Santiago Iturriaga

• PhD in Computer Science
• Research Assistant at Universidad de la República de Uruguay

Demand response programs allow consumers to participate in the operation of a smart electric grid by reducing or shifting their energy consumption, helping to match energy consumption with power supply. This article presents a bio-inspired approach for addressing the problem of colocation datacenters participating in demand response programs in a s...

This article presents the application of high performance computing for efficient simulations of granular media in silos. Granular media are extensively used in industry, where storage and proper treatment pose several challenges to the scientific community. A relevant problem concerns the study of granular media stored in a silo. Determining the b...

This article presents a distributed approach for autonomous exploration and surveillance using unmanned aerial vehicles. The proposed solution applies the agent-oriented paradigm to implement a cooperative approach to solve the problem efficiently. A specific state machine is proposed for unmanned aerial vehicles to implement the coordination neede...

This article presents demand response techniques for the participation of datacenters in smart electricity markets under the smart grid paradigm. The proposed approach includes a datacenter model based on empirical information to determine the power consumption of CPU-intensive and memory-intensive tasks. A negotiation approach between the datacent...

This article presents an approach for the participation of datacenters and supercomputing facilities in smart electricity markets. This is a relevant problem in modern smart grid systems to implement demand response strategies for a better use of resources to guarantee energy efficiency. The proposed approach includes a datacenter model based on em...

This article explores the application of evolutionary algorithms and agent-oriented programming to solve the problem of searching and monitoring objectives through a fleet of unmanned aerial vehicles. The subproblem of static off-line planning is studied to find initial flight plans for each vehicle in the fleet, using evolutionary algorithms to ac...

This article describes a proposal for the participation of supercomputing platforms and datacenters in the electric market, by implementing demand response techniques and ancillary services. Supercomputing and datacenters are appropriate candidates to adjust their power consumption in order to help the electric network to fulfill specific goals, ei...

This article describes the national initiative for installing and operating a collaborative scientific HPC infrastructure in Uruguay (Cluster-UY). The project was conceived as a mean to foster research and innovation projects that face complex problems with high computing demands. The main ideas and motivations of the Cluster-UY project are describ...

Content Distribution Networks (CDN) are key for providing worldwide services and content to end-users. In this work, we propose three multiobjective evolutionary algorithms for solving the problem of designing and optimizing cloud-based CDNs. We consider the objectives of minimizing the total cost of the infrastructure (including virtual machines,...

This work addresses the multi-objective resource provisioning problem for building cloud-based CDNs. The optimization objectives are the minimization of VM, network and storage cost, and the maximization of the QoS for the end-user. A brokering model is proposed such that a single cloud-based CDN is able to host multiple content providers applying...

This article presents an empirical evaluation of power consumption of synthetic benchmarks in multicore computing systems. The study aims at providing an insight of the main power consumption characteristics of different applications when executing over current high performance computing servers. Three types of applications are studied executing in...

This chapter presents a new kind of cloud brokering model called virtual broker. The virtual broker owns and manages what we call a virtual cloud, composed by a set of reserved VMs from a number of public cloud providers. This new broker sublets its resources to its customers as on-demand VMs, at lower prices than those offered in the market. This...

This article presents the application of heuristic algorithms to solve the affinity scheduling problem in multicore computing systems. Affinity scheduling is a technique that allows efficient utilization of heterogeneous computing systems, by assigning a set of tasks to cores, taking into account specific efficiency and quality-of-service criteria....

Este artículo presenta los avances en la definición de un modelo para la planificación de eficiencia energética en centros de supercómputo considerando la utilización de energías renovables. Se plantea la gestión energética como un problema de optimización multiobjetivo y se propone una metaheurística evolutiva multiobjetivo para su resolución. Los...

This work presents a multi-objective approach for scheduling energy consumption in data centers considering traditional and green energy data sources. This problem is addressed as a whole by simultaneously scheduling the state of the servers and the cooling devices, and by scheduling the workload of the data center, which is comprised of a set of i...

This article studies the application of multiobjective evolutionary algorithms for solving the energy-aware scheduling problem of workflows in a distributed system that is composed by a federation of datacenters. Nowadays, energy efficiency is a major concern when using large distributed computing systems, including novel grid and cloud computing f...

This article presents a multiobjective approach for scheduling large workflows in distributed datacenters. We consider a realistic scheduling scenario of distributed cluster systems composed of multi-core computers, and a multi-objective formulation of the scheduling problem to minimize makespan, energy consumption and deadline violations. The stud...

This article presents sequential and parallel metaheuristics to solve the virtual machines subletting problem in cloud systems, which deals with allocating virtual machine requests into pre-booked resources from a cloud broker, maximizing the broker profit. Three metaheuristic are studied: Simulated Annealing, Genetic Algorithm, and hybrid Evolutio...

niveles con un planificador de alto nivel que planifica la ejecución de los trabajos a nivel de centros de datos, y un planificador bajo nivel que planifica la ejecución de los trabajos dentro de cada centro de datos. Se proponen dos algoritmos evolutivos multi-objetivo basados en Multi-objective Cellular Genetic Algorithm (MOCell) y Non-dominated...

This article introduces a new kind of broker for cloud computing, whose business relies on outsourcing virtual machines (VMs) to its customers. More specifically, the broker owns a number of reserved instances of different VMs from several cloud providers and offers them to its customers in an on-demand basis, at cheaper prices than those of the cl...

This article presents the application of evolutionary algorithms to solve the affinity scheduling problem in multicore computing systems. Affinity scheduling is a technique that allows the efficient utilization of heterogeneous computing systems, by assigning a set of task taking into account specific efficency and quality-of-service criteria. The...

Mobile ad hoc networks (MANETs) are infrastructure-less communication networks spontaneously created by a number of mobile devices. Due to its highly fluctuating topology, finding the optimal configuration of communication protocols is a complex and crucial task. Additionally, different objectives must be usually considered. In our previous work, w...

Multi-Bulk Synchronous Parallel (MultiBSP) is a recently proposed parallel programming model for multicore machines that extends the classic BSP model. MultiBSP is very useful to design algorithms and estimate their running time, which are hard to do in High Performance Computing applications. For a correct estimation of the running time, the main...

This article presents an empirical evaluation of energy-aware schedulers under uncertainties in both the execution time of tasks and the energy consumption of the computing infrastructure. We address an important problem with direct application in current clusters and distributed computing systems, by analyzing how the list scheduling techniques pr...

This article presents the parallel implementation on CPU/GPU of two variants of a stochastic local search method to efficiently solve the scheduling problem in heterogeneous computing systems. Both methods are based on a set of simple operators to keep the computational complexity as low as possible, thus allowing large instances of the scheduling...

This article presents a new parallel hybrid evolutionary algorithm to solve the problem of virtual machines subletting in cloud systems. The problem deals with the efficient allocation of a set of virtual machine requests from customers into available pre-booked resources from a cloud broker, in order to maximize the broker profit. The proposed par...

This article introduces the formulation of the VirtualMachine Planning Problem in cloud computing systems. It deals with the efficient allocation of a set of virtual machine requests from customers into the available pre-booked resources the broker has in a number of cloud providers, maximizing the broker profit. Eight list scheduling heuristics ar...

Mobile ad hoc networks are infrastructureless communication networks that are spontaneously created by a number of mobile devices. Due to the highly fluctuating topology of such networks, finding the optimal configuration of communication protocols is a complex and crucial task. Additionally, different objectives must be usually considered. Small chan...

This article introduces ME-MLS, an efficient multithreading local search algorithm for solving the multiobjective scheduling problem in heterogeneous computing systems. We consider the minimization of both the makespan and energy consumption objectives. The proposed method follows a fully multiobjective approach, applying a Pareto-based dominance s...

This work studies the problem of scheduling independent tasks in heterogeneous computing grid systems. A new bi-objective formulation of the scheduling problem is introduced, which aims at minimising the makespan and weighted response ratio objectives. A novel parallel micro evolutionary algorithm is developed in order to efficiently solve the prob...

This article presents the application of a parallel evolutionary algorithm implemented in both CPU and Graphic Processing Units (GPU), to solve large instances of the noisy OneMax problem with up to one billion variables. Actually, new GPU platforms provide the computing power needed to apply massively parallel strategies to solve large problems. W...

This article introduces an efficient multithreading local search algorithm for solving the multiobjective schedul-ing problem in heterogeneous computing systems consider-ing the makespan and energy consumption objectives. The proposed method follows a fully multiobjective approach using a Pareto-based dominance search executed in paral-lel. The exp...

This work presents the application of a parallel micro-CHC evolutionary algorithm to the scheduling problem in heterogeneous computing environments, to minimize the make span and weighted response ratio objectives. The studied problem is NP-hard, and significant effort has been made to develop efficient methods to compute accurate schedules in redu...