http://hdl.handle.net/10468/2482 2017-10-30T17:46:44Z 2017-10-30T17:46:44Z Wahbi, Mohamed Grimes, Diarmuid Mehta, Deepak Brown, Kenneth N. O'Sullivan, Barry http://hdl.handle.net/10468/4548 2017-08-25T18:01:15Z 2017-06-01T00:00:00Z

A distributed optimization method for the geographically distributed data centres problem Wahbi, Mohamed; Grimes, Diarmuid; Mehta, Deepak; Brown, Kenneth N.; O'Sullivan, Barry Salvagnin, Domenico; Lombardi, Michele The geographically distributed data centres problem (GDDC) is a naturally distributed resource allocation problem. The problem involves allocating a set of virtual machines (VM) amongst the data centres (DC) in each time period of an operating horizon. The goal is to optimize the allocation of workload across a set of DCs such that the energy cost is minimized, while respecting limitations on data centre capacities, migrations of VMs, etc. In this paper, we propose a distributed optimization method for GDDC using the distributed constraint optimization (DCOP) framework. First, we develop a new model of the GDDC as a DCOP where each DC operator is represented by an agent. Secondly, since traditional DCOP approaches are unsuited to these types of large-scale problem with multiple variables per agent and global constraints, we introduce a novel semi-asynchronous distributed algorithm for solving such DCOPs. Preliminary results illustrate the benefits of the new method.

2017-06-01T00:00:00Z Manna, Carlo http://hdl.handle.net/10468/3548 2017-02-01T12:01:03Z 2016-11-05T00:00:00Z

On-line dynamic station redeployments in bike-sharing systems Manna, Carlo Adorni, Giovanni; Cagnoni, Stefano; Gori, Marco; Maratea, Marco Bike-sharing has seen great development during recent years, both in Europe and globally. However, these systems are far from perfect. The uncertainty of the customer demand often leads to an unbalanced distribution of bicycles over the time and space (congestion and/or starvation), resulting both in a loss of customers and a poor customer experience. In order to improve those aspects, we propose a dynamic bike-sharing system, which combines the standard fixed base stations with movable stations (using trucks), which will able to be dynamically re-allocated according to the upcoming forecasted customer demand during the day in real-time. The purpose of this paper is to investigate whether using moveable stations in designing the bike-sharing system has a significant positive effect on the system performance. To that end, we contribute an on-line stochastic optimization formulation to address the redeployment of the moveable stations during the day, to better match the upcoming customer demand. Finally, we demonstrate the utility of our approach with numerical experiments using data provided by bike-sharing companies.

2016-11-05T00:00:00Z Wallace, Richard J. http://hdl.handle.net/10468/4023 2017-05-25T18:00:50Z 2016-11-01T00:00:00Z

Preprocessing versus search processing for constraint satisfaction problems Wallace, Richard J. A perennial problem in hybrid backtrack CSP search is how much local consistency processing should be done to achieve the best efficiency. This can be divided into two separate questions: (1) how much work should be done before the actual search begins, i.e. during preprocessing? and (2) how much of the same processing should be interleaved with search? At present there are two leading approaches to establishing stronger consistencies than the basic arc consistency maintenance that is done in most solvers. On the one hand there are various kinds singleton arc consistency that can be used; on the other there are several variants of restricted path consistency. To date these have not been compared directly. The present work attempts to do this for a variety of problems, and in so doing, it also provides an empirical evaluation of the preprocessing versus search processing issue. Comparisons are made using the domain/degree and domain/weighted degree variable ordering heuristics. In general, it appears that preprocessing with higher levels of consistency followed by hybrid-AC processing (i.e. MAC) gives the best results, especially when the weighted degree heuristic is used. For problems with n-ary constraints, this difference seems to be even more pronounced. In some cases, higher levels of consistency maintenance established during preprocessing leads to performance gains over MAC of several orders of magnitude.

2016-11-01T00:00:00Z Browne, David Manna, Carlo Prestwich, Steven http://hdl.handle.net/10468/4461 2017-08-15T18:00:48Z 2016-09-01T00:00:00Z

Relevance-Redundancy Dominance: a threshold-free approach to filter-based feature selection Browne, David; Manna, Carlo; Prestwich, Steven Greene, Derek; MacNamee, Brian; Ross, Robert Feature selection is used to select a subset of relevant features in machine learning, and is vital for simplification, improving efficiency and reducing overfitting. In filter-based feature selection, a statistic such as correlation or entropy is computed between each feature and the target variable to evaluate feature relevance. A relevance threshold is typically used to limit the set of selected features, and features can also be removed based on redundancy (similarity to other features). Some methods are designed for use with a specific statistic or certain types of data. We present a new filter-based method called Relevance-Redundancy Dominance that applies to mixed data types, can use a wide variety of statistics, and does not require a threshold. Finally, we provide preliminary results, through extensive numerical experiments on public credit datasets.

2016-09-01T00:00:00Z