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We are in a ‘personal data gold rush’ driven by advertising being the primary revenue source for most online companies. These companies accumulate extensive personal data about individuals with minimal concern for us, the subjects of this process. This can cause many harms: privacy infringement, personal and professional embarrassment, restricted access to labour markets, restricted access to highest value pricing, and many others. There is a critical need to provide technologies that enable alternative practices, so that individuals can par- ticipate in the collection, management and consumption of their personal data. In this paper we discuss the Databox, a personal networked device (and associated services) that col- lates and mediates access to personal data, allowing us to re- cover control of our online lives. We hope the Databox is a first step to re-balancing power between us, the data subjects, and the corporations that collect and use our data.

We present unikernels, a new approach to deploying cloud services via applications written in high-level source code. Unikernels are single-purpose appliances that are compile-time specialised into standalone kernels, and sealed against modification when deployed to a cloud platform. In return they offer significant reduction in image sizes, improved efficiency and security, and should reduce operational costs. Our Mirage prototype compiles OCaml code into unikernels that run on commodity clouds and offer an order of magnitude reduction in code size without significant performance penalty. The architecture combines static type-safety with a single address-space layout that can be made immutable via a hypervisor extension. Mirage contributes a suite of type-safe protocol libraries, and our results demonstrate that the hypervisor is a platform that overcomes the hardware compatibility issues that have made past library operating systems impractical to deploy in the real-world.

We explore the idea of making aesthetic decorative patterns that contain multiple visual codes. We chart an iterative collaboration with ceramic designers and a restaurant to refine a recognition technology to work reliably on ceramics, produce a pattern book of designs, and prototype sets of tableware and a mobile app to enhance a dining experience. We document how the designers learned to work with and creatively exploit the technology, enriching their patterns with embellishments and backgrounds and developing strategies for embedding codes into complex designs. We discuss the potential and challenges of interacting with such patterns. We argue for a transition from designing ‘codes to patterns’ that reflects the skills of designers alongside the development of new technologies.

Smartphones have exploded in popularity in recent years, becoming ever more sophisticated and capable. As a result, developers worldwide are building increasingly complex applications that require ever increasing amounts of computational power and energy. In this paper we propose ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud. ThinkAir exploits the concept of smartphone virtualization in the cloud and provides method-level computation offloading. Advancing on previous work, it focuses on the elasticity and scalability of the cloud and enhances the power of mobile cloud computing by parallelizing method execution using multiple virtual machine (VM) images. We implement ThinkAir and evaluate it with a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for a N-queens puzzle application and one order of magnitude for a face detection and a virus scan application. We then show that a parallelizable application can invoke multiple VMs to execute in the cloud in a seamless and on-demand manner such as to achieve greater reduction on execution time and energy consumption. We finally use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements. © 2012 IEEE.

OS virtualization and cloud computing have radically changed the way Internet services are deployed: enterprises share third-party datacenters, deploying existing applications with minimal changes. Recent measurements reveal a lack of traffic isolation capabilities within the datacenter with network performance exhibiting high variability. We advocate addressing this problem by allowing applications to express their own forwarding logic using OpenFlow to achieve application specific optimal performance. We present an OpenFlow implementation within the Mirage application synthesis framework, in the form of library implementations of a modular controller and an extensible OpenFlow-enabled switch, able to expose the underlying network infrastructure to cloud applications. By linking into the application, this provides a safe yet highly extensible framework for programming network control that, although unoptimised, still provides reasonable performance when compared with existing controllers.

We present a generative model for representing and reasoning about the relationships among events in continuous time. We apply the model to the domain of networked and distributed computing environments where we fit the parameters of the model from timestamp observations, and then use hypothesis testing to discover dependencies between the events and changes in behavior for monitoring and diagnosis. After introducing the model, we present an EM algorithm for fitting the parameters and then present the hypothesis testing approach for both dependence discovery and change-point detection. We validate the approach for both tasks using real data from a trace of network events at Microsoft Research Cambridge. Finally, we formalize the relationship between the proposed model and the noisy-or gate for cases when time can be discretized., Comment: Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI2008)

The Homework project has examined redesign of existing home network infrastructures to better support the needs and requirements of actual home users. Integrating results from several ethnographic studies, we have designed and built a home networking platform providing detailed per-flow measurement and management capabilities supporting several novel management interfaces. This demo specifically shows these new visualization and control interfaces (1), and describes the broader benefits of taking an integrated view of the networking infrastructure, realised through our router's augmented measurement and control APIs (2). Aspects of this work have been published: the Homework Database in Internet Management (IM) 2011 [3] and implications of the ethnographic results are to appear at the SIGCOMM W-MUST workshop 2011 [2]. Separate, more detailed expositions of the interface elements and system performance and implications are currently under submission at other venues. A partial code release is already available and we anticipate fuller public beta release by Q4 2011.

Smartphones have exploded in popularity in recent years, becoming ever more sophisticated and capable. As a result, developers worldwide are building increasingly complex applications that require ever increasing amounts of computational power and energy. In this paper we propose ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud. ThinkAir exploits the concept of smartphone virtualization in the cloud and provides method level computation offloading. Advancing on previous works, it focuses on the elasticity and scalability of the server side and enhances the power of mobile cloud computing by parallelizing method execution using multiple Virtual Machine (VM) images. We evaluate the system using a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for the N-queens puzzle and one order of magnitude for a face detection and a virus scan application, using cloud offloading. We then show that if a task is parallelizable, the user can request more than one VM to execute it, and these VMs will be provided dynamically. In fact, by exploiting parallelization, we achieve a greater reduction on the execution time and energy consumption for the previous applications. Finally, we use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements., Comment: 17 pages

Research into, and design and construction of mobile systems and algorithms requires access to large-scale mobility data. Unfortunately, the research community lacks such data. For instance, the largest available human contact traces contain only 100 nodes with very sparse connectivity, limited by experimental logistics. In this paper we pose a challenge to the community: how can we collect mobility data from billions of human participants? We re-assert the importance of large-scale datasets in communication network design, and claim that this could impact fundamental studies in other academic disciplines. In effect, we argue that planet-scale mobility measurements can help to save the world. For example, through understanding large-scale human mobility, we can track and model and contain the spread of epidemics of various kinds. © 2010 ACM.

Shair is a system allowing contracted mobile phone users to benefit from their unused quota minutes and text messages. Unused minutes and texts are shared via opportunistic local connectivity, allowing other Shair users to use sharers' phones within reach of short range radio such as Bluetooth. © 2009 ACM.

Research into, and design and construction of mobile systems and algorithms requires access to large-scale mobility data. Unfortunately, the wireless and mobile research community lacks such data. For instance, the largest available human contact traces contain only 100 nodes with very sparse connectivity, limited by experimental logistics. In this paper we pose a challenge to the community: how can we collect mobility data from billions of human participants? We re-assert the importance of large-scale datasets in communication network design, and claim that this could impact fundamental studies in other academic disciplines. In effect, we argue that planet-scale mobility measurements can help to save the world. For example, through understanding large-scale human mobility, we can track and model and contain the spread of epidemics of various kinds., Comment: 6 pages, 2 figures

Many models have been proposed to generate Internet Autonomous System (AS) topologies, most of which make structural assumptions about the AS graph. In this paper we compare AS topology generation models with several observed AS topologies. In contrast to most previous works, we avoid making assumptions about which topological properties are important to characterize the AS topology. Our analysis shows that, although matching degree-based properties, the existing AS topology generation models fail to capture the complexity of the local interconnection structure between ASs. Furthermore, we use BGP data from multiple vantage points to show that additional measurement locations significantly affect local structure properties, such as clustering and node centrality. Degree-based properties, however, are not notably affected by additional measurements locations. These observations are particularly valid in the core. The shortcomings of AS topology generation models stems from an underestimation of the complexity of the connectivity in the core caused by inappropriate use of BGP data.