentication methods do not correspond with simplistic functionality of those services. We approach this issue at several fields - workflow systems, domain specific languages and cloud computing. Our work focus on two points, optimizing existing application models by supplementing them with additional tools and providing new features, and develop new models that are based on our research on applications in the cloud environment.

Many-core hardware is targeted specifically at obtaining high performance, but reaching high performance is often challenging because hardware-specific details have to be taken into account. Although there are many programming systems that try to alleviate many-core programming, some providing a high-level language, others providing a low-level language for control, none of these systems have a clear and systematic methodology as a foundation. In this article, we propose stepwise-refinement for performance: a novel, clear, and structured methodology for obtaining high performance on many-cores. We present a system that supports this methodology, offers multiple levels of abstraction to provide programmers a trade-off between high-level and low-level programming, and provides programmers detailed performance feedback. We evaluate our methodology with several widely varying compute kernels on two different many-core architectures: a Graphical Processing Unit (GPU) and the Xeon Phi. We show that our methodology gives insight in the performance, and that in almost all cases, we gain a substantial performance improvement using our methodology.

Estimation of the execution time is an important part of the workflow scheduling problem. The aim of this paper is to highlight common problems in estimating the workflow execution time and propose a solution that takes into account the complexity and the randomness of the workflow components and their runtime. The solution proposed in this paper addresses the problems at different levels from task to workflow, including the error measurement and the theory behind the estimation algorithm. The proposed estimation algorithm can be integrated easily into a wide class of schedulers as a separate module. We use a dual stochastic representation, characteristic / distribution functions, in order to combine tasks' estimates into the overall workflow makespan. Additionally, we propose the workflow reductions - the operations on a workflow graph that do not decrease the accuracy of the estimates, but simplify the graph structure, hence increasing the performance of the algorithm.

Storing data in the cloud is becoming a common trend, for both end-customers and data center operators. We propose a method for deciding where to host data storage tasks under the constraint of minimal greenhouse gas emission. The decision on whether to store data locally or store it remotely at a cleaner data center relies on the models for the local and remote data centers and the network connecting them. We conclude that the transport network that connects a local node and a 'cleaner' remote data center plays a significant role in the decision of where to store data, and that the frequency of access of the data is an important and related factor. © 2014 IEEE.

Computational and storage tasks can nowadays be offloaded among data centers, in order to optimize costs and or performance. We set out to investigate what are the environmental effects, namely the total CO2 emission, of such offloading. We built models for the various components present in these offloading scenarios, and we incorporated these models into an online calculator available to users. Our online calculator determines the total CO2 emitted by performing computational or storage tasks at a local or remote data center, provided that the energy sources of the data centers and the network connecting them are known. The demonstration we propose will allow conference visitors to use our website/calculator to get a sense for the impact of offloading their storage and computational tasks.

This discussion paper introduces the concept of the Virtual Artery as a multiscale model for arterial physiology and pathologies at the physics–chemistry–biology (PCB) interface. The cellular level is identified as the mesoscopic level, and we argue that by coupling cell-based models with other relevant models on the macro- and microscale, a versatile model of arterial health and disease can be composed. We review the necessary ingredients, both models of arteries at many different scales, as well as generic methods to compose multiscale models. Next, we discuss how this can be combined into the virtual artery. Finally, we argue that the concept of models at the PCB interface could or perhaps should become a powerful paradigm, not only as in our case for studying physiology, but also for many other systems that have such PCB interfaces. This article is part of the themed issue ‘Multiscale modelling at the physics–chemistry–biology interface’.

BPEL4People and WS-HumanTask (WS-HT) specify models to integrate human resources into business processes. The emerging use of these standards will result in more challenging authorization decisions in the context of the execution of workflows. Compared to a classical scenario where a workflow management system controls the mapping of tasks to a specific set of resources, human interaction introduces an additional mapping scheme in which humans were separately mapped to tasks. Basically the authorization framework needs to be adapted from a push-based model to a push-/pull-based approach. This short paper introduces the concepts of a security framework for a WS-HT implementation. It presents a generic framework that supports a pull-based work distribution strategy in distributed environments with the help of a task repository that mediates tasks between resources and workflow instances.

Scientific applications are frequently modeled as a workflow that is executed under the control of a workflow management system. One crucial requirement during the execution is the validation of the generated results and the traceability of the experiment execution path. The automated tracking and storage of provenance information during workflow execution could satisfy this requirement. To collect provenance data using the Grid-enabled scientific workflow management system WS-VLAM, experimentation was made with two different implementations of the provenance concepts. The first one adopts the Open Provenance Model (OPM) as basis to represent, store, and share scientific experiments metadata using the Provenance Layer Infrastructure for e-Science Resources (PLIER). The second one is the history-tracing XML (HisT) which was developed for e-Business provenance. HisT provides a specific model to store provenance data within layered XML documents, whereby each layer is related to one individual workflow task. This paper explores these two provenance models by using an example workflow application and describes how they are integrated into WS-VLAM including implementation details of the provenance architecture. It finally gives a comparison of the two different approaches with a special regard to provenance for human actors.

For electronic payments, the communication link hoB to be reliable. Dedicated short range communication is a proposed solution for the automatic debiting of vehicles without disturbing the traffic flow. The requirements on the reliability of such a system are high, which implies that only large scale simulations with a lot of detail are effective to analyse an occasional error. In thiB article, an hierarchical approach is worked out that allows such simulations of the communication link with a 80% reduction of the computational effort compared to simulation with full detail.