Micael Gallego

GRASP and path relinking for the max-min diversity problem

The max-min diversity problem (MMDP) consists in selecting a subset of elements from a given set in such a way that the diversity among the selected elements is maximized. The problem is NP-hard and can be formulated as an integer linear program. Since the 1980s, several solution methods for this problem have been developed and applied to a variety of fields, particularly in the social and biological sciences. We propose a heuristic method-based on the GRASP and path relinking methodologies-for finding approximate solutions to this optimization problem. We explore different ways to hybridize GRASP and path relinking, including the recently proposed variant known as GRASP with evolutionary path relinking. Empirical results indicate that the proposed hybrid implementations compare favorably to previous metaheuristics, such as tabu search and simulated annealing.

A branch and bound algorithm for the maximum diversity problem

This article begins with a review of previously proposed integer formulations for the maximum diversity problem (MDP). This problem consists of selecting a subset of elements from a larger set in such a way that the sum of the distances between the chosen elements is maximized. We propose a branch and bound algorithm and develop several upper bounds on the objective function values of partial solutions to the MDP. Empirical results with a collection of previously reported instances indicate that the proposed algorithm is able to solve all the medium-sized instances (with 50 elements) as well as some large-sized instances (with 100 elements). We compare our method with the best previous linear integer formulation solved with the well-known software Cplex. The comparison favors the proposed procedure.

Tabu Search with Strategic Oscillation for the Maximally Diverse Grouping Problem

We propose new heuristic procedures for the maximally diverse grouping problem (MDGP). This NP-hard problem consists of forming maximally diverse groups—of equal or different size—from a given set of elements. The most general formulation, which we address, allows for the size of each group to fall within specified limits. The MDGP has applications in academics, such as creating diverse teams of students, or in training settings where it may be desired to create groups that are as diverse as possible. Search mechanisms, based on the tabu search methodology, are developed for the MDGP, including a strategic oscillation that enables search paths to cross a feasibility boundary. We evaluate construction and improvement mechanisms to configure a solution procedure that is then compared to state-of-the-art solvers for the MDGP. Extensive computational experiments with medium and large instances show the advantages of a solution method that includes strategic oscillation.

Hybrid heuristics for the maximum diversity problem

Abstract The maximum diversity problem presents a challenge to solution methods based on heuristic optimization. We undertake the development of hybrid procedures within the scatter search framework with the goal of uncovering the most effective designs to tackle this difficult but important problem. Our research revealed the effectiveness of adding simple memory structures (based on recency and frequency) to key scatter search mechanisms. Our extensive experiments and related statistical tests show that the most effective scatter search variant outperforms state-of-the-art methods.

A black-box scatter search for optimization problems with integer variables

The goal of this work is the development of a black-box solver based on the scatter search methodology. In particular, we seek a solver capable of obtaining high quality outcomes to optimization problems for which solutions are represented as a vector of integer values. We refer to these problems as integer optimization problems. We assume that the decision variables are bounded and that there may be constraints that require that the black-box evaluator is called in order to know whether they are satisfied. Problems of this type are common in operational research areas of applications such as telecommunications, project management, engineering design and the like.Our experimental testing includes 171 instances within four classes of problems taken from the literature. The experiments compare the performance of the proposed method with both the best context-specific procedures designed for each class of problem as well as context-independent commercial software. The experiments show that the proposed solution method competes well against commercial software and that can be competitive with specialized procedures in some problem classes.

Authentication, Authorization, and Accounting in WebRTC PaaS Infrastructures: The Case of Kurento

WebRTC server infrastructures are useful for creating rich real-time communication (RTC) applications. Developers commonly use them for accessing capabilities such as group communications, archiving, and transcoding. Details on how to implement and use such infrastructures securely are of increasing interest to the engineering community. Kurento is an open source project that provides a WebRTC media server and a platform as a service cloud built on top of it. The authors present the Kurento API and analyze different security models for it, investigating the suitability of using simple access control lists (ACLs) and capability-based security schemes to provide authorization. Using minimal implementation, they discuss the advantages and drawbacks of each scheme and conclude that, for the proposed schemes, ACLs are less scalable but provide more granularity.

Tabu search for the Max-Mean Dispersion Problem

In this paper, we address a variant of a classical optimization model in the context of maximizing the diversity of a set of elements. In particular, we propose heuristics to maximize the mean dispersion of the selected elements in a given set. This NP-hard problem was recently introduced as the maximum mean dispersion problem (MaxMeanDP), and it models several real problems, from pollution control to ranking of web pages. In this paper, we first review the previous methods for the MaxMeanDP, and then explore different tabu search approaches, and their influence on the quality of the solutions obtained. As a result, we propose a dynamic tabu search algorithm, based on three different neighborhoods. Experiments on previously reported instances show that the proposed procedure outperforms existing methods in terms of solution quality. It must be noted that our findings on the use of different memory structures invite to further consideration of the interplay between short and long term memory to enhance simple forms of tabu search.

GRASP with path relinking for the single row facility layout problem

The single row facility layout problem (SRFLP) is an NP -hard problem that consists of finding an optimal arrangement of a set of rectangular facilities (with equal height and different lengths), placing them next to each other along a line. The SRFLP has practical applications in contexts such as arranging rooms along corridors, setting books on shelves, allocating information on magnetic disks, storing items in warehouses, or designing layouts for machines in manufacturing systems. This paper combines the greedy randomized adaptive search procedure (GRASP) methodology, and path relinking (PR) in order to efficiently search for high-quality solutions for the SRFLP. In particular, we introduce: (i) several construction procedures, (ii) a new fast local search strategy, and (iii) an approach related to the Ulam distance in order to construct short path relinking trajectories. We also present a new set of large challenging instances, since previous sets do not allow to determine significant differences among advanced metaheuristics. Experiments show that our procedure outperforms state-of-the-art methods in all of the scenarios we considered. Firstly, the GRASP with PR finds the best known solutions for previous instances used in the literature, but employing considerably less computing time than its competitors. Secondly, our method outperforms the current state-of-the-art methods in 38 out of 40 new instances when running for the same amount of computing time. Finally, nonparametric tests for detecting differences between algorithms report p-values below 10 - 11 , which supports the superiority of our approach.

WebRTC Testing: Challenges and Practical Solutions

WebRTC comprises a set of novel technologies and standards that provide Real-Time Communication on Web browsers. WebRTC makes simple the embedding of voice and video communications in all types of applications. However, releasing those applications to production is still very challenging due to the complexity of their testing. Validating a WebRTC service requires assessing many functional (e.g. signaling logic, media connectivity, etc.) and non-functional (e.g. quality of experience, interoperability, scalability, etc.) properties on large, complex, distributed and heterogeneous systems that spawn across client devices, networks and cloud infrastructures. In this article, we present a novel methodology and an associated tool for doing it at scale and in an automated way. Our strategy is based on a blackbox end-to-end approach through which we use an automated containerized cloud environment for instrumenting Web browser clients, which benchmark the SUT (system under test), and fake clients, that load it. Through these benchmarks, we obtain, in a reliable and statistically significant way, both network-dependent QoS (Quality of Service) metrics and media-dependent QoE (Quality of Experience) indicators. These are fed, at a second stage, to a number of testing assertions that validate the appropriateness of the functional and non-functional properties of the SUT under controlled and configurable load and fail conditions. To finish, we illustrate our experiences using such tool and methodology in the context of the Kurento open source software project and conclude that they are suitable for validating large and complex WebRTC systems at scale.

Designing and evaluating the usability of an API for real-time multimedia services in the Internet

In the last few years, multimedia technologies in general, and Real-Time multimedia Communications (RTC) in particular, are becoming mainstream among WWW and smartphone developers, who have an increasing interest in richer media capabilities for creating their applications. The engineering literature proposing novel algorithms, protocols and architectures for managing and processing multimedia information is currently overwhelming. However, most of these results do not arrive to applications due to the lack of simple and usable APIs. Interestingly, in this context in which APIs are the critical ingredient for reaching wide developer audiences, the scientific literature about multimedia APIs and their usability is scarce. In this paper we try to contribute to fill this gap by proposing the RTC Media API: a novel type of API designed with the aim of making simple for developers the use of latest trends in RTC multimedia including WebRTC, Video Content Analysis or Augmented Reality. We provide a specification of such API and discuss how it satisfies a set of design requirements including programming-language agnosticism, adaptation to cloud environments, support to multisensory multimedia, etc. After that, we describe an implementation of such an API that has been created in the context of the Kurento open source software project, and present a study evaluating the API usability performed in a group of more than 40 professional developers distributed worldwide. In the light of the obtained results, we conclude that the usability of the API is adequate across the main development activities (i.e. API learning, code creation and code maintenance), with an average usability score of 3.39 over 5 in a Likert scale, and that this result is robust with respect to developers’ profiles, cultures, professional experiences and preferred programming languages.