Laura Cruz-Reyes

Instituto Tecnológico de Ciudad Madero, División de Estudios de Posgrado e Investigación, Faculty Member

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Laura Cruz Reyes,Computer Science PHD
National Research II SNI
Research interests: Metaheuristics, Machine Learning and Algorithmics
Application areas: Complex Systems (Logistics and Distributed Systems)
Phone: +52 833 3574820 Ext. 3024 (cubicle)
Address: Instituto Tecnológico de Ciudad Madero, ITCM
Division de Estudios de Posgrado e Investigación
Juventino Rosas y Jesús Urueta, C.P. 89440 Cd. Madero, Tam. MEXICO

Personal Office: Second Level of System and Computer Laboratory, cubicle C1

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CG GS

Marcela Quiroz

Jennifer Bermudez

Universidad Distrital Francisco Jose de Caldas, Bogota, Colombia

Iván Mauricio Salgado Flores

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Jairo R. Montoya T.

Universidad de La Sabana

Daniel Echeverria

Universidad Central de Chile

Amabiles Nuñez

Pablo Banda

Universidad Finis Terrae

Ana Caceres

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Papers by Laura Cruz-Reyes

A Statistical Approach for Algorithm Selection

by Juan Frausto Solis and Laura Cruz-Reyes

This paper deals with heuristic algorithm characterization, which is applied to the solution of a... more This paper deals with heuristic algorithm characterization, which is applied to the solution of an NP-hard problem, in order to select the best algorithm for solving a given problem instance. The traditional approach for selecting algorithms compares their performance using an instance set, and concludes that one outperforms the other. Another common approach consists of developing mathematical models to relate performance to problem size. Recent approaches try to incorporate more characteristics. However, they do not identify the characteristics that affect performance in a critical way, and do not incorporate them explicitly in their performance model. In contrast, we propose a systematic procedure to create models that incorporate critical characteristics, aiming at the selection of the best algorithm for solving a given instance. To validate our approach we carried out experiments using an extensive test set. In particular, for the classical bin packing problem, we developed models that incorporate the interrelation among five critical characteristics and the performance of seven heuristic algorithms. As a result of applying our procedure, we obtained a 76% accuracy in the selection of the best algorithm.

Distributed VoIP Load Balancing in Cloud Computing

by Laura Cruz-Reyes, Jorge Mario Cortes Mendoza, and Andrei Tchernykh

Cloud computing is widely being adopted by many companies because it allows to maximize the utili... more Cloud computing is widely being adopted by many companies because it allows to maximize the utilization of their resources. Unfortunately, the management of cloud infrastructure is a challenging task. Reliability, security, quality of service, and cost-efficiency are important issues in these systems, they require resource optimization at multiple layers of the infrastructure and applications. The complexity of cloud computing systems makes infeasible the optimal or near-optimal resource allocation, especially in presence of uncertainty of very dynamic and unpredictable environment. Hence, load balancing algorithms are a fundamental part of the research in cloud computing. We formulate the problem and describe several load balancing algorithms in distributed computer environments. The goal is to understand the main characteristics of load balancing algorithms and how they can be adapted for the domain of VoIP computations on federated clouds. We conclude by showing how none of these works directly addresses the problem space of the considered problem, but do provide a valuable basis for our work.

Caracterización del Proceso de Optimización de Algoritmos Heurísticos

Laura Cruz-Reyes

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