Vegard Engen

BonFIRE: A Multi-cloud Test Facility for Internet of Services Experimentation

BonFIRE offers a Future Internet, multi-site, cloud testbed, targeted at the Internet of Services community, that supports large scale testing of applications, services and systems over multiple, geographically distributed, heterogeneous cloud testbeds. The aim of BonFIRE is to provide an infrastructure that gives experimenters the ability to control and monitor the execution of their experiments to a degree that is not found in traditional cloud facilities.

BonFIRE: The Clouds and Services Testbed

BonFIRE is a multi-site test bed that supports testing of Cloud-based and distributed applications. BonFIRE breaks the mould of commercial Cloud offerings by providing unique functionality in terms of observability, control, advanced Cloud features and ease of use for experimentation. A number of successful use cases have been executed on BonFIRE, involving industrial and academic users and delivering impact in diverse areas, such as media, e-health, environment and manufacturing. The BonFIRE user-base is expanding through its free, Open Access scheme, daily carrying out important research, while the consortium is working to sustain the facility beyond 2014.

Understanding Human-Machine Networks: A Cross-Disciplinary Survey

In the current hyperconnected era, modern Information and Communication Technology (ICT) systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such Human-Machine Networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, or following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of socio-technical systems, actor-network theory, cyber-physical-social systems, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends.

Machine Agency in Human-Machine Networks; Impacts and Trust Implications

We live in an emerging hyper-connected era in which people are in contact and interacting with an increasing number of other people and devices. Increasingly, modern IT systems form networks of humans and machines that interact with one another. As machines take a more active role in such networks, they exert an in-creasing level of influence on other participants. We review the existing literature on agency and propose a definition of agency that is practical for describing the capabilities and impact human and machine actors may have in a human-machine network. On this basis, we discuss and demonstrate the impact and trust implica-tions for machine actors in human-machine networks for emergency decision support, healthcare and future smart homes. We maintain that machine agency not only facilitates human to machine trust, but also interpersonal trust; and that trust must develop to be able to seize the full potential of future technology.

Human-Machine Networks: Towards a Typology and Profiling Framework

In this paper we outline an initial typology and framework for the purpose of profiling human-machine networks, that is, collective structures where humans and machines interact to produce synergistic effects. Profiling a human-machine network along the dimensions of the typology is intended to facilitate access to relevant design knowledge and experience. In this way the profiling of an envisioned or existing human-machine network will both facilitate relevant design discussions and, more importantly, serve to identify the network type. We present experiences and results from two case trials: a crisis management system and a peer-to-peer reselling network. Based on the lessons learnt from the case trials we suggest potential benefits and challenges, and point out needed future work.

Predicting application performance for multi-vendor clouds using dwarf benchmarks

Future Internet applications are becoming increasingly dynamic and can be composed of a wide range of services controlled and hosted by different stakeholders. This paper addresses the challenge of resource provisioning for applications that have specific Quality of Service (QoS) requirements and where consumers of Cloud resources want to avoid lock-in to any specific Infrastructure-as-a-Service (IaaS) provider. Application modelling can be used to predict performance of applications given certain resources, workload and configuration. However, application modelling is a significant challenge for Cloud consumers due to the limited and varying information IaaS providers disclose about infrastructure resources. We demonstrate in this paper how Dwarf benchmarks can be used as a uniform and informative way of characterising compute resources, which is successful for application modelling, achieving high prediction accuracy on a range of applications.

Automation in human-machine networks: how increasing machine agency affects human agency

Efficient human-machine networks require productive interaction between human and machine actors. In this study, we address how a strengthening of machine agency, for example through increasing levels of automation, affect the human actors of the networks. Findings from case studies within air traffic management, emergency management, and crowd evacuation are presented, shedding light on how automation may strengthen the agency of human actors in the network through responsibility sharing and task allocation, and serve as a needed prerequisite of innovation and change.

The Interplay Between Human and Machine Agency

Human-machine networks affect many aspects of our lives: from sharing experiences with family and friends, knowledge creation and distance learning, and managing utility bills or providing feedback on retail items, to more specialised networks providing decision support to human operators and the delivery of health care via a network of clinicians, family, friends, and both physical and virtual social robots. Such networks rely on increasingly sophisticated machine algorithms, e.g., to recommend friends or purchases, to track our online activities in order to optimise the services available, and assessing risk to help maintain or even enhance people’s health. Users are being offered ever increasing power and reach through these networks by machines which have to support and allow users to be able to achieve goals such as maintaining contact, making better decisions, and monitoring their health. As such, this comes down to a synergy between human and machine agency in which one is dependent in complex ways on the other. With that agency questions arise about trust, risk and regulation, as well as social influence and potential for computer-mediated self-efficacy. In this paper, we explore these constructs and their relationships and present a model based on review of the literature which seeks to identify the various dependencies between them.

BonFIRE: a multi-cloud experimentation-as-a-service ecosystem

Editors: Martin Serrano, National University of Ireland Galway, Ireland Nikolaos Isaris, European Commission, Belgium Hans Schaffers, Saxion University of Applied Sciences, Netherlands John Domingue, Open University, United Kingdom Michael Boniface, IT Innovation, United Kingdom Thanasis Korakis, Polytechnic Institute of NYU, USA ISBN: 9788793519121 e-ISBN: 9788793519114 Available From: June 2017 Price: € 90.00The demand for ways to explore and understand how applications and services behave in a shared software defined infrastructures is increasing. Completely new applications are emerging, alongside “Big Data” and the convergence of services with mobile networks and the Internet of Things (IoT) all exploiting Cloud scalability and flexibility along with integration with software defined networks. These innovative technologies are creating opportunities for industry that requires a new collaborative approach to product and services that combines, commercial and funded research, early-stage and close-to-market applications, but always at the cutting edge of ideas.New media applications and services are revolutionising social interaction and user experience in both society and in wide ranging industry sectors. The rapid emergence of pervasive human and environment sensing technologies, novel immersive presentation devices and high performance, globally connected network and cloud infrastructures is generating huge opportunities for application providers, service provider and content providers. These new applications are driving convergence across devices, clouds, networks and services, and the merging of industries, technology and society. Yet the developers of such systems face many challenges in understanding how to optimise their solutions (Quality of Service – QoS) to enhance user experience (Quality of Experience – QoE) and how their disruptive innovations can be introduced into the market with appropriate business models.

Forecasting impact of technology developed in R&D projects: The FITMAN approach

A typical two-or three-year research project has an impact that is only really visible after the project has come to an end, at a time when there are no resources to monitor that impact. As a consequence, projects need to estimate/predict their future impact before they end. In this paper we describe the impact activity monitoring method in the FITMAN project. This method addresses the problem by accounting for actions to raise impact during a project and the planning for such actions after a project has ended. We also describe the socio-economic impact assessment methodology created in FITMAN, showing how this links to the impact activity monitoring method. Key to both is the assessment and monitoring of impact in three different areas: industry, society and the scientific community. Each area represents different challenges and we discuss their relative value to the overall assessment. We also report on our early experiences of applying this to ten industry-led use case trials in the FITMAN project. The insights gained by applying these methodologies can be more widely applied across domains related to technology management.