Remotely hosted services and 'cloud computing'

Emerging technologies for learning report - Article exploring potential of cloud computing to address educational issue

Reviews

500 Computing Tips for Teachers and Lecturers by Phil Race and Steve McDowell, London: Kogan Page, 1996. ISBN: 0–7494–1931–8. 135 pages, paperback. £15.99

Measurement-device-independent quantum communication with an untrusted source

Measurement-device-independent quantum key distribution (MDI-QKD) can provide enhanced security, as compared to traditional QKD, and it constitutes an important framework for a quantum network with an untrusted network server. Still, a key assumption in MDI-QKD is that the sources are trusted. We propose here a MDI quantum network with a single untrusted source. We have derived a complete proof of the unconditional security of MDI-QKD with an untrusted source. Using simulations, we have considered various real-life imperfections in its implementation, and the simulation results show that MDI-QKD with an untrusted source provides a key generation rate that is close to the rate of initial MDI-QKD in the asymptotic setting. Our work proves the feasibility of the realization of a quantum network. The network users need only low-cost modulation devices, and they can share both an expensive detector and a complicated laser provided by an untrusted network server.Comment: 13 pages, 4 figures. arXiv admin note: the security proof technique is based on arXiv:0802.2725, arXiv:0905.4225

Pilot decision making in a computer-aided flight management situation

An experimental representation of a computer-aided multi-task flight management situation has been developed. A computer aiding program was implemented to serve as a back-up decision maker. An experiment was conducted with a balanced design of several subject runs for different workload levels. This was achieved using three levels of subsystem event arrival rates, three levels of control task involvement, and three levels of availability of computer aiding. Experimental results compared quite favorably with those from a computer simulation which employed a queueing model. It was shown that the aiding had enhanced system performance as well as subjective ratings, and that the adaptive aiding policy further reduced subsystem delay

Addressing the Challenges in Federating Edge Resources

This book chapter considers how Edge deployments can be brought to bear in a global context by federating them across multiple geographic regions to create a global Edge-based fabric that decentralizes data center computation. This is currently impractical, not only because of technical challenges, but is also shrouded by social, legal and geopolitical issues. In this chapter, we discuss two key challenges - networking and management in federating Edge deployments. Additionally, we consider resource and modeling challenges that will need to be addressed for a federated Edge.Comment: Book Chapter accepted to the Fog and Edge Computing: Principles and Paradigms; Editors Buyya, Sriram

Why (and How) Networks Should Run Themselves

The proliferation of networked devices, systems, and applications that we depend on every day makes managing networks more important than ever. The increasing security, availability, and performance demands of these applications suggest that these increasingly difficult network management problems be solved in real time, across a complex web of interacting protocols and systems. Alas, just as the importance of network management has increased, the network has grown so complex that it is seemingly unmanageable. In this new era, network management requires a fundamentally new approach. Instead of optimizations based on closed-form analysis of individual protocols, network operators need data-driven, machine-learning-based models of end-to-end and application performance based on high-level policy goals and a holistic view of the underlying components. Instead of anomaly detection algorithms that operate on offline analysis of network traces, operators need classification and detection algorithms that can make real-time, closed-loop decisions. Networks should learn to drive themselves. This paper explores this concept, discussing how we might attain this ambitious goal by more closely coupling measurement with real-time control and by relying on learning for inference and prediction about a networked application or system, as opposed to closed-form analysis of individual protocols