**Êtes-vous un étudiant de l'EPFL à la recherche d'un projet de semestre?**

Travaillez avec nous sur des projets en science des données et en visualisation, et déployez votre projet sous forme d'application sur GraphSearch.

Publication# Dynamically Selecting Composition Algorithms for Economical Composition as a Service

Résumé

Various algorithms have been proposed for the problem of quality-driven service composition. They differ by the quality of the resulting executable processes and by their processing costs. In this paper, we study the problem of service composition from an economical point of view and adopt the perspective of a Composition as a Service provider. Our goal is to minimize composition costs while delivering executable workflows of a specified average quality. We propose to dynamically select different composition algorithms for different workflow templates based upon template structure and workflow priority. For evaluating our selection algorithm, we consider two classic approaches to quality-driven composition, genetic algorithms and integer linear programming with different parameter settings. An extensive experimental evaluation shows significant gains in efficiency when dynamically selecting between different composition algorithms instead of using only one algorithm.

Official source

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Concepts associés

Chargement

Publications associées

Chargement

Publications associées (2)

Chargement

Chargement

Concepts associés (11)

Algorithme

thumb|Algorithme de découpe d'un polygone quelconque en triangles (triangulation).
Un algorithme est une suite finie et non ambiguë d'instructions et d’opérations permettant de résoudre une classe de

Optimisation linéaire en nombres entiers

L'optimisation linéaire en nombres entiers (OLNE) (ou programmation linéaire en nombres entiers (PLNE) ou integer programming (IP) ou Integer Linear Programming (ILP)) est un domaine des mathématiqu

Parameter

A parameter (), generally, is any characteristic that can help in defining or classifying a particular system (meaning an event, project, object, situation, etc.). That is, a parameter is an element

Heinz Blatter, Martin Funk, Laurent Michel, Marco Picasso

A shape optimization algorithm is presented that estimates the ice thickness distribution within a three-dimensional, shallow glacier, given a transient surface geometry and a mass-balance distribution. The approach is based on the minimization of the surface topography misfit in the shallow ice approximation by means of a primal-dual procedure. The method's essential novelty is that it uses surface topography and mass-balance data only within the context of a time-dependent problem with evolving surface topography. Moreover, the algorithm is capable of computing some of the model parameters concurrently with the ice thickness distribution. The method is validated on synthetic and real-world data, where the choice of its Tikhonov regularization parameter by means of an L-curve criterion is discussed. (C) 2014 Elsevier Ltd. All rights reserved.

In today's digital world, sampling is at the heart of any signal acquisition device. Imaging devices are ubiquitous examples that capture two-dimensional visual signals and store them as the pixels of discrete images. The main concern is whether and how the pixels provide an exact or at least a fair representation of the original visual signal in the continuous domain. This motivates the design of exact reconstruction or approximation techniques for a target class of images. Such techniques benefit different imaging tasks such as super-resolution, deblurring and compression. This thesis focuses on the reconstruction of visual signals representing a shape over a background, from their samples. Shape images have only two intensity values. However, the filtering effect caused by the sampling kernel of imaging devices smooths out the sharp transitions in the image and results in samples with varied intensity levels. To trace back the shape boundaries, we need strategies to reconstruct the original bilevel image. But, abrupt intensity changes along the shape boundaries as well as diverse shape geometries make reconstruction of this class of signals very challenging. Curvelets and contourlets have been proved as efficient multiresolution representations for the class of shape images. This motivates the approximation of shape images in the aforementioned domains. In the first part of this thesis, we study generalized sampling and infinite-dimensional compressed sensing to approximate a signal in a domain that is known to provide a sparse or efficient representation for the signal, given its samples in a different domain. We show that the generalized sampling, due to its linearity, is incapable of generating good approximation of shape images from a limited number of samples. The infinite-dimensional compressed sensing is a more promising approach. However, the concept of random sampling in this scheme does not apply to the shape reconstruction problem. Next, we propose a sampling scheme for shape images with finite rate of innovation (FRI). More specifically, we model the shape boundaries as a subset of an algebraic curve with an implicit bivariate polynomial. We show that the image parameters are solutions of a set of linear equations with the coefficients being the image moments. We then replace conventional moments with more stable generalized moments that are adjusted to the given sampling kernel. This leads to successful reconstruction of shapes with moderate complexities from samples generated with realistic sampling kernels and in the presence of moderate noise levels. Our next contribution is a scheme for recovering shapes with smooth boundaries from a set of samples. The reconstructed image is constrained to regenerate the same samples (consistency) as well as forming a bilevel image. We initially formulate the problem by minimizing the shape perimeter over the set of consistent shapes. Next, we relax the non-convex shape constraint to transform the problem into minimizing the total variation over consistent non-negative-valued images. We introduce a requirement -called reducibility- that guarantees equivalence between the two problems. We illustrate that the reducibility effectively sets a requirement on the minimum sampling density. Finally, we study a relevant problem in the Boolean algebra: the Boolean compressed sensing. The problem is about recovering a sparse Boolean vector from a few collective binary tests. We study a formulation of this problem as a binary linear program, which is NP hard. To overcome the computational burden, we can relax the binary constraint on the variables and apply a rounding to the solution. We replace the rounding procedure with a randomized algorithm. We show that the proposed algorithm considerably improves the success rate with only a slight increase in the computational cost.