Dipartimento di Matematica e Informatica

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Distributed Optimization Over Large-Scale Systems for Big Data Analytics
(Università della Calabria, 2020-01-17) Shahbazian, Reza; Leone, Nicola; Grandinetti, Lucio; Guerriero, Francesca
A large-scale system is defined as one that supports multiple, simultaneous users who access the core functionality through some network. Nowadays, enormous amount of data is continually generated at unprecedented and ever-increasing scales. Large-scale data sets are collected and studied in numerous domains, from engineering sciences to social networks, commerce, bimolecular research, and security. Big Data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process with acceptable latency. Usually, Big Data has one or more of the characteristics including high volume, high velocity, or high variety. Big Data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Generally, Big Data comes from sensors, devices, video or audio, networks, log files, transactional applications, web, and social media, in a very large-scale. Big Data is impossible to analyze by using traditional central methods and therefore, new distributed models and algorithms are needed to process the data. In this thesis, we focus on optimization algorithms for Big Data application. We review some of the recent machine learning, convex and non-convex, heuristic and stochastic optimization techniques and available tools applied to Big Data. We also propose a new distributed and decentralized stochastic algorithm for Big Data analytics. Our proposed algorithm is fully distributed to decide large-scale networks and data sets. The proposed method is scalable to any network configuration, is near real-time (in each iteration, a solution is provided although it might not be the optimum one) and more critical, robust to any missing data or communication failures. We evaluate the proposed method by a practical example and simulations on cognitive radio networks. Simulation results confirmed that the proposed method is efficient in terms of accuracy and robustness. We assume that the distributed data-sources should be capable of processing their data and communicate with neighbor sources to find the network objective as an optimal decision. Some challenges are introduced by new technologies such as 5G or high-speed wireless data transfer, including imperfect communications that damage the data. We propose an optimal algorithm that uses optimal weighting to combine the shared data coming from neighbors. This optimal weight improves the performance of the decision-making algorithm in terms of error and convergence rate. We evaluate the performance of the proposed algorithm mathematically and introduce the step-sized conditions that guaranteed the convergence of the proposed algorithm. We use computer simulations to evaluate the network error. We prove that in a network diagram with ten datasources, the network performance of the proposed algorithm outperforms some of the known optimal solutions such as Metropolis and adaptive combination. Keywords: Optimization, Big Data, Large-Scale, Distributed, Optimal Weight.
Omega our multi ethnic genetic algorithm
(2014-03-13) Cerrone, Carmine; Grandinetti, Lucio; Gaudioso, Manlio
Combinatorial optimization is a branch of optimization. Its domain is optimization problems where the set of feasible solutions is discrete or can be reduced to a discrete one, the goal being that of nding the best possible solution. Two fundamental aims in optimization are nding algorithms characterized by both provably good run times and provably good or even optimal solution quality. When no method to nd an optimal solution, under the given constraints (of time, space etc.) is available, heuristic approaches are typically used. A metaheuristic is a heuristic method for solving a very general class of computational problems by combining user- given black-box procedures, usually heuristics themselves, in the hope of obtaining a more e cient or more robust procedure. The genetic algorithms are one of the best metaheuristic approaches to deal with optimization problems. They are a population- based search technique that uses an ever changing neighborhood structure, based on population evolution and genetic operators, to take into account di erent points in the search space. The core of the thesis is to introduce a variant of the classic GA approach, which is referred to as OMEGA (Multi Ethnic Genetic Algorithm). The main feature of this new metaheuristic is the presence of di erent populations that evolve simultaneously, and exchange genetic material with each other. We focus our attention on four di erent optimization problems de ned on graphs. Each one is iii iv proved to be NP-HARD. We analyze each problem from di erent points of view, and for each one we de ne and implement both a genetic algorithm and our OMEGA.

Dipartimento di Matematica e Informatica - Tesi di Dottorato

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