Insight Centre for Data Analytics - Book chapters

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    Globally optimised energy-efficient data centres
    (InTechOpen, 2017-03-22) Pesch, Dirk; Rea, Susan; Torrens, J. Ignacio; Zavrel, Vojtech; Hensen, J.L.M.; Grimes, Diarmuid; O'Sullivan, Barry; Scherer, Thomas; Birke, Robert; Chen, Lydia; Engbersen, Ton; Lopez, Lara; Pages, Enric; Mehta, Deepak; Townley, Jacinta; Tsachouridis, Vassilios; Seventh Framework Programme
    Data centres are part of today's critical information and communication infrastructure, and the majority of business transactions as well as much of our digital life now depend on them. At the same time, data centres are large primary energy consumers, with energy consumed by IT and server room air conditioning equipment and also by general building facilities. In many data centres, IT equipment energy and cooling energy requirements are not always coordinated, so energy consumption is not optimised. Most data centres lack an integrated energy management system that jointly optimises and controls all its energy consuming equipments in order to reduce energy consumption and increase the usage of local renewable energy sources. In this chapter, the authors discuss the challenges of coordinated energy management in data centres and present a novel scalable, integrated energy management system architecture for data centre wide optimisation. A prototype of the system has been implemented, including joint workload and thermal management algorithms. The control algorithms are evaluated in an accurate simulation‐based model of a real data centre. Results show significant energy savings potential, in some cases up to 40%, by integrating workload and thermal management.
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    Spreading of infection on temporal networks: an edge-centered perspective
    (Springer, 2019-10-30) Koher, Andreas; Gleeson, James P.; Hövel, Philipp; Deutsche Forschungsgemeinschaft; Deutscher Akademischer Austauschdienst; Science Foundation Ireland
    We discuss a continuous-time extension of the contact-based (CB) model, as proposed in [Koher et al. Phys. Rev. X 9, 031017 (2019)], for infections with permanent immunity on temporal networks. At the core of our methodology is a fundamental change to an edge-centered perspective, which allows for an accurate model on temporal networks, where the underlying time-aggregated graph has a tree structure. From the continuous-time CB model, we derive the infection propagator for the low prevalence limit and propose a novel spectral criterion to estimate the epidemic threshold. In addition, we explore the relation between the continuous-time CB model and the previously proposed edge-based compartmental model, as well as the message-passing framework.