Resource management in UAV-assisted MEC: state-of-the-art and open challenges

Wireless Networks - Unmanned aerial vehicles (UAV) have been widely used in various fields because of their high mobility and portability. At the same time, due to the rapid development of...     

Cloud computing for small research groups in computational science and engineering: current status and outlook

Cloud computing could offer good business models for small computational science and engineering (CSE) research groups because these groups often do not have enough human resources and knowledge to manage the complexity of computational and data infrastructure for their research, while cloud computing aims to eliminate that complexity from the user. In this paper, we have analyzed current status of supporting tools for small CSE groups to utilize cloud computing. Although cloud computing is perceived as an interesting model, we have identified several issues that prevent a wide adoption of cloud computing from small CSE research groups. We also present recommendations for addressing these issues in order to attract small CSE groups to utilize cloud computing.     

Group Decision Making on Urban Planning Using Desktop Multimedia Conferencing

Recent developments on multimedia systems and networking technology show that using desktop multimedia conferencing for group decision making on wide area networks such as the Internet is possible. In this paper we review the design, hardware and software requirements and organizational issues in a desktop multimedia conferencing system. We draw on our experiences from multiple multimedia conferences on the Internet and in particular we focus on a case study on urban planning using desktop multimedia conferencing on the Internet. Further we discuss implications for further research on desktop multimedia conferencing.     

Weighted fuzzy clustering for capability-driven service aggregation

Workflow design, mashup configuration, and composite service formation are examples where the capabilities of multiple simple services combined achieve a complex functionality. In this paper, we address the problem of limiting the number of required services that fulfill the required capabilities while exploiting the functional specialization of individual services. Our approach strikes a balance between finding one service that matches all required capabilities and having one service for each required capability. Specifically, we introduce a weighted fuzzy clustering algorithm that detects implicit service capability groups. The clustering algorithm considers capability importance and service fitness to support those capabilities. Evaluation based on a real-world data set successfully demonstrates the effectiveness of and applicability for service aggregation.     

Dynamic Instrumentation, Performance Monitoring and Analysis of Grid Scientific Workflows

While existing work concentrates on developing QoS models of business workflows and Web services, few tools have been developed to support the monitoring and performance analysis of scientific workflows in Grids. This paper describes novel Grid services for dynamic instrumentation of Grid-based applications, performance monitoring and analysis of Grid scientific workflows. We describe a Grid dynamic instrumentation service that provides a widely accessible interface for other services and users to conduct the dynamic instrumentation of Grid applications during the runtime. We introduce a Grid performance analysis service for Grid scientific workflows. The analysis service utilizes various types of data including workflow graphs, monitoring data of resources, execution status of activities, and performance measurements obtained from the dynamic instrumentation of invoked applications, and provides a rich set of functionalities and features to support the online monitoring and performance analysis of scientific workflows. Workflows and their relevant information including performance metrics are stored and utilized for comparing the performance of constructs of different workflows and for supporting multi-workflow analysis. The work described in this paper is supported in part by the Austrian Science Fund as part of the Aurora Project under contract SFBF1104 and by the European Union through the IST-2002-511385 project K-WfGrid.     

Modeling and mining of dynamic trust in complex service-oriented systems

The global scale and distribution of companies have changed the economy and dynamics of businesses. Web-based collaborations and cross-organizational processes typically require dynamic and context-based interactions between people and services. However, finding the right partner to work on joint tasks or to solve emerging problems in such scenarios is challenging due to scale and temporary nature of collaborations. Furthermore, actor competencies evolve over time, thus requiring dynamic approaches for their management. Web services and SOA are the ideal technical framework to automate interactions spanning people and services. To support such complex interaction scenarios, we discuss mixed service-oriented systems that are composed of both humans and software services, interacting to perform certain activities. As an example, consider a professional online support community consisting of interactions between human participants and software-based services. We argue that trust between members is essential for successful collaborations. Unlike a security perspective, we focus on the notion of social trust in collaborative networks. We show an interpretative rule-based approach to enable humans and services to establish trust based on interactions and experiences, considering their context and subjective perceptions.     

Caramba—A Process-Aware Collaboration System Supporting Ad hoc and Collaborative Processes in Virtual Teams

Organizations increasingly define many business processes as projects executed by virtual (project) teams, where team members from within an organization cooperate with outside experts. Virtual teams require and enable people to collaborate across geographical distance and professional (organizational) boundaries and have a somewhat stable team configuration with roles and responsibilities assigned to team members. Different people, coming from different organizations will have their own preferences and experiences and cannot be expected to undergo a long learning cycle before participating in team activities. Thus, efficient communication, coordination, and process-aware collaboration remain a fundamental challenge. In this paper we discuss the current shortcomings of approaches in the light of virtual teamwork (mainly Workflow, Groupware, and Project Management) based on models and underlying metaphors. Furthermore, we present a novel approach for virtual teamwork by tightly integrating all associations between processes, artifacts, and resources. In this paper we analyze (a) the relevant criteria for process-aware collaboration system metaphors, (b) coordination models and constructs for organizational structures of virtual teams as well as for ad hoc and collaborative processes composed out of tasks, and (c) architectural considerations as well as design and implementation issues for an integrated process-aware collaboration system for virtual teams on the Internet.     

Are our homes ready for services? A domotic infrastructure based on the Web service stack

The increase in computational power and the networking abilities of home appliances are revolutionizing the way we interact with our homes. This trend is growing stronger and opening a number of technological challenges. From the point of view of distributed systems, there is a need to design architectures for enhancing the comfort and safety of the home, which deals with issues of heterogeneity, scalability and openness. By considering the evolution of domotic research and projects, we advocate a role for Web services in the domestic network. We ground our claim by proposing a concrete architecture for a home in which the health of an elder is monitored. The architecture is implemented on a heterogeneous set of devices, which allows us to evaluate it and draw conclusions on the feasibility of using service-oriented approaches in ubiquitous computing.     

IoTSim-Edge: A simulation framework for modeling the behavior of Internet of Things and edge computing environments

With the proliferation of Internet of Things (IoT) and edge computing paradigms, billions of IoT devices are being networked to support data-driven and real-time decision making across numerous application domains, including smart homes, smart transport, and smart buildings. These ubiquitously distributed IoT devices send the raw data to their respective edge device (eg, IoT gateways) or the cloud directly. The wide spectrum of possible application use cases make the design and networking of IoT and edge computing layers a very tedious process due to the: (i) complexity and heterogeneity of end-point networks (eg, Wi-Fi, 4G, and Bluetooth); (ii) heterogeneity of edge and IoT hardware resources and software stack; (iv) mobility of IoT devices; and (iii) the complex interplay between the IoT and edge layers. Unlike cloud computing, where researchers and developers seeking to test capacity planning, resource selection, network configuration, computation placement, and security management strategies had access to public cloud infrastructure (eg, Amazon and Azure), establishing an IoT and edge computing testbed that offers a high degree of verisimilitude is not only complex, costly, and resource-intensive but also time-intensive. Moreover, testing in real IoT and edge computing environments is not feasible due to the high cost and diverse domain knowledge required in order to reason about their diversity, scalability, and usability. To support performance testing and validation of IoT and edge computing configurations and algorithms at scale, simulation frameworks should be developed. Hence, this article proposes a novel simulator IoTSim-Edge, which captures the behavior of heterogeneous IoT and edge computing infrastructure and allows users to test their infrastructure and framework in an easy and configurable manner. IoTSim-Edge extends the capability of CloudSim to incorporate the different features of edge and IoT devices. The effectiveness of IoTSim-Edge is described using three test cases. Results show the varying capability of IoTSim-Edge in terms of application composition, battery-oriented modeling, heterogeneous protocols modeling, and mobility modeling along with the resources provisioning for IoT applications.     

Auction-based crowdsourcing supporting skill management

Crowdsourcing is a promising approach for enterprises to maintain a flexible workforce that is able to solve parts of business processes formerly processed in-house. Companies perceive crowdsourcing as a concept that allows receiving solutions quickly and at little cost. Similar to cloud computing where computing power is provided on demand, the crowd promises a flexible on-demand workforce. However, businesses realize that these benefits entail a lack of quality control. The main difference compared to traditional approaches in business process execution is that tasks or activities cannot be directly assigned to employees but are posted to the crowdsourcing platform. Its members can choose deliberately which tasks to book and work on. In fact, crowdsourcing is heavily affected by the loose-coupling of workers to crowdsourcers and the dynamics of the environment. Hence, it remains a major challenge to guarantee high-quality processing of tasks within the prescribed time limit. A further obstacle for adoption of crowdsourcing in enterprises is the fact that it is hard to specify a fair monetary reward in advance. The concepts introduced in this work allow to smoothly integrate new workers, to keep them motivated, and to help them develop and improve skills needed in the system. We present a crowdsourcing marketplace that matches complex tasks, requiring multiple skills, to suitable workers. The key to ensuring high quality lies in skilled members whose capabilities can be estimated correctly. To that end, we present auction mechanisms that help to correctly estimate workers and to evolve skills that are needed in the system. Crowdsourcers do not need to predefine exact prices but only maximum prices they are willing to pay since the actual rewards for tasks are formed by supply and demand. Extensive experiments show that our approach leads to improved crowdsourcing, in most cases.