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 increasing 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 implications 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.

Abstract-With increasing availability of Cloud computing services, this paper addresses the challenge consumers of Infrastructure-as-a-Service (IaaS) have in determining which IaaS provider and resources are best suited to run an application that may have specific Quality of Service (QoS) requirements. Utilising application modelling to predict performance is an attractive concept, but is very difficult with the limited information IaaS providers typically provide about the computing resources. This paper reports on an initial investigation into using Dwarf benchmarks to measure the performance of virtualised hardware, conducting experiments on BonFIRE and Amazon EC2. The results we obtain demonstrate that labels such as 'small', 'medium', 'large' or a number of ECUs are not sufficiently informative to predict application performance, as one might expect. Furthermore, knowing the CPU speed, cache size or RAM size is not necessarily sufficient either as other complex factors can lead to significant performance differences. We show that different hardware is better suited for different types of computations and, thus, the relative performance of applications varies across hardware. This is reflected well by Dwarf benchmarks and we show how different applications correlate more strongly with different Dwarfs, leading to the possibility of using Dwarf benchmark scores as parameters in application models.

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 humancentric 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 sociotechnical 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.

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Abstract. 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. The BonFIRE architecture has been designed to support key functionalities such as: resource management; monitoring of virtual and physical infrastructure metrics; elasticity; single document experiment descriptions; and scheduling. As for January 2012 BonFIRE release 2 is operational, supporting seven pilot experiments. Future releases will enhance the offering, including the interconnecting with networking facilities to provide access to routers, switches and bandwidth-on-demand systems. BonFIRE will be open for general use late 2012.