Spatio-Temporal Point Pattern Analysis Using Genetic Algorithms

The effectiveness of emergency service systems is measured in terms of their ability to deploy units and personnel in a timely, and efficient manner upon an event’s occurrence. A typical methodology to deal with such a task is through the application of an appropriate location - allocation model. In such a case, however, the spatial distribution of demand although stochastic in nature and layout, when aggregated to a specific spatial reference unit, appears to be spatially structured or semi – structured. Aiming to exploit the above incentive, the spatial tracing and analysis of emergency incidents is achieved through the utilisation of Artificial Intelligence. More specifically, in the proposed approach, each location problem is dealt with at two interacting levels. Firstly, spatio-temporal point pattern of demand is analysed over time by a new genetic algorithm. The proposed genetic algorithm interrelates sequential events formulating moving objects and as a result, every demand point pattern is correlated both to previous and following events. Secondly, the approach provides the ability to predict, by means of an artificial neural network, how the pattern of demand will evolve and thus the location of supplying centres and/or vehicles can be optimally defined. The proposed neural network is also optimised through genetic algorithms. The approach is applied to Athens Metropolitan Area and the data come from Fire Department’s records for the years 2003-2004.

End-to-end elasticity control of cloud-network slices

The design of efficient elasticity control mechanisms for dynamic resource allocation is crucial to increase the efficiency of future cloud-network slice-defined systems. Current elasticity control mechanisms proposed for cloud- or network-slicing, only consider cloud- or network-type resources respectively. In this paper, we introduce the elaSticity in cLOud-neTwork Slices (SLOTS) which aims to extend the horizontal elasticity control to multi-providers scenarios in an end-to-end fashion, as well as to provide a novel vertical elasticity mechanism to deal with critical insufficiency of resources by harvesting underused resources on other slices. Finally, we present a preliminary assessment of the SLOTS prototype in a real testbed, revealing outcomes that suggest the viability of the proposal.Peer ReviewedPostprint (published version

Stochastic modelling of the effects of interdependencies between critical infrastructure

An approach to Quantitative Interdependency Analysis, in the context of Large Complex Critical Infrastructures, is presented in this paper. A Discrete state–space, Continuous–time, Stochastic Process models the operation of critical infrastructure, taking interdependencies into account. Of primary interest are the implications of both model detail (that is, level of model abstraction) and model parameterisation for the study of dependencies. Both of these factors are observed to affect the distribution of cascade–sizes within and across infrastructure

Boltzmann meets Nash: Energy-efficient routing in optical networks under uncertainty

Motivated by the massive deployment of power-hungry data centers for service provisioning, we examine the problem of routing in optical networks with the aim of minimizing traffic-driven power consumption. To tackle this issue, routing must take into account energy efficiency as well as capacity considerations; moreover, in rapidly-varying network environments, this must be accomplished in a real-time, distributed manner that remains robust in the presence of random disturbances and noise. In view of this, we derive a pricing scheme whose Nash equilibria coincide with the network's socially optimum states, and we propose a distributed learning method based on the Boltzmann distribution of statistical mechanics. Using tools from stochastic calculus, we show that the resulting Boltzmann routing scheme exhibits remarkable convergence properties under uncertainty: specifically, the long-term average of the network's power consumption converges within $\varepsilon$ of its minimum value in time which is at most $\tilde O(1/\varepsilon^2)$, irrespective of the fluctuations' magnitude; additionally, if the network admits a strict, non-mixing optimum state, the algorithm converges to it - again, no matter the noise level. Our analysis is supplemented by extensive numerical simulations which show that Boltzmann routing can lead to a significant decrease in power consumption over basic, shortest-path routing schemes in realistic network conditions.Comment: 24 pages, 4 figure