Sébastien Nicolas

A Bio-Inspired Resource-Saving Approach to Dynamic Client-Server Association

This novel method for client-server association draws inspiration from the nest-building behavior of social insects. It focuses on reducing active, and thus resource-consuming, servers.

Modeling quality of service vs. Peak Reduction trade-offs in A/C-based Demand-Side Management

A large number of A/C units potentially represents a substantial flexible load susceptible to be leveraged for Demand-Side Management (DSM) purposes. However, in such a scenario, a drop in quality of service (QoS), i.e. an uncomfortable increase in temperature, is to be expected as some cooling-down is delayed to achieve peak reduction. We present the results of a simulation study designed to test a decentralised control framework based on probabilistic activation of individual A/C units and reactive pricing [1]. Performance is evaluated and discussed, primarily in terms of the trade-off between QoS and DSM efficiency.

A self-organizing client / server allocation algorithm for applications with non-linear cost functions

We subject a previously presented client-server allocation algorithm to a revised performance criterion to account for non-linear cost functions. We identify the conditions under which the original approach is not guaranteed to outperform the benchmark approach and propose a revised cost prediction method to improve performance for those scenarios. We present empirical results to show the measured benefit of the revised approach over the benchmark as well as the original work.

Energy-Aware Communities Using Mobile Clients

This article distinguishes two aims: (1) to showcase an approach to identify clusters of users of mobile devices on the basis of shared common attributes, and (2) to investigate a social network-based awareness campaign which shows significant promise to reduce the individuals as well as the groups energy consumption. One possible way of progressively driving down the power consumption of individuals is to improve real-time and fine-grained awareness of their energy signature (or carbon footprint) relative to their peers. In this paper, we explore the possibility to support such an awareness program using gossiping in a network of mobile clients. We identify and address key challenges, namely how to propagate required information and, critically, how to filter out meaningless and / or counter-productive comparisons by detecting (through machine-learning techniques) boundaries between user communities. Results of a preliminary simulation study designed for evaluating performance in terms of our frameworks ability to identify suitable comparison groups and to subsequently drive down the number of false positives / negatives are presented.

Simulating Stochastic Activation Functions

The article discusses the benefits of stochastic load activation in the context of demand-side management for energy-aware building management systems. We describe our model and report on results from simulations investigating the trade-off between quality of service and peak reduction as well as the ability to prioritise activation in times of resource shortage.